Why most companies still think too small about AI

Enterprises focus on incremental AI gains

Most companies using AI today are still thinking small. They’re using it to save time or automate repetitive tasks, but they’re not using it to reinvent their business. Forrester’s Accelerate Your AI Voyage report makes this clear: 43% of AI decision-makers measure productivity improvements, 41% measure efficiency, yet only 32% connect AI results to profit or revenue. These numbers show that most enterprises are still working to get marginal improvements rather than breakthroughs.

That mindset limits what AI can deliver. AI isn’t just another tool, it’s a catalyst for changing how organizations work. If you build your AI strategy only around saving a few hours here and there, you’re missing the bigger opportunity to reshape how value is created in your company. True transformation happens when AI becomes part of how you compete.

For leaders, the challenge is moving beyond comfort zones. Incremental progress feels safe because it’s measurable and easy to justify. But safety can become stagnation. Capturing AI’s full value means setting bolder goals, using AI not just to optimize what already exists, but to redefine what’s possible. Efficiency helps in the short term, but transformation builds long-term advantage.

Executives should demand more than process improvements. Ask how AI can help create new capabilities, unlock new markets, or change how decisions are made. That’s where the real competitive edge lies. The organizations that reframe AI as a strategic driver will lead in the next wave of industry evolution.

Few organizations have comprehensive, effective AI strategies

Most enterprises still lack a clear, company-wide AI strategy. They’re experimenting with isolated projects, some automation here, a chatbot there, but not connecting these efforts to business outcomes. Forrester’s estimate puts it plainly: only between 5% and 15% of organizations have an effective AI strategy, and the actual number is likely closer to 5%. That leaves the vast majority reacting to trends instead of setting direction.

Brian Hopkins, Vice President for Emerging Technology at Forrester, calls this out directly. He says efficiency is not strategy. It’s project management. Too many organizations are “incrementally investing in productivity,” expecting it to translate into transformation. It doesn’t. This approach leads to slow improvement but no real innovation. Worse, it often depends on job cuts to realize short-term savings, something that can damage morale and culture before any real progress is made.

C-suite leaders need to lead differently. AI success requires more than a roadmap; it requires a vision that aligns technology with measurable business outcomes. A strong AI strategy integrates cross-functional collaboration, investment in data infrastructure, and clarity on ethics and governance. It also demands that leaders break silos, AI cannot sit only in IT or data science. It has to cut across operations, marketing, product, and finance.

For business leaders, this is about moving from experimentation to orchestration. Stop viewing AI as a tool you add to existing systems. Treat it as a foundation for new capabilities that drive revenue, growth, and resilience. That’s how companies evolve from cautious adopters into true innovators, and how they stay ahead of those who are just incrementally automating.

Reliance on narrow AI tools limits innovation potential

Many organizations are locked into limited use of AI because they depend on off-the-shelf tools instead of developing custom solutions that fit their business needs. Decidr’s survey confirms that around 40% of U.S. companies get most of their AI value from ChatGPT-style applications. These tools may offer quick gains, but they often fail to deliver lasting differentiation. They’re easy to implement but harder to scale into systems that drive competitive strength.

Business leaders should recognize that depending solely on general-purpose AI can hold the company back. It’s convenient, but it doesn’t create a barrier that competitors can’t cross. When every organization has access to the same generative AI products, the real distinction comes from how well a company integrates and extends AI into its core processes. Executives must look beyond front-end adoption and examine how AI can influence decisions, products, and performance metrics across the organization.

There’s no need for complexity here, just precision. Companies that build or customize AI models around their own data, customer base, and workflows will see bigger returns. They’ll also retain more control over intellectual property and business insight. To do this effectively, leadership must be willing to invest in training, infrastructure, and internal expertise.

The opportunity is clear. Short-term reliance on prebuilt tools might boost productivity, but long-term competitiveness demands deeper integration. Leaders need to decide whether they want to participate in AI or lead in it. Those who commit to developing proprietary or value-aligned AI will shape entire industries rather than simply keeping up with them.

Legacy thinking constrains real AI transformation

The biggest obstacle to meaningful AI transformation is mindset. Many leaders still drive AI adoption with old management habits, focusing on efficiency within narrow functions instead of rethinking how work itself should evolve. Christine Park, Chief AI Transformation Officer at Branch, emphasizes that leaders are optimizing within silos when they should be enabling new ways of working across the enterprise. That’s why most companies are seeing limited results.

Traditional operational models were built for linear processes. AI changes that dynamic. True transformation requires leadership to connect departments, remove redundant work, and redesign workflows to fit a more fluid, data-driven framework. AI becomes far more powerful when organizations coordinate across divisions instead of competing for resources or improvements in isolation.

Executives must also approach AI as a human transformation. Park notes that transformation includes redefining how people are trained, how roles are structured, and how success is measured. This is critical for long-term scalability. A company can only get as far as its people are prepared to go. If teams view AI as a separate function rather than part of how they deliver value, resistance grows and adoption slows.

For decision-makers, the shift involves rebalancing priorities, less focus on micromanaging incremental savings, and more on fostering shared capability and innovation. Leaders who encourage collaboration and experimentation create conditions where AI can drive structural progress, not just task-level improvements. Those who remain tied to legacy thinking risk being outpaced by companies that treat AI not as an add-on but as a central driver of growth and reinvention.

Workflow redesign is key to capturing AI’s full value

Many organizations are automating tasks but not rethinking the workflows those tasks belong to. This partial automation leads to inefficiencies that limit AI’s impact. Automating a process without redesigning it from end to end often results in duplication of effort, unnecessary complexity, and escalating operational costs. Mike Flynn, Technology Sector Consulting Leader at EY, describes this as “trapped work,” where AI adds technology overhead without eliminating real workload.

Executives need to look at the larger system. Instead of layering AI on top of existing workflows, leaders should assess where automation can fundamentally reshape how work is structured. Integrating AI into process design from the outset allows companies to remove repetitive steps, streamline communication between teams, and make better use of both human and machine strengths. This requires cross-department collaboration, because value is created when workflows connect.

C-suite leaders should also approach workflow transformation as a strategic investment. A well-designed AI workflow can adapt faster to changing customer needs and market conditions, giving companies more operational flexibility. Flynn emphasizes that true transformation involves re-engineering operational processes. This means mapping every stage of work to identify where AI can deliver the most leverage and aligning human roles to those high-value points.

Companies willing to commit to this kind of comprehensive redesign will see compounding value over time. They not only reduce inefficiencies but also gain the structural agility to integrate future AI developments. Those holding onto piecemeal deployments will continue to face diminishing returns and rising complexity. AI at scale only works when the foundation, workflow design, is intentionally built for it.

Structural and governance barriers hinder large-scale AI adoption

Even organizations that understand AI’s potential struggle to scale it due to weak governance and risk management frameworks. Many businesses are constrained by compliance requirements, data governance issues, and limited oversight mechanisms. Without these systems, they can only deploy AI in non-critical areas. Thomas Prommer, former President at Huge, explains that most enterprises use “copilot” tools for internal productivity because they don’t require model risk committees or advanced governance.

For leaders, this reveals a clear gap. AI cannot move into revenue-generating or mission-critical applications without confidence in the integrity, transparency, and security of the systems behind it. Many executives underestimate how much regulatory readiness and operational discipline are needed before AI can be fully embedded across functions like supply chain or pricing strategy. Governance isn’t bureaucracy, it’s the framework that keeps scalability safe and sustainable.

Breaking through these barriers takes executive-level ownership. Prommer notes that transformative AI projects often succeed when a CEO, investor, or single P&L owner drives the change with accountability tied to tangible results. Relying solely on CIO-led initiatives limits momentum, as CIOs often lack the authority to enforce enterprise-wide process shifts or risk frameworks. Leadership alignment is essential to unlocking enterprise-scale transformation.

C-suite teams should treat governance, auditability, and ethical management of AI as strategic pillars. Implementing proper model oversight, establishing transparent data policies, and integrating legal and compliance functions early will accelerate safe scaling. When governance is weak, AI stays experimental. When governance is strong, it becomes central to business strategy, driving real competitive advantage.

Path forward, strategy-driven, outcome-oriented AI transformation

To move from incremental results to meaningful impact, organizations must build AI strategies that start with clear business outcomes. Forrester’s latest guidance outlines the fundamentals: define success metrics upfront, align use cases with those metrics, establish a structure for testing and deployment, and scale through modern infrastructure like cloud systems and frontier models. The companies that accomplish this don’t just use AI to make processes faster, they use it to change the trajectory of their business.

Brian Hopkins, Vice President for Emerging Technology at Forrester, puts it simply: strategy is where you apply massive force based on insight. He emphasizes that organizations must identify unique strengths that competitors cannot easily replicate, and then channel AI investments toward those strengths. This means looking beyond surface gains and focusing on structural advantages, such as how AI can enhance customer experience, optimize decision-making, and create new growth models.

Executives should see AI transformation as a companywide effort that spans technology, process, and culture. Success depends on collaboration between IT, data teams, and business units to ensure AI initiatives are not isolated in technical silos. Leadership has to connect the dots between operational execution and strategic intent. The aim is not to deploy more tools but to design an ecosystem where insights move freely, systems learn continuously, and outcomes are measured in tangible business terms like profit margins, speed to market, and customer satisfaction.

For leaders, discipline and focus define the path forward. That means setting ambitious, measurable targets and holding the organization accountable to them. It also means investing in scalable infrastructure and upskilling teams to handle complex AI systems safely and effectively. When strategy, governance, and execution align, AI becomes more than a productivity enhancer, it becomes the foundation for durable competitive strength.

Hopkins and other experts, including Mike Flynn of EY and Thomas Prommer, formerly of Huge, agree that this level of transformation requires operational redesign and strong leadership commitment. Organizations that take this integrated, outcome-based approach are positioned to realize not just better efficiency, but a new level of agility, innovation, and resilience that will define the next generation of market leaders.

Recap

AI has matured fast, but most organizations haven’t kept pace. Many are still chasing short-term productivity instead of using AI to reshape their business. The difference between incremental improvements and full transformation comes down to strategic intent and leadership courage.

Executives set the tone. Transformative AI depends less on the technology itself and more on the clarity of vision behind it. Leaders who connect AI investments to measurable business outcomes, redesign workflows for flexibility, and strengthen governance will unlock far greater value than those focused only on efficiency.

This is a moment for decisive action. The tools now exist to make AI central to growth. Companies that align strategy, culture, and execution will redefine what’s possible in their industries. Those that hesitate will watch faster, bolder competitors move ahead.

The opportunity is clear. AI rewards leadership that thinks beyond improvement and aims for reinvention.