AUTOMATION WITH A MISSION

BOB DE CAUX, VP OF AUTOMATION AT IFS, ON HOW TO IMPROVE SUSTAINABILITY OUTCOMES USING AI AND AUTOMATION IN THE ENERGY, UTILITIES AND RESOURCES SECTOR

The energy sector needs to shift from a ‘tech-first’ to an ‘outcome-first’ mindset if it is to make the pivotal connection between data and sustainability in the years to come. Automation is rightly regarded as the hero to connect the two but is currently lacking a clear mission.

The need to collate, analyse and utilise a company’s own information for improved decision-making and enhanced customer personalisation is matched by a simultaneous need to become more efficient, more streamlined, more environmentally conscious, and more ethical.

To connect the two and become more sustainable through automated collection and analysis of data requires a shift in mindset and, inevitably, investment into technologies that can help to meet these goals. However, what too few businesses are doing at present is approaching the challenge in that exact order: mindset and culture first, before turning to those solution suites thereafter.

Solutions such as machine learning can only feed off the questions being asked of it, and the data being presented as a result.

If the initial culture isn’t geared up to ask the right questions and push towards achievable and clear goals on the sustainability front, then the tech won’t be able to flex its muscles.

For many, it might be time to take one step back and reassess the original sustainability mission.

False starts

A familiar trend across all industries, and indeed in the energy domain, has seen businesses in such a rush to collect more data, that they’ve not put in place a structure that points this data in suitable, tailored directions.

This actually works against notions of sustainability, as companies then spend extra time and money to keep up with their own data deluge as it comes out the other side of the automation filter – be it a new data scientist hire, or additional tech to try and better connect functions. It derives from a misunderstanding of the tech that has been originally deployed to theoretically do all of this hard work for them - namely, artificial intelligence.

Automation through AI is sold as this efficiency-, and sustainability-inducing, silver bullet. But, without guidance and an idea of what the company wants to get out of the resultant data, the tool is simply digesting information, rather than channelling or optimising it. Quickly, those same companies have become wearied and frustrated with the lack of progress being made from a sustainability perspective, as a result of their concerted and expensive – if not strategic – automation efforts.

And that’s why industry players need to slow down, even take a step back, and make sure that the roadmap forward is based on clear targets behind the data being collated, and stated objectives geared around their own, bespoke sustainability challenges.

Calculating the value of sustainability

While this direct link between data and sustainability has yet to be made by any sector completely, some are further along than others at this stage. Aerospace and defence, for example, are ahead of manufacturing, which has been slower to the uptake.

Perhaps this indicates why many in industrial realms, including energy and utilities, have struggled to tick the culture box as part of their digital transformations. A speedy ‘invest and deploy’ attitude has seen them try to meet expectations around automation, in a vain hope to become more sustainable as a by-product. But really, only a few have realised that the true potential of automation should be triggered with sustainability in mind from the planning stage.

Have the calculations been made to understand the true value of reduced downtime, improved maintenance forecasting, enhanced workforce deployment, optimised administrative tasks, and – of course – improved production efficiencies?

All are proposed outcomes when pitting current practice against best practice driven by data. But all are also at risk of being missed out on, if these stated aims are not guiding AI and machine learning from the very outset.

Giving automation a clear mission

The need to not only become more efficient, but to clearly, outwardly display how you as a company are financially, socially, and environmentally sustainable will be a market differentiator very soon; if it isn’t already. It’s a differentiator for customers, who are becoming more and more demanding about how their service providers are being run. And it’s certainly a priority for regulators, who will be requesting more audits and reports around sustainability records, which clear data will be key in bringing to light.

Ultimately, sustainability at the back end needs to be visible, transparent and auditable, and this can only be achieved if the initial goals are equally clear and laid out.

Thankfully, for those having a rethink following their false starts, there are solutions providers and specialist consultants on hand to help with the formulation of this roadmap. These specialists don’t just aid the data collation process, but begin from step one, in ensuring that stated KPIs are achievable, suitable and connected to the practical applications at their disposal.

What this correct first step will do most effectively is debunk a significant myth that has driven the datasustainability relationship so far. That is, that the latter is a happy coincidence once improved data analytics, AI or IoT connections have been enacted.

The truth is that there is no coincidence or foregone conclusion when it comes to the upshots of automation tools. Rather, the likes of AI serve as a middle ground between the strategies that decision makers put in place, and the insight yielded at the other side. Without the strategy stage, that middle ground becomes an unguided phase simply collecting information without a clear end reason in play. The likelihood of becoming more sustainable as a result of this wild west approach is slim, and points to where industry operators need to focus instead: the start.

The greatest data scientists working with the most sophisticated solutions won’t be able to meet end targets if there aren’t any clear targets to begin with.

With this in mind, the first solutions that companies invest in, and the initial digital partnerships forged, moving forward, should be those that can help establish clear sustainability KPIs in the first place. From that point on, automation can finally be the hero it’s designed to be, with data as its weapon, and a clear mission to set out on.

BRIDGING THE GAP

ED HOPPITT, EMEA DIRECTOR APPS AND CLOUD PLATFORMS AT VMWARE, ON WHY BOARDS AND APP DEVELOPERS NEED TO SPEAK THE SAME LANGUAGE AND COLLABORATE CLOSELY.

As key drivers of the modern digital economy, applications are the enablers of experiences that attract, retain and empower customers and employees.

It is clear that business leaders understand the critical importance of apps. We only have to look at the demand for app developer talent to understand how valued they are, and frankly how desperate companies are to acquire those skills. An analysis of job listing data by VMware found that yearon-year demand for development roles increased by 38% across Europe from Q1 of 2021 to the same period in 2022.

These are highly technical, specialist roles that need filling if businesses are to deploy the sort of applications that wow users. The problem is boards generally aren’t highly technical. The language they use will be different, which can cause a disconnect with developers if the latter doesn’t have the requisite business knowledge and skills to talk in a way the board recognises. Ideally, businesses want developers who can speak that language to reinforce the business case for apps and ensure organisational goals are understood and met.

The research suggests that businesses instead hire developers that have the requisite technical skills but may struggle to fully understand what their employers expect apps to deliver. Across EMEA, barely a quarter (26%) of the top skills featured in developer vacancies are business-related, and half (49%) of all listings fail to include a single business skill as one of their top 10 requirements.

These skills and their attendant knowledge are all critical to engaging senior executives and making a compelling case for apps. Yet they rarely feature in employers’ requirements for developer talent: just five percent of listings mention the need for finance knowledge, six percent highlight stakeholder management abilities and seven percent want project management skills.

What this creates is a digital disconnect between the technical and non-technical, between business and developers. It’s like any situation where two people don’t speak the same language to the right standard: while both parties might be able to communicate their vision broadly, the nuance will invariably be lost. When it comes to app development, this could mean apps struggle to fulfil their potential. They might not properly meet the needs of users or fully engage them, aren’t deployed fast enough, or struggle to deliver the experience the company needs to capitalise on in digital-first marketplaces.

Turning the digital disconnect into an app opportunity

To turn this challenge into an opportunity, two things need to happen.

First, businesses need to recalibrate their recruitment. It’s an understatement to say that the market is ridiculously competitive now – some parts of Europe saw demand for certain developer roles top 300%. Employers are adjusting employee value propositions, looking for new ways to work, and creating better benefits packages, as well as upping salaries. All to attract highly prized talent.

Why go to all that effort but overlook critical attributes that are sorely needed?

Prospective employers need to be prioritising those that can manage different stakeholder expectations. Developers need to be able to talk to senior managers and leaders in the business language these executives are accustomed to.

Secondly, developers should look at how they can make themselves even more attractive and develop these in-demand skills themselves. While the rate of demand might suggest that developers do not need to be doing more to stand out, they should be considering what other skills they can add to their arsenal. Just as they wouldn’t want their technical abilities to become out of date, so they would be wise to review what additional capabilities they can develop to make themselves even more indispensable. No one knows what is round the corner, and the present gap offers an opportunity for developers to be the bridge that both brings together non-tech leaders and tech specialists. If businesses have a choice between a technically proficient candidate and one that pairs deep technical knowledge with an ability to communicate in business terms, they should choose the latter every time.

As boards’ demand for app delivery grows, there is a real danger that businesses could invest time, energy and money into acquiring talent that has extensive app development experience, but ultimately isn’t equipped to help them capitalise on the modern digital economy.

Businesses do need to rethink what they are actually looking for, but at the same time there is an opportunity for developers to hone their business skills. Do that, and they will become more visible and instrumental among business leaders in app modernisation.