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TXNM had an AI mandate, but figuring out where to start required weeks of expensive, manual discovery work.
TXNM Energy's board and CEO had made one thing clear: they needed to adopt AI, and fast.
Like every enterprise, they knew AI would transform their business. So the question wasn't whether to adopt it. It was where to start.
That mandate fell on Dr. Tobe Phelps, who leads the Technology PMO. But he knew you can't jump straight to solutions. Building an AI roadmap requires one thing first: a deep understanding of how work actually gets done.
So his team went department by department to find out— asking employees to walk through their processes one by one. Sessions stretched into days, then weeks.
The traditional approach is horribly painful. Large teams stuck in a conference room, sticky notes on the wall, trying to map out what their process flows are."
This approach meant pulling multiple employees away from their day jobs, and convincing some that processes they'd relied on for years were actually costing the organization — even when nothing felt broken.
It was slow, expensive — yet critical —work.
Because without a clear picture of how work actually happens, TXNM couldn't build a credible AI roadmap, prioritize the right opportunities, or prove impact to the board.
Building an AI strategy is hard enough. Traditional process discovery methods were making it harder. Dr. Phelps knew there had to be a better way.
TXNM found a way to build their AI roadmap in days, without a single workshop.
When Tobe learned that Scribe — the Workflow AI platform his team already relied on to document work — launched a product that could analyze and improve that work, he called his account manager right away.
What caught his attention most: Scribe Optimize could passively capture workflow context across the entire organization — analyzing it and surfacing prioritized recommendations on where to automate and implement AI. No workshops. No pulling people out of their day jobs. It could build his AI roadmap for him. In days.
He wanted to see it for real, though.
When it came time to run a trial, he didn't go easy on it. He chose to trial it with their cybersecurity team who rely on heavily manual, complex workflows across a diverse range of tools. If Optimize could handle them, it could handle anything. Over three weeks, it ran silently in the background — capturing clicks and keystrokes in approved apps, then running it all through AI to surface complete, end-to-end workflows.
The recommendations were right on point: integrations to build, manual processes to automate, systems that could get connected.
"We deliberately chose the team with the most complex workflows we could find. We wanted to really push this product. So I dug into the data from every angle I could think of. What Scribe’s AI came back with was impressive."
Tobe brought in the cybersecurity team's own leadership to pressure-test the data. What they found surprised them.
Not only did Optimize accurately capture their workflows, it surfaced inefficiencies that had never been visible before. In a single review session, they had more actionable insight into their own team than weeks of workshops had ever produced.
"Letting leaders see their team's inefficiencies on the screen, and knowing that's backed by real data, completely changes the conversation. We stop debating and start prioritizing."
With Optimize, TXNM finally had the data to build a credible AI roadmap.
For TXNM, Optimize has quickly become the foundation for their AI strategy. Tobe thinks about AI adoption as scaffolding: you have to build it in the right order, or the whole thing is unstable. Optimize is the context layer that makes everything else possible: clear visibility into how work happens, a prioritized view of where AI can win, and the data to back every decision up.
"Optimize will change the game for TXNM because we will finally have a basis for moving forward with our AI adoption."
And that foundation is built on data that can't be argued with. When recommendations are grounded in real workflow analysis, there are no competing opinions, no gut-feel prioritization. Tobe can walk into any conversation with scored, quantified evidence for exactly where automation will have the biggest impact, and direct the organization's time and resources accordingly.
"The data is accurate, up to date, and so much cleaner than anything we had before. We start the conversation about our AI roadmap at a higher level, and it's quicker to move through."
The impact on time has been just as significant. The manual discovery process that once consumed weeks of workshops and hundreds of employee hours has been replaced by automatic capture.
"Optimize has already saved us an infinite number of hours. Easily hundreds of hours per person per department."
TXNM set out to build an AI roadmap. With Optimize, they finally have one.
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