TBtech February Edition

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STORIES INSPIRED BY MODERN LIVING.

FEBRUARY 2023

The future is now.

FOCUSING ON AI AND MACHINE LEARNING.

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The era of software-defined everything is pushing the pace of innovation and transforming market segments ranging from aerospace to industrial, defense to medical, and networking to automotive.

DELIVERING EV CHARGING INFRASTRUCTURE TO MEET 2030 NEEDS.

Those responsible for the deployment of EV infrastructure need to understand their user’s needs, which should inform long-term roll-outs, and design procurement processes that incentivise charging point providers to deliver quality long-term deployments.

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Meet your tbtech team.

Joe has vast experience and knowledge accumulated and honed as a New Business Development Manager and Relationship Manager. Responsible for generating new business opportunities, looking after the growth of the company and strategy, sourcing new ventures and managing the company.

Luke is Digital Editor at TBTech and has history working closely with the worlds biggest tech brands to deliver campaigns. Luke is an advocate of tech across business and commercial applications.

Paul stops at nothing to innovate and create value for our customers. His mission is help those we work with to win in their markets. Passionate about delivering customer success and have had the pleasure of supporting many of the world’s leading technology brands for over 15 years.

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PAUL WHITTALL

We have been working behind-the-scenes to elevate the readers experience.

Erin’s love for advertising and design has led her to Tbtech as a Media Marketing Apprentice. As a new member of the team, she is looking forward to exploring new skills and learning more about the tech sector .

Driven by storytelling, Jessie’s writer-designer duo allows her to combine the power of synergy across different mediums. She believes a strong marketing strategy begins with understanding the brands mission and audience, together with the market, in order to position yourself as a leading brand, speaking directly to your clients and customers desires.

William’s passion starts and ends with design, timeless aesthetic and creative solutions. Having worked on numerous creative campaigns ranging from car manufacturers, leading tech companies, property investors as well as local artisans, the goal is to create the ultimate brand experience between the client and the consumer.

Matt is Operations Manager at TBTech, he has spent the last 15 years working with multinational IT companies building campaigns, GTM strategies, leading both Sales and Marketing teams to achieve organisational goals. With a love of computer science, history, and psychology he is an advocate for change, operational efficiency and automation. Value across the business for all our customers.

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Escalating concerns in AI for 2023 –and what to do about them.

When people think of artificial intelligence, what comes to mind is a cadre of robots uniting as sentient beings to overthrow their masters. AI has woven its way into the daily lives of consumers, arriving in the form of good recommendation engines for online shopping, recommending solutions for customer service questions from the knowledge base, or suggestions on how to fix grammar when writing an email.

By Ramprakash “Ram” Ramamoorthy, Head of AI Research for Zoho.

Escalating concerns in AI for 2023 –and what to do about them.

According to McKinsey’s “The State of AI in 2021” report, 57 per cent of companies in emerging economies had adopted some form of AI, up from 45 per cent in 2020. In 2022, an IBM survey found that, though AI adoption is gradual, four out of five companies plan to leverage the technology at some point soon. The expectation is for industry to further embrace AI to continue software evolution. Users will witness the technology providing contextual understanding of written and spoken language, helping arrive at decisions faster and with better accuracy, and telling the bigger picture story behind disparate data points in more useful and applicable ways.

AI is going to move the needle for enterprises in 2023, but it is not without mounting concerns. Privacy, or the lack thereof, will likely remain a central fear among consumers. AI training under the current processes also have a likelihood of biases from misunderstanding spoken language or skewing data points. Simultaneously, the media and international governance have not caught up to where AI currently sits and is headed.

AI FULLY ENTERS THE MAINSTREAM

Mainstream adoption of AI is on the way with devices, apps and employee experience (EX) platforms likely to come equipped with AI out of the box. No longer will consumers be able to optin, which will accelerate and heighten concerns within AI that exist in the mainstream already. Privacy reigns supreme given the volume of public data breaches in recent years, including LinkedIn, MailChimp and Twitch, with consumers understandably wary of giving out personal information.

One of the central issues in privacy is that there isn’t any consensus on what best practices look like across the industry, it’s tough to garner data if the concept of ethical collection is fluid. AI technology is still in its infancy and governance has not yet matured to the point where there is consistency across companies.

To prepare for the inevitability of AI-first technology, companies could demonstrate full transparency by providing easy access to their privacy policies— or, if none exist, compose them as soon as possible and make them readily available to view on the company’s website.

Granular language, while generally frowned upon in communicating with a non-tech savvy audience, is welcome in this instance, as consumers with a full understanding of how their data gets used are more likely to share bits and pieces.

BIASES IN AI MUST BE ELIMINATED

Biases are often invisible, even if their effects are pronounced, which means their elimination is a difficult thing to guarantee. And, despite its advanced state, AI currently remains just as prone to biases as its human counterparts. Biases are usually introduced early in the process. AI needs to be trained, and many companies opt for either purchasing synthetic data from third-party vendors— prone to distinct biases—or have it comb the general internet for contextual clues. However, no one is regulating or monitoring the world wide web for biases, and they’re likely to creep into an AI platform’s foundation. Financial investments in AI aren’t likely to trivialize anytime soon, so it’s of particular importance to establish processes and best practices to scrub as many biases, known or unknown, as quickly as possible.

One of the most effective safeguards against bias is to install a human safeguard between the data collection and processing phases of AI training. For example, at Zoho, some employees join the AI in combing through publicly available data to first scrub any trace of personally identifiable data—not only to protect these individuals but to ensure only crucial pieces of information make it through. Then, the data is further distilled to include only what’s relevant. An important thing to remember about bias is that it remains an evolving concept and a moving target, particularly as access to data improves. That’s why it’s essential for companies to ensure they are routinely scanning for new information and accordingly updating their criteria for bias.

THE MEDIA NARRATIVE REMAINS RELENTLESS

At the center of the above two issues sits the media, who are prone to repeat and reemphasize two conflicting narratives. On the one hand, they report that AI is a marvelous piece of technology with the potential to revolutionize our daily lives in both obvious and unseen ways. On the other, though, they continue to insinuate that AI technology is one step away from taking people’s jobs and declaring itself supreme overlord of Earth. As AI technology becomes more ubiquitous, expect the current media’s approach to remain mostly the same. It’s reasonable to anticipate a slight increase in stories about data breaches, though, as more access to AI will lead to a greater possibility that a consumer could find themselves affected. This trend could exacerbate a bit of a catch-22: AI cannot truly improve without increased adoption, yet adoption numbers are likely to stagnate due to lags in technology improvement.

CUSTOMERS GUIDE THE FUTURE OF AI

Best of all, a strong customer base in 2023 opens lines of communication between vendor and client. Direct, detailed feedback drives relevant, comprehensive updates to AI. Together, companies and their customers can forge an AI-driven future that pushes the technology envelope while remaining responsible with safe, secure, and unbiased data collection. Just don’t tell the robots.

How retailers can embrace AI to build customer loyalty.

In the on-demand economy, there’s a growing pressure on retailers to deliver. Customers expect brands to know who they are, to receive personalised interactions, and to be able to engage with retailers for a range of customer queries at the click of a button and on the channel of their choice. Growing trends in sustainability and diversity have added new elements of complexity for meeting customers’ needs. Interactions must be seamless and personalised as customers pivot from smartphone to online and chatbot to human advisor. Brands are turning to technology to outperform their competitors and improve sales and customer retention.

By Dan Hartveld, CTO at Red Ant.

How retailers can embrace AI to build customer loyalty.

The answer lies in retail data analytics, in collecting and utilising their customer and product data to create actionable insights. Using dedicated technology such as AI can proactively engage customers’ interests, make a sale, and assess post-purchase experience to cross and up-sell further. But a key challenge for retailers is to get their data ‘in order’, as the amount of data brands create, use and store is increasing exponentially. In fact, a recent Red Ant survey revealed that over a third of retailers (39%) lack confidence in the quality of their data. Being truly data-ready will enable them to invest in AI for a seamless, personalised customer experience that builds shopper loyalty.

WHAT IS RETAIL DATA ANALYTICS?

Retail data analytics uses digital technologies such as AI and machine learning to analyse data in order to make intelligent and meaningful business decisions. It is a process of using accumulated data to optimise areas of a retail operation. This includes understanding customers’ buying needs so that they can focus on areas that have high demand. AI can help improve data quality by using mechanical tools to spot relationships and patterns in data that are beyond human capabilities and can build a business process to make sense of these patterns. At a basic level, success in AI relies on training the system on whether a suggestion is good or bad and what that looks like by putting in feedback mechanisms - specialists in data transformation can help with this. Telling AI that it made a good recommendation is the secret to making intelligent decisions which will impact customer loyalty and the bottom line.

THE OPPORTUNITY FOR AI IN RETAIL

AI-driven insights are defining a new era for retail, where retailers can truly understand their customers and what makes them tick to boost customer loyalty and increase revenue. There are two main areas for deploying AI in a retail environment. The first is about how to make good product and service recommendations to customers based on data and how AI can develop feedback mechanisms that allow these predictions. An example of this might be noticing a customer always buys perfume of a particular 50ml size so that the retailer can take this information and recommend the right product to them.

AI can also be used to surface a ‘next best action’ – identify customers, their propensity to respond positively to outreach, and also how best to engage them. A retailer can use AI to drive the customer to respond to something, for instance going in store or inviting them to an event.

AI can recognise the habits and

behaviours of a customer to prompt them into action using a set of rules which determine how they are likely to react, and using natural language transformers like ChatGPT can provide store associates with message prompts that will encourage them to engage. Embracing AI is most successful as part of adopting an omnichannel retail strategy which harnesses data from all customer channels by bringing essential retail apps together with a retailer’s existing systems, content, and data into a single colleague hub. Retailers that utilise data analytics in this way to improve their business tend to outperform their competitors as they gain a single customer view of the entire shopping journey.

Retail data analytics can also improve everyday in-store operations. They can make predictions to help with inventory management and streamline backroom processes. Furthermore, retail data analytics can help pinpoint product popularity, stock levels, voucher redemption, speed of service, average purchase value and more,

allowing brands to efficiently manage in-store activity, make real-time changes and improve overall retail performance. Other key applications for AI in retail include price and promotional optimisation, in-store / onshelf availability, social media monitoring / sentiment analysis, demand forecasting and even fraud/threat detection, allowing them to monitor activity and make changes that are relevant to both the customer and the business.

THE RISE OF CLIENTELING

A key retail tech approach for 2023 is clienteling, which uses AI to offer customers the best of both worlds – the human touch coupled with the most personalised digital experiences. It means the establishment of long-term relationships with shoppers by using customer data that provides real-time insight into their preferences, behaviours, and purchases. This strengthens the bond between retailer and shopper, enhances the customer experience, and encourages them to return to the store.

Accumulated data gathered from a single customer means that store associates can view a selected customer’s purchase history, wish list, returns and even notes created about them including product preferences and vertical-specific information such as allergies or a specific shade of skin colour for cosmetics. This provides a truly personalised experience. With relevant data recommendations, a trusted store associate can upsell effectively while ensuring every product is suited to the shopper. This results in more repeat purchases with the retailer and avoids them straying to alternative brands.

THE POWER OF GETTING SMARTER ABOUT CUSTOMERS

Despite the vast potential of AI technology, implementing it in today’s complex retail environment can seem like a daunting task. However, using data insights has clear advantages for retailers to compete in the increasingly challenging digital and economic landscape and it’s never too late to get smarter about understanding customers. Bringing all the insights into one place on a customer analytics dashboard gives retailers the power to monitor and analyse activity online and on the shop floor with smart, actionable insights that drive store performance. These can be further customised for different user levels, so store associates, managers and head office can easily access retail analytics that are most relevant to their roles and responsibilities. Once all of the retailer’s data is in one place, empowering store associates with the right information on customers and products can deliver a next level experience that keeps customers coming back.

Unlock the Power of WiFi 6.

How To Leverage It For Your Business With DrayTek.

Are you tired of being left behind in the technological world? Well, fear not! WiFi 6 is here to save the day and bring your business into the future. With unprecedented speeds and a host of new capabilities, WiFi 6 is the must-have technology for any business looking to stay ahead of the curve. Read on to learn all about the amazing features of WiFi 6 and how it can benefit your business, as well as to see our top picks for WiFi 6 enabled routers and the best prices on the market right now. Don’t be left behind, join the technological elite with WiFi 6.

By Jessie Pethrus, Marketing, TBtech.

Unlock the Power of WiFi 6.

WHAT IS WIFI 6?

As the latest iteration of WiFi technology, WiFi 6 has been making waves since its release in 2019. With major improvements in speed, range, and overall connectivity, users can expect up to twice the speed of previous WiFi standards, as well as a 50% increase in range. In addition to these impressive improvements, WiFi 6 also offers increased privacy and security measures, including individual device authentication and the option for end-to-end encryption. Not only does WiFi 6 provide enhanced performance, but it is also more power-efficient and eco-friendly than previous iterations. It is also backwards compatible with WiFi 5 and WiFi 4, meaning that your devices will still be able to connect to WiFi 6 networks, although they will only do so at the lower speed of WiFi 5.

BENEFITS OF WIFI 6 FOR BUSINESSES

Think of WiFi 6 as a highway for your devices to connect to networks. Not only can more cars (or devices) join the road at once without causing traffic jams (or unstable connections), but the speed limit has also increased, allowing for faster data transmission. This is especially handy when streaming video or gaming online, where a smooth ride is essential. But what about those times when there are more cars on the road than usual, like during rush hour? That’s where DrayTek’s WiFi 6 comes in with its dual-band technology. Imagine two lanes on the highway, one with a 2.4GHz frequency and the other with a 5GHz frequency. The 2.4GHz lane may not be as fast as the 5GHz lane due to its smaller and more crowded space, but it can sometimes navigate through obstacles, like walls and floors, better.

This means that in some home and office installations, the 2.4GHz lane can reach spots that the 5GHz lane may struggle to provide good performance to, such as a room on the other side of the house from the Access Point.

But the benefits of WiFi 6 don’t stop at speed and connectivity. It also has advanced security measures, including advanced encryption protocols, to keep hackers at bay. With DrayTek’s WPA3 Wireless Security and its 192-bit encryption strength for networks using RADIUS and 802.1x authentication, you can feel confident using WiFi in public places, like airports. In short, WiFi 6 is the ultimate package for faster, more secure connectionsmake sure to upgrade to DrayTek’s WiFi 6.

WHAT ARE THE DIFFERENCES BETWEEN WIFI 6 AND PREVIOUS GENERATIONS?

WiFi 6 is a major step up from WiFi 5 and WiFi 4, with a few standout features that set it apart. For starters, WiFi 6 is a breeze to set up, making it a breeze to get your network up and running. And with its lower power usage, WiFi 6 is a win for both your wallet and the environment. But the real game-changer for WiFi 6 is its individual device authentication. Unlike WiFi 5 and WiFi 4, which rely on group authentication, WiFi 6 can precisely identify individual devices and give them more tailored access to the network. So why settle for less when you can have the best with WiFi 6? Upgrade now and experience the power of individual device authentication.

HOW CAN BUSINESSES USE WIFI 6 TO THEIR ADVANTAGE?

With the increased bandwidth, improved signal strength, and enhanced security of WiFi 6, businesses can benefit in many ways. For example, WiFi 6 makes it easier to stream audio and video content, including VoIP calls and video conferences. Additionally, WiFi 6 networks can be used to connect IoT devices, such as sensors, automated doors, or security cameras. WiFi 6 can help businesses improve internal communication and workflow by facilitating better connections among employees. Furthermore, it can be used to create a more dynamic, engaging customer experience by improving WiFi hotspots at public locations. As previously mentioned, businesses can also use WiFi 6 to connect with other businesses and partners, enabling a new level of collaboration and information sharing.

Some of the notable upgrades that set WiFi 6 apart from WiFi 4 and WiFi 3 is:

1. Quicker and easier setup: No need to spend hours configuring your network, WiFi 6 can be up and running in no time.

2. Lower power usage: WiFi 6 is more energy-efficient, which is good for both your wallet and the environment.

3. Individual device authentication: Unlike WiFi 5 and WiFi 4, which use group authentication, WiFi 6 can identify individual devices and provide them with more precise access to the network.

WHAT HARDWARE AND SOFTWARE DO YOU NEED TO USE WIFI 6?

Ready to upgrade to WiFi 6 and unlock its full potential? You’ll need the right tools for the job. First and foremost, you’ll need devices with WiFi 6 capability to connect to a WiFi 6 network. While WiFi 5 devices can technically connect, they’ll only do so at the slower WiFi 5 speed. For the full WiFi 6 experience, you’ll want devices with WiFi 6 capability, like those from DrayTek.

In addition to WiFi 6 capable devices, you’ll also need WiFi 6 compatible access points or routers to get your network up and running. And if you want to extend the range of your WiFi 6 network, you’ll need compatible range extenders. Finally, don’t forget about WiFi 6 compatible network management software to keep everything running smoothly.

Unlock the Power of WiFi 6.

TIPS FOR SETTING UP A WIFI 6 NETWORK

There are a few important steps to take when setting up a WiFi 6 network. First, decide whether you want to set up a new WiFi 6 network or migrate your existing network to WiFi 6. If you decide to migrate your network, you will want to be mindful of the fact that WiFi 5 and WiFi 4 devices will only be able to connect at slower WiFi 5 speeds. Once you decide which path is best for your business, you can begin the process.

When setting up a new WiFi 6 network, you will want to ensure you have the necessary hardware. You can set up a WiFi 6 network with WiFi 6 routers and WiFi 6 access points. Granted, if you are migrating to WiFi 6 from a previous WiFi standard, you can also use WiFi 6 range extenders. Once you have the hardware in place, you will want to set up your network management software to ensure the network runs smoothly.

BEST PRACTICES FOR LEVERAGING WIFI 6

WiFi 6 is quickly becoming the standard for wireless internet and is revolutionizing how we stay connected. The new technology offers faster speeds, better range, and improved efficiency. With so many exciting possibilities, it is important to understand the best practices for leveraging WiFi 6 to its full potential.

MAKE SURE YOU HAVE THE RIGHT HARDWARE

WiFi 6 routers and access points come with a new radio frequency that your devices must support. Some devices are incompatible with the latest technology, so check before making any purchases.

Second, consider the number of devices you need to connect WiFi 6 can handle up to four times the number of devices as the previous standard, so ensure you have enough access points to accommodate your needs.

Take advantage of the improved range.

WiFi 6 can cover larger areas than the previous standard, so you can reduce the number of access points you need.

Make sure to keep your network secure.

With so many devices connected to the same network, there is an increased risk of security breaches. Take the necessary steps to protect your network and data. Following these best practices will ensure you get the most out of your WiFi.

Security considerations for WiFi 6 networks

You can take a few steps to help mitigate security risks and protect your network. First, ensure all devices are authenticated on the network, meaning they will only be allowed to connect if they use the correct credentials. Next, set up network access control so that only authorized devices can access the network.

Finally, use a VPN to protect data in transit and make sure your sensitive data is protected. The latest WiFi 6 technology brings many benefits for businesses, but it is important to ensure that your network is secure. WiFi 6 brings with it stronger security features, including individual device authentication, group encryption, and advanced protection. However, businesses should still take steps to protect their networks and keep data safe from malicious actors. Moreover, wireless security is ever more important for businesses and homes, as wireless links have become fast enough to match cabled networking with far greater convenience. With that convenience comes the risk that wireless networking does not have the physical security of cabling, instead utilising wireless security protocols with strong encryption to protect against unauthorised network access, or eavesdropping on client connections to steal data.

However, DrayTrek WPA3 Wireless Security brings a number of security improvements and design changes that require hardware and software/operating system support, both for wireless clients and wireless access points.

CONCLUSION

WiFi 6 is the latest iteration of WiFi technology, bringing with it faster speeds and improved connectivity. With the help of DrayTek, businesses can upgrade their networks to WiFi 6 and take advantage of its benefits. DrayTek’s solutions are tailored to your unique needs and can provide an array of benefits, including increased bandwidth, stronger signal strength, and improved security. With DrayTek’s help, businesses can ensure their networks are running on the latest and greatest WiFi technology and make the most of the opportunities that WiFi 6 presents.

Let’s review the benefits again:

1. Increased bandwidth: WiFi 6 boasts a 3x increase in bandwidth compared to WiFi 5. This means you can stream and download content more efficiently and seamlessly, giving you better access to the content you need.

2. Stronger signal strength: WiFi 6 has a larger coverage area than previous versions, so even if you have a larger space to cover, your WiFi 6 network will provide a strong enough signal to support all connected devices.

3. Improved security: WiFi 6 requires individual devices to be authenticated, and it can be set to use end-to-end encryption, ensuring privacy and safeguarding against malicious actors.

4. Increased device support: WiFi 6 has been designed to work with a wider range of devices, including IoT devices.

5. Easier set-up: WiFi 6 has a simpler set-up process than previous WiFi iterations.

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The economics of transformative AI.

2022 has seen dramatic advances in AI, particularly in the space of large language models, with progress being made much faster than expected. For the past couple of months, OpenAI’s chatbot ChatGPT has caught the world’s imagination, demonstrating its impressive writing skills through countless articles, and even outperforming Wharton University students in MBA exams. ChatGPT has gained more than 100 million users in its first two months of existence, marking the fastest digital product launch in history, and is now estimated to produce a volume of text every 14 days that is equivalent to all the printed works of humanity. Its potential is such, and the stakes so high, that Google has decided to launch its own version, Bard, using Google’s existing large language model LaMDA, that can engage in free-flowing conversations with humans.

By Anton Korinek, Professor, Darden School of Business, University of Virginia and Rubenstein Fellow, Brookings.

The economics of transformative AI.

Against the backdrop of these developments at the beginning of 2023, I would like to share some facts, thoughts and opinions on the implications of the future developments in the field of AI, and what shape the technology might take in the future.

1. Cognitive automation: This is set to be a major trend in 2023 and beyond. As the capabilities of large language models continue to expand, cognitive workers (such as myself) are increasingly at risk of being automated. This means that economists must abandon the notion that automation only affects routine jobs, and that human creativity is somehow — miraculously — immune to automation. To keep up with this new reality, economic models must be adapted accordingly.

2. Language models — from assistants to tutors: in coming years, cognitive workers will increasingly incorporate large language models into their daily workflow. In the short term, this will make cognitive workers more productive — they can outsource countless small tasks

to their new digital assistants. Humans will have to focus on their comparative advantage: whereas content creation is increasingly commoditized by large language models, discriminating which content is useful is still something that humans are better at. Over time, the digital assistants will increasingly turn into digital tutors – teaching their users new concepts and expanding our horizon. I have recently completed a guide for how cognitive workers can take advantage of large language models. Although I focus on applications to my own field, economics, the lessons apply broadly to all cognitive workers.

3. Exponential growth in compute: Progress in AI is continuing relentlessly, propelled by a combination of advances in hardware and software as well as ever-growing training budgets. This has resulted in doubling times in compute of about six months for cutting-edge models — much faster than Moore’s Law — a regularity that has held for almost a decade now. Note that the dollar cost of training runs is also growing exponentially for cutting-

edge models — and currently in the realm of eight-digit dollar amounts. Since expenditure on compute is growing much faster than the overall economy, an evergrowing portion of our economy’s resources is devoted to compute — the beginning of an AI take-off!

4. Economic growth: Compared to the macroeconomy, investment in AI is still relatively small, and it will take several more rounds of doubling for the macroeconomic effects to be felt. The economy is like a large vessel that takes a long time to turn, so I don’t anticipate that the recent advances will be reflected in investment, productivity and growth numbers at the macro level in 2023.

5. Growing public attention: OpenAI’s ChatGPT, released in November, gave the public a firsthand experience of the power of advanced AI and significantly increased public awareness of the abilities of large language models. (If you haven’t already, try it out to get a glimpse of what the personal assistants of the future will look like.) Technically, the system is just one customization

of one large language model (GPT3.5) among several others that have demonstrated a growing level of general intelligence. But it represents yet another small step forward toward artificial general intelligence (AGI), i.e., toward AI systems that can perform all cognitive tasks that humans can perform. The next generation of language models are rumored to be released soon and will display even greater general intelligence.

6. Expanding Overton window and AGI governance: As conversations about AGI become more commonplace, the Overton window — the range of ideas and policy options that people view as reasonable to discuss — is rapidly expanding. This will bring AGI governance to the forefront of the public discourse. Important questions include both how AGI will interact with existing governance structures and how AGI itself should be governed. Economists have much to contribute to these topics, providing ample opportunities for cutting-edge research papers and dissertations. Our Oxford Handbook of AI Governance, set

to be published in early 2023, will make an influential contribution — and many of the chapters are already available online.

7. Preparing for the non-existent future of work: In the medium term, our society will have to adjust to a world in which human labor is largely redundant. And this may happen sooner than many expect, perhaps even within the current decade. Cognitive automation is making policies such as a universal basic income more urgent and more appealing. To better understand how to prepare for this non-existent future of work, I recently published a report on the topic. What is more, if cognitive work becomes redundant, we must also re-evaluate the purpose of education at a fundamental level.

8. Life after cognitive automation: Finally, although it’s crunch time for research on how to govern AGI, I am also taking time to reflect on life after cognitive automation has made me redundant as an economist and to prepare myself mentally so that I’m ready when the time comes.

How bad data management is hindering AI advancements.

Experts conjecture that the global market for AI is set to grow 37.3 percent from 2023 to 2030. This is testament to how fundamental AI has become to steering business endeavors in the 21st century, as well as how enthusiastic those with a finger to the pulse are for increased developments. Yet many businesses are struggling to capitalize on the opportunities already offered by AI, or worse still, are unknowingly blunting their competitive edge.

By Guro Bakkeng Bergan, VP GM

EMEA, Fivetran.

How bad data management is hindering AI advancements.

The varying states of progress between AI-hungry companies is best demonstrated by the results of a recent survey of senior IT and data science professionals, conducted by Vanson Bourne. The survey revealed that 85 percent of organizations use Machine Learning (ML) or AI methodologies to build models, but only ten percent of those had been doing so for over a year. When queried on why organizations were not further ahead with AI progress, nearly half cited that data leaders within the organization were too busy with other tasks.

With 99 percent of those surveyed agreeing their data strategies had room for improvement, organizations would be wise to evaluate what is currently in place. Only then can the right steps be taken to rectify bad data and elevate insights.

AUTOMATE PIPELINES TO ELEVATE INSIGHT

To reach AI maturity, businesses need to ensure that data processes within the organization are compatible with practical applications of AI. This means, first and foremost, that data pipelines –which transport the data between applications, databases and analytics tools – must be flowing with clean, fresh and reliable data. Yet, all too often, this fundamental step is where the process breaks down, leading to bad AI insights further downstream.

When you picture the steps every business must complete to make raw data analysis-ready –including ingestion from multiple disparate sources such as business systems and applications; preprocessing; and transformation

– it’s obvious that managing it completely manually, and atscale, will create issues and waste resources. In fact, in the Vanson Bourne research, when data professionals were asked what part of the AI workflow process could be improved through automation, the two most-selected

responses were data ingestion and data transformation – two steps intrinsically linked with data pipelines. A stark result when considering that 9 out of 10 businesses also claimed they still manually build and manage their data pipelines.

This approach will ultimately provide businesses with stale data that is either unusable and unreliable when fed into ML algorithms. Indeed, 71 percent of data analysts struggle to even access the data needed to run AI programs.

DEVELOPING TRUSTWORTHY DATA

This is where we find the other great barrier to successful AI adoption: distrust. When surveyed, a colossal 86 percent of respondents claimed they would struggle to trust AI to make all business decisions, because of ongoing concerns around underperforming AI models built using inaccurate or low-quality data. The process then becomes a vicious cycle, as the lack of trust complicates buy-in from stakeholders who control budgets and strategy. It’s a mistake to assume that every stakeholder will have the same practical knowledge of data processes as data teams, so communicating the negative impact of bad data is of business-critical importance. If this education is not done, the data infrastructure underpinning AI programs will continue to miss out on the attention and investment it needs to deliver material results.

Today, underperforming AI programs are costing organizations as much as five percent of their global annual revenue – in the future, this financial and opportunity cost can grow even further.

On the other hand, making simple improvements to the underlying data management processes can catalyze innovation far surpassing decision-makers’ expectations. Automating data pipeline management and centralizing data in a central location – such as a data lake – make a great place to start. By outsourcing the burden of building these systems from scratch, businesses can free up data talent to work on high value-added tasks and rest assured that data insights feeding into dashboards, reports and AI programs are clean, fresh and –crucially – reliable. Avenues for growth, that once seemed marred by hurdle after hurdle, will become obstacle free and stakeholders’ confidence in AI-led decisions will subsequently improve.

MODERNIZATION STARTS FROM WITHIN

As has always been the case, businesses cannot expect new results from old habits. With underperforming AI programs already eating into organizations’ competitive edge, now is the time to lift the veil on bad data processes and ensure all the cogs are turning smoothly.

The good news is that many businesses are already close to using their data as a springboard into AI-driven decision-making. By removing the barriers to data access and insight, they can now set their sights on the future.

How to ‘Bake’ AI into direct store delivery.

For those outside the grocery industry, direct store delivery (DSD) is a distribution model used by manufacturers/CPGs that entails delivering products directly to retail store shelves. This model is extremely conducive for perishable food categories. During your typical grocery shopping, you may notice that store employees these days do relatively little replenishment in the aisles. For many brand-name goods, those tasks are now the responsibility of the respective delivery person from the manufacturer or CPG, who wheels pallets off their delivery truck and directly restocks those items themselves within the designated shelf space negotiated between the company and the store.

By Jasneet Kohli, Global Head/VP of Solution Consulting, antuit.ai

How to ‘Bake’ AI into direct store delivery.

That front-line driver or agent spends most of their workday making stops at multiple stores along their regular delivery route. With limited space on the truck, and variable demand patterns affecting different stores, CPGs must optimise their delivery systems, assuring every retail outlet consistently maintains right-sized quantities—or what we term optimal stock position— across every SKU, particularly for perishable foods.

One prime example of a DSD category is bread and other baked goods. As a perishable food, the bread market is uniquely shaped around three challenges:

1. Fresh bread has a relatively shorter shelf life. Major bread companies have typically conceded an intrinsic (or dare we say it, baked-in?) return rate of 6-7% of their product as it passes its sell-by dates on store shelves. With the razor-thin margins of grocery products, waste reduction—shrinking that spoilage rate however possible—has become an important KPI in this space.

2. In this age of instant gratification, where new product categories rapidly redefine the marketplace, bread consumers in particular tend to be quite discerning. Those who aren’t fiercely brand-loyal may still be motivated around health-driven options—whole wheat, organic, non-GMO and so forth. When their preferred choices become chronically understocked at one grocer, they’ll eventually seek them out among competitors. Grocery retailers rely upon the efficiency of DSD CPGs to retain both customers and revenue.

3. Demand patterns for baked goods are hyperlocal, influenced in large part by consumer demographics and store attributes. Other key variables—seasonality, holidays, events, price changes, product transitions, and new product introductions—make it even harder to predict true, unconstrained consumer demand.

With so many variables in play, where can advanced artificial intelligence and machine learning (AI/ML) data science factor into a predictive ordering tool— leveraging a wealth of data sets to optimise on-shelf availability across every retail outlet, while minimising waste? An ideal solution would encompass five key points:

•Generate an unconstrained statistical forecast at a day/ store granular level, augmenting statistical baselines with emerging demand variables that AI can track in real time—such as seasonality (in the case of bread, demand for sandwiches during back-to-school), retailer promotion schedules, even localised weather conditions.

●•Automatically convert those forecasts into optimal/suggested order quantities, leveraging business rules and inherent DSD constraints such as tray rounding, service days, and material availability.

• Perform exception-based reviews, spanning various dimensions such as customer and route, as well as incentivising drivers’ performance for speed and efficiency.

• Include capabilities to make late adjustments on-the-fly, based on perpetual inventory recommendations and in the field last-minute insights.

• Drive business performance by measuring KPIs throughout the lifecycle of the product. Facilitate rapid corrective actions around business decisions or model tuning.

The technology behind DSD predictive ordering has already made huge strides, thanks largely to leading-edge AI/ ML innovations. Specifically, dynamic aggregation algorithms, leveraging cloud-scale computing power, are successfully refining enormous volumes of data at a time series level. This ultimately gleans the most actionable data points into what we term the optimal order—or the right-sized quantities for every DSD deliver route, driven by AI forecast and visibility into components such as base, promotion, seasonality, and more.

This data can be directly accessed by delivery drivers and other frontline personnel via a tablet-based user interface, an essential tool for every driver as they efficiently service every stop along their daily delivery routes.

Over the past few years, we’ve witnessed the continuing evolution of grocery DSD firsthand, as antuit.ai customers already account for an overwhelming majority of the retail bread market in North America, spanning iconic brands as well as popular niche products. The innovation of AI-powered predictive ordering that has already helped redefine the baked goods space can also be successfully applied across multiple other consumer goods categories as well. The innovation of AI-powered predictive ordering that has already helped redefine the baked goods space can also be successfully applied across multiple other consumer goods categories as well.

AI Tech: A game-changer for enhanced influencer marketing campaign results.

Artificial Intelligence (AI) tools have been embraced by an array of Tech industries, with the world of Influencer Marketing being no stranger to applying AI for better campaign performance. Among the many reasons to use AI tools, strategically developed AI-led campaigns can help businesses gain traction from their most desired audiences, not to mention, AI holds the power to generate a long-lasting impact, resulting in better ROI and customer retention.

By Daria Belova, Marketing and PR Director of HypeFactory.

AI Tech: A Game-Changer.

GOOD-TO-KNOW BEFORE GETTING STARTED WITH AI

With the industry boom in influencer marketing, it’s become increasingly difficult for even veteran marketers to keep up with industry trends and to discover a top-performing influencer between the masses. Although it may seem like a valid reason to implement AI to ease the job, marketers continue to have their share of reservations and difficulties with AI.

At present, the influencer marketing industry holds enormous potential for combining creative human talent with AI to produce results-driven marketing campaigns and, regarding the return on investment, it looks like a lucrative investment. However, in reality, AI is just a smarter technology to improve campaign results, not a silver bullet.

There exists many ethical concerns about using AI in marketing as these automated tools rely on an abundance of personal data for the tool to keep learning and improve accuracy of personalised and tailored marketing

suggestions. Nonetheless, when data handling is treated with transparency, AI in marketing has changed the game for valuable lead generation.

USES OF AI TECHNOLOGY IN INFLUENCER MARKETING

The online playground has become a very competitive space for influencers to secure brand partnerships. With amplified competition, many influencers resort to black-hat tactics like buying followers to bump-up their follower count and engagement ratings. This in a bid to attract clients and partnerships. These influencer frauds are very costly to brands, causing damages of 1.3 billion USD. AI has been a game-changer to ensure brands are partnering with authentic influencers.

Identifying Influencers with Appropriate Target Audience: A popular issue that AI can take care of is to determine the influencer’s follower profile and confirm it matches the brand’s intended audience. With appropriate AI tech-tools, influencer accounts go

through an extensive, multi-layered selection process that uses filters such as age, gender, language, location, interests, engagement, and advertising of competitive brands. This helps marketers to narrow down the focus on key audiences. For example, HypeFactory, for instance, uses a multi-layered selection process that includes 38 filters to identify the best influencers for a brand’s campaign.

Fraud Detection: As AI matures and becomes more sophisticated, it becomes incredibly difficult for influencers to be dishonest about the quality and ROI potential of their channels and audiences. AI algorithms can detect fraudulent influencer behaviour, like buying fake followers. This ensures that brands partner with a genuine influencer and engages a lucrative audience. Essentially, Ai prevents any undesirable campaign outcomes that could work against the brand’s reputation.

3 TIPS FOR BETTER INFLUENCER MARKETING PERFORMANCE

Continuous testing and learning, scaling up best-performing ideas, co-creating content with creators and integrating it into creative and product, along with experimenting with new social media platforms, are critical elements that need to be implemented for better marketing performances. With or without the presence of AI tools.

1. Focus on the brand’s niche: Keep influencers from the brand’s specific market vertical in mind, but don’t forget about the brand’s other interests in neighbouring niches. Examine what these interests might be, scrutinise and gauge influencers from those areas.

2. Tailor brand messaging: Make ads stand out from the rest by personalising it to the brand, and take note of current trends, for example, many people are drawn to ads with virtual influencers.

To make campaigns even more captivating, consider using additional engaging mechanics such as AR-masks.

3. Build long-term relationships with high-performing influencers: Spend more time crafting quality relationships, rather than constantly pursuing new ones. Save time by automating the process of finding new influencers. Also allocate enough time to develop creative strategies that will capture the user’s attention. Additionally, building trust with an influencer’s audience takes time and can’t be accomplished with just one campaign.

HARNESSING THE POWER OF AI-GENERATED MARKETING CONTENT

With AI-generated content becoming the latest digital trend, particularly in the context of virtual influencers and the growth of the metaverse, AI can be used to generate virtual influencers, create copyright-free images for influencer marketing campaigns and verify the uniqueness of a brand’s creative ideas. This can

help increase the effectiveness of campaigns, make brands stand out from competitors, and prevent copyright infringement. Note that the success of virtual influencers still rely on content marketing teams who know how to use AI tools effectively. Brands cannot place all their faith in AI-generated content to achieve top performance without people operating behind the scenes.

CONCLUSION

The use of AI technology is undeniably a breakthrough for influencer marketing. In essence, the use of AI in influencer marketing can help brands to be more strategic and efficient in their influencer marketing efforts, ultimately driving better business results. Brands are able to take advantage of AI tools by optimising campaigns more effectively, following KPI’s better, and getting a greater insight into who they’re targeting. AI technology is expected to play a crucial role in influencer marketing as the market continues to grow and evolve.

AI: The brains behind energy saving, customer service and product development.

Reducing carbon emissions from our built environment is an increasingly urgent conundrum that’s giving rise to a new generation of ‘smart’ buildings that interconnect technologies like artificial intelligence (AI), the internet of things (IoT) and data analytics to optimize performance and minimise energy use. Today’s buildings are responsible for approximately 40 percent of global energy-related carbon emissions1, with almost a third of this resulting from energy used to light, heat or cool homes, offices and shopping malls. The remainder are ‘embodied’ emissions from the building process, maintenance and demolition.

By Bertrand Vandewiele, Global Head of Commercial Building Solutions at ABB.

AI: The brains.

Internal building facilities that can be controlled and optimized by an automated, centralized system include heating, ventilation and air conditioning (HVAC) and lighting, and can account for as much as 50% of energy use in an average commercial building2. Reducing day-to-day ‘operational’ emissions from HVAC systems is one way that building operators can satisfy increasingly stringent environmental legislation, as well as keep energy costs down in light of spiralling electricity prices.

AI platforms that connect directly to a building’s HVAC system, and utilize data to optimize control, can help reduce a building’s carbon footprint, supporting net zero and cost reduction goals.

AI also is being used to power chatbot tech support for smart home and commercial building technology which has a doubleadvantage of also gathering customer feedback data for future product innovation and development.

INTELLIGENT HVAC SYSTEMS

Existing HVAC systems, even in larger buildings, will likely use a basic, non-programmable thermostat that automatically triggers the heat or air conditioning through an electrical connection when the temperature deviates from the desired setting. Using AI transforms the building’s HVAC system from reactive to pre-emptive, monitoring it 24/7 and making decisions based on advanced deep learning models. Autonomously driving HVAC systems in this way eliminates the need for human interaction.

Let’s take the scenario of an office lobby to demonstrate how an HVAC system can be upgraded to learn, reason, and even solve problems. Newly installed revolving doors in the lobby let outside air in. A standard HVAC system would have wasted energy as it struggled to regulate the fluctuating temperatures, whereas the AI upgrade quickly learns and adapts to its changing surroundings.

Similarly, using machine learning (ML), AI reasons that the office’s communal area will grow warmer at lunchtime as more workers congregate in that particular space. Not only that, predicting that the room will be warmer at this time, AI can automatically adapt and adjust the air conditioning prior to lunchtime, simultaneously ensuring maximum comfort for the occupants while conserving energy.

The system can even optimize the energy mix by turning on or off earlier or later in a given zone, based on energy costs (in peak times), runtime hours and energy mix – further improving the ROI and reduction in carbon footprint.

Connecting software and control hardware to existing HVAC systems so they become selfadaptive and more intelligent can delivers an ROI of more than 150 percent4, with building owners and operators report decreased energy costs of 30-50 percent through investing in smart automation.

BUILDING A CLEANER FUTURE

Our recent collaboration with pioneering Montreal-based scale-up BrainBox AI, is a realworld example of how AI is being used to great effect to optimize HVAC systems in the urban built environment across a wide range of asset classes, including commercial office towers, retail shopping centers and hotels.

The resulting technology solution is built on a digital platform and powered by cloud-based artificial intelligence, in order to provide the owners of smart buildings with a simple way of optimizing their energy consumption.

It does this by employing predictive algorithms that eliminate the need for human intervention, as well as an array monitors, analyses and reports that help building owners and managers to track energy efficiency, all of which can be seamlessly integrated into a building’s existing HVAC system.

The solution is currently being used in around 100,000,000 ft2 of commercial real estate and BrainBox AI report companies achieving up to 40% decreases in carbon footprint, up to 25 percent in energy costs, and up to 60 percent improvements in occupant comfort. This adds up to 50 percent to the lifecycle of HVAC equipment.

As a result the solution is already providing cost and energy savings in buildings as diverse as shopping centers in Australia, office and retail buildings in Canada, and an 82,500ft2 medical center in New York, US.

HOW AI IS OPTIMIZING CUSTOMER SUPPORT

Another collaborative innovation involves an AI-driven hardware support platform called Mavenoid, which combines AI and ML to reduce the complexity and cost of customer support by solving enquiries with high success rates and limiting the number of escalations to human service teams.

The technology utilizes data from sources such as manuals, product documentation and online communities to train its ML and AI engines to resolve repetitive support requests. Providing automated support via the existing App, enabling self-resolution and significantly reducing the number of support cases referred to help centers.

Despite $1.3trn8 being spent globally every year on 256 billion customer support calls, 50 percent of them still go unresolved. In addition to responding to this challenge and improving customer satisfaction, the Mavenoid’s platform allows installers to add new services and leverage end-user feedback to drive future product developments, and is already being used in selected markets with smart home solutions.

These latest innovations not only demonstrate the flexibility and scale of industry 4.0 technologies such as AI, ML and the internet of things across multiple industries and use cases, but also their almost limitless potential to reduce both complexity and cost, improve efficiencies and contribute to a cleaner, more sustainable world.

The Agile Mindset: Getting business evolution right.

Following the pandemic, as organisations across the globe were urged to drive their digital agenda to adapt their way of working, many tech companies benefited from the universal need for their products and services. In fact, some of the tech giants, which were initially worried about how Covid-19 and the ensuing restrictions may affect them, reported record revenues. And it wasn’t just the big tech companies that benefited during that period. Smaller scale tech companies were also growing faster and became more profitable than they had been in years.

By Steve Martin, CEO, Agilisti.

The Agile Mindset: Getting business evolution right.

Does this mean that tech companies have emerged invincible and stronger than ever after the pandemic and will continue to grow? Enter 2023 and it’s not looking that likely. A jittery market braced for recession coupled with ongoing skills and labour shortages and retention issues means that these days the tech industry is more focused on dealing with challenging times ahead rather than growth. And judging from the thousands of jobs which were recently slashed by big tech companies, it looks like the tech industry is bracing itself for a downturn.

With all that in mind, it’s more than likely that 2023 will be a year when many tech players will be prioritising the consolidation of existing processes, leaner operations and making the most of their human capital. The question is, how can they do this without compromising their growth journey and without losing sight of how they can continuously add value to their customers? The answer is simple: Agile can help them do that. The implementation of Agile? Not so simple. Or at least it isn’t for organisations that doesn’t fully understand how to make it an integral part of their evolution.

Agile has been used across industries, from manufacturing to IT, for a while now and yet there are many misinterpretations of what it is and how it can be of particular relevance to the tech sector right now. If the success of the tech sector relies on doing what it currently does but more efficiently while retaining its talent, then it should go beyond Agile practices which simply focus on how everything works. It needs a complete mindset pivot that embeds Agile into the organisational culture and trickles it down and across the organisational matrix. Changing the way teams work should only be part of this process, deep diving into its cultural roots to become a truly Agile organisation is where the real evolution happens. The first step to growth and evolution is accepting that there is a need for change. Only once this reset has taken place can leaders look into how to truly cultivate trust in their organisations and give teams the freedom to experiment and innovate.

The original Agile Manifesto, which was created over 20 years ago, focused on four core values which included: responding to change rather than following a plan, individuals over processes, working software over comprehensive documentation and customer collaboration over contract negotiation. In today’s environment, these values can only take the tech industry so far, especially since most tech companies have a wider scope than just offering tech solutions. Getting started and succeeding in the road of optimum Agility takes strong and committed leaders who want to implement change across the organisation’s ethos and culture and are willing to make allowances for teams to adapt and give them what’s needed to succeed while supporting those who may not share the same vision. And they can only do that through trust and transparency.

Adopting these values is what can lead to continuous growth. Agile organisations and teams are always looking for avenues to become more efficient and effective in a sustainable way and have the culture to adapt accordingly. In truly Agile organisations, the growth focus is not purely on product development and service delivery and is not siloed away in teams which are traditionally responsible for those areas. Continuous improvement and experimenting with new ways of working, measuring, learning and adapting are the cornerstones of crossfunctional, high-performing teams. This doesn’t mean steering clear of systems, quite the opposite. Continuous improvement should rely upon teams that adopt a systemised approach using key data points and relevant feedback sources to identify and make changes.

The first step to embracing the Agile mindset is to start with what you have. Take a step back from the operation and look at the steps required to optimise and improve. The great thing about Agile is that its versatility is the perfect match for fast-moving industries. It can be adapted into any scenario, team and service area. It gives teams on the ground the power to make decisions and offer insight that feeds into growth plans and it gives leadership teams a full view of the organisation. And it’s this 360° perspective that allows organisations to brace for bumps on the road and emerge stronger, driving real value to their stakeholders.

Improving public safety with AI.

Data is a key element for authorities and emergency services to improve public safety. Dispatchers and first responders need access to the right information quickly and easily to help them handle incidents in the most appropriate way possible. This includes information to ensure the safety of the responders themselves. Nonetheless, the constant flood of information from alarms, alerts, calls for service, CCTV, social media, and other systems can overwhelm already stretched public safety personnel. While data is of course imperative for public services to run efficiently, there can be too much of a good thing. Recent advances in artificial intelligence (AI) are helping organizations sift through data to uncover insights both during and after an emergency. This assistive AI can help streamline processes by doing the heavy lifting, making connections and recommendations. From dispatchers and first responders to crime analysts, detectives and city officials, everyone can benefit.

By Peter Prater, managing director, Hexagon’s Safety, Infrastructure & Geospatial division UK.

Improving public safety with AI.

HELPING FRONTLINE RESPONDERS AND DISPATCHERS

Assistive AI can work to optimize and automate repetitive, laborintensive activities, streamlining operations and enabling emergency services personnel to dedicate their time to incidents that require their attention. For example, AI embedded within dispatch systems can provide actionable insights during an emergency, detecting patterns from among the different emergency calls and making recommendations to dispatchers, allowing insight into possible linked situations. AI can also help first responders monitor incidents in real time and anticipate ongoing risks where possible.

By integrating with AI, operational analytics and data can provide new levels of assistive insights about emergencies as and when they unfold in real time – from determining links among related calls and events to understanding a deteriorating incident. Reports can also be generated that can predict peak call times enabling earlier intervention and quicker response times, therefore addressing public demand.

PRESENTING A UNIFIED VIEW FOR DETECTIVES AND CRIME ANALYSTS

In the past, agencies have relied on database research of past events to help detectives solve crimes. Today, agencies are increasingly relying on AI to help solve cases much more efficiently and quicker than before.

AI systems can be set up to monitor data pertaining to specific locations or keywords and alert detectives and analysts when a new, relevant incident or report is logged. These proactive notifications can help police stay on top of events and make connections faster than before as well as constantly analyzing patterns in data, that might otherwise go unnoticed.

Considering crime analysts need to mine data from multiple sources, identify patterns and analyze trends to help detectives and patrol divisions do their jobs effectively, it seems the utilization of AI is best able to achieve results, keeping communities safer.

REAL-TIME OPERATIONS CENTERS

A growing global trend in public safety is the emergence of real-time operations centers. Though a new concept in the UK, these centers are expected to gain popularity as the need for real-time data increases. They can operate continuously, deliver information in real time, and provide full visibility of incidents and information across a city or region, which can help guide resource deployment and investigations.

Whilst these centers can combine multiple data sources and systems, as expected, not all data may be relevant to the problems that these agencies are trying to solve. For example, the London Assembly has launched a four-step data methodology to better design data projects. This framework helps organizations have more effective conversations about which data sets are needed to achieve a given outcome. As a framework, it encourages data sharing and collaboration across the entire data governance process to make

it easier for boroughs to build automation into the data exchange process, reducing the time from analysis to action or intervention.

Similarly, with the utilization of AI, operations centers can function much more efficiently. For example, it would be possible to flag anomalies and trends from city-wide data and make proactive recommendations that teams can act upon quickly.

In addition to enhancing public safety, these operations centers can streamline processes by helping multiple city services collaborate to see the full picture of data and incidents happening in their jurisdictions. In the UK, city departments, like traffic management or utilities, tend to function independently, with little to no communication between them. As such, efficiency is significantly reduced, which could negatively impact crisis situations.

Leveraging AI within an operations center enables the mining of public safety data as well as data from traffic management, public works, utilities and more. By sharing

data from different departments in an integrated operational view, coordination within cities can be vastly improved, which helps to keep cities safer.

SECURING A SAFER FUTURE FOR THE PUBLIC WITH AI

It is clear there are an array of benefits in leveraging data to drive better decision making for public safety. The utilization of assistive AI enables agencies to streamline processes, working alongside dispatchers, investigators, and analysts, to name a few. By making connections between data, it can help deliver greater insights between departments and better outcomes for the public overall

AI-Augmented TestingA Developer’s Superpower.

Artificial Intelligence (AI) has been captivating our imaginations for a while now, but the main point of difference today is that the technology is no longer considered a novelty. With the rise of Machine Learning (ML) and innovations such as ChatGPT, society is making giant leaps in adopting AI into our daily lives. Moreover, enterprises and organisations across multiple sectors are using AI to help them increase efficiency and enhance productivity.

By Chris Trueman, SVP LiveCompare, Tricentis.

AI-Augmented TestingA Developer’s Superpower.

AI IN SOFTWARE DEVELOPMENT AND TESTING

The evolution of AI is quickly becoming a group project, with multiple Big Tech companies such as Google and Microsoft building powerful AI/ML developer tools. AI is no longer considered a “nice to have” - today, it’s a critical part of digital transformation to increase business’ stability, flexibility, and scalability.

Software development and applications are at the core of these digitalisation initiatives. However, too many companies still need to rely on legacy tools when it comes to testing. This remains a challenge, as modernising testing isn’t always perceived as being of business value, with other technology functions often invested in first. Quality Assurance (QA) is also often viewed as secondary to development efforts, with DevOps opting for obsolete ‘good-enough’ tools that lack scalability and enterprise-grade features.

Fortunately, more than 86% of CIOs and senior tech leaders consider AI a key criterion for selecting QA solutions. In addition, with more organisations shifting their workload to the cloud, there is an urgent need for low-code/ no-code applications, and AIpowered test automation is key for this transition to be successful.

While some questions may remain around AI adoption, it’s clear that the most valuable teams within enterprises are those that work to harness the full power of the tools available. In the case of AI, its adoption will help to further intertwine developers and testers to work more efficiently together, thereby ensuring that their work will contribute direct business value. In today’s fast-moving world it is the fast and efficient businesses that will thrive. So, how can DevOps teams leverage AI positively in order to fuel innovation and positively impact their business operations?

UNDERSTANDING THE AI BENEFITS IN TESTING

It’s imperative for organisations to understand how much automation they are trying to achieve. According to recent research from Forrester, there are five levels of automation that can be applied to software testing, which help clarify where human intervention will diminish as automation evolves. Whilst most companies might aspire to achieve a fully automated state to increase business velocity, most companies have only achieved levels 0 to 2 out of the possible 5 levels of automation, which means machines can help build API, UI or unit tests after showing them how. So, let’s have a closer look at current use cases of AI in the testing market:

Visual Testing - used for finding cosmetic bugs in applications by comparing a baseline screenshot to a future one, visual testing helps discover issues not picked up by functional testing tools querying the Document Object Model (DOM). It’s imperative to ensure that applications run smoothly, however, it’s impossible for DevOps to test every single device manually. Equally, applications can pass functionality tests whilst failing to scale when, for example, switching from Android to iOS devices. This is where AI-powered solutions can help detect visual issues at scale. However, a human tester is still required to review and accept suggestions, and AI will need to be taught which visual cues to look out for.

Test building from mock-ups –with the help of ML, testers can create the assets by scanning the mock-up designs of an application. This feature allows for testing to start earlier in the development process thus leading to fewer issues as time pressures mount. This also helps accelerate UI maturity and allows testers to run the same test cases

without modifications. With many applications having native web and mobile interfaces, reusing the same tests allows DevOps to ship products faster with higher quality. Should any drastic changes be made, self-healing AI - a system to auto-fix broken test cases - can be applied to detect visual changes such as location, control type and design.

Intelligent bug hunting - AI can analyse common failure patterns and prioritise tests with the most common bugs. However, in this case, AI is limited to analysing test case failure data, creating blind spots in high-risk areas of an application; therefore, human input is still required.

Smart impact analytics - powered by AI - helps prioritise what test should be run to verify changes to applications in code, configuration, integration, and security. This dramatically increases risk coverage by focusing on the right testing.

Test design and optimisationhelps analyse production data and process discovery and automatically generates the right combination of test cases. As a result, testers can prioritise features and test cases.

As organisations rush to implement and deliver new digital innovations faster than ever, many still need to rely on clunky and high-maintenance legacy tools, leaving critical gaps in their business operations. AI integration will provide a helping hand for DevOps by empowering exploratory testers to find more bugs and issues upfront, streamlining the process from development to deployment.

Implementing augmentation helps inform decision-making processes for DevOps teams by finding patterns and identifying outliers. This frees up time for teams to focus their brain power on the tasks that they want to do and that are more strategically important to the business.

Why AI technology will take away the nightmare of business admin and reporting.

AI technology is the future and the big tech players are gambling on it. Just recently, Microsoft confirmed its ‘multimillion dollar investment’ in ChatGPT bot maker, OpenAI, bringing AI even higher up the agenda. The race is most certainly on, with Google introducing their own conversational AI service Bard, and Chinese company, Baidu announcing its own ChatGPTstyle service as well. But what is it? And what does it mean for the future of work and business? AI, otherwise known as artificial intelligence, refers to the development of computer systems that can perform tasks that would typically require human intelligence. AI technologies, such as ChatGPT, come in a wide range of techniques and approaches, such as speech recognition, machine learning, natural language processing and robotics.

By Ambroży Rybicki, CEO and co-founder, ARP Ideas.

AI technology takes away the nightmare of admin and reporting.

And as the hype around AI has accelerated over recent months, businesses are looking for new ways to optimise and promote their products, services and operations with AI. In fact, AI technology has already begun to permeate through so many aspects of businesses, from data analytics to building models. Businesses are dealing with complex, and vast amounts of data thanks to the rapid digital transformation we saw during the pandemic - and AI has become the key to sorting and unlocking the true meaning and value of this data.

So, how can AI transform business admin and reporting? What are the advantages in the workplace?

Improved efficiency and productivity: AI can automate many time-consuming and repetitive tasks, freeing up employees to focus on more complex and strategic jobs. By automating repetitive and timeconsuming tasks, businesses can benefit from increased efficiency and productivity in the workplace. Employees tend to be dissatisfied when they don’t feel motivated

or engaged with their work, particularly when it comes to administrative functions. Therefore, AI can also be a huge support in improving employee satisfaction and retention.

Improved accuracy: When it comes to repetitive administration, it’s particularly easy to make typos and small errors, however with AI, this risk is reduced. This means businesses can trust the reliability of the information produced, and lower the risk of human error, saving businesses money by preventing potentially very costly and overlooked mistakes.

Enhanced customer experience: AI is transforming customer communications, making it easier for businesses to connect with consumers and provide 24/7 support. With AI-powered chatbots and virtual assistants, businesses can not only improve their response times, but also provide more personalised and valuable support at a fraction of the cost. By enhancing customer experience with AI, businesses are likely to generate and maintain higher levels of customer satisfaction and loyalty. With this

high quality customer service and experience, businesses can even gain a competitive advantage in the market.

And, when it comes to sales, cutting edge AI and collaborative capabilities of Microsoft Dynamics 365 Sales takes customer experience to the next level, giving sellers a 360 degree view of the customer. This solution allows businesses to better learn from customer history, access information from external data sources and identify the next best step to take - all within one AI based tool. With this, sellers can see, act on and suggest actions to sell more, faster, and with greater impact.

Report generation: The beauty of AI is that it goes beyond sorting data, and provides actionable insights and reports based on its extensive, accurate and rapid, data analysis. With this level of analysis, businesses can make more informed decisions, based on recommendations that surface from AI technologies. Solutions like Power BI, which features AI, offers businesses data analytics features

to really bring this data to life with tools for aggregating, analysing, visualising and sharing data. With this, businesses can access image recognition and text analytics, create machine learning models using automated ML capabilities and integrate with Azure Machine Learning. As well as benefiting from better visualisation of data, business leaders can rely on the credibility of these reports and identify trends, as it is all backed up by extensive and accurate information. This can be particularly useful in areas such as financial forecasting, inventory management and customer service too.

Cost savings: Amid a challenging economic climate, businesses are tightening their belts and looking to reduce expenditure where they can - particularly on marketing. This is where AI can step in. By providing insights on the customer journey, activity and preferences, AI makes marketing much more efficient, strategic, and far less costly than spending money on unnecessary and ineffective marketing costs - for instance, additional staff, sales teams and

campaigns that fail to generate sales effectively. Therefore, by automating tasks, businesses can save on costs associated with hiring additional staff to manage these processes, while driving sales forward in more strategic ways that better connect with customers.

These are just a few benefits, but the list goes on!

While AI technologies do of course come with an upfront cost, the long-term benefits will save companies millions, boost performance, provide remarkable reporting insights and ensure highly accurate and efficient processes. Looking ahead, we can expect widespread adoption in the business world, in the same way it will permeate our personal lives as consumers of the likes of ChatGPT.

However, successful implementation of AI remains key as, if it’s done poorly, it misses the point altogether and can actually worsen business performance. To successfully implement AI technology, organisations must develop a clear plan for its

implementation, starting with the business problems they need to solve and identifying the technologies and tools they need to resolve pain points and boost operations. Importantly, businesses are not alone, and collaborating with AI experts and IT consultants, such as ARP Ideas, will be essential in its implementation. Outsourcing technical expertise is a brilliant way to kick start and build out your strategy. Experts with experience in machine learning and data science, will be able to advise on and provide bespoke solutions for each business problem, integrating

AI-based technologies with existing business systems, and implementing them at a fraction of the cost of hiring in-house.

In summary, AI technology is rapidly advancing business operations - from automating repetitive business tasks, boosting efficiencies, enabling better decision-making and enhancing customer service. It’s inevitable that these technologies will be more present within the workplace, particularly when it comes to streamlining business admin tasks and reporting - taking the nightmare away, and allowing businesses to focus on the more challenging and complex work.

The Marriage of Man and Machine: Why the Success of AI depends on Humans.

The marriage between man and machine is more important than ever. Artificial Intelligence (AI) is rapidly transforming how we work, play and interact with one another. However, it is not just the technology that will shape the future of AI, it is the humans behind it. We are past the movie-portraying view of robots ‘taking our jobs’ or ‘taking over our world’ idea of artificial intelligence, and we accept that AI will play a significant role in the future. We need to embrace it, not for the sake of apathy or cost-efficiency but for the sake of innovation, growth, and evolution. More than that, it’s exciting. The technology we have is amazing, mind-blowing even, however AI is still in its infancy and there is a long way to go until it becomes truly human-like and before we can rely on it fully. For us to do that it would need to be better than a person, and it’s not there currently.

By Pete Hanlon, Group CTO, Moneypenny.

The Marriage of Man and Machine.

And far from making humans obsolete, AI will superpower humans to excel. That combination of humanity and technology is the real future of business. So, let’s explore why the success of AI depends on humans and how this union can be leveraged to create better solutions.

PEOPLEPOWER –THE FUTURE.

A people-based business has a culture that focuses on employees first, including customer relationships - the human element. And for such a business, people buy from people - that’s who they do business with. This is not going to change. AI will, however, enhance their ‘power’, allowing people-based companies to become more bespoke, more tailored; exceeding customer expectations and delivering a truly personalized experience. And that is where the critical advantage lies.

Allowing and embracing AI to do the hard work of data collection and analysis frees up your biggest asset - your people - to do what they do best. It provides the building blocks for better decisionmaking and problem-solving. It is the specialized intelligence to our generalized intelligence.

For Moneypenny, like many other people-based businesses, it provides a massive opportunity. Think of it like visiting a luxury hotel, where everyone knows what you like to be called, what paper you like to read over breakfast, how you like your eggs. We strive to be akin to that high- end concierge, the right-hand person worth their weight in gold that guests/clients simply cannot do without.

Rather than wait for the finished article, we know where the future is going, and we want to be a part of it. In adopting the technology early, we can fully understand it, and its potential. In using it as it advances, we are evolving alongside it, learning as the technology advances and making it a part of our future.

TECH ALONE IS NOT THE ANSWER.

The perfect marriage is that of people and technology. Technology is the enabler, but only when it is combined with the right people to adopt it properly and accelerate and escalate it. but only when it is combined with excellent people will it produce a first-class customer service. The challenge of business is to figure out the perfect combination of AI and people to produce that outstanding service every time. Using automation is scalable for business. It will perform well under an ever-increasing workload. Without the humanity, however, it can become impersonal. There is a delicate balance to be found, and it is do-able with the right infrastructure and culture.

HOW HUMAN AND ARTIFICIAL INTELLIGENCE CAN WORK TOGETHER.

AI is here. And it is a certain part of the future. It may even change the way we do business forever. But we are not there yet. Today, we must adopt early, embrace the potential, and see the amazing places the journey could take us. There will always be an opportunity for those who follow the route of mass-produced automation. How many places (covering all hotel star-ratings) now offer a fully automated check-in service, embracing the technology to give customers what they want, with in-App check-in to fully online?

We have discovered this amazing technology and now businesses are looking to take it to the next level. Humans and AI have complementary strengths and weaknesses. While AI is extremely good at processing large amounts of data, humans can make decisions based on their experience, intuition and creativity. This creates an opportunity for the two to work together to achieve powerful results.

For example, AI can be used to automate mundane tasks, freeing up humans to focus on the more creative and innovative tasks. AI can also analyze vast amounts of data quickly and accurately, providing humans with insights and actionable intelligence, leading to better decision-making together than either could make independently. What’s more, most recently, AI has been making leaps and bounds in its ability to not only solve complex problems but be creative. Adding voices of a certain artist or style to a song or recreating works of art. By leveraging the power of machine learning, AI is gaining the ability to generate new ideas and

express them through music, art, and writing. The results, however, are heavily reliant on information quality from the inputter, the human. High-quality input results in high-quality output.

When working together, humans and AI can generate solutions that are both efficient and effective. For example, AI can help to find solutions to problems faster than humans can, while humans can use their expertise to refine the solutions generated by the AI to ensure they are appropriate.

In summary, the Marriage of Man and Machine provides a powerful opportunity for humans and AI to collaborate in ways that neither could do alone. By combining their complementary strengths, humans can leverage AI to amplify their own capabilities and work smarter. They are the ones that will accelerate the technology, make it scalable and send business on a sustainable growth trajectory whilst also doing better business.

How AI is an ad stack supercharge.

In the face of a challenging macroeconomic climate, the UK digital advertising market remains remarkably strong, and is expected to reach $40.37bn in 2023. Indeed, with advertisers increasingly relying on digital channels for driving brand awareness and sales, platforms like Connected TV (CTV), digital audio and digital out-ofhome (DOOH) are picking up a larger slice of the ad spend pie. In contrast to just a few years ago, this investment would have traditionally been allocated to offline media.

By Louisa Jones, UK Senior Sales Director, Scibids UK

How AI is an ad stack supercharge.

This is not to say that the industry is without its problems however. The economic situation, amongst other geopolitical pressures, is having an adverse effect on the sector, forcing media planners to think more short-term and reactively. The digital media landscape itself is also looking increasingly complicated, with the development of privacy regulations making ad targeting and measurement less transparent, useful and understandable for media managers.

Thankfully, there are solutions today that digital marketers can utilize to make their advertising more effective than ever - namely, Artificial Intelligence (AI). AI solutions have been recognized for a few years as presenting huge opportunities for digital marketers, but it is customizable algorithms in particular which are transforming media-buying, and are essential for any competitive ad stack moving forward.

This is because AI solutions offer a significant improvement on human intelligence in media-buying, utilizing the full potential of data across the ad stack to supercharge ad performance and scale while reducing waste.

BENEFITS OF AI TO MEDIA BUYING

For several years, digital marketers have enjoyed being able to pursue better performance with Demand Side Platforms (DSPs). A key part of the open, digital ecosystem, marketers are nevertheless faced with growing challenges in this area. The volume of data available in a DSP, along with increasingly fragmented reporting, is making analyzing this data a big obstacle. This means that the valuable insights that can be gained are eluding digital marketers.

To solve this, more and more DSPs are opening up their APIs to enable brands to bring in their own third-party software. Here, customizable AI, and its ability to analyze data at scale, is supercharging the optimisation process. This explains the interest we are seeing brands express in customizable AI solutions, with more and more RFPs being issued for this purpose, and we expect further DSPs to recognise their worth as the industry moves forwards.

CLEANER TARGETING THROUGH AI

Customizable AI is also being sought after in solving the cookie issue. Indeed, with the deprecation of cookies on the horizon, marketers are eager to explore ways that can ensure they maintain effective targeting without user tracking and profiling.

Certain solutions can provide this targeting by forgoing the ‘dirty fuel’ of third-party cookies in favor of the non-user specific semantic and contextual metadata. Coupled with the ability of customizable AI to combine this with their accrued first-party data and consented third-party data, the result for digital marketers is increased media productivity through more effective ad targeting.

UNCOVERING DATA

Customizable AI’s capability with ingesting and analyzing data extends across the entire media stack, including the data traditionally siloed within different sections of the business. For example, a large retailer will likely have within its customer relationship management (CRM) systems and customer data platforms (CDPs) huge amounts of actionable data across categories like price position, inventory and POS data. The problem is that this data is often too massive and complicated for a human to draw insights from.

Customizable AI meanwhile can make it more readily available and therefore inform media-buying. Furthermore, as these algorithms are constantly being updated and fed back into the DSPs, media buying remains aligned with the latest business information in near real time.

CUSTOM AICUSTOM KPIS

With marketing teams under growing pressure to show measurable business outcomes from their budgets, customizable AI solutions can be leveraged to ensure that this spend is quantifiable against the KPIs that matter. For example, a metric which is of increasing worth to marketers is attention. However, this is not available as a standard metric within the DSPs; they require measurement providers to deliver these. Only through customizable algorithms can this type of data be actionable in the media buying process. This offers marketers the twin opportunity to further optimize investments against these more useful KPIs, as well as highlight their worth to key stakeholders more clearly.

As digital marketers contend with justifying and maximizing media spend in an increasingly difficult economic context, customizable AI will be key to achieving success. Its ability to analyze and make actionable data from across the media stack will serve brands well as they look to prepare themselves for future challenges. And for its sheer versatility, it is likely we will see customizable AI leveraged across digital marketing practices.

Conversational AI: Differentiating between relationship and function.

Conversational AI is growing in popularity for customers and businesses, and its capabilities have developed significantly since the technology first emerged. But as it grows, striking the right balance of its purposes is becoming more and more essential. So, what’s the difference between chatbots and regular bots? And what makes function-based resolutions and relationship value-add opportunities different?

By Rob Mead, Head of Strategic Marketing at Gnatta.

Conversational AI: Differentiating between relationship & function.

THE DIFFERENCE BETWEEN “BOT” VS “CHAT”

Chatbots and bots are the product of combining natural language processing (NLP) with traditional software. While both can help customers through typed and spoken interfaces, they aren’t entirely the same.

SO, WHAT’S THE KEY DISTINCTION BETWEEN A BOT AND A CHATBOT?

The terms themselves give it away. The major difference is that a bot is an automated tool designed to complete a specific task, while a chatbot does the same thing, only with a focus on the conversation.

While bots have mastered automated tasks, chat doesn’t necessarily have a set functional component. Think of it this way: robots in a factory don’t look like humanoids out of science fiction and instead serve as functional equipment. Meanwhile, setting the world to rights with your friends doesn’t necessarily achieve anything beyond human interaction. Therefore, the thing that sets apart bots and chatbots is that they have different outcomes. One facilitates relationships, while the other resolves issues with functionality.

FUNCTIONAL RESOLUTIONS VS RELATIONSHIP VALUE-ADD OPPORTUNITY

Function-based resolutions are likely to have one single input for a single output. They’re basically a one-in-one-out system. A relationship value-add opportunity does exactly what it says – it adds value in many ways, with multiple outputs for one single input.

HOW DOES THIS WORK IN PRACTICE?

Automation allows companies to create bots capable of answering simple queries and concerns like, “what is my estimated delivery date window?” or “where is my parcel in the delivery journey?”. When a company offers products and services, giving customers an easy option to find out quick information can be essential, and keeps you ahead of the competition that might have slower customer service processes.

Functional resolutions streamline services, give quick and easy access to information, and show that your brand can be trusted.

Meanwhile, relationship valueadded opportunities can arise from conversational AI that allows a company to get to know its customers better. Whether this means providing a personal shopper experience or simply using technology capable of personalising customer experiences – customers place value on the relationship a company forges with them.

Building meaningful consumercompany relationships improves loyalty and retention, shows attention to detail, and provides exciting and unique customer experiences.

HOW CAN BOTH WORK IN A CUSTOMER MIX, AND AT WHAT STAGES?

There’s space for function and relationship in the customer mix, but often at different stages. For example, in fashion retail, an AI program for online shopping that delivers a personal shopperstyle experience – wherein the technology learns a person’s style, general fit, and what they want to project through clothing – is worth the machine learning investment because you’re looking to build a relationship.

Using the same example, when a customer wants to know where their order is in the delivery journey, this is a functional post-sale query – of which an automated bot would be better to use. Although queries can arise at any stage, they’re most important right before a purchase and afterwards. At these stages, customers want a quick, no-fuss response and usually aren’t thinking about the relationship with the company. Meanwhile, customers might think more about the relationship before they even find your company and during the buying process. As more and more customers switch to buying products and services online, competing for attention will require personalisation.

UNDERSTANDING WHERE TO SPLIT INVESTMENT

Splitting investment requires a deeper understanding of what’s right for your business and customers. After all, functionality and relationship-building tools are needed throughout various stages – hence the need to find the right balance.

Say a fashion retailer is missing an automated bot that can resolve post-sale queries, for example. Customers would then need to find another way to get a response to their questions or concerns. As a result, the customer might feel that the company hasn’t invested enough in this area of the customer journey.

Similarly with a fashion retailer not offering an AI program allowing customers to get a personal shopper-style experience; the customer might feel that the business hasn’t invested enough into creating consumer-business relationships. Striking the perfect balance between the two will depend on what your customers expect from you, and the type of results you want to see from the investment. A decent chunk of investment should be poured into the relationship-building side if boosting customer loyalty and retention is the goal. If you know your customers care less about the relationship and more about getting answers, then the investment should flow into automation facilitating functionality.

THE FUTURE OF CONVERSATIONAL AI

In my view, companies differentiating between relationship value-add opportunities and functional resolution, while also pouring in appropriate amounts of AI and automation investment, will likely become market leaders soon.

Just look at the current market leaders and how they’re using AI and automation. Most – if not all – will be using the appropriate amount for their customers and are continuing to invest back into the right places. They’re leading the way by embracing AI and machine learning technology.

Data is invaluable but businesses need a responsible steward and the right skills to handle it.

Today’s businesses deal with a huge amount of data, but who is ultimately responsible for it? And are you equipped to handle it appropriately? Speaking at a discovery hearing for the ongoing lawsuit over the mishandling of user information stemming from the Cambridge Analytica scandal, two experienced Facebook engineers admitted they did not know.

By Jason Foster, CEO and Founder, Cynozure.

Data is invaluable but businesses need a responsible steward.

They told the court that the company doesn’t keep track of all a user’s personal data. When pressed about exactly what information Facebook stores, and where it’s located, Engineering Director Eugene Zarashaw replied that there is unlikely to be a single person who would know. “It would take a significant team effort to even be able to answer that question,” he said.

Whilst such a lack of oversight and accountability is shockingespecially given Facebook’s remit of billions of digital footprints - it isn’t surprising. This dearth of knowledge when it comes to data management is common across many businesses – recent research found that one in five (21%) senior executives in financial institutions in the UK don’t know where data is held. This is especially concerning when considering that – similar to the Facebook example - these are organizations that handle a large volume of sensitive personal data on a daily basis.

DATA POWERING THE BUSINESS WORLD

Data is proliferating at an unprecedented rate. Data access, management, and analysis have therefore swiftly become critical issues for businesses of any size.

Companies obtain their data from a myriad of sources, such as internal systems, digital platforms, wearable devices driven by technologies like the Internet of Things (IoT), artificial intelligence (AI), and other emerging innovations. Whenever customers, or employees, interact with search engines, social media, or apps, they produce a phenomenal amount of data. However it’s rarely the case that there is a person internally who is responsible for overseeing this influx of data, or a clear strategy in place to deal with it. This needs to change, and fast.

Data presents businesses with an array of opportunities, including valuable customer data and insights to enhance propositions –94% of senior finance executives agree that using data effectively is central to successful business. But it can also expose weaknesses in employee skillsets and IT infrastructures, highlighting areas where investment is needed to ensure data is properly stored and protected, as well as legally and ethically used.

Many organizations still lack the sufficient expertise and tools to extract business intelligence from their data. Whilst there are solutions to help manage volume – such as shifting data to cloud providers – it’s essential that your staff are data literate and the appropriate people have the understanding and clarity about where data is, what’s held, and how it’s used.

STEWARDSHIP, STRATEGY, AND SKILLS

The concept of a Chief Data Officer barely existed a decade ago, but the role is now fundamental to a successful business. It’s the cornerstone to adopting a data-guided business model that’s fit for the digital future.

The Chief Data Officer can set the strategy but will still require data expertise - a highly coveted skillthroughout an organization.

Unfortunately, as the demand for data expertise grows, so too does the gap between the number of people with these skills and the growing number of roles to fill. This is now recognised as a national issue in the UK, with the government last year publishing a paper, Quantifying the UK Data Skills Gap, as it seeks to ensure that businesses across all sectors can access the dataliterate employees they need. The government’s research highlighted that almost half of businesses recruiting for roles requiring data skills have struggled to do so over the last two years. This is a problem that the business world, alongside the government, must unite to solve.

There are a number of things organizations can do to improve their chances of a positive outcome when recruiting for data roles. They must first understand what skills are needed and be clear about the role profile – this is something a Chief Data Officer can shape as part of the strategy. It’s also advisable to break out of comfortable, traditional places to recruit, diversifying the talent pool to widen out the available supply of skills.

As well as hiring externally, businesses looking to become more data literate shouldn’t forget to equip existing employees with data skills and data management tools. Upskilling your workforce can be an untapped goldmine – invest in knowledge and the latest technology to support your people. Traditional training courses are one option, but specialist data literacy organizations and EdTech platforms that provide live and on-demand lessons in how to use, understand and extract value from data are better ones.

Collaboration is also key to building the data workforce of the future. Some organizations, such as not-for-profits, will find it harder to access and retain data expertise. It’s incumbent on others to help them, where they can.

The positive and open use of data can unlock a better future for the business world. But it’s crucial that organizations adopt a clear strategy, with defined roles and an emphasis on embedding the right skills and tools throughout the workforce. It’s easy to be appalled by the quote from Facebook’s Eugene Zarashaw, but can you honestly say you know exactly what information is stored and where, in your organization?

Financial services industry set for AI overhaul.

Artificial Intelligence (AI) is set to play a central role in the future of financial services, and advisors are already recognising the potential this tech has to offer – a resounding 98% believe AI will impact the future of financial advice. So, which areas of wealth management will AI revolutionise moving forwards? And how is the sector already underutilising its capabilities?

By Ville Somppi, Vice President of Industry Solutions at M-Files.

Financial services industry set for AI overhaul.

MANAGING INFORMATION WITH AI AND META-DATA

Metadata can help find information easily by enriching the search criteria. Not only is the system looking for a specific keyword, but it knows to limit searches to defined categories, such as project names or specific users. Typically, this metadata is added manually, but AI can help automatically classify data to simplify future searches. For example, AI can determine that a document is a non-disclosure agreement for a designated project and customer, and file it according to this information.

Developers will place added emphasis on understanding user intent to deliver relevant search results, even if the criteria provided is unclear. Once this tech is implemented, AI will boost search capabilities for financial service providers by pinpointing the snippet of text staff are looking for, instead of leaving them to comb through an entire document to access what they need.

MEETING THE COMPLIANCE BURDEN

Thanks to AI, financial service providers can automatically classify information and then apply pre-defined compliance rules. For example, if a document is an agreement that needs to be signed, contains PIIdata (personally identifiable information) that needs to be protected with GDPR compliant rules or is part of a specific client project, the access rights can automatically be set according to client confidentiality policy.

Furthermore, firms that innovate information management processes will benefit from electronic audit trails, where all information can be protected according to the rules of the business. Therefore, when a financial provider is audited, they can very simply show who has viewed or modified a piece of information, prove that all users have read and accepted the latest standard operating procedure and demonstrate that only certain users have the authority to approve commercial transactions exceeding a certain threshold.

SURFACING CONTENT RECOMMENDATIONS

AI has already infiltrated our daily lives through content recommendations seen in internet searches, social media browsing, online shopping and entertainment streaming. The same technologies are emerging in the business world, providing the information people need, before they even look for it.

Through AI, financial advisors can autopoulate homescreens based on the client meetings in their calendar. As a result, there is no need to manually dig-out clientbooks, correspondence or investment recommendations that match the risk profile of each client – AI will automatically supply all the relevant information.

KNOWLEDGE MANAGEMENT

Cashing in on knowledge management capabilities will help wealth managers produce, present and scale their offerings.

Analysing how historic assets were built and the market conditions that impacted their performance is a good starting point in the creation of new offerings.

Part of a financial asset’s success is the way wealth managers are able to present it. As such, knowledge workers are codifying past successes into a template that captures all necessary product information, and better resonates with the target audience.

Financial service providers are also building scalability through two methods: showcasing products that perform well, and leveraging scalable sales machinery that finds new clients and money to invest. Document management platforms act as the central hub of information, boosting the ability to develop these areas.

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on-premises. One key trend is the adoption of consumption-based models such as Fujitsu uSCALE billing based on actual usage. uSCALE supports business resilience, increases financial and capacity that is deployed in your data center ahead of business needs.

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