Key Takeaways from Growth Acceleration Partners’ Technology Executives Roundtable on “Future-Proofing Your AI”
By: Jocelyn Sexton, GAP’s VP of Marketing
AI isn’t a project. It’s an evolving system, and the big challenge is designing AI that improves over time, rather than decays. Enterprises are shifting from building proofs of concept to building permanence and ROI. The best leaders are rethinking AI as an evolving architecture, rather than a single project or tool.
In November 2025, Growth Acceleration Partners (GAP) hosted an in-person Technology Executives Roundtable on the topic of “Future-Proofing Your AI” to discuss strategy, share experiences and explore solutions in a collaborative, peer-to-peer environment.
We invited technology leaders, including CTOs, VPs of Engineering and Data Governance Leads, to join us in New York City to investigate three key topics:
- The problem with AI investments
- The challenge of building AI that lasts
- The solution of adaptive, scalable systems
We discussed how organizations seek to engineer intelligence that endures and delivers value long after deployment, but one unique characteristic of AI is how quickly anyone can build an experiment or proof of concept. Very quickly, enterprises are seeing a messy collection of brittle prototypes that quickly lose the promised productivity and financial gains that once looked so bright.
What began as AI experimentation is now an engineering and organizational challenge: how to build AI that delivers value not just in month one, but year three.
This summary captures patterns from GAP’s Roundtable event: what works, what doesn’t, and how to build AI systems that are scalable and sustainable. It explores how organizations can build adaptive, production-grade AI that delivers continuous ROI through governance, scalability and feedback. It also reframes ROI beyond cost savings, emphasizing trust, adaptability and knowledge retention as measures of success.
Additionally, the discussion during the Roundtable highlighted how sustainable AI depends on both architecture and culture. True longevity requires simplicity over complexity, curiosity over compliance, and discipline over speed. Through clear frameworks for governance, retraining and human-in-the-loop oversight, enterprises can transform isolated AI projects into self-correcting ecosystems built not just to perform, but to evolve.
From these conversations, clear patterns took shape around how to move from experimentation to sustainable impact. This report outlines the critical lessons learned and emerging best practices shaping the future of enterprise AI.
The Problem — AI Investment Dilemmas: Are We Chasing the Hype or Building for ROI?
The hype phase is over, and AI is now a business expectation. Quick wins like automating manual steps, improving response times and reducing backlogs don’t create a durable advantage. Now it’s about integrating AI into everything so the impact compounds, instead of plateaus.
Everyone has seen encouraging and disappointing quantitative data about AI. One participant mentioned a Menlo Ventures report titled “2025: The State of AI in Healthcare,” where health systems shortened their average buying cycles from 8.0 months to 6.6 months (about an 18% acceleration) and outpatient providers reduced their buying cycles from 6.0 months to 4.7 months (about a 22% improvement). Another mentioned a survey in ServiceNow, Inc.’s “Enterprise AI Maturity Index 2025,” where fewer than 1% of respondents scored over 50 on the 100-point AI maturity scale.
Leaders are caught between two priorities: show results quickly, and build systems that still make sense in two years. “It’s about what we can benefit from today, with an eye toward what lasts,” said one executive, describing this dual focus as the tension between immediate ROI and enduring advantage.
AI demands discipline, governance and a forward-looking view of strategic value. One technology leader described it this way: “The first wave of AI projects often felt like adrenaline — quick, exciting, but unsustainable. The second wave must feel like architecture.”
Also, ROI in AI doesn’t always look like a simple cost reduction. Sometimes it’s time reclaimed, talent unlocked or knowledge preserved. These softer metrics related to efficiency, trust and adaptability can become an additional true currency of transformation.
Defining ROI Beyond Cost Savings
Traditional metrics struggle to capture AI’s true impact. Cost saving is the easiest ROI to measure, but long-term impact shows up in three ways:
- Trust: People rely on the system’s output.
- Adaptability: Systems improve as the business changes.
- Knowledge Retention: Ensure expertise doesn’t walk out the door.
For example, some teams now measure success not in outputs but in trust levels — confidence in model accuracy, explainability and decision-making. And trust can manifest efficiency. One participant mentioned AI systems processing emails or claims with 90 percent accuracy and freeing employees to take on more complex work.
“Value creation is what we can measure today,” the participant said. “Value building is what we’re doing for tomorrow — cultivating trust in the results and building a healthy backlog of ideas that feed future innovation.”
Even in highly regulated sectors like healthcare and insurance, where legacy processes are notoriously slow, AI has cut time-to-market from years to weeks. The result isn’t just faster execution; it’s a cultural shift toward continuous improvement and a visible increase in developer satisfaction, collaboration and creativity. Productivity is no longer a static goal — it’s an evolving capability.
One leader described how AI reduced the “retirement risk” of legacy knowledge: “Our senior engineers used to be the only ones who understood certain systems. Now, anyone can query AI for context. It has reduced dependence on single experts by 30%.”
These are the less visible but ultimately more powerful returns on AI: shared knowledge, scalable expertise and creative momentum.
The People Problem with Change Management and Curiosity
For all its promise, AI’s toughest challenge remains human, not technical. Every AI conversation eventually circles back to people. The technology moves fast; humans, less so. The greatest barrier to progress isn’t technical complexity — it’s fear, inertia, resistance, fatigue and lack of imagination.
“People were genuinely frightened,” one R&D leader admitted. “We introduced AI to help, but for many it felt like a threat. It takes patience… holding hands, not just writing policies.”
Others echoed the need for empathy-driven transformation. Mandates to “use AI or risk being left behind” often backfire, breeding resentment rather than adoption. “Attitude matters more than aptitude,” said another executive. “You can’t force curiosity — you have to inspire it.”
Change management has become the silent engine of successful AI adoption. Teams need encouragement, not mandates. Inspiration, not enforcement. When employees are told to “use AI or be left behind,” the result is anxiety, not innovation.
The most successful teams are those that cultivate AI sherpas — early adopters who share stories, demonstrate results, and spark enthusiasm across departments. It’s a grassroots approach that transforms compliance into creativity. The leaders succeeding in this space share real examples, celebrate quick wins and make AI feel accessible. They treat adoption as a cultural journey, not a compliance requirement.
Curiosity, once considered a soft skill, has become a hard requirement. Those willing to ask better questions — about data, outcomes and processes — are the ones shaping the next phase of business intelligence. As automation absorbs repetitive work, curiosity becomes the new job security.
As one participant observed, “The biggest risk isn’t that people can’t learn AI. It’s that they stop imagining what’s possible.”
The Challenge — Building AI with Lasting Governance
The initial wave of AI enthusiasm has given way to a more sober focus on governance and resilience. As organizations move past experimentation, the next frontier is establishing frameworks that balance innovation with control. Enterprises are learning that responsible AI isn’t just an ethical obligation — it’s an operational necessity.
AI governance is emerging as a multidisciplinary effort spanning legal, risk, HR, and technology. Some companies now operate cross-functional AI councils that oversee everything from model registration to ethics reviews and license management. Many have introduced sandbox environments for safe experimentation, ensuring freedom without compromising compliance. Other robust governance frameworks now include policies for data usage and model registration. These systems don’t exist to slow innovation, but to ensure it happens with accountability.
“Governance defines how we build,” said one moderator. “Feedback defines how we evolve. Together, they transform disconnected projects into cohesive ecosystems that can adapt as fast as the technology itself.”
Continuous monitoring, model retraining, and user feedback loops that bring real-world performance back into the development cycle are essential for detecting drift — when models begin to deviate from reality. In production environments, even small drifts can have cascading business impacts. Therefore, AI models must be monitored, audited and retrained regularly. Mature organizations pair automated monitoring tools with “human-in-the-loop” systems to validate accuracy and retrain models in near real time.
This level of feedback ensures AI doesn’t just launch successfully, but stays relevant. This evolving balance of oversight and agility is what turns AI projects into dynamic, self-correcting systems that learn as fast as the world changes around them.
Thinking back to the earlier conversation about trusting outputs, GAP’s CTO Paul Brownell warned about conflating measurement of value with measurement of quality and scalability.
“Measuring success of outputs — of model accuracy — is really measuring whether AI is robust and enterprise-quality,” Brownell said. “It’s not really about measuring the value AI gives to the business; it’s how valuable AI is to accelerate your car faster if it is pointed in the wrong direction? The engineering, design and operation of AI systems must focus on successful outputs. Because who cares how much faster you can generate inadequate results?”
The Solution — Blueprints for Adaptive AI: Scalable Systems for a Moving Target
As enterprises push further into AI adoption, the conversation shifts from experimentation to endurance. The focus is no longer just on what can be built, but on what can last.
The most effective organizations are taking a measured approach to scalability. They’re learning that sustainable AI starts with simplicity. “If you can do it on paper, don’t use AI,” one participant said, capturing the emerging mindset.
The group agreed: AI is not the solution to every problem or opportunity. AI should never replace sound engineering judgment — it should enhance it; so if older methods work well, continue to use those processes. When teams have mastered the fundamentals, AI becomes an accelerator rather than a distraction.
That pragmatism extends to every layer of design. Leaders are beginning to ask more nuanced questions: How old is this data? What version of the model generated this output? What governance level applies? Understanding the lineage and context of AI decisions has become essential for establishing trust.
Equally important is redefining the scope of success. The new AI discipline is about knowing when not to overbuild. In many cases, fewer, better-tuned models outperform sprawling AI ecosystems that are expensive to maintain. “Maybe we don’t need ten models,” one participant noted. “Maybe four or five — done well — are enough.” Scaling, in this sense, is not about multiplying models, but multiplying impact.
At the organizational level, sustainability requires vision and measurable milestones. Enterprises that succeed in scaling AI do so with clear thresholds, checkpoints and feedback loops that prevent overspending or drifting from intended goals. The healthiest systems balance innovation with discipline. As one leader explained, “The basics matter. We can’t lose high-level discipline just because the tools make it easy to move fast. So we must stay fast, but not reckless.”
Scope creep, however, is an ever-present challenge. Projects evolve quickly, often in ways that make the original objective feel outdated before it’s achieved. This demands agility and a willingness to recalibrate without abandoning direction. In the words of one participant, “This is the definition of scope creep. It will keep changing. The goal isn’t to prevent it, but to manage it intelligently.”
Another common realization: the success of AI initiatives hinges on clarity of purpose. Many teams are embracing lean, outcome-focused models that rely on small expert groups to define and test ideas before scaling. The approach is less about massive teams and more about agile “SWAT units” that move fast, validate quickly and translate insights into strategy. The goal is to ensure the organization understands why a solution exists before it invests in how to build it.
External expertise can play a critical role here. Consultants or strategic partners can surface blind spots, facilitate honest assessment and accelerate maturity. They help organizations ask hard questions: Is our data ready? Where are we in our AI journey? What levers truly create value? When the right people — internally and externally — align on purpose, progress accelerates.
At the foundation of every adaptive AI program lies one unshakable truth: talent is the moat. The most advanced systems mean little without people who are curious, imaginative and disciplined enough to use them well. “The biggest moat is talent plus ambition,” one voice reflected. AI tools may be ubiquitous, but it’s human intent and curiosity that separate real progress from noise.
What Production-Grade Really Means
When leaders talk about “production-grade AI,” they’re not describing technology alone — they’re describing a lifecycle. Responsible systems combine technical rigor with ethical oversight and human judgment.
Production-grade AI begins with explainability. Teams must be able to see not just what an AI decided, but why. Transparency is now a competitive advantage as it enables users to verify outputs, detect bias and continuously improve. But explainability alone is not the solution. As one expert observed, “It doesn’t fix bias — it reveals it. We need systems that show why a decision was made to help build trust.”
AI systems mirror the humans who create them, carrying the same assumptions, limitations and, at times, blind spots. Bias cannot be fully eliminated, but it can be surfaced, understood and managed. The goal is not perfection, but accountability. When organizations build transparency into their systems, they empower both machines and humans to learn together.
Monitoring and retraining are therefore non-negotiable. Biases evolve, data shifts and user behavior changes. A model that performed flawlessly last quarter may drift out of relevance today. Continuous oversight, human validation and retraining cycles are essential to maintaining accuracy and trust.
Transparency also guards against emerging risks — from adversarial manipulation to misinformation baked into training data. “Bad actors are already teaching models the wrong lessons,” said one attendee. “Feedback loops can be gamed.” Robust governance, data lineage and the ability to deactivate compromised pipelines quickly are critical safeguards.
Ultimately, production-grade AI is not a finish line but a feedback loop. It’s a system designed to improve through use, to learn responsibly and to stay accountable.
A Practical Operating Pattern
From the discussion, a pragmatic pattern emerges:
- Frame the why, then size the fix. Define success without AI. If a paper process or a SQL view solves it, do that first — then add models where they create disproportionate value.
- Architect for context. Version data and models. Stamp outputs with lineage, policy tier, and freshness so users understand limits.
- Design for change. Expect scope to evolve. Limit model count. Wire monitoring for drift, abuse, and business KPIs — not just model metrics.
- Close the loop with humans. Put SMEs in the review gates that matter. Treat their time as a lever, not a cost.
- Accelerate with allies. Bring in external specialists to compress discovery — not to own your strategy. Keep governance and IP tight.
- Invest in the posture, not just the platform. Hire for curiosity. Reward simplification. Celebrate teams that decommission models when the simple answer wins.
Simplicity and Scalability Principles
If there’s a unifying thread running through these discussions, it’s that simplicity is the new sophistication. AI is most powerful when applied thoughtfully, not reflexively. The future belongs to organizations that build systems grounded in purpose, governed by trust and powered by people who ask why before how.
To say this even more boldly: There’s a growing recognition that not every problem needs an AI solution. In fact, one of the most powerful lessons emerging from early adopters is this: AI should be the last tool you reach for, not the first.
True innovation starts with clarity. Before investing in complex models, organizations must ask, “What problem are we solving?” and “Is AI the simplest, most effective way to solve it?” Often, the best use of AI comes after strong engineering fundamentals are already in place.
The most effective AI architectures are modular, explainable and scalable. They balance flexibility with control, combining sandbox freedom with enterprise-grade governance. They make it possible to trace how each decision was made, while continuously learning from new data and user feedback.
Simplicity is not the enemy of sophistication — it’s the foundation of sustainable sophistication. And it always has been.
“AI doesn’t defy the fundamental truths of the universe,” GAP’s CTO Paul Brownell said. “Sophisticated systems require simple architectures. If all you have is a hammer, everything looks like a nail. So experiment quickly, and implement thoughtfully.”
The real blueprint for adaptive AI isn’t a technology roadmap. It’s a mindset that values clarity over complexity, ethics over expedience, and imagination over inertia. And perhaps that’s the enduring lesson from this wave of AI transformation: progress doesn’t mean building faster; it means building smarter, with the patience to make intelligence truly sustainable.
The Future: From AI Projects to AI Ecosystems
AI is evolving from a series of isolated initiatives into an interconnected ecosystem of learning, experimentation, and feedback.
The companies that will lead in this new era share a common DNA:
- Vision with discipline — clear roadmaps that balance ambition and control
- Governance with flexibility — rules that protect creativity, not restrict it
- Technology with humanity — systems that amplify human curiosity, rather than replace it
One attendees said this near the end of the Roundtable: “AI is not here to replace what people do — it’s here to expand what they can imagine.” The organizations that will last are the ones that hold both truths at once: build systems that learn and adapt, and build cultures that do the same.
The hype cycle may fade, but the intelligence ecosystem it unleashed is here to stay. The future of AI isn’t about bigger models or faster automation. It’s about designing adaptive systems — both technological and human — that can think, learn and evolve together.
Success will come not from scaling models faster, but from engineering systems that are explainable, accountable and designed to last. True transformation happens when architecture and culture evolve together. Governance, curiosity and continuous learning will drive enduring impact.
At Growth Acceleration Partners, we believe sustainable AI is engineered, not improvised. GAP helps enterprises achieve this balance through proven frameworks and scalable architectures that turn experimentation into enduring impact. By engineering adaptive, responsible AI, organizations can deliver continuous ROI and create intelligence that grows stronger with every iteration.