Outlast the Hype with AI That Lasts The Next Competitive Edge Delivers ROI at Scale (and Keeps Getting Smarter)

Key Takeaways from Growth Acceleration Partners’ Technology Executives Roundtable on “Future-Proofing Your AI”

By Jocelyn Sexton, GAP’s VP of Marketing

AI Is No Longer a Project — It’s an Evolving System

Artificial intelligence has moved beyond novelty and reached a pivotal inflection point. What was once a bold experiment has now become a business imperative. For many organizations, it now functions as a layer of infrastructure that must be designed with the same rigor as any enterprise system. And if built hurriedly or without governance, it quickly becomes unstable and difficult to maintain.

Leaders at a November 2025 Technology Executives Roundtable in New York City hosted by Growth Acceleration Partners (GAP) agreed that the path forward requires treating AI as an adaptive architecture, not an isolated initiative.

At the event, CTOs, engineering executives and data governance leaders gathered to explore three big questions:

1. The Problem — What’s holding back AI investments?
2. The Challenge — What does it take to build AI that lasts?
3. The Solution — How do we engineer adaptive systems that continue returning value over time?

Throughout the discussion, participants described the same tension — the need to show results quickly while also building foundations sturdy enough to evolve. The conversation revealed a shift from “How fast can we build AI?” to “How do we build AI that delivers meaningful value not just in month one, but in year three?”

The Problem

The ROI Dilemma: Hype, Pressure and Misaligned Expectations

The hype phase of AI is over. What remains is an expectation that AI will deliver business impact, yet many organizations still struggle to prove sustained ROI. Quick wins like automating manual tasks or reducing ticket backlogs are helpful, but they rarely produce a durable advantage.

Leaders described feeling torn between proving AI’s value now and ensuring the work won’t fall apart later. One executive summarized the challenge: “It’s about what we can benefit from today, with an eye toward what lasts.”

While some industries report encouraging progress — shorter buying cycles, faster workflows, reduced errors — others reveal stark gaps in maturity. A participant pointed to a survey showing fewer than 1% of enterprises scoring above 50 on a 100-point AI maturity scale.

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.

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 — Do people rely on the system’s output?
• Adaptability — Can the system evolve with the business?
• Knowledge Retention — Are we reducing single-expert dependency?

One participant shared how AI reduced “retirement risk” for their legacy systems. “Our senior engineers used to be the only ones who understood certain components. Now anyone can query AI for context. It reduced dependence on single experts by 30%,” he said.

Another noted that improved trust often leads directly to improved efficiency. When AI systems consistently process claims or emails with high accuracy, “employees get to focus on the complex work they were hired for,” the leader said.

These softer returns — shared knowledge, increased trust and more engaged teams — are increasingly recognized as core to long-term transformation.

Curiosity, fear and the pace of change were big topics during the discussion. Despite its technical complexity, AI’s toughest challenge remains human. Leaders repeatedly returned to the same theme — change management is the real barrier.

“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.”

Mandates like “use AI or risk being left behind” rarely inspire adoption. Instead, they create anxiety and resistance. The most successful organizations cultivate a culture of curiosity and experimentation rather than compliance.

Patterns in Successful AI Cultures

Patterns in successful AI cultures include:

• Encouraging early adopters to become “AI sherpas” who share their wins
• Highlighting small victories to show real, practical value
• Creating safe spaces for experimentation instead of enforcing mandates

The Roundtable participants agreed we all need leaders 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.

“The biggest risk isn’t that people can’t learn AI,” one participant observed. “It’s that they stop imagining what’s possible.”

The Challenge

Governance: The Foundation for AI That Lasts

As enterprises move beyond proofs of concept, governance becomes the defining discipline of sustainable AI. It’s no longer optional. It’s necessary due to risk, compliance and the need for reliability.

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, automated retraining and user-in-the-loop feedback are the operational backbone of any production-grade system. You already know drift isn’t theoretical. It’s inevitable, and even minor deviations compound quickly when models are embedded in pricing, routing, risk scoring or clinical workflows.

The organizations that stay ahead treat drift as an engineering problem, not an after-hours dashboard exercise. They instrument models the same way they instrument distributed systems — with telemetry, alerting thresholds and auditable pathways back to the data and features that shaped each prediction.

The goal isn’t just to keep models online; it’s to keep them aligned with real-world behavior. That requires an interplay between automated pipelines and human oversight that can adjudicate edge cases and high-impact exceptions. When this feedback loop is healthy, the system becomes adaptive in the truest sense — continuously correcting, recalibrating and improving. This is how AI moves from a point-in-time deployment to a living architecture that adjusts as fast as the conditions around it change.

GAP’s CTO Paul Brownell also emphasized the risk of confusing speed with value. “Measuring success of outputs — of model accuracy — is really measuring whether AI is robust and enterprise-quality,” he 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?”

Brownell’s point underscored a shift in mind-set — from celebrating output volume to prioritizing quality, context and relevance.

The Solution

Blueprints for Adaptive AI: Pragmatic, Scalable Systems

Participants agreed that sustainable AI begins with simplicity. And it may surprise some to know the leaders at the Roundtable often repeated a now-familiar line from the event:

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.

AI should enhance sound engineering judgment, not replace it. If an older method solves the problem well, use it. If AI is required, use only what’s necessary. Overbuilding makes systems expensive and fragile.

Many teams are now discovering that fewer, better models outperform sprawling networks of loosely governed ones. As one attendee noted, “Maybe we don’t need 10 models. Maybe four or five — done well — are enough.” Scaling, in this sense, is not about multiplying models, but multiplying impact.

Principles of Sustainable Scalability

• Understand data lineage — its age, version and governance tier
• Set thresholds and checkpoints to prevent scope drift
• Use small, expert teams to validate ideas before scaling
• Bring in external experts to accelerate discovery and reveal blind spots

This measured approach protects teams from racing ahead without clarity or discipline. One participant described it well: “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.”

Additionally, scope creep is constant in AI. Objectives shift as data, users and constraints evolve, and the original problem statement often becomes stale before the system ships. The answer isn’t to eliminate scope changes — it’s to manage them with discipline. As one participant put it, “It will keep changing. The goal isn’t to prevent it, but to control it intelligently.”

The teams navigating this well anchor everything in clarity of purpose. They rely on small, senior “SWAT units” that validate ideas quickly, kill weak assumptions early and only scale what proves out. It’s a move away from big-project thinking and toward outcome-first engineering — making sure everyone understands why something should exist before committing resources to how it gets built.

External expertise can accelerate this maturity curve. The right partners surface blind spots, pressure-test readiness and force hard conversations about data quality, architectural debt and real value drivers. When internal and external perspectives align, progress compounds.

Underneath all of this is the real moat: talent. Tools are ubiquitous, but the ability to ask the right questions, simplify the complex and push for disciplined execution is not. As one leader said, “The biggest moat is talent plus ambition.” Curiosity and intent are what separate durable AI systems from noise.

Production-grade requires a lifecycle of transparency, monitoring and human judgment.

When leaders at the Roundtable talked about “production-grade AI,” they weren’t describing a technical milestone. They were describing an operational lifecycle. Production-grade AI is explainable, monitored and continually retrained. It surfaces bias rather than hiding it. It includes guardrails that prevent misuse and drift.

The group discussed rising concerns about adversarial behavior and misinformation embedded in training data. One participant noted, “Bad actors are already teaching models the wrong lessons. Feedback loops can be gamed.” Robust governance, data lineage and the ability to deactivate compromised pipelines quickly are critical safeguards.

This reinforced the importance of governance structures that are not just strong, but adaptable. 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, repeatable framework emerged from the Roundtable:

• 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 is the thread that connects it all. If there was 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.

The organizations achieving lasting AI impact are not building indiscriminately. They’re asking better questions, defining clearer problems and applying models only where they matter most.

Before building, they ask:

• What problem are we solving?
• Is AI the simplest way to solve it?

GAP’s CTO Paul Brownell captured it with a reminder: “AI doesn’t defy the fundamental truths of the universe. Sophisticated systems require simple architectures. If all you have is a hammer, everything looks like a nail. So experiment quickly, and implement thoughtfully.”

Final Reflections

AI is evolving into an interconnected ecosystem of experimentation, feedback and continuous learning. The organizations positioned to lead this future share a common DNA:

• Vision with discipline — ambition paired with intentional guardrails
• Governance with flexibility — structures that protect creativity
• Technology with humanity — systems that amplify imagination, not replace it

Near the end of the Roundtable, one attendee summed it up beautifully: “AI isn’t 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 companies that thrive will be those building systems and cultures that evolve together — responsibly, sustainably and with purpose.

The hype will fade, but the intelligence ecosystems organizations build today will define the next competitive edge. True AI transformation doesn’t come from scaling models faster, but from engineering transparency, accountability and simplicity into every layer of the system.

At Growth Acceleration Partners, we help enterprises navigate this shift with frameworks that support adaptive, responsible AI — accelerating experimentation while ensuring enduring impact. By building systems that learn with every iteration, organizations can deliver continuous ROI and create intelligence designed to last.