27 Sep

5 Blockers Preventing Scaled Adoption of AI in Energy & Utilities and How to Overcome Them

Tom Jenkin

The energy and utilities sector is under pressure to modernise and innovate. They’re facing soaring operating costs amidst volatile markets, while striving to hit ambitious net zero targets.

Yet frustratingly, they still aren’t capitalising on the myriad of possibilities offered by embracing AI.

Last year alone, it’s estimated that businesses who adopted AI generated an additional $2.9 trillion in corporate value, as well as a staggering 6.2 billions hours of increased worker productivity.

So, what’s stopping the energy and utilities sectors from joining the ML and AI revolution?

Blocker #1: Businesses are bogged down with archaic infrastructures

People and processes within multiple energy and utility companies are still being managed in exactly the same way as they were 30 years ago. And in some cases it’s even longer.

This approach means that data is being handled in a very admin-centric way that doesn’t capitalise on the vast amount that is being generated by the businesses. Processing this data on legacy platforms and spreadsheets, makes it difficult for businesses to access it in a timely manner that could reap real benefits.

Working in this way leaves many companies unable to scale new developments at a rapid pace which leaves the sector lagging behind when it comes to adopting AI and ML.

Solution: Invest in making data highly discoverable across your organisation

Making data highly accessible across your organisation will make it a far more valuable asset. It will boost operational efficiency and greatly reduce the risk of human error.

In order to make this a reality, your organisation will need to undergo a series of operational changes. Data ownership and accountability principles will need to be agreed across domains, new automated approaches to data governance must be taken and a shift to treating data as a product must be implemented. New technology capabilities, broadly powered by the cloud, should be rolled out organisation-wide whilst establishing a culture of self-service provisioning for data infrastructure.

Unlike traditional ways of working with data, companies will be able to innovate quickly which will ultimately lead to business value that could run into hundreds of millions.

Blocker #2: The workforce needs more data skills

Data science is absolutely critical for reaching transformation goals in this extremely dynamic sector.

However, the energy industry struggles to attract and train enough data scientists (as well as broader, higher-level data awareness among the organisation).

Due to the historically quite static nature of energy (oil, coal, electricity, gas) and so on, energy companies have evolved to be stable and slow-moving. As a result, there’s an ingrained reluctance to innovate due to a lack of understanding of how data initiatives could transform roles in a positive way.

Solution: Paint your organisation as cutting-edge and emphasise the importance of data

There are two prongs that energy companies need to adopt.

On the one hand, they need to attract the top data minds that are coming out of education and in the industry.

By using data to transform and innovate, energy companies – who have traditionally struggled to attract young people into the industry – can paint themselves as forward-thinking and cutting-edge places to work and help to drive the recruitment of the data engineering specialists of tomorrow.

On the other hand, in order to get everyone across the organisation brought into new, data-centric ways of working, they must foment a general understanding of the importance of data across the organisation.

Educating the wider business about, for example, the importance of a unified data governance strategy will help to encourage people from all parts of the company to embrace the data journey.

Blocker #3: A large number of organisations are siloed

Many businesses are set up in such a way where teams work independently of each other, with data held and managed within small units, rather than being accessible across the wider company.

This leads to a fragmented approach in respect of data ownership and governance.

Even in instances where companies have already employed data science teams, these are often siloed and therefore not aligned to overall business goals which makes it difficult for them to make any impact.

Solution: Create cross-functional teams to support data initiatives

Breaking down these silos within organisations will allow businesses to adopt a more agile approach to data innovation.

By decentralising data and pushing it out to teams across the business, workers will be  empowered to use data to optimise their day-to-day activities.

These new teams will help to support new capabilities across the business, while making data more accessible to all.

Blocker #4: There is a shortage of data expertise within the industry

A lack of understanding of data has led to a distrust in data initiatives in many organisations.

Many within the industry even fear that any decentralisation of information could lead to vulnerability from cyber-attacks or system failures.

The current lack of data expertise sitting within the industry also means that any attempts at building tools for automation often happens without input from the teams who would benefit most on a day-to-day basis.

Ultimately, without these insights at the beginning of the process there will be no significant benefit to the business.

Solution: Embed data ownership and skills across the organisation

It’s important that there is clear leadership of data initiatives within the organisation and that these leaders communicate the key messaging around data and champion how it will impact day-to-day business alongside overall goals.

Organisations should look to embed data ownership and skills across the organisation with specialist support provided where needed.

As well as being made aware of how these initiatives will benefit the wider business, the workforce should be upskilled and provided with self-service tools that enable them to shift away from the offline processing of data.

Blocker #5: There is a lack of access to high quality data

It’s important to note here that the issue within the sectors isn’t lack of data. In fact, most energy and utilities businesses are sitting on a huge amount of data, much of it real time.

The problem is that it’s hosted on legacy platforms, leaving many parts of the business with insufficient access to data that could make a real difference to their day-to-day operations.

In addition, many energy organisations are now in a continual cycle of data clean-up because they aren’t sorting out the data issues at source, which has led to backlogs - sometimes stretching back years.

Solution: Start by fixing the quality of your underlying data

It’s critical that businesses first address the quality of their underlying data.

By getting the basics right, they will be able to generate good quality, stable data  that is accessible by everyone.

Greater data quality and discoverability will help to improve operational efficiency when it comes to issues such as backlog management, and will help them to unlock real value from machine learning.


With the potential contribution to the global economy from AI predicted to be $15.7 trillion by 2030, it’s important that all sectors embrace the possibilities and start their journey today.

By combining high quality, accessible data alongside machine learning capabilities, the energy and utilities sectors can unleash real business value as they tackle the big issues facing them including operating costs, backlogs and carbon emissions.

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