Many energy data teams do a great job of serving requests from stakeholders across operations, trading, asset management, and regulatory compliance. They deliver insights on time, build robust, scalable solutions, and even find time to share knowledge across the organisation.

But it’s easy to get a bit stuck in a rut.

Data and analytics isn’t easy. It takes time. Energy markets are volatile, infrastructure is ageing, and integrating renewables creates new complexities. Developing AI-driven forecasting models, grid optimisation tools, or predictive maintenance solutions takes time and multiple iterations. On top of that, energy data teams are in high demand, with their skills able to add value across multiple departments – from trading desks to network operators to sustainability teams.

That can lead to overload – lots of different people competing for your time and attention to sprinkle some of your data and analytics magic on their workload.

You can end up on the treadmill, stuck in that “order-taker” role. Working away at whatever crosses your Kanban backlog and, if you’re lucky, getting through it at about the same rate it’s growing. Some of the great ideas you’ve had sit on the “one day” shelf, gathering ever more dust while you build yet another ETL pipeline to replace some fractal VBA nightmare.

At least someone cares

What’s worse than many people looking for your support?

No one looking for it.

Maybe you’re capable, available, keen, and ready to go but the one area of the organisation you think you could make the most impact in just aren’t interested. They don’t get it. They’re not data people. They don’t care. Or they just don’t see how things might be improved with a little help from their local friendly data team.

How do you break free if you’re in either of the above situations?

How do you get stakeholders engaged?

How do you take some control over what gets built – being proactive instead of reactive?

The Art of the Possible Workshop

This may be my favourite tool developed over the last few years of leading and consulting for data engagements. The Art of the Possible Workshop aims to get stakeholders excited about what they can do with data and analytics. It helps them spot opportunities across their organisation, and most importantly it gets them to prioritise a big shopping list of potential workstreams so you can take them on to the next step.

The goal of this workshop is threefold:

• Get stakeholders excited about the possible impact data and analytics can have
• Gather a curated list of pain points and potential opportunities to drive positive change in the organisation
• Get collective commitment and prioritisation on what to do next.

This starts the whole co-design process. People often love the sound of an idea if they think it was theirs (even if they were coached to it). This workshop allows you to guide the group to have a bunch of great ideas for your team to start implementing.

Why it works for energy organisations

The energy industry is evolving rapidly, with increasing pressure to enhance efficiency, integrate renewables, and meet regulatory requirements. However, challenges remain:

• Struggling to identify impactful use cases – Lots of ideas, but no clear prioritisation leads to inefficiency and slow progress.
• Lack of a roadmap for implementation – Blue sky thinking is great, but without a clear path forward, promising ideas never materialise.
• Disconnect between business and technology priorities – Different departments have competing objectives, making it hard to unify efforts around data and AI initiatives.

The participants

The Art of the Possible Workshop is typically delivered to between 4-8 people (too few and you don’t get a wide enough picture, too many and people don’t get enough share of the time/space). If you need more people, consider splitting it up into sub groups or categorising by role. This should be somewhat additive, so don’t be afraid to run it with a similar group at a later date once some changes have been made.

Who should be in the room depends on the remit of the data team or those you’re trying to engage. If running this workshop at a smaller company, involving senior leaders as spokespeople for their departments can ensure broad representation. If the company is large or you’re looking to engage just one function/team (say finance), then you might want to get really specific and try to invite at least one of each job role and seniority level in that function.

Workshop structure

This workshop is typically delivered in-person over a single day but can also be run remotely or in smaller sessions. The process follows a structured approach:

1. Case study showcase – Inspire participants with real-world examples from the energy sector.
2. Opportunity discovery – Identify key challenges and inefficiencies.
3. Discussion – Explore potential solutions and align them with business priorities.
4. Theming – Group ideas into broader categories (e.g., asset optimisation, market forecasting, grid flexibility).
5. Ideation – Refine concepts into actionable initiatives.
6. Prioritisation – Rank initiatives based on impact, feasibility, and strategic alignment.
7. Commitment – Secure buy-in and define next steps.

Case study showcase – 60 minutes

This part takes the most time to prepare. You want to prepare a number of case studies that are relevant to the people you have in the room. The objective here is to get people excited and discussing blue sky, no limits things that are possible. Aim for roughly 10 examples, use short GIFs and video demos over pictures where possible. You don’t need to go into details. If you can link to hard, citable numbers that’s even better. Case studies by big vendors and consulting firms is where I usually start my search.

Draw these case studies from four places, in order of preference:

• Direct competitors and industry leaders – What are other utilities, grid operators, or energy traders doing?
• Adjacent industries – Learn from renewables, oil & gas, or transport companies that face similar challenges.
• Cross-industry examples – Finance, healthcare, and logistics all offer insights into how AI and data can drive efficiency.
• Generally applicable case studies that will impress – Think good data catalogues, ChatGPT etc.

I always try to include some interesting general stuff that they might not be aware of – data discoverability tools, RPA, self-serve reporting, data quality tools and more.

Pro tip: Use videos, GIFs, or live demos where possible—these are far more engaging than static slides. If you can cite real numbers (e.g., “Company X reduced maintenance costs by 30% using predictive analytics”), even better.

Once you’ve done this, your audience should be primed for some participation.

Head over here for part 2 of the Art of the Possible series. Learn more about Hypercube’s Art of the Possible Workshop below.