Many companies have adopted AI without achieving business impact
AI adoption is officially mainstream. Most companies claim they’re using it, some are even hiring Chief AI Officers, launching pilots, deploying chatbots, and talking up their AI strategy on earnings calls. And yet, when you actually look at the bottom line, something’s missing.
According to McKinsey, 78% of companies say they’re using AI in some form. That’s a big number. But more than 80% of them haven’t seen any tangible business impact, especially at the enterprise level. BCG data confirms the same: only 4% of companies are getting real value from AI, while 74% are struggling to show meaningful returns.
The problem is that most organizations are using it to slightly improve things they already know. That mindset won’t cut it anymore. Using new technology to clean up old workflows limits what’s possible. Businesses that win in this space are the ones that use AI not to rethink the system entirely.
There’s also a governance problem. In the race to keep up with competitors and technology trends, many firms forget to clean up the data feeding their models. The focus stays on volume and accuracy, without any checks on bias, redundancy, or security. That leads to outputs that don’t scale, don’t deliver insight, and can’t be trusted.
AI transformation requires a bimodal strategy
Trying to run a company by scaling one AI use case after another doesn’t work. It’s scattered, short-term, and it adds complexity without long-term payoff. The companies that are outperforming are the ones building AI into their business strategy directly, as a central driver of growth and innovation.
According to PwC’s 2025 AI Business Predictions, 49% of technology leaders said AI is already fully integrated into their company’s core strategy. That sounds strong, but many of these efforts are just bolting AI onto yesterday’s processes. High levels of integration don’t always mean high levels of impact.
The right way to build an AI-first company is to use a bimodal strategy. Mode 1 is about locking in operational excellence, eliminating bottlenecks, automating repetitive work, tightening up your supply chain. Mode 2 is where you explore the ideas that give your company new revenue streams, products, and markets. They’re both important. But what makes them work is running both at the same time.
This concept of bimodal thinking has been around for years. Gartner defined it in 2014. And today, with AI, it’s more relevant than ever. Mode 1 gives you control. Mode 2 gives you velocity. Ignore either one, and you’re flying blind.
Dan Priest, PwC’s US Chief AI Officer, nailed it when he said: “Top performing companies will move from chasing AI use cases, to using AI to fulfill business strategy.” That’s where the shift happens, from scattered projects to scalable advantage. Focus your team on building clear strategies for both modes. Nail today’s operations. Then use the capacity you gain to build what comes next.
Mode 1 focuses on securing mission-critical operations and driving operational efficiency
If your core business operations are unstable, AI won’t help much. You need to start by identifying what drives revenue and what absolutely cannot fail, production lines, logistics systems, digital infrastructure, customer support, core software platforms. These are the pieces that hold the company together. That’s Mode 1.
The role of AI here isn’t to reinvent your business. It’s to make sure you’re running lean, precise, and scalable. That means improving throughput, removing process friction, eliminating delivery bottlenecks, and upgrading customer and employee experiences. You apply AI to amplify what you’re already good at and stabilize any weak links in the system.
In Mode 1, you’re building dependable gains, cost reductions, optimized operations, smoother workflows. That’s how you free up internal capacity, financial and human. And this efficiency gives you room to make real strategic moves when it comes to innovation.
The leaders who get this right are the ones asking the right questions: What in our operation is inefficient? Where are we losing time and value that AI could reclaim? What pain points are slowing us down today? Once you answer these, you start applying AI with purpose.
The companies that fall short usually start too wide or too vague. A successful Mode 1 strategy has clear boundaries, direct impact, and measurable outcomes. Don’t overcomplicate it. Focus on running critical systems better, faster, and smarter.
Mode 2 emphasizes innovation, agility, and the creation of new growth avenues
Once you’ve established operational stability, you can channel the gains into areas that drive future success. That’s Mode 2. It’s about using AI to challenge how your company competes, enters new markets, and creates entirely new streams of growth.
Mode 2 is where you test faster, build smarter, and release products or services that didn’t exist in your plan five years ago. You’ve already made the space by improving productivity and cutting internal waste. Now the goal shifts, use that capacity to do things that shift value.
You don’t need a bigger company to do this. You need a better one. A recent McKinsey report tracked micro-, small-, and medium-sized enterprises (MSMEs) and found that 17% of today’s publicly traded companies worth $10 billion or more were MSMEs back in 2000. Among public tech companies, nearly 25% came from that group. That tells you what happens when agility and innovation align with long-term execution.
AI gives you options, new product lines, service offerings, go-to-market strategies, and global expansion paths. But none of this happens unless you take action from the top. Mode 2 requires leadership commitment to invest in experimentation and implementation. You will be testing assumptions and adjusting strategy at a faster pace than legacy structures are built for.
You need to build teams that are comfortable with real-time shifts and results that change weekly, not quarterly. If you want to lead in this new environment, don’t ask for permission to experiment. Build the systems and the talent pipeline that make experimentation a permanent part of your company’s operation. Mode 2 is the foundation for where your next decade of growth will come from.
Parallel pursuit of modes 1 and 2 is key for successful AI transformation
Most companies struggle with AI because they try to operationalize it in sequence, optimize now, innovate later. That’s a mistake. Mode 1 and Mode 2 are parallel priorities. You need to run your business efficiently while building its next chapter. Both tracks move in sync.
Treating optimization and innovation as separate projects fragments progress. What actually works is using the capacity you unlock through Mode 1 efficiencies to accelerate Mode 2 outcomes. Efficiency increases aren’t there to pad margins, they give your teams more time, more resources, and more clarity to focus on making real improvements where it matters most.
Executives need to stop thinking of AI as a siloed function. It’s part of every priority. Your revenue targets, talent development, product roadmap, and role in the market should all reflect what AI is enabling across the business. The clearer you are about this integration, the faster your organization moves.
This means strategy, resources, and measurement frameworks need to be defined for both modes. Teams working on optimization should have visibility into innovation timelines, and vice versa. That cross-functionality ensures that what gets built stays aligned with what matters.
If leadership treats these areas independently, organizations fall back into fragmentation, one team chasing efficiency, another proposing unscalable innovation. That creates internal conflict and wastes time. The goal isn’t compartmentalized excellence. It’s unified progress driven by real-time execution across both layers.
Organizations should repurpose AI-generated efficiencies
Once your operations are faster and smarter, the next step is to decide what to do with that advantage. Ignoring that reallocation step is where most companies fail to turn automation into strategic transformation.
Process improvements from AI change what teams are capable of. That means upskilling employees, rethinking roles, mapping out new capabilities, and expanding how you solve business problems using internal talent. It’s not about replacing people. It’s about enabling the ones you have to focus on higher-value work.
The real competitive edge comes when AI becomes part of how teams think, not just how they execute. You build that edge by putting the right people in the right environments, where experimentation is rewarded, ideas move quickly, and failure is treated as part of the loop, not the end of it.
This requires a culture shift. The kind that starts with leadership. You can’t say you’re building for the future if every efficiency goes toward cost reduction. That sends the wrong signal internally, and it slows down momentum. Make it clear that time saved is time reinvested in growth, product development, research, new customer models, or employee training.
In “How AI can drive business transformation,” I highlighted that the time AI frees up should be considered high-value capital, not just a bonus. When that time is strategically redirected, it becomes core fuel for reshaping how organizations operate. That’s where long-term differentiation starts. Not in the tools, but in the systems and teams using them.
Main highlights
- Most AI adoption lacks strategic impact: Leaders should stop using AI to make legacy processes slightly better and instead target business model reinvention. Surface-level integration doesn’t drive returns.
- Bimodal strategies make AI work: Capture real value from AI by running optimization (Mode 1) and innovation (Mode 2) simultaneously. This dual-track model drives both short-term performance and long-term competitive edge.
- Mode 1 demands laser focus on core operations: Use AI to streamline what’s mission-critical, logistics, service uptime, production, and internal processes. These gains create the bandwidth needed for future-facing work.
- Mode 2 unlocks exponential growth: Reinvest Mode 1 efficiencies into building new markets, products, and revenue streams. Agile companies that align innovation cycles with execution outperform legacy incumbents.
- Optimization and innovation must run in parallel: Don’t wait to finish optimization before starting innovation. Align both strategies to increase organizational agility, reduce internal friction, and scale value creation.
- AI efficiency should fuel transformation: Redirect time and resources gained through automation into upskilling, experimentation, and strategic initiatives. This reinforces culture, capability, and future readiness.
