Frank Carnevale, Country Head, Canada for iGreenTree.ai | AI, Digital Innovation & Cleantech | Energy & Utilities.
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Artificial intelligence is no longer a future concept. It is already influencing how organizations plan, operate and compete. Yet, despite all the excitement around AI, many leadership teams still struggle with a basic question: Where do we currently sit on our AI journey?
That question is harder to answer than it sounds. Most organizations have scattered pilots, promising ideas and isolated investments. One team may be testing generative AI, another may be working on automation, and a third may be trying to improve data quality. But without a clear way to measure progress across the enterprise, leaders are left with activity instead of insight.
In my day-to-day, I oversee the growth of a North American digital systems implementor for utilities, and I have developed several AI agents to support this work. Based on what I've seen in the industry, this is why organizations need an AI-enablement dashboard.
An AI-enablement dashboard is not simply a scorecard for technology teams. It's a practical way to show where the organization stands across its most important domains, how far each domain is from its desired north star and what needs to happen next. It creates shared language for executives, boards, regulators, staff and even the public to better understand how your AI readiness is evolving.
AI success should not be measured by how many tools an organization buys or how many pilots it launches. It should be measured by how well the organization is building the foundations needed to create real business value over time. That means looking at AI through several lenses at once.
First, there is data readiness. Is the data accessible, trustworthy, secure and usable? Many organizations want AI-driven outcomes, but poor-quality data, disconnected systems and weak governance often block progress before it starts.
Second, there are AI capabilities. Does this organization have the right mix of talent, platforms, model workflows and governance to move from experimentation to execution?
Third, there are innovation areas. Where are the best opportunities to create measurable value? Not every use case matters equally. Some will have clear business impact, while others may be interesting but not urgent.
Fourth, there is AI maturity. Is a domain still in the awareness stage? Running pilots? Scaling successful use cases? Or operationalizing AI in a disciplined repeatable way?
When these dimensions are brought together in one dashboard, leaders gain something many organizations currently lack: visibility. They can see where the enterprise is strong, where it is falling behind and where investments should go next.
This is especially valuable in complex industries such as utilities, where organizations must balance innovation with reliability, compliance, safety and public trust. In these environments, AI cannot be treated as a side project. It has to be connected to operational priorities and measured in a structured way.
That is where domain-based framework becomes so useful. My team has developed a framework that identifies 15 specific domains for utilities, but it can be replicated across any industry, really. Rather than asking whether an industry or segment is simply “good with AI,” the framework breaks the question into meaningful business areas and scores each one against its own north star. In this case, this kind of approach aligns with a broader industry emphasis on benchmarking, prioritizing and scorecarding AI-enabled outcomes—while linking readiness to data, process and security foundations.
This creates a much more realistic picture of progress.
A company may be more advanced in one domain, such as asset intelligence, but still early in another, such as customer operations or grid-edge analytics. One domain may have strong innovation potential but low data readiness. Another may have mature data practices but limited AI capability. A scoring framework makes those differences visible and actionable.
In any given industry, you should select the domains that are critical to the success of your business. AI enablement is not a binary state. An organization is never simply done. It is always moving. New priorities emerge. Regulations evolve. Data improves. Teams mature. Use cases shift from experimental to operational. The journey is continuous, and your success metrics will tell you how that journey continues to progress.
An AI-enablement dashboard helps organizations manage their journey with discipline. It can give boards confidence that progress is being measured responsibly. It can help executives make better investment decisions. It can give staff clarity on where effort is needed. And it can give regulators and the public a transparent way to understand whether AI adoption is being pursued thoughtfully and safely.
Perhaps most importantly, it changes the conversation. Instead of asking, “Are we using AI?” leaders can ask better questions: Which domains are closest to value? Where are we not ready yet? What is improving? What is stalled?
Those are the questions that turn AI from hype into management.
Not sure where to start with your dashboard? I have two holistic tips for leaders from any organization.
First, you will want to have conversations with the teams running your business units (domains) to properly identify what is possible in your various AI use cases.
Second, you will also want to do your homework so that the north star you are picking for those domains isn't going to be outdated in the next six months. You need to adapt and constantly update, but aim further out to make sure you're surveying what will be possible soon.
In the months and years ahead, the most successful organizations may not be the ones with the most AI tools. They may be the ones with the clearest view of where they stand, what progress looks like and how they can keep moving toward their north stars.
That is the real value of an AI-enablement dashboard: It does not suggest that transformation is finished. It makes the journey visible.
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