This article is sponsored by NLP Logix and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.
Government and industry leaders increasingly agree that governance is now foundational to AI — not optional — because generative and predictive systems are already shaping critical decisions in the public sector.
Generative AI guidance from the Colorado Office of Information Technology shows why: Nearly a quarter of organizations reported inaccurate outputs and 16% reported cybersecurity issues, underscoring how adoption can outpace governance.
A recent OECD report asserts that fragmented data, legacy systems, and weak impact measurement often keep government AI stuck in pilot programs. The report goes on to argue that governance must define accountability and measurement early.
NLP Logix defines AI governance across ethics, policy, and testing. In practice, such policies mean documenting models, enforcing human review in sensitive workflows, and running standardized bias/robustness tests pre- and post-deployment. This perspective positions governance as both risk control and an enabler of scalable, trustworthy AI.
In a special, NLP Logix-sponsored series, Emerj Editorial Director Matthew DeMello sat down with Naveen Kumar, Head of Insider Risk, Analytics, and Detection at TD Bank, Matt Berseth, Co-founder and CIO at NLP Logix, and Russell Dixon, Strategic Advisor at NLP Logix, all to examine how organizations can effectively deploy AI tools, balance innovation with governance, and measure real business impact.
Their discussions underscore that AI initiatives falter when controls, training, and measurement are treated as afterthoughts. This article analyzes three core insights for successful AI adoption centered on robust governance, measurable business outcomes, and strategic deployment:
- AI governance as a built-in control layer: Enforcing role-based access, strict data classification, phased rollouts, and mandatory human oversight for safer deployment.
- Plan, govern, train, and measure AI: Deploying AI tools with a clear strategy, defined use cases, upfront governance, user training, and measurable adoption to ensure effective outcomes and ROI.
- Enforce strategic planning and metrics for AI success: Planning AI deployments with clear goals, metrics, and usage tracking to prevent tool creep and drive measurable value.
AI Governance as a Built-in Control Layer
Episode: Governing AI for Fraud, Compliance, and Automation at Scale – with Naveen Kumar of TD Bank
Guest: Naveen Kumar, Head of Insider Risk, Analytics, and Detection, TD Bank
Expertise: Regulatory Compliance, Fraud and Threat Detection
Brief Recognition: Naveen has over 16 years of experience in AML, Insider Risk, Fraud, and Sanctions. Previously, he has worked with PwC and Stellaris Health Network. He holds a Master’s in Science in data modelling from The Rochester Institute of Technology.
In his interview, Kumar argues that AI governance starts with traceability: knowing what data is used, who can access it, and how AI interacts with it.
“I think role-based AI is like a polite bouncer. It only provides information based on role — if there’s an insider investigation going on, finance has nothing to know about it. Putting it into the AI shouldn’t return anything. Guardrails are an invisible force, period. These are rules AI simply cannot break, no matter what prompt it receives. That stops people from gathering information by asking a series of questions and revealing things an attacker shouldn’t know.”
– Naveen Kumar, Head of Insider Risk, Analytics, and Detection at TD Bank
He portrays balancing innovation with customer obligations and constraints around regulatory and security compliance as an act that demands significant time and deliberate trade-offs. His recommendation: a phased rollout that starts with narrowly scoped use cases and minimal data access, then expanding permissions and sources only after controls prove out.
Classification also plays a central role for which Naveen recommends that leaders label data as safe, sensitive, or critical, and exclude critical data from early iterations. In his view, this structured, step-by-step approach is what helps organizations navigate the tension between usefulness and risk.
He also emphasizes that how AI is used depends heavily on the domain, drawing a clear distinction between compliance and retail use cases. On the retail side, where the goal is acquiring customers, a more aggressive use of AI may make sense. In compliance, however, the opposite approach is necessary — organizations need to be far more conservative.
Using the example of Suspicious Activity Reports, Kumar explains that while AI can support the process, it should not be allowed to run end-to-end without human review.
The challenge is balancing automation with oversight. To manage this, Naveen suggests thinking in terms of speed versus precision: automate low-risk alerts, and route higher-risk cases to human reviewers. Ultimately, he says, the right balance depends on the domain and the use case. In some situations, AI should be positioned as an efficiency layer or a first draft rather than a fully autonomous, end-to-end solution.
He then lays out a set of practical steps for moving forward with AI in a controlled way: he recommends starting in safe sandboxes, building a complete inventory of internal and vendor AI, and involving compliance early. The goal is visibility into what models exist, what data they touch, and how they’re governed.
Plan, Govern, Train, and Measure AI for ROI
Episode: Making Microsoft Copilot and ChatGPT Enterprise Work for You – with Matt Berseth and Russell Dixon of NLP Logix
Guest: Russell Dixon, Strategic Advisor, NLP Logix
Expertise: Technology Innovation, Business Transformation, Information Technology
Brief Recognition: Dixon is a Strategic Advisor at NLP Logix, specializing in global operations and business transformation. With over 20 years of experience in information technology, he advises organizations on deploying AI solutions and cloud technology. Russell’s expertise includes enterprise sales and business automation, with a focus on identifying high-value use cases to drive ROI.
During his podcast appearance, he argues that while tools like ChatGPT and Microsoft Copilot are almost universally applicable, they only pay off when deployment includes training, guardrails, and measurement of adoption and productivity.
Without that structure, Russell warns, simply releasing AI tools into the organization will not deliver results, nor ROI. Instead, users are likely to become frustrated, look for alternatives, or worse, to conclude that the tools offer no real value.
Thus, governance must be defined before AI tools are deployed. Without a realistic use case, deployment plan, and user training strategy, he argues, organizations won’t get the results they expect.
“There’s a governance piece to this as well. How am I going to use this tool, and what guardrails am I going to put around the tool, so that I’m assuring that my internal and client data is protected? Finally, you’ve got to ask yourself how you’re going to measure productivity. Are you going to rely on user feedback, or do you put more formal measurement tools and processes in place along the way to measure adoption in usage as the project gets deployed?”
– Russell Dixon, Strategic Advisor at NLP Logix
Dixon closely ties the success of AI projects to how well the use case is defined. The more generic the use case, the higher the likelihood of success. For example, deploying tools like Copilot or ChatGPT to support general workplace productivity should come with fairly high expectations, especially when the goal is a broad productivity lift across common office tasks.
By contrast, he says highly specific or specialized use cases carry more risk. The narrower the solution, the greater the chance it may not deliver the desired outcomes. He agrees with colleague and fellow podcast guest Matt Berseth, Co-founder and CIO of NLP Logix, that aiming for around an 80% success rate is reasonable, and that some level of failure is expected — even necessary — if organizations are pushing innovation.
However, Russell emphasizes that early signals matter. If adoption and results aren’t showing up quickly, organizations should pause and reassess. In his view, the technology itself is capable; when projects struggle, the root cause is more likely tied to user behavior or a mismatch between the tool and the use case, rather than limitations of the AI.
Enforce Strategic Planning and Metrics for AI Success
Episode: Making Microsoft Copilot and ChatGPT Enterprise Work for You – with Matt Berseth and Russell Dixon of NLP Logix
Guest: Matt Berseth, Co-founder and CIO, NLP Logix
Expertise: AI, Data Science, Software Engineering
Brief Recognition: Berseth is the Co-founder and CIO of NLP Logix and leads the delivery of advanced machine learning solutions for industries including healthcare, logistics, and finance. With over 20 years of technical leadership, he previously held engineering and architectural roles at Microsoft and CEVA Logistics. He serves as an adjunct professor and holds a Master’s in Software Engineering from North Dakota State University.
Matt describes a successful AI deployment as one that is measured, deeply understood, and continuously reinforced, not just rolled out and counted. He distinguishes adoption from value — usage patterns matter more than license counts — and recommends combining user feedback with telemetry on who uses the tools, how often, and for what workflows.
Adoption, he says, is just “bodies in the tool.” What really matters are usage patterns — how different users, teams, and departments are applying AI in high-leverage ways.
He also describes what can go wrong without proper planning, warning of an emerging phenomenon commonly referred to in development circles as “tool creep”:
“I think what’s happening now is tool creep. These tools become just another thing we don’t know how to use. We’re buying licenses but not seeing value. I use ChatGPT at home and like that interface better; at work, I don’t want to learn a new tool that keeps changing. The real issue is that you have to see these tools as a strategic part of your enterprise AI strategy. You need a plan, clear goals, metrics, and a way to drive adoption across the organization. If you do that, you’ll achieve your goals. If not, in three or six months, you’ll be back trying to fix a rollout that started on the wrong foot.”
– Matt Berseth, Co-founder and CIO, NLP Logix
In contrast, he emphasizes that some failure is necessary to drive innovation. Nearly 80% of his team’s AI POCs reach production and stay for a year, showing that the technology is capable. When projects struggle, the issue is usually poor use-case selection, not the tools. Today, with accessible AI like ChatGPT, it’s easier than ever to create value; organizations simply need to pick the right problems to solve.
Berseth argues that if an organization claims a 100% proof-of-concept success rate, it may actually be avoiding risk rather than pushing innovation. Some failure is expected and even desirable when teams test new ideas.
On the governance front, Matt reiterates that successful AI deployment requires structured planning, clear goals, and defined metrics. He emphasizes that organizations must strategically select use cases, monitor adoption, and track both tool-level and business-level outcomes to ensure responsible, effective, and measurable use of AI tools like ChatGPT and Microsoft Copilot.
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