From Experimentation to Execution: How KMS Technology and Addepto are Redefining AI Readiness

In December 2025, KMS Technology — a U.S.-based Digital Engineering, Data, and AI services company headquartered in Atlanta — acquired Addepto, a leading AI and data consulting firm headquartered in Poland. In this exclusive interview, Chewie Quek, Chief Growth Officer at KMS Technology, and Edwin Lisowski, Chief Technology Officer at Addepto, share their perspectives on the AI transformation landscape, how enterprises can overcome industry-wide pain points, and what lies ahead for the market.

AI TRANSFORMATION LANDSCAPE

What key trends and metrics are you currently seeing in global enterprises on their AI transformation journey?

Chewie Quek: We’re seeing a clear shift from AI experimentation to AI accountability. Three trends stand out. First, AI initiatives are increasingly tied to specific KPIs: cost reduction, revenue uplift and operational efficiency. Second, data readiness has become the ultimate gatekeeper; without modern data governance, AI stalls. Finally, C-suites are paying closer attention to technical debt, ensuring AI isn’t layered onto legacy systems without architectural discipline. The most common metrics include time-to-value for AI use cases, percentage of AI models successfully deployed into production, data quality scores, and the operational cost of maintaining AI systems post-deployment.

Edwin Lisowski: If you zoom out, the story is about making AI work reliably and at scale. Pilots are turning into portfolios. Leaders don’t want 20 experiments; they want 3–5 programs that move core KPIs. We are also seeing AI “leave the chat window.” The real value shows up when AI is embedded directly into workflows—sales ops, customer service, finance, claims. Consequently, the metrics have evolved. We look at unit economics (cost per outcome), human-override rates (to gauge quality), and system reliability.

From a C-level perspective, how do you benchmark an organization’s AI capabilities across the five most critical dimensions?

Chewie Quek: We assess maturity across five core dimensions: Data Foundation, Architecture & Platforms (MLOps), Talent & Operating Model, Use-case Prioritization, and Governance & trust. Organizations that score consistently across all five tend to move faster, incur less technical debt, and see AI as a long-term capability rather than a series of disconnected projects.

Edwin Lisowski: To add to that, you need a framework that is simple enough to govern but concrete enough to drive action. The “tell” is straightforward: how fast can you go from idea to production safely, and replicate that success across multiple domains without it turning into chaos.

How do KMS and Addepto’s combined offerings help companies prevent the technical debt that typically occurs when AI is added to legacy systems?

Chewie Quek: Technical debt often happens when AI is treated as an “add-on.” Together, we address this by designing AI into the digital core from the start. By aligning AI with long-term platform modernization, we help organizations modernize incrementally, avoiding the “hidden costs” that typically surface 12–24 months post-deployment.

Edwin Lisowski: Many transformations fail because AI is “patched” on, creating a fragile maze of scripts. Our joint approach treats AI as a first-class part of the architecture. We build clean integration patterns (APIs/events) and implement automated monitoring—quality gates, drift checks, and rollback paths—from day one, ensuring AI evolves without breaking the business.

INDUSTRY-WIDE PAIN POINTS

As raw model intelligence potentially plateaus—the “AI Wall”—does ‘AI Readiness’ become an operational race rather than a technology one?

Chewie Quek: Competitive advantage is shifting from the model to operationalization. It’s about how quickly a company can integrate AI into its decision systems and run those systems at scale. By focusing on the full AI value chain—modern data platforms, production-grade architecture, MLOps, and enterprise integration, we design end-to-end systems that allow AI to move from insight to action.

Edwin Lisowski: In a world where models are interchangeable, the winner is the one who runs AI best in production. We help clients build an “Operating System for AI”—reusable foundations and production discipline that make every subsequent deployment faster, cheaper, and safer. In a world where the model is not the differentiator, execution becomes the moat.

How should a CIO’s mindset evolve when they are effectively ‘hiring’ digital labor capable of planning its own actions?

Chewie Quek: This is a fundamental shift. AI systems are no longer passive tools; they are digital workers. CIOs must think less like software deployers and more like workforce architects. The question changes from ‘Does it meet requirements?’ to ‘How does this AI behave under uncertainty, how do we monitor its decisions, and how do humans stay in the loop?’

Edwin Lisowski: CIOs are moving from managing software to managing capacity. Agents aren’t features—they’re digital workers that can take actions, and change the governance model. You have to define roles, permissions, and accountability and measure performance like a team: success rate, exception handling, and ROI per workflow. If you treat agents like apps, you’ll get surprises. If you treat them like a workforce, you can scale safely.

MARKET & ECONOMIC PREDICTIONS

In the post-efficiency era, what happens after the low-hanging fruit of automation is exhausted?

Chewie Quek: That’s when true AI Transformation begins. We move from doing the same things faster to doing entirely new things that weren’t possible before—redesigning how work gets done and creating entirely new business models around AI-enabled services.

Edwin Lisowski: The value moves up the stack. It’s about changing the economics of decisions. We are looking at faster, smarter exception handling, proactive customer experiences, and AI-native products that generate revenue rather than just savings. Efficiency is the entry ticket; reinvention is where the long-term winners appear.

As the ‘Agentic Era’ matures, do you anticipate the rise of ‘Zero-Operations’ companies? How does this change the provider-enterprise relationship?

Chewie Quek: Ultra-lean organizations with dramatically higher revenue-per-employee are becoming real. This fundamentally changes our role: it’s no longer about delivering tools; it’s about becoming a long-term partner in how the business operates and governs its digital workforce. We help clients design the guardrails that allow AI to take on more responsibility without losing control.

Edwin Lisowski: “Zero operations” is more direction than destination—exceptions, accountability, compliance, and brand risk don’t disappear. What changes is the expectation: Partners won’t be judged on implementation alone, but on operational outcomes—reliability, governance, continuous improvement, and measurable business impact.

What is your strategic vision for KMS and Addepto’s investment to stay ahead of the curve?

Chewie Quek: Our strategy is built around that shift toward AI Transformation. We are investing in agent-based architectures, modern data platforms, and production-grade AI operations. Crucially, we are investing in people—engineers and architects who understand both the technology and the business context. Staying ahead isn’t about chasing the next model release. It’s about building durable capabilities that help organizations turn AI into a sustainable advantage, even as the technology itself keeps changing.

Edwin Lisowski: Our joint thesis is that winners won’t be those who demo the most, but those who deliver safely. We are investing in AI Delivery Systems (monitoring/governance accelerators), Industry-Ready Assets (reusable components), and Multidisciplinary Teams. Models will change, but the foundation of operating discipline shouldn’t.