Modulating Your AI Strategy for Long-Term Success

In this episode, Jessica Lidh, Kristin Rydland and Curren Katz of modulAIre discuss the growing challenges organizations face when navigating the complex and rapidly evolving AI landscape. They introduce a new collaborative engagement between Triad and modulAIre designed to help public sector clients move beyond the confusion of point solutions and isolated pilots. Curren emphasizes that many organizations waste time and resources by jumping into AI without a clear strategy, often relying on consumer-grade AI experiences or fragmented vendor solutions that don’t scale. The proposed engagement offers a refreshing alternative: a focused, four-week planning process built around interactive workshops where clients simply show up and share their challenges—no homework, no endless forms. modulAIre brings deep, pre-2021 AI expertise to assess current state, identify high-impact use cases, and deliver a comprehensive, executable roadmap complete with data strategy, governance frameworks, and a clear business case. Curren shares real-world examples of costly missteps clients have made—from investing millions in data initiatives that weren’t AI-ready to scaling isolated pilots into unmanageable operational messes—underscoring the value of getting the foundation right from the start. Jessica and Kristin highlight how this approach is uniquely suited for the public sector, offering a low-risk, budget-friendly entry point that works within procurement constraints and delivers actionable tools to build confidence, secure stakeholder buy-in, and set organizations up for sustainable, scalable AI success.

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Relevant Insights

Curren Katz

PhD is the Founder and Co-CEO of modulAIre (www.modulaire.ai), an AI solutions and service company bringing proven, scalable, and trustworthy AI to organizations.

Prior to starting modulAIre, as a Fortune 100 executive at Johnson & Johnson, Multiplan, and Highmark Health, Dr. Katz has led large-scale AI initiatives across healthcare and pharma. At Highmark, she built and scaled a full Data Science department and Research Institute, developing AI models to improve patient outcomes, reduce costs, and streamline operations. Her work has earned patents and international recognition through collaborations with IBM, Bosch and NASA. Dr. Katz is also an IEEE Certified Artificial Intelligence Systems Lead Assessor, authorized to evaluateAI ethics and issue CE mark certifications for AI Systems. Curren holds a PhD in Cognitive Neuroscience from Humboldt University in Berlin, a Master’s in Mind, Brain, and Education from Harvard, and a Bachelor’s in Early Childhood Education & Special Education from New York University.

What You’ll Learn in This Podcast:

Why most organizations struggle to develop a successful AI strategy—and how they often waste time and money on the wrong approach
The critical difference between consumer AI and enterprise AI, and why confusing the two leads to failure
How point solutions and fragmented vendors create integration nightmares, operational overhead, and tech debt
What to look for when vetting AI vendors—including why deep AI experience that predates 2021 is essential
How the AI Planning Engagement works: a four-week, workshop-based process with no homework, no endless forms, and minimal client lift
What tangible deliverables clients receive, including a current state assessment, data strategy, prioritized use cases, governance frameworks, and an executable roadmap
Real-world examples of costly AI missteps, including millions spent on data initiatives that weren’t AI-ready and isolated pilots that couldn’t scale
Why getting the foundation right before investing in data or technology prevents expensive rework down the line
How this engagement is uniquely tailored for public sector clients—working within procurement constraints, budgeting cycles, and the need for secure, governed solutions
The power of starting small while building a scalable, modular foundation that sets organizations up for sustainable AI success

From Chaos to Clarity: How We Find the AI Opportunities That Actually Matter:

modulAIre, we’ve seen and we’ve implemented AI so many times in so many places, and seen what works and what doesn’t, how to really find those valuable use cases, that it’s much easier for us to, you know. Talk to the client and just get right to that point. And it’s easy, it doesn’t require any expertise on anyone else’s part to just talk about, here are the challenges, don’t not worry about if it’s an AI challenge or not, just talk about, this is what we’re seeing, here’s the opportunities, here’s what we’re worried about, here’s what we’d love to fix. So we start there, and then we look at, again, the client’s; data, infrastructure, where they are today, their systems. Also, very seamless, it does not require tons of time and expertise. But from there, we can put together where’s the future? Where do we want to go? And the value in going there? What do we do first, second, third?”

The Vendor Trap: Why Point Solutions Sabotage AI Success:

“There are a couple of different aspects as to what’s different and what allows us to really help clients succeed. One is, all vendors are not the same, so when you’re talking to a vendor, they may be selling a piece of a solution, right? It may be a small piece of what’s needed to achieve results with AI, and it could be even a really a fine piece, a good piece. I’ve seen some solutions where I think, oh, this would be great again, and personal life, consumer world. The challenge we see is that these small, focused, pieces are not enough and what organizations end up with, our clients, sometimes when we go in, they’ve been down this path, what they end up with is a ton of point solutions, and then they have to integrate all of those. And suddenly, they’re dealing with vendors and integrations between all those tools, a bunch of different contracts, and it quickly just completely overwhelms the client, and all of the value in going down that path is lost. You have to hire departments of people just to maintain all the integrations and manage all the vendors.”

No Homework, No Spreadsheets. The Workshop Experience That Makes AI Planning Fun:

“From an end-user experience, what they’re doing is really they’re participating in workshops. They’re not being sent, endless spreadsheets or forms to fill out, which it can be completely overwhelming, it takes a ton of time. They’re not, constantly having to go and gather information and gather materials. That is, they have a full-time job, and, you know, if the end user knew exactly what they needed to focus on and what was relevant, that would things would be easy, right? And we totally understand that, and we’ve, over many, many years and decades, refined this so it’s very interactive, it’s, I’ll say, fun, it’s enjoyable. No preparation is really needed. You come to the workshops and just think about it. We have ways for everyone to participate. It’s sort of, you can say, design thinking. Just talk about, okay, let’s take a few minutes and just talk about who are the, you know, people, different types of stakeholders impacted. Just scribbling things down on post-it notes, nothing high pressure. And then going through what are the challenges that these people are facing, so that the end user, the client, they’re just participating in these workshops, and we have found, truly, it’s a lot of fun. It feels like, okay, a refreshing way to talk about business. There’s no pre-work, there’s no homework afterwards. From there, you know, we take that and group everything together, synthesize everything, any other materials, information we have, and decide together on, okay, here’s what we see. Here’s what is potentially high impact? Is this highly feasible? Because we want to start with things that are very high impact, they’re going to deliver.”

What You Actually Walk Away With: The Deliverables That Turn AI Strategy into Action:

“First of all, I’ll have, like, a high-level executive summary… The scope, what were the findings, and just the recommendations mapped to business outcomes. We’ll also give them, of course, the current state assessment. So this is where you are today: systems, people, processes, culture… We’re looking at that whole data landscape and saying, here’s the strategy to get your platform and data into a place where you can maximize the value with AI. And then the AI strategy… what are the AI opportunities? And also, what are the guardrails that are needed? The evaluation? How are you going to know if an AI opportunity is performing well? …maturity and capability scorecards… so there’s a very clear sense of what types of training programs they might need. And then the roadmap, like, an actual executable roadmap… What are the dependencies? What’s the timeline? What’s needed? So this is, like, a not high-level theory, but really what needs to happen.”

Don’t Stare Off the Cliff: Why a Quick, Focused Plan Prevents Costly Missteps:

“AI has moved on, and technology has moved on, so this is meant to be a focused, quick, get exactly those artifacts that a client can actually move forward with, whichever direction they go. So, we have, we have clients that use this to move forward internally. Maybe they have, you know, internal team, they just want to make sure they’re focused and going in the right direction and filling in just where they need it, right? They might use it that way. We just want to give an organization a chance to be successful with a good plan, however they move forward. To not look over, you know, just a cliff and not even know how far down they’re going. To give them a sense of, here’s what you need to do, here’s the value it can deliver, here’s what to promise your organization. We’ve seen a lot of, a lot of challenges and a lot of situations with clients when we come in that would have been avoided with this process. We’re just really excited to whatever the next steps after this are, to help as many companies as possible avoid those pain points.”