A structured, multi-step framework is essential for successful generative AI implementation
You don’t need more meetings. You need a clear process. Generative AI offers scale, speed, and intelligence, but without structure, it can sink time and amplify risk faster than it creates value. Rest, one of Australia’s largest superannuation funds, got that early. Instead of opening the floodgates, they created a four-step framework rooted in the Lean Startup philosophy: Test, Measure, Expand, Amplify.
This approach works because it keeps you grounded. It removes guesswork and replaces it with verified outcomes. Each step is controlled. Teams run real experiments, evaluate returns through hard numbers, and only then go bigger. That discipline is rare, but critical.
If you’re leading a heavily regulated business or operating with narrow margins, this structure is non-negotiable. Without it, you’re gambling with trust, accountability, and speed of execution. You need to show outcomes, not optimism. This framework does that. It connects AI with business outcomes.
There’s a lesson for every executive here: Don’t wait for a vendor to shape your AI strategy. Own it. Build systems that serve your business. That’s how you lead from the front.
The test phase prioritizes small-scale, controlled experimentation to validate AI ideas
When you have a new tool as powerful as generative AI, the first impulse is usually to scale it fast across the entire company. Wrong move. Rest understood the value of restraint. They didn’t build from zero, they launched RestGPT, using OpenAI’s engine inside their own secure infrastructure. Clean, fast deployment. No wasted motion.
The power here is in narrowed scope. They ran focused experiments in areas like employee productivity. They set guardrails, a clear responsible-use policy built around internal governance standards. They tagged specific use cases to test… and more important, they measured effort before and after deployment. That told them exactly where time was saved or value was added.
This level of control matters. It forces clarity. You define what success looks like and track it against baseline behavior. You don’t rely on “feel-good” feedback. You get evidence.
For business leaders, here’s the signal: Deploying AI doesn’t start with enterprise-wide rollouts. You start with conviction, clarity, and a test zone that’s both honest and measurable. That creates the only result that matters, proof.
Skip that, and you’ll end up with excitement, but no ROI. Do it right, and you create wins the business can rally behind. That’s how momentum starts, and how it scales.
The measure phase uses precise metrics to determine the viability and value of AI initiatives
Execution without measurement is noise. Rest didn’t settle for surface-level metrics like tool adoption. They went deeper, isolating impact tied directly to business outcomes. One standout example was their finance team using RestGPT to analyze market insight reports. With AI involved, that task saw an 85% cut in processing time.
During the pilot, Rest observed that around 90% of employees were using RestGPT, but they didn’t mistake high usage for high value. They made it clear: usage is a signal, not the goal. If an AI solution isn’t improving quality or saving time in ways that matter, it shouldn’t scale. The projects that didn’t move the needle got shut down.
This discipline in how success is defined is essential. Generative AI is an investment. And like any investment, you track returns. Time saved. Accuracy improved. Complexity reduced. That kind of rigor forces teams to prove the benefit, not just assume it.
Leaders should follow this model not just to protect budget, but to keep the organization focused. Measurable outcomes cut through internal skepticism. They also unlock clearer prioritization, which matters when you’re running multiple transformations at once.
The expand phase focuses on scaling successful pilots while adjusting strategies based on real adoption
Once a pilot shows results, the natural move is to expand. But again, Rest didn’t chase momentum blindly, they tested assumptions before scaling. They explored new use cases beyond chat-based AI, pushing into broader automation like support tools for real-time chat responses. Technically, it worked. The AI delivered accurate message drafts. But here’s what happened: employees didn’t trust it. Usage lagged. The company made a fast decision, they pulled the plug after two and a half weeks.
This self-correction is what scaling responsibly looks like. You don’t force a tool because the technology checks out. You validate behavior. You make sure people actually use it, and that they get value out of it. Adoption isn’t a detail. It’s the difference between success and wasted investment.
In contrast, other tools in the call center proved more immediately valuable. A speech-to-text transcription solution slashed post-call work by 50%. That result was directly useful, and easier for teams to embrace. It moved from test to scale because it made sense for both humans and systems.
As you move from pilot to scale, you need to be honest about where friction exists. Technology alone won’t fix that. It’s focus and readiness, and a willingness to shift tactics, that make scaled AI work. If you can read the signs early, you save time, resources, and internal credibility. Let the data drive the path forward.
The amplify phase aims to integrate high-impact AI solutions organization-wide to maximize strategic benefits
Real results are what allow you to scale with confidence. Rest hit this point after proving generative AI wasn’t just functional, it delivered measurable efficiency. In the Amplify phase, they moved beyond isolated use cases and into enterprise-wide deployment. They expanded RestGPT access to over 800 employees, integrating it with platforms like ServiceNow, M365, Atlassian, and desk booking systems. The goal wasn’t novelty, it was operational leverage.
This transition wasn’t about simply putting AI everywhere. It was about tying AI to workflows people already use and tracking time saved across roles, from junior analysts to senior execs. For leadership, this level of traceability matters. You’re not guessing the value. You’re seeing where productivity improves and where information flows better across systems and teams.
They also applied this phase to their call center. By combining AI-driven guidance with human reps, they improved the experience for both employees and customers. The results were concrete: they handled 1,600 calls a day, cutting an average of 2.5 minutes from each call. That created an estimated annual time savings of 20,000 hours. Those aren’t projections. Those are operational gains, already realized.
Executives tend to ask when to scale. The answer: when outcomes are clear, adoption is strong, and infrastructure can support it. You get that when performance justifies expansion and systems are ready to handle the load.
Generative AI initiatives can reveal unforeseen strategic insights and augment data analysis capabilities
You often don’t see the full value of a tool until you start using it at scale. Rest didn’t just get productivity gains from their AI projects, they uncovered data they weren’t fully leveraging before. After rolling out AI in their call center, they began analyzing real-time customer interactions. That gave them sharper insights into the issues their members cared about most, insights that were previously hard to extract at volume.
This wasn’t planned as a primary benefit. It emerged. But it created immediate business relevance. Knowing what members care about lets you improve services, anticipate problems, and align more closely to your users. It turns routine customer service into a source of business intelligence.
For executive teams, this is key. You’re not just deploying AI to save hours, though that matters. You’re deploying it to extend visibility. The more you can see, in real-time, across functions, the tighter your feedback loops become. This improves how you make decisions, manage resources, and drive strategy at the executive level.
The opportunity in AI isn’t only in automation. It’s also in awareness. If you’re not using that signal to sharpen strategy, you’re underutilizing the tech. Rest proved that real value often shows up downstream, once momentum builds, and smart organizations are ready to capture it.
A phased, strategic AI implementation approach is critical for balancing innovation and regulatory compliance
Generative AI moves fast. That doesn’t mean your deployment should. Rest understood this early. As one of Australia’s largest super funds, they operate in a highly regulated space with fiduciary responsibilities. Rolling out new technology recklessly isn’t just risky, it’s unacceptable. Their phased approach, Test, Measure, Expand, Amplify, kept innovation controlled, measurable, and aligned with internal governance standards.
What they did right was matching pace with structure. They knew that experimentation without risk controls wouldn’t scale. And they didn’t bolt AI onto the business just to say they were using it. Every move was tied directly to efficiency, compliance, or customer value. That’s key for any executive running a business with operational constraints, public accountability, or large-scale risk exposure.
The benefit of this kind of framework is repeatability. Once you prove it works in one domain, whether that’s market analysis, customer support, or internal productivity, you can carry that logic into other business functions. You get visibility. You reduce uncertainty. And you improve quality at scale without sacrificing speed or compliance.
For leadership teams, especially those in finance, healthcare, infrastructure, or other tightly governed sectors, this model matters. It shows that you can be bold with AI while remaining responsible. You don’t need to slow down innovation; you just need to contain it until it proves its worth. That’s not caution, it’s strategy. Rest didn’t adopt AI just to keep up. They made it part of how they move forward with intent.
Concluding thoughts
Too many companies approach generative AI like it’s a box to check. The smart ones treat it like infrastructure. What Rest proved is this: if you want ROI, adoption, and alignment with strategy, you don’t need more tools, you need structure. Their four-step model didn’t just reduce noise; it produced verified impact, trimmed hours, and gave the business better visibility into what actually worked.
You don’t need to be in finance to benefit from this. Any executive responsible for transformation, performance, or compliance can apply the same thinking. Validate fast. Measure what matters. Scale with purpose. Amplify where the business is ready. That’s how AI becomes operational, not ornamental.
