The discussion focuses on how Southern Company is using advanced metering infrastructure (AMI) data, strong data governance, cloud-based architecture and business-led innovation to improve grid operations, customer engagement and AI adoption.
Utilities are under increasing pressure to modernize the grid, support electrification, improve reliability and deliver better customer experiences. AI is becoming central to that transformation, but successful adoption depends on more than deploying new tools.
The real differentiator is trusted, governed and business-ready data.
Many utilities still struggle with fragmented systems, siloed data ownership and disconnected analytics environments, making it difficult to scale AI initiatives across the enterprise. Southern Company’s approach focuses on bringing AMI and operational datasets together in a shared cloud environment while strengthening governance, stewardship and collaboration between business and IT teams.
Rather than focusing first on technology, the strategy centers on enabling better business decisions through accessible and trusted data. This helps align IT, analytics teams and business users around a shared goal: creating reusable data products that support operational decision-making, analytics and AI innovation.
AMI data is evolving beyond billing and meter operations into one of the utility sector’s most valuable operational assets.
With millions of smart meters generating continuous streams of information, utilities can use AMI data to improve grid visibility, customer engagement and operational efficiency.
One of Southern Company’s first analytics initiatives focused on validating meter-to-transformer relationships, a foundational capability for maintaining grid health and balancing electrical load across the network.
Using voltage signatures and data science techniques, analytics teams were able to identify incorrectly connected or missing meters while recommending more accurate transformer assignments.
These initiatives demonstrate how AMI data can support both operational reliability and customer-focused innovation when it is governed and accessible across the enterprise.
Data governance is often viewed as a barrier to speed and experimentation. Increasingly, utilities that are successfully scaling AI are treating governance as an enabler of trust, adoption and long-term innovation.
Governance was embedded into Southern Company’s platform from the beginning, allowing teams to innovate within established standards while maintaining data quality, privacy and security controls.
The shift away from monolithic enterprise platforms toward more federated, domain-based approaches aligned to business use cases and data products is becoming increasingly important for utilities scaling AI. This allows teams, reports and AI models to consume trusted data from a shared governed foundation without recreating infrastructure for every initiative.
AI adoption can accelerate quickly when utilities establish trusted data foundations and reusable platforms.
Early analytics initiatives delivered measurable operational value across Southern Company, encouraging broader participation across the organization—what Peter Warren describes as the “popcorn effect.” As more business units gained access to governed data, shared infrastructure and modern AI capabilities, teams were able to focus less on rebuilding technical foundations and more on delivering operational and customer value.
Southern Company’s data strategy is also enabling more advanced AI capabilities, including retrieval-augmented generation (RAG), large language models (LLMs) and AI agent deployment.
As utilities expand into generative AI, the importance of trusted data, governance and security becomes even more critical. AI models and agents are only as effective as the data foundations supporting them.
Building scalable AI in utilities requires AI-ready data foundations, strong governance, reusable platforms and a culture that enables business and technology teams to innovate together.
- 1. Introduction: Exploring the future of AI, data and innovation in utilities
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Peter Warren:
Hello everyone and welcome back to our ongoing series on the ever-changing world of energy and the transition and how things are moving forward. We've got a great session for you today. I've got a wonderful couple of guests. I've got Joyce Solomon, the data analytics manager for AMI for Southern Company and also Doug Leal. Doug's joined us before, our VP from Consulting for Data Analytics. Hi, Joyce. Do you want to say a few words and introduce yourself further?
Joyce Solomon:
Yes, thank you, Peter. Hi everyone. I'm Joyce Solomon, the data analytics manager in the AMI space in Southern Company. I have the privilege of managing 4.4 million meters across Southern Company. Very excited to be here.
Doug Leal:
Excellent. And Doug, yourself.
Hello everyone. Yes, Doug Leal. As Peter mentioned, I'm a vice president of data analytics and AI here at CGI, helping our clients drive the most value out of their data. I've been doing data projects and machine learning before machine learning was on the mainstream, before machine learning was considered trendy, for many years. I'm very happy to be here and looking forward to the conversation.
Peter Warren:
That's wonderful. And I often forget to introduce myself. I'm Peter Warren. I'm the global industry lead for energy and utilities here at CGI.
Joyce and Doug, you've done a bunch of different things together presentation-wise recently because of the successes you're having. Earlier this year you were at DistribuTECH in the United States. You did a great presentation on stage and that received a lot of kudos. You also just recorded a podcast with Energy Central. Doug, do you want to maybe recap what that is so people can seek that out as well?
- 2. Building a strong data strategy for utilities
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Doug Leal:
Yes, absolutely. It was always great to get together with Joyce and talk about our projects and initiatives.
On that podcast we talked a lot about the goal and vision for a solid data strategy to get the most value out of our data. We dove into the details around the focus of the data platform that we started building with Joyce's team, which was all around bringing the data close to the business and driving value out of that data.
We also dove into data governance, everyone's favorite topic. We discussed how to enable data governance as an accelerator instead of a brake to the entire process.
We talked about the federated model and how instead of one monolithic enterprise data platform, we followed more of a mesh approach, which is domain-based data platforms aligned with data products. Those data products focus on providing information for that domain to consumers of that data. When I say consumers, it could be a team, a machine learning model, a Power BI report or a Tableau report. Whoever is consuming that data has all the information in one place.
I'll hand over to Joyce as well for her input on what else we discussed during that 30-minute podcast.
Joyce Solomon:
Not only did we talk about the foundation that was built with the data, but also all the insights, the value proposition and the AI niche that we were able to accomplish and continue to accomplish.
Interestingly enough, one of the questions that was asked was around how AI is received in Southern Company. I remember us having a detailed discussion on that and I'm pretty sure every organization today that touches AI, that's the topic of the matter. Happy to participate and happy to talk about that here today too if that's a topic of discussion.
- 3. Why culture matters for AI success
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Peter Warren:
Each year CGI does a Voice of the Client survey and we talk to our customers. Southern Company fits into the category that is succeeding with their investment in AI and data. They're doing things correctly.
It was interesting that there's a common characteristic, and only about 30% of companies are doing this, so you're in an elite group. It was around the culture, having a platform, having a standard strategy and also executive support.
One of the things I really enjoyed after hearing all the technical things you did was hearing how the culture of Southern Company is working together and receiving this.
Why don't we just dive into that, Joyce? What's your thoughts on all of that? How is your leadership supporting you? How are your users supporting you?
Joyce Solomon:
Peter, I have to say being in this role excites me the most because I see a lot of support coming from the leadership that I report to. They have the same sentiment as me and my organization on moving the niche together with turning data into insightful information that could be given to other business units or directly to the customer as well.
With the journey that we have made, it has been such a great journey because not only is the leadership behind us, but we've partnered very well with the technology organization that we have internally as well as vendor partners like CGI that have helped us build that fundamental architecture that keeps enabling us to quickly and nimbly bring value propositions and use cases to life.
Of course, any business unit that we serve from the AMI perspective, that's just a rule of thumb that I always like to have champions joining us in the journey because they are the SMEs in their area. They know what value proposition each of the use cases can deliver and how they will use it.
Putting all of that together in a circle is how I've seen great success coming out of my organization.
Peter Warren:
I love that because it's less about the technology and more about the organization. We do see that as a big thing.
Doug, how would you like to comment on how you've been working with the organization? You walk a line between the IT department and the lines of business. How does that all work?
- 4. Aligning IT and business teams around data
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Doug Leal:
It is a very interesting dynamic because we need to make sure we are following all the guidelines, procedures and standards under the IT organization while making sure we are delivering value to business teams.
I think both organizations are aligned on the purpose. There is a very clear signal when the conversation starts, whether it is on the business side or with the technology organization. It always starts on enabling business decisions. It never is about a tool or how we're going to use some new technology. It is all about the business decision.
How are we going to enable the business to make better decisions? How are we going to bring the data close to the business so they can innovate?
That has been a very rewarding process. Not focusing on the technology, but leveraging technology to solve business problems with IT supporting us during the entire process.
- 5. Breaking down data silos in the utility industry
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Peter Warren:
Joyce, I talk a lot to the 70% that aren't getting the success. What's the secret recipe between your organization? I know you don't just have a CIO, you have a CITO. That's a factor. Even the definition of your role is a little bit different about data and who has it. I don't believe you have a lot of silos or problems with silos.
Joyce Solomon:
I think we did have that, Peter. We have to acknowledge that. At the beginning of the journey there were a lot of silos. Data was in all different locations and accountability was only on individual data sets.
I think the barrier of including technology tools and enablement, and also being a partner in the journey rather than taking over, was the culture shift for every organization to come together and put the data in one location, which is the cloud environment that we have today.
It also helped each other and serviced each other off the data products. That helped build a clearer role around stewardship of data, how we put governance around it and then the first use case that was built showed value instantaneously.
Everybody wanted to jump in. Everybody wanted to use the data. Everybody wanted to be part of that journey.
It was an awesome journey that we took with the understanding that the tool, the data and the insights are not owned by one organization. It is shared across many different business units to see value.
- 6. Real-world AI use cases using smart meter data
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Peter Warren:
I love that. I see that as one of the many benefits you had. Are there some use cases you two would like to highlight that come to mind?
Joyce Solomon:
Yes, absolutely. Believe it or not, I am so privileged to handle the AMI data that comes out of all our smart meters. I will go anywhere and today I would want to say that AMI data is the most sought-after data and is the most valuable data. So I feel very empowered having that data set, owning it and governing it, and then providing it as insights to others.
The first use case that I was challenged on building upon in the AMI data was identifying how a meter is connected correctly to a transformer or not. When you look at that use case you might think this is not really exciting or user appealing, but it's actually fundamental for the grid's health.
You've got to connect the right types of meters, the right size and the right amount of meters to the transformers so that the upstream load is well balanced and your grid becomes healthy.
We used data science to do that. Not only did we use voltage signatures for this, but we were able to identify missing meters. With the insights that we delivered, we were also able to do recommendations on what the next best transformer this meter should have been connected to. Some automation went into that for self-correction as well.
That use case became so popular in the power delivery space and people started asking, "What else, Joyce? What else can you do? What else are you seeing out of this?"
When we were doing that use case, we were able to discover a new use case, which was usage theft. As the models were running we were finding anomalies on meters that are still drawing usage but are not tied to an account.
We were able to do triangulation architecture using data science and spot those meters that are considered loss meters because energy theft is taking place. For every operating company we were able to bring back revenue.
A couple of months later we were able to use just AMI data to detect EV charging at home, charging behavior and charging times. That helped us proactively communicate with customers and educate them on becoming a new EV user and the rates and programs that we have.
That ties directly to customer satisfaction and also keeping our grid healthy so that we educate customers not to charge their EVs at home during peak hours.
The next thing we discovered from that was HVAC heating or cooling inefficiencies and energy scoring as well. That caught the eye of our leadership and today we are able to use these insights to target customer satisfaction and keep our grid ready and healthy.
- 7. The ‘popcorn effect’ of AI innovation
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Peter Warren:
When we started talking about this initially with Doug and yourself, other people coined it the popcorn effect. One group had success then another group said, "I want that."
Change management of these people coming towards you, and the push-pull between the lines of business and the IT department, became interesting. But because they wanted the outcome, they adopted the IT rules.
Doug, do you want to talk about that push-pull that created this popcorn effect?
Doug Leal:
Yes, absolutely. It all comes down to the company culture, which is enabling the business to make decisions based on data. They are the subject matter experts of that domain.
Giving business teams access to trusted data and the tool set that enables them to leverage large language models and the latest AI technologies was the key differentiator to make teams innovate and deliver value.
What Joyce described is the long-term benefit, which is this whole compounding value of solutions being delivered.
Once business teams have access to trusted data within this shared model environment that we established, the second and third use cases become faster than the first. That's when you start seeing enterprise value and enterprise momentum.
Going back to your point, Peter, the popcorn effect is where teams see that something was successful and say, "I have a similar use case. Let me get onboarded to this platform and leverage all the infrastructure and automation that was already established."
Those teams can just focus on the use case. They don't need to worry about infrastructure as code because that problem was already solved. They come to the platform and focus on the use case.
I say "only on the use case" like it's simple. It is not. There's a lot of complexity. But the more we can remove from the business in terms of deployment and infrastructure, the better they can move forward and move fast.
- 8. Creating a culture of innovation with AI
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Peter Warren:
That's brilliant.
As we start to wrap up here, Joyce, maybe I can give it back to you for a few minutes to talk about that culture of innovation that's happening. It really moved from a culture of command and control to a culture of innovation. How is that working and how is that going for you?
Joyce Solomon:
It's doing great, actually, Peter, because not only do I get to innovate and have been given the space to do so, I have been successfully influencing other leaders like myself in the organization to move forward and produce and be at the forefront of innovation.
Today, with the tools that we have, just like what Doug said, we've moved into the space of RAG, LLM and AI agent deployment.
Of course, with all of this comes the safeguard of our data as well. We have data privacy that we have to handle, governance that we have to handle and leadership having their trust in leaders like me to deliver those things for the betterment of the company and to make decisions.
I'm in a great spot. I enjoy my job and I want to continue to influence others to do the same.
- 9. Final thoughts on AI, data governance and innovation
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Peter Warren:
That's brilliant. Maybe we'll end on that wonderful point about how you guys have taken what people would think is governance as being something that blocks progress and made it a strength, a power and a foundation to move things forward.
It's a very inspiring story and thank you very much for sharing it.
Doug, I'll give you a goodbye and Joyce a goodbye, and we'll talk to everybody in the audience another time.
Doug Leal:
Thanks everyone. It's been a pleasure to be here. Great conversation as always.
It is a great time to be working with data and AI, so I'm looking forward to the next 10, 20 or 30 years. It's a very exciting time.
Joyce Solomon:
Thank you all for having me and happy to participate with CGI. CGI has been a great partner and collaborator for us, so we continue to look forward to this partnership and for us to innovate together. Thank you.
