Future of AI
“Take the presidential deepfakes to appreciate the urgency of the issue.”
Rebecca Finlay, CEO, Partnership on AI Page 06
“The explosive rise of generative AI is shaking up the business world.”
Lamia Kamal-Chaoui, Director of Entrepreneurship, SMEs, Regions and Cities, OECD Page 10
Global standards for local impact: pursuing universal principles for AI governance
Major efforts are underway to establish robust governance and ethical mechanisms, safety measures and national/cross-national artificial intelligence (AI) strategies.
Initiatives to establish AI governance are unified by Immanuel Kant’s Categorical Imperative, which emphasises universal moral principles. Applied to AI, this principle highlights the need for global standards, ensuring uniform ethical guidelines as AI technologies impact various aspects of human life.
Addressing dilemmas through AI governance
The Collingridge dilemma, which posits that a technology’s consequences are most uncertain when it is still malleable and controllable, highlights the critical juncture at which AI development currently stands. International cooperation is imperative at this crossroads to shape AI’s trajectory for maximum benefit and minimal risk.
AI’s rapid development poses a moving target for regulators and ethicists. Governance must match the dynamism of the technology itself. As AI matures, its societal and economic impacts complicate regulation efforts.
Practices and infrastructures may become so entrenched that modifying them grows increasingly difficult and costly. This dilemma amplifies the need for a global AI governance framework beyond local or national policies.
Flexible framework of trust and cooperation
As AI’s influence transcends borders, international cooperation is essential. Its rapid evolution often surpasses policy development, risking outdated regulatory frameworks. Therefore, fostering dynamic global governance is crucial for managing AI’s global impact responsibly and ensuring its beneficial development. In this rapidly evolving landscape, governance should shift from rigid, prescriptive mechanisms to a flexible
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framework of trust and cooperation within the international AI community. This approach involves not only setting initial guidelines but also continuously engaging diverse stakeholders in discussions about future directions and implications of AI developments. The aim is to build and maintain a trusted consensus that supports adaptive and informed decision-making, ensuring governance strategies evolve alongside AI.
Trust-based governance for equitable AI
Establishing trust in the consensus is crucial for effective governance and for ensuring that AI benefits are distributed equitably. Trust-based governance can facilitate transparent sharing of information and best practices, reduce competitive tensions and help set a common ethical baseline that respects all stakeholders’ interests. By fostering an environment where every global community member can contribute to AI’s trajectory, we enhance the likelihood of universally fair and beneficial AI development.
UN leads global AI governance
We have a unique opportunity to build global governance and enable a trusted consensus, and the United Nations’ efforts, including the Secretary-General’s High-level AI Advisory Body, are essential steps towards this development. The UN’s upcoming Summit of the Future this September will be pivotal for advancing the necessity of collaborative international frameworks to harness AI’s transformative power for the global good. This Summit is crucial for cementing our collective commitment to responsible AI governance, ensuring that advancements benefit all humanity.
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WRITTEN BY Mehdi Snène AI and Digital Transformation Senior Advisor, Office of the UN Secretary-General’s Envoy on Technology
Business travel trends: how investing in AI supports human connection
Despite AI’s growing role in creating efficiencies and cutting costs, the value of connecting in person remains crucial for business success.
Artificial intelligence (AI) can make things better for businesses — if applied correctly. As a global business travel management platform, we’re taking a different AI path; a human-first approach leveraging technology with the product and people in mind. Business leaders must automate back-end repetitive tasks, so their people have more time to interact with colleagues, customers and partners. This is a game-changer to engage teams and build a positive future in a human-first world, where AI fills the gap.
AI boosts the need for business travel
Our ‘Value of Business Travel Report
2024’ explores how AI is shaping the way companies invest in travel. The good news: it’s not going anywhere. Around 76% of CEOs agree that increasing travel budgets positively impacts their company’s revenue, and over one-third (38%) say AI will increase the need for in-person meetings facilitated by business travel.
meet customers and do essential in-person work, has a strong positive impact not just on company financial performance but on employee engagement and retention. We found that 85% of employees who travel for work said that in-person social events boost morale and engagement. Meanwhile, companies that increased their travel budgets to connect employees have a 29% lower employee turnover than companies that reduced travel spend.
If business leaders want to reap the benefits of AI, they should focus on automating mundane tasks to help teams feel more engaged. It’s a win-win for companies and their people.
Business leaders must automate backend repetitive tasks, so their people have more time to interact with colleagues, customers and partners.
Despite economic headwinds, half of the 2,000 companies we surveyed, including 500 in the UK, expect their travel budgets to increase in 2024 as people attend more conferences and events.
The impact on company culture
Humans aren’t designed to interact only online. Our research found that investment in travel to connect teams,
Top trends in AI-powered intelligent customer experience for the modern consumer
Learn the latest trends in AI, automation and data-driven personalisation to stay ahead with intelligent customer experience (CX) solutions.
Modern consumers demand secure, personalised interactions with companies, driven by data utilisation, automation and intelligent solutions. “Messaging has become one of the most important support channels, and automation is on the rise,” says Matthias Göhler, Chief Technology Officer EMEA at Zendesk. “This is, of course, partly due to the advancement of technological innovations and partly to the growing demands of consumers.”
Rapid shift to intelligent CX
According to the Zendesk CX Trends Report 2024, there is a rapid shift towards intelligent CX solutions, integrating AI, automation and data analysis — with 70% of consumers anticipating complete changes in their interactions with companies within two years. “Consumers are ready to step into the age of intelligent CX; it’s high time for companies to
AI enhances human support roles
At TravelPerk, solving real pain points has been our secret sauce to AI innovation. When people travel for work, they want to know that they will get to where they need to be on time. If an airline cancels a flight and it’s far enough from your departure date, AI can help by offering alternative rebooking options.
However, if the flight is today, and you experience a disruption, it’s a real person you will want to talk to — not an AI bot. We’ve invested heavily in automating processes like flight changes, so our human agents can resolve issues fast when customers need it most. AI technology needs to be leveraged to enhance your teams, not replace them.
“Customers expect tailored interactions,” says Göhler.
To enhance AI-driven CX capabilities, Zendesk has acquired Ultimate, expanding service automation beyond chatbots. It offers problem-solving AI agents, can automate about 80% of support requests and customise solutions for complex customer needs.
Comprehensive consumer data protection
follow suit now,” insists Göhler. AI technologies, such as chatbots, are pivotal in meeting consumer expectations. They can respond to common inquiries, allowing human agents to focus on more complex tasks. As a result, 64% of CX leaders are increasing investments in enhancing chatbot capabilities. He adds: “Managers should further develop chatbots now and integrate them more closely into the customer journey to improve CX and strengthen customer loyalty.”
Personalised integration challenges
Despite the potential benefits, AI adoption also introduces challenges. Many CX leaders feel pressure to integrate AI tools into their strategies, as 62% recognise the transformative potential of generative AI in reshaping customer journeys. This technology not only promises hyper-personalisation but also drives more human-like interactions.
However, hyper-personalisation success relies on robust safeguarding measures to protect consumers’ personal data. This includes encryption technologies, multi-factor authentication and AI-powered fraud detection.
“Ensuring the privacy and security of customer data should always come first before the data is used for personalisation purposes,” adds Göhler. “This is increasingly falling on the shoulders of CX leaders.” Threequarters of CX experts plan to boost cybersecurity budgets, underscoring its importance in maintaining customer trust amid AI-driven personalisation efforts.
Prioritising customer expectations
The future of CX lies in responsibly integrating advanced technologies to bridge the gap between expectations and service delivery. “Intelligent CX can close this gap and bring CX closer to where it should always be: the customer,” Göhler notes.
INTERVIEW WITH Matthias Göhler
Chief Technology Officer
EMEA, Zendesk
WRITTEN BY
Angelica O’Toole
WRITTEN BY
Avi Meir
CEO and Co-Founder, TravelPerk
How to harness the transformative power of GenAI
It is time for businesses to develop a clear vision for how they can scale the applications of generative AI that will deliver truly transformative benefits.
Business leaders appear to have satisfied themselves with the transformative potential of generative AI (GenAI). The PwC CEO Survey found that 64% of UK business leaders believe GenAI will increase the efficiency of their workforce, 57% say it will improve products and services, and 45% say it will increase both revenue and profitability.
Mark Nicholls
Realising combined GenAI benefits Combined, such outcomes can represent a step change in the fortunes of any business. However, turning that potential into realised value cannot be achieved solely through the kinds of serial trials and discrete use cases that many businesses have been exploring.
Business leaders must identify which applications of GenAI have the most transformative potential, then focus their strategy and investment on scaling those, says Johan Jegerajan, Chief Technology Officer for PwC’s Consulting arm in the UK and EMEA.
From potential to transformative impact PwC is using GenAI to unlock value for clients across industries, delivering efficiency gains and increasing productivity, but also helping to transform how they create value for their customers, employees and shareholders.
Examples include powering faster, more effective decision-making in business-critical areas such as tax, legal and compliance. The technology can help experts quickly analyse huge bodies of text and data, such as contracts and legal and regulatory documents. They can interrogate that content, validate the outcomes and act quickly, reducing the need for painstaking human review.
Jegerajan explains: “We are putting incredibly powerful technologies into the hands of humans in a way that means they can solve problems at a speed they never thought possible.” This doesn’t remove the critical importance of a highly trained expert overseeing the inputs and outputs of GenAI, but it does make that expert far better equipped to create value.
“It’s like having a really smart colleague, sitting on your desktop, who can find important information, make suggestions, perform analysis and create first drafts — all with exceptional speed,” says Jegerajan. “That frees you up to focus on how that information can create value for you and your clients or customers.”
Creating
new growth opportunities
Beyond augmenting the skills and capabilities of the workforce, other transformative use cases increasingly draw upon the role of GenAI in delivering a more hyper-personalised customer experience and the new revenue opportunities that
creates.
“Initially, there has been a significant focus on efficiency and productivity and those use cases are compelling,” says Jegerajan. “But, as businesses look at which use cases will create the greatest long-term value, we start talking about business model reinvention and genuinely transformative applications that don’t just tackle existing tasks more quickly but create new growth opportunities that will deliver more revenue, more value to customers and take more market share.”
Jegerajan says businesses that are not looking to scale the most transformative uses of GenAI risk falling behind.
The power of shared experience
Such claims may be commonplace when it comes to businesses talking up the potential of emerging technologies. That is why PwC is focused on ‘walking the walk’ as well as ‘talking the talk.’
The firm positions itself as ‘client zero,’ meaning it has adopted GenAI throughout its own organisation and is basing its recommendations to clients on a solid foundation of firsthand experience.
The firm has been working extensively with AI for over a decade and has integrated a number of AI tools across its business. Last year, as part of an ‘AI for All’ strategy planned to put AI tools into the hands of all staff, it rolled out its own GenAI tool called ‘ChatPwC’. That gives its employees access to a private, secure GenAI platform that keeps the firm’s data separate from public large language models. Currently, over 15,000 of its UK employees have been set up with GenAI skills and tools.
INTERVIEW WITH Johan Jegerajan CTO for Consulting, PwC
WRITTEN BY
Navigating GenAI risks responsibly
Jegerajan says: “By talking our clients through our own journey, taking the learnings from how we are using GenAI ourselves, we are able to more quickly cut through the noise about GenAI and some lingering concerns around the technology.”
Establishing such credibility means also being equipped to talk to clients about the risks which come with GenAI, and the critical steps needed to assure and secure the data being used.
“Embedding GenAI into business transformation comes with its own risks,” says Jegerajan. “Not least how trusted the outputs can be. We have been focused on responsible AI for many years, and we continue to work with technology partners, regulators and policymakers to drive this discussion forward. Ethics, responsibility and trust must sit at the heart of any use of GenAI; you need to know the data is secure and cannot be breached or controlled by threat actors.”
A foundation for long-term success
Much of the work the firm does with clients starts with building a foundation for the effective longterm use of GenAI, which will enable any use case to scale. That work is critical to moving GenAI from tactical, discreet use cases to a core component of ongoing transformation.
This includes ensuring the right data and cloud infrastructure are in place to power GenAI. However, as Jegerajan says: “For many organisations, the modernisation of their technology
infrastructure is still in flight. We are seeing the acceleration of many cloud and data transformation programmes in response to the potential of GenAI, with leading organisations defining the value they want to derive from the technology and using that to inform a refined data strategy.”
Yet, Jegerajan says responsibility is as much an essential building block of those foundations. “Without trust, all of that transformative potential is weakened. Without an ethical lens on applications of GenAI, the upsides can quickly become downsides, mired in regulatory and reputational damage.”
Embedding GenAI into business transformation comes with its own risks.
Upskilling for GenAI success
According to the PwC CEO Survey, nearly two-thirds of UK business leaders say they expect to reskill most of their workforce within the next three years to capitalise on GenAI. As already identified, there is huge value to be created from empowering skilled employees to augment the work they do with GenAI.
Adoption is clearly vital to achieve that value, so, as businesses put tools in the hands of their people, it will be important to monitor usage to
understand the volume and nature of adoption. Engaging skilled employees is also essential to ensure GenAI is used with the necessary expert human oversight, providing high-quality critical thinking, logic and governance.
A foundation for ongoing change
“While in many contexts, GenAI has the power to astound, it is still a long way from providing the answer to every business problem,” says Jegerajan. “It remains fallible, and its capabilities in complex logical reasoning and real-world understanding are very nascent. Companies therefore need to apply the technology with these limitations in mind, and an awareness of the oversight their people need to provide in areas such as identifying hallucinations and proactively addressing the risk of bias.”
Ultimately, these many threads to transformative GenAI success rely upon collaboration — bringing together those who understand the technology and those who understand the business, its customers and the environment it operates in, including risk and regulation.
Alongside the right technology, and an approach built around responsibility and ethics, the ability to draw upon those multiple perspectives will be a vital building block for the future. “Organisations don’t just need to think about how they change,” says Jegerajan. “They need to think about how they are set up to keep changing, again and again. Transformation is not an event; it is an ongoing process.”
We are seeing the acceleration of many cloud and data transformation programmes in response to the potential of GenAI.
Lessons on responsible use of generative AI from industry heavyweights
Amidst the rapid evolution of generative AI technologies, global policymakers are racing to regulate potential risks. Learn how collaborative, voluntary efforts in business, media and civil society can inform both policy and practice.
Ensuring safe and responsible AI has always been the mission of Partnership on AI (PAI). Through years of work on deepfakes and other synthetic media, it’s clear that any guidance or regulation on how to build, create or distribute AI-generated audio, video and images will struggle to keep pace with the field’s development.
Magnitude of synthetic media governance
Take, for instance, the plethora of AI-image-generating apps or presidential election deepfakes to appreciate the urgency of the issue. To fill the guidance gap, PAI created its framework on Responsible Practices for Synthetic Media, following consultation with companies, media and civil society organisations across the generative media ecosystem.
Additionally, we published an unprecedented body of work showing how organisations address issues such as transparency, digital dignity, safety and expression. Media organisations like the BBC and CBC, technology companies, AI startups and more contributed case studies offering an inside look into how synthetic media governance can be applied, augmented, expanded and refined for use in practice.
Enhancing institutional transparency
Our case studies’ transparency helps ensure accountability in the responsible creation, distribution and use of synthetic media. The cases highlight institutional practices
Why industrial adoption of AI solutions is key to long-term business success
In a rapidly evolving industrial climate, adoption of AI solutions can enable businesses to cut carbon emissions and optimise operational efficiency for long-term commercial success.
Artificial intelligence (AI) solutions can improve operational efficiency and productivity across sectors while allowing organisations of all sizes to achieve their sustainability goals.
AI instrumental to sustainable innovation From aiding leaders in implementing sustainable measures to optimising energy use within operations, AI can be a powerful tool for businesses aiming for net zero emissions in the long term. Pressure to decarbonise is rising alongside environmental concerns from stakeholders, partners and customers.
As sustainability becomes central due to regulations and business preferences, many leaders may remain unaware of AI’s value in meeting sustainability goals. However, it
and tactics to address questions like how to build disclosure mechanisms and how to develop policies to prevent misuse. There is a clear need for policymakers and other actors to have access to this information.
Governance recommendations complexity
The diversity of use cases for synthetic media made our goal of producing clear and tangible governance recommendations much more difficult. This was even more complex because of our ecosystem approach.
The cases touched on vast societal dynamics ranging from freedom of speech, the meaning of harm, transparency, creative endeavour and consent — topics that each warrant their own specific analysis exercises.
Guiding synthetic media governance
In direct response to the rapid pace of AI development and details learned throughout case reflection, we will continue to iterate the framework to advance better organisational practice. Government regulation and policy are key complements to the Synthetic Media Framework and our governance activities at PAI.
The centrality of consent, transparency, support for creative expression and harm mitigation are essential to synthetic media policymaking. These policies, which have been tested in practice, have the potential to provide a foundation to inform regulatory momentum.
holds the potential to reduce carbon emissions through analysis of energy consumption data. AI can enhance visibility throughout supply chains, tracking and tracing products at every stage of production and distribution, thus increasing accountability.
How AI can support industry decarbonisation
At Digital Catapult, we develop pioneering AI solutions to help leading businesses achieve sustainability goals. On the AI for Decarbonisation Virtual Centre of Excellence (ADViCE) programme, we explore new use cases for AI to cut carbon emissions in key sectors such as energy, agriculture and manufacturing. This includes a series of seven grand challenges where AI can be utilised to support industry decarbonisation:
(1) unlocking domestic decarbonisation; (2) enabling net zero infrastructure; (3) maximising flexibility in energy networks; (4) decarbonising manufacturing inputs; (5) improving manufacturing process efficiency; (6) minimising methane in agriculture; and (7) optimising soil management.
Soil management and achieving net zero For instance, in soil management, AI can accurately and quickly determine the amount of fertiliser required to limit emissions. AI predicts changes in soil health and carbon storage potential from future weather events, changing climate or environmental changes. This can allow businesses to model soil attribute changes and help plan more resilient sites.
Additionally, the Tenfold NetZero Accelerator Programme in Northern Ireland is demonstrating the strategic value of AI in helping businesses across the region to achieve their net zero ambitions. Through the programme, Digital Catapult is supporting SMEs to reduce glass waste, improve quality inspection and make progress towards producing zero-emission glass with the help of AI.
The programme was established to decarbonise key sectors in Northern Ireland, including agri-industries and manufacturing. By adopting AI the right way, industries can get one step closer to achieving their sustainability goals without compromising operational efficiency.
WRITTEN BY Dr David Pugh Director of Sustainable Industry, Digital Catapult
WRITTEN BY Rebecca Finlay CEO, Partnership on AI
How generative AI can transform business backup data safely into knowledge
Technology experts underline the importance of safety and security as businesses and organisations introduce generative artificial intelligence to improve efficiencies.
Generative artificial intelligence (genAI) is transforming the enterprise landscape with more businesses looking to bring it into their operations. However, it is essential to safely introduce responsible data access for it to be effective.
AI unlocking complex technical data
Greg Statton, IT expert from Cohesity, is responsible for the company’s data and AI strategies. He points to the growing role of generative AI chatbots, such as ChatGPT, in translating complex technical data into human-readable language in seconds.
“With CEOs worldwide wanting to see investment into generative AI because they hope for efficiency gains, every IT team will sooner or later have to find answers on how they can utilise AI tools. However, if IT teams use AI too ambitiously and quickly, they could unknowingly open up new security gaps.”
Responsible AI integration
Statton’s advice to IT teams looking to leverage the potential of this technology without creating new gaps is to introduce AI in a controlled, responsible way. Cohesity’s responsible AI principles include: transparency, to protect access to the data with role-based access controls; governance, to ensure the security and privacy of data used by AI models
and the workforce; and access, to integrate indexed and searchable data securely and easily while ensuring data is immutable and resilient.
Statton, who has seen the company evolve from a small enterprise to a major technology company over the last decade, became intrigued by GPT3 technology’s ability to generate new text on a given input.
From that, he developed a prototype that could unlock data internally to answer questions using a large language model. However, he realised that customers may also benefit from a product that could unlock data from their long-term storage and backups.
The approach is designed to help customers use their backup data safely and efficiently. It is based on four main pillars: data protection; data security; data mobility; and data access.
Enhancing backup data utilisation
Backup data requires protection against cyberattacks, must be easily movable for seamless efficiency and be accessible on demand. “We wanted to see what more value we could bring to that,” Statton says. “We wanted to be able to provide tools to help unlock value or hidden potential of that data for our customers, so we introduced our insight pillar.
“Cohesity Gaia is the first release within that insight pillar, which combines the power of large language models with that vast repository of data to help drive conversational search, knowledge management and question and answering,” he adds.
Semantic indexing for data retrieval
“Customers can create a semantically aware index of data, allowing the system to find source material deep inside their own data to help answer their questions. All of this is using ‘natural language,’ meaning no need for complex code or query languages.” Backup infrastructure is the only platform in the data centre that maintains a complete copy of all the enterprise data in one place and across all time. Statton adds: “If that backup platform is intelligent enough, it can leverage that data to drive operational efficiencies with the help of generative AI.”
A company could ask any businessrelated or technical question that can be answered by reading all documents ever created by the enterprise — but without having to search, consume or understand that vast library. Companies reuse backup data while safeguarding proprietary information from accidental exposure. “On top of that, you can apply access controls to the end user,” he adds.
Transforming tech with natural language
Statton concludes: “GenAI is really changing the expectations that individual consumers and enterprise customers have on technology; they are realising they can interact with technology in a more natural language way. These are important technologies to bring to the enterprise, but we can’t forget the guiding principles of safety and security when doing so.”
Paid for by Cohesity
WRITTEN BY Mark Nicholls
INTERVIEW WITH Greg Statton IT Expert, Cohesity
Why high-density SSD storage secures the future of AI
Solid-state drives help AI perform to its full potential — it’s time for data centres to modernise.
Artificial intelligence (AI) is growing and scaling up at an exhilarating rate. Without improvements to crucial digital infrastructure, that pace is at risk.
How SSDs revolutionise AI data storage
Today, approximately 90% of data centres use hard disk drives (HDDs) to store the reams of information that make all types of AI work, from machine learning to large-language models. However, HDDs, which were invented in the 1950s, are not well suited to incredibly data-intensive AI.
Enter solid-state drives (SSDs), which are smaller, higher density, enable lower cost, last longer and require much less power to run. Quad-level cell (QLC) technology, the most advanced SSD as of today, allows for four bits of data to be stored in each memory cell.
“Whether AI is running image recognition in a doctor’s office or doing quality control checks on a smart factory floor, it needs a massive amount of data, and it needs to be able to access that data at speed,” says Roger Corell, senior director of leadership marketing at California-headquartered Solidigm, which has the second-largest share of the global enterprise SSD market. “Compared to HDDs, SSDs can enable a lower total cost of ownership, are orders of magnitude faster and, with their massive capacities can deliver huge infrastructure efficiency improvements,” he adds.
Inefficient HDDs hinder AI Corell argues that the advent of AI requires a complete hardware overhaul. “The number one challenge to scaling AI data centres is power consumption. Rightfully so, everyone talks about GPUs (graphics processing units) — but storage is a big factor as well because you need more HDDs to meet performance requirements. They are always on and consume a massive amount of power,” he says.
got a further saving when it comes to end-of-life because you’ve got fewer products to dispose of,” adds Corell.
While AI presents the most urgent storage issue for data centres, it’s not the only technology putting pressure on them. “There are other very dataintensive workloads such as big data analytics and high-performance computing that need the density and performance of high-cap SSDs,” says Corell.
SSD long-term efficiencies outweigh cost
According to the International Energy Agency, data centres accounted for about 1% of global electricity demand in 2020. This is only expected to grow as AI and cloud computing advance dramatically. This means that as tech companies reduce their carbon emissions in other areas, for many, their overall emissions are going up due to the data centre boom.
A single rack of SSDs can store the same amount of data as around three racks of HDDs.
HDDs are slow, so you have to combine many of them to get to the performance of a single SSD. “They can’t store nearly as much data as an SSD. Those are big problems, particularly with all the news about power constraints holding back AI data centre buildouts. For some AI usages, HDDs can account for up to 35% of that energy consumption,” explains Corell.
SSDs improve infrastructure efficiency
A single rack of SSDs can store the same amount of data as around three racks of HDDs. “In that situation, you’ve reduced your data centre footprint, you’ve reduced your power consumption by about 80%, you’ve reduced your cooling cost, and you’ve
SSDs come with a higher upfront cost than HDDs, but due to their operational and performance efficiencies and longer lifecycle, they’re more cost-effective over time. “Once you look at total cost analysis, there’s a crossover point — particularly as our capacities increase — where SSDs are more economical in the long term,” says Dave Sierra, data centre solutions marketing at Solidigm.
“That’s because you need fewer drives, and you get more efficient power savings too. We’re focused on ensuring our customers evaluate the full solution, not just the initial upfront cost.”
High-density SSDs reduce energy consumption
As AI continues to evolve, its energy consumption cannot be ignored. High-density storage solutions, such as those pioneered by Solidigm, offer a promising path toward more sustainable data centre operations. By deploying hyper-dense QLC storage, data centres can significantly reduce their physical and energy footprints.
INTERVIEW WITH Roger Corell Sr. Director Leadership Marketing, Solidigm
INTERVIEW WITH Dave Sierra
Data Center Solutions Marketing, Solidigm
SPREAD WRITTEN BY Lizzie Meager
Edge AI advantages: how SSDs drive efficiency and reliability
While SSDs are revolutionising data centres, it’s not their only use case. They’re also incredibly effective for edge AI applications.
By storing data close to the source of generation, SSDs contribute to the effectiveness of edge AI by enabling real-time processing and reducing dependency on distant data centres minimising reliance on cloud infrastructure.
Edge AI benefits from SSDs SSDs for edge AI are particularly useful for critical organisations like hospitals and factories, which need minimum latency. It’s also effective for data security and protection, particularly with the increasing number of data protection regulations governing
grab a whole rack of servers and HDDs (hard disk drives) and put it in the trunk of a driverless car — although in the early stages of development, that’s exactly what [tech companies] were doing,” says Wouhaybi.
SSDs are faster, smaller and more efficient. They have no moving parts, making them less prone to damage from shocks and vibrations, which are common in edge environments. This durability enhances lifespan, reduces mechanical failures and cuts down on AI system downtime and maintenance costs.
Form factor versatility of SSDs In certain edge applications like
INTERVIEW WITH Rita Wouhaybi AI fellow, Solidigm
Why generative AI tools can help SMEs catch up to the competition
With generative AI tools, SMEs can swiftly generate detailed data analysis of their operations, personalised business strategies, new product concepts and custom promotional materials within seconds.
While artificial intelligence has long been harnessed by industry giants to dissect data and streamline operations, the advent of affordable and accessible generative AI tools has now put that potential in the hands of SMEs.
Rapid adoption of generative AI
Recent insights from the OECD’s new Digital for SMEs Survey show clear momentum among SMEs in adopting generative AI services. Within a year of these tools becoming publicly available, 18% of SMEs have been experimenting with them. This whirlwind of change presents new opportunities but also new risks for SMEs and the governments seeking to support them.
Digital divide risks widening
First, there is the risk of widening digital divides. The SMEs that were quickest on the uptake were those that were already the most digitally savvy: 23% of digitalised businesses use generative AI, compared to 13% of their less digitalised counterparts, putting the latter at risk of falling further behind in digitalisation.
SME cyberattack vulnerability
Second, there is the risk that generative AI tools leave SMEs more open to more sophisticated cyberattacks. Worryingly, given that most security breaches are caused by human error, our survey showed that only 11% of respondent businesses engage in cybersecurity training, and 6% perform regular cybersecurity assessments.
Support SMEs’ secure tech adoption
Governments must now up their game to help SMEs to adopt these technologies securely and strategically. Some initiatives have already emerged, such as the UK’s Department for Science Innovation and Technology’s GBP 7.4 million ‘Flexible AI Upskilling Fund pilot’ to subsidise AI skills training for SMEs in the professional business services sector, providing grants that cover up to 50% of training costs.
How governments can break through to SMEs
Yet, one of the toughest jobs governments will face will be to ‘cut through’ to busy entrepreneurs. We found that only 18% of reporting SMEs are knowledgeable about government support for digital tool adoption. Reaching them and winning their trust could get even harder as malicious actors themselves use AI tools to impersonate government services.
As generative AI continues to create a buzz in business, this groundbreaking technology could level the playing field for SMEs. Governments must provide support to make this happen — and unleash their potential to thrive in an increasingly digital world.
WRITTEN BY Lamia Kamal-Chaoui Director of Entrepreneurship, SMEs, Regions and Cities, OECD
How AI is transforming communication networks and other industries
Artificial intelligence (AI) has exploded into the collective consciousness with the advent of content creation tools like ChatGPT. However, AI is broader than generative AI and has many other applications.
Artificial intelligence is rapidly transforming how to manage, deploy and develop communications networks — and this is only the beginning of the journey. AI can support networks — and vice versa — throughout their entire life cycle, from design to day-to-day management. This makes them smarter, more efficient, more resilient and less costly to run.
BY
Elizabeth Meager
Designing mobile networks to be fully AI-native is already underway in research. However, achieving this will require comprehensive standardisation efforts, likely coinciding with the adoption and rollout of 6G, expected to start in 2030.
AI potential in networks and industries “Right now, AI in the popular domain has been focused on content creation. We’ve not fully moved it into the industrial domain, which is where we can harness the biggest potential for business metrics, such as productivity, efficiency, safety and sustainability,” says Thierry E. Klein, President of Bell Labs Solutions Research, Nokia’s industrial research arm.
“Networks will become much more optimised and automated, and that’s where we will see performance benefits — as well as reduced energy consumption of the networks and massive improvements on the user experience side.”
In the shorter term, AI tools are already being utilised for energy usage optimisation within networks. “As traffic patterns change, we can analyse the data using AI to change the configuration of the network to reduce energy consumption by up to 30% and still maintain the service and expectations of the customer, and that’s happening today,” says Klein.
Why AI power depends on data Any AI tool is only as powerful as the data on which it’s trained, insists Klein. As impressive as ChatGPT is, it has several limitations. That’s why Nokia has created a large language model (LLM), trained on 60,000 internal documents, which is much smaller but highly tailored to its products and users.
“These LLMs are not going to write you a great poem or cooking recipe, but that’s not their intended purpose,” says Klein. “I don’t think we’ll end up with one large-language model that everybody uses. I expect a collection of models more attuned to specific business contexts, which is far more equitable and democratised.”
There is a lot of potential in collaborating with customers and other technology partners to progress this technology. “By applying information communication technologies to other sectors — including healthcare, logistics, manufacturing and transportation — we see a huge potential benefit when it comes to digitising those industries,” he adds.
AI trust and governance
The future of AI relies heavily on trust, says Klein and, in many of its public deployments thus far, that trust has not come naturally. Like all other new technologies that have come before it, AI has been greeted with a healthy degree of scepticism from the public.
Some have already called for the creation of a regulatory body governing AI. However, the technology is moving incredibly fast and the need for oversight must be carefully balanced with harnessing the transformational benefits of AI and encouraging innovation.
Building responsible AI
Realising this need early on, Nokia Bell Labs defined six pillars of responsible AI: fairness, reliability, privacy, transparency, sustainability and accountability. “We don’t believe in building AI tools and figuring out the responsibility aspect later,” says Klein. “If you leave it until you’ve built the system, you’re very likely to have to undo and redo things, which is time-consuming and costly.”
For this reason, the company has kept its responsible AI principles front of mind from the beginning to guide AI research and development and encourage industry collaboration.
“On the technology side, we see a lot of potential in this technology, and we want to make sure we — and everyone — can harness and benefit from that potential in a responsible way,” says Klein.
Discover the exponential potential of networks, built for AI, with AI
INTERVIEW WITH
Thierry E. Klein
President, Bell Labs Solutions Research, Nokia
WRITTEN
As AI’s influence transcends borders, international cooperation is essential.
~Mehdi Snène,
AI and Digital Transformation Senior Advisor, Office of the UN SecretaryGeneral’s Envoy on Technology
