Generative AI only delivers real value when it’s at the core
Most companies still think of generative AI as a tool, something you can plug in to boost efficiency or automate small tasks. That mindset misses the point. Generative AI forces a rethinking of how value is created, how work is done, and how your business competes. You don’t capture the full upside by running pilots or stacking AI on top of old workflows. You get it by building the business around AI from the ground up.
Let’s be clear, AI doesn’t automatically bring ROI. You don’t see the returns just by adopting it. You have to reshape your internal architecture: decision-making, processes, customer interaction, and speed of execution. You need to question everything that was once standard operating procedure. This is about redesigning for scale and competitive advantage in a world where intelligence isn’t limited to people.
Execs who get it are structuring business models with AI embedded into core functions. They’re thinking across the whole system, not just pockets of improvement. And they’re moving fast. According to Bain & Company’s latest global survey, less than 20% of enterprises have scaled generative AI in a meaningful way. The leaders aren’t experimenting more, they’re moving more deliberately, with scale in mind from day one.
If your business is still stuck in “pilot mode,” it’s time to level up. Treat AI not as a tool for productivity, but as infrastructure for growth.
AI transformation must be executive-led
If AI isn’t a top priority in the boardroom, it won’t become one on the ground. True transformation starts at the top. If you’re a leader, your job is to champion AI as a core part of your strategy and company culture.
What does that look like? It’s CEOs and executives aligning AI usage to actual business outcomes, revenue, efficiency, differentiation. It’s setting measurable goals, funding the right initiatives, making time to understand the technology, and challenging teams to explain why AI isn’t being used, not why it should be. That mindset shift matters.
At Shopify, employees now have to justify why a task can’t be done by AI before they’re allowed to do it manually. That creates forward momentum. Other companies are tying AI initiatives to incentive structures and rolling out company-wide upskilling programs, so people at every level are growing alongside the technology.
Executives in winning companies actively use AI tools themselves. They’re setting the example. By showing that AI is strategic, they drive real uptake. And when AI is visibly linked to performance and outcomes, adoption becomes self-sustaining.
Executives who lead from the front accelerate their company’s shift from early experimentation to real transformation. Those who delegate or defer will stay stuck.
Focus on fewer, high-impact AI domains
Most companies make the mistake of spreading their AI efforts too thin. They pilot dozens or even hundreds of isolated use cases, hoping something sticks. That doesn’t create impact. It burns time, money, and focus. The companies moving fastest and furthest are doing the opposite, they’re making fewer, bolder bets.
Leaders are targeting four or five critical domains where AI can change the game. These are areas tightly linked to strategy and value creation. In tech, it’s advanced software development. In healthcare, it’s drug discovery, regulatory handling, and personalized patient engagement. In consumer and retail, it’s dynamic pricing, forecasting, personalization, and faster content cycles. These domains offer leverage, transform one, and you reshape how a significant chunk of the business performs.
Each of these domains connects to larger systems of work. Take B2B sales, it’s not a discrete process. It’s a structure made up of micro-tasks across messaging, targeting, pricing, delivery, and feedback. If you don’t rewire the full system, the AI won’t deliver compounding value.
The top players invest hard time up front: define the domain, build the business case, identify the friction, and architect systems that scale. They’re building feedback engines to measure and adjust over time, not waiting for perfect information, but iterating based on real signal from inside the business.
Leadership needs to be clear and committed here. Focus generates acceleration. Once you’ve identified the right domains, your teams can move faster, test better, and scale what works. Companies that try to address everything struggle to deliver meaningful progress anywhere.
Rebuild processes, don’t just add AI to broken ones
If you apply AI to a bad process, the process doesn’t get better. You just get bad results, faster. The companies that are winning today aren’t stacking AI on top of legacy systems, they’re stripping them down and rebuilding from scratch, with AI integrated from the foundation.
This is what zero-based design is about. Start with the outcome, then build the supporting process with AI at the core. One major bank did this when it looked at customer engagement. Instead of using generic sales campaigns at fixed intervals, they built system-driven “trigger” moments that respond based on actual customer behavior. For example, customers withdrawing cash from fee-based ATMs now get notified about nearby no-fee options, automatically. Fast, targeted, and relevant.
They set up teams focused on specific moments in the customer lifecycle, using custom AI tools to surface insights, test ideas, and push out changes quickly. The result? A process that used to take months now cycles in a single day. Campaign agility like that isn’t possible if you’re still running on traditional playbooks.
Most enterprises are still trying to add tech on top of broken processes. That won’t cut it. Business leaders need to make the call to unbuild dated systems. Look at core workflows, customer service, supply chain, marketing ops, product delivery, and ask: if you were starting today with AI built in, what would this look like?
The real ROI doesn’t come from incremental upgrades. It comes from hard changes. Design your operations for speed, for data flow, for learning and responsiveness. Put your teams in a position where they’re testing, optimizing, and deploying continuously, not waiting on quarterly reviews.
In this shift, boldness matters. Don’t waste AI on legacy baggage. Clear it out, and redesign what your customers and teams actually need.
Build an operating model that can scale change
Even with strong leadership and well-defined AI priorities, transformation stalls without an operating model built to handle continuous change. AI doesn’t just need execution, it needs infrastructure. That means teams, processes, and systems designed not just to run the business, but to evolve it in real time.
The companies that are breaking through right now have organized their AI efforts around small, high-performance transformation teams. These teams aren’t there to run the whole show, they exist to support solution teams inside business functions. They remove friction, increase transparency, and keep things moving. When something works, they scale it. When it doesn’t, they iterate and clean up fast.
There are six core areas these teams focus on. First, end-to-end processes, cutting across traditional silos to reach the real value. Second, supporting rapid mobilization by giving solution teams the resources and clarity to keep building. Third, strong data infrastructure, collecting, managing, and governing unstructured and synthetic data the right way. Fourth, scaling, proven AI systems must be rolled out quickly across geographies, product lines, or segments. Fifth, adoption strategies, using feedback tools like weekly reports to spot bottlenecks and accelerate uptake. And sixth, alignment between business and tech, making sure platform capabilities, governance, and reuse are never separate from business intent.
Leaders who get this right are operating with two speeds at once: executing current business needs while rewiring the system for next-generation growth. This dual capability is what prepares companies to handle AI agents, complex data types, and future workforce configurations, without being constantly reactive.
The infrastructure you build now will determine how far your transformation can go. Speed, adaptability, and internal coordination aren’t soft features, they’re strategic assets.
Competitive edge comes from intentional transformation, not just adoption
At this point, everyone is experimenting with AI. That’s no longer a differentiator. The companies that stand out are those treating AI not as an experiment, but as a strategic transformation. This shift is what creates long-term advantage.
It’s not about the number of pilots launched or tools tested. It’s about how deeply AI is integrated into how your company competes. The ones pulling ahead are making bold decisions. They’re reimagining workflows, removing friction from operations, and applying pressure on the system to evolve continuously. They’re not doing this off the side of their desk, they’re doing it at the center of how they operate.
This requires discipline. It’s about stepping back, reshaping priorities, and executing with measurable impact. Leaders who’ve made progress are combining precise strategic choices with execution rigor that holds up under pressure.
Companies still stuck in early-stage trials can break through, but not by waiting. The next stage will be defined by enterprise-wide shifts. That includes decision rights, workforce training, workflow automation, data architecture, and cross-functional collaboration.
The bottom line: your competitors are figuring out how to make AI a permanent, productive part of their business model. The advantage now belongs to those who transform with intent, and follow through.
Key takeaways for leaders
- Treat AI as infrastructure, not a tool: Generative AI delivers value when it’s embedded into core business models and operations, not when it’s limited to isolated pilots. Leaders should redesign workflows and strategy with AI at the center to unlock real performance gains.
- Lead it from the top: AI transformation scales when actively led by the C-suite. Executives must align AI initiatives with business strategy, model usage themselves, and tie adoption to meaningful incentives to drive companywide momentum.
- Focus where it matters most: Broad experimentation slows impact. Leaders should identify and commit to four or five high-value AI domains, aligned to business outcomes, and build scalable, interconnected systems in those areas.
- Redesign, don’t retrofit: Layering AI on weak legacy processes limits results. Companies should zero-base design key workflows to enable real-time data use, faster decision-making, and continuous improvement driven by AI.
- Build a structure that can evolve: A dedicated operating model is essential for sustaining AI at scale. Invest in agile transformation teams, data governance, feedback systems, and cross-functional alignment to keep innovation moving fast.
- Move with intent, not volume: Pilots won’t separate the leaders anymore, systems and scale will. Competitive advantage now comes from bold, strategic transformation that permanently integrates AI across the business.
