91% of mid-market companies use generative AI, yet only 25% have fully integrated it into their main operations, and 74% cannot demonstrate tangible value from their AI investments¹². This gap is not about technology. It is about strategy. Most companies buy AI tools before defining the business problem or setting up the governance needed to sustain long-term value.
For mid-market leaders, closing this strategy gap is now a key competitive challenge. This article shares how Warp approaches AI strategy—and what our team consistently sees that separates companies that realise real value from those stuck in pilot mode.
The strategy gap in numbers
The adoption numbers are impressive on the surface. McKinsey’s 2025 State of AI survey³ found 88% of organisations use AI in at least one business function, up from 55% two years earlier. But maturity tells a different story: nearly two-thirds of companies are still in the experimenting or piloting stage, and only 1% of C-suite executives describe their generative AI rollouts as “mature.”
For mid-market companies specifically, 63% are operating without a clear strategic framework, more than half admit they were only “somewhat prepared” at adoption, and 70% say they need outside help to extract value¹. At the workforce level, Gallup⁵ found 70% of employees report receiving no guidance on how to use AI at work, and half have received zero training.
At Warp, we think the most useful measure of AI maturity isn’t how many tools you’ve deployed. It’s whether your organisation has made the shift from efficiency to amplification.
Most AI journeys begin in the same place: doing things faster and cheaper. Automating manual processes, reducing headcount on repetitive tasks, and compressing turnaround times. These are legitimate gains, but they have a ceiling. Efficiency optimises what you already do. It doesn’t change what you’re capable of.
The organisations we see generating the strongest returns from AI have moved beyond that. They use AI to amplify human judgement, not replace it. To create new sources of revenue, not just protect existing margins. To reshape how leadership makes decisions, not just how teams execute tasks.
This is where the strategy gap matters most. Organisations stuck in the efficiency phase tend to layer AI on top of existing processes and wonder why the results plateau. Those that embrace amplification rethink their processes and unlock performance that efficiency alone can never achieve.
We’ll unpack our full view of AI maturity in future work. But the shift from efficiency to amplification is the lens we apply to every engagement and the benchmark we believe mid-market leaders should measure themselves against.
The tools are there. The strategy is not.
Why most AI investments fail to deliver
Over 80% of AI projects fail — twice the rate of traditional IT projects, according to RAND Corporation⁷. In 2025, 42% of companies abandoned most of their AI initiatives, up from 17% the year before⁸. The average company invested $1.9 million in generative AI projects in 2024, yet fewer than 30% of CEOs were satisfied with the returns.
In almost every case, the technology did what it was supposed to do. The real problems were the absence of strategic decision-making before the project started and weak governance throughout. These are not technology failures — they are strategy failures.
The 10-20-70 rule: what separates winners
The most important approach for understanding the strategy gap is BCG’s 10-20-70 principle⁹. According to this model, 10% of AI success depends on algorithms, 20% on technology and data infrastructure, and 70% on people, processes, and organisational culture.
BCG validated this empirically across client engagements, and the principle has held consistently across their research since 2020. McKinsey’s findings align: the biggest differences between AI leaders and laggards are in strategy and adoption, not technology — with leaders outperforming laggards by 2 to 6 times in total shareholder returns³.
At Warp, we identify with three things that really stand out when working with our clients:
- First, they decide before they build. Clients that start with a clear plan to address a specific business problem and ensure key stakeholders are aligned report meaningful value from AI. They take the time to redesign workflows end-to-end before selecting tools.
- Second, they focus deeply instead of spreading efforts too thin. Often, in early client engagements, clients share that their AI pilots have been unsuccessful. We find that success comes more readily when clients tackle the low-hanging fruit or top pain points first, focusing on two to three areas with clear goals. This sets them up for early wins, and the momentum that follows builds from there.
- Third, they invest in change management alongside the technology. Projects with dedicated change management resources achieve a higher success rate. Training and communication with your workforce significantly helps with adoption and the success of implementation.
The pattern is clear: companies realising AI value make key strategic decisions up front. Tool selection alone does not ensure success.
Leadership is the strongest predictor of AI outcomes.
BCG’s September 2025 research⁶ found that C-level executives deeply engaged with AI are 12 times more likely to be among the top 5% of companies generating real value. McKinsey’s AI high performers are three times more likely to report strong senior leadership ownership. Gartner found that 91% of high-maturity organisations have appointed dedicated AI leaders.
The change in who makes AI decisions shows this shift. BCG’s 2026 AI Radar¹⁰ found that 72% of respondents now see the CEO as the main AI decision-maker, up from just one-third the year before. The stakes are personal. Half of CEOs believe their job security depends on getting AI right in 2026.
Yet alignment continues to be elusive. Adecco Group’s 2025 survey¹¹ found that 53% of leadership teams struggle to agree on AI strategies, and confidence in those strategies fell 11 percentage points over the prior year. Only 10% of companies qualify as “future-ready” for AI disruption.
Real-world examples back up the data. Brightstar Capital Partners, a mid-market private equity firm, succeeded by choosing augmentation over automation. They used AI to help people make better decisions rather than replace them. Sharp Business Systems brought inactive accounts back to life by using AI-powered sales intelligence to solve a specific, known bottleneck. In both cases, the strategic decision came first.
Compare this to Zillow, which invested heavily in AI-powered pricing algorithms but failed to account for the unpredictable factors that drive property markets. This led to losses of over $500 million. (Zillow abandons its home-flipping algorithm, 2021) Snapchat also added an AI chatbot to its platform without clear value for users, which caused its app rating to drop and led to a 488% increase in deletion searches. (“Delete Snapchat” Searches Increase by 488% After AI Features Roll Out to Everyone, 2023)
Consulting firm AArete sums it up well: the winners picked one or two real problems in their current workflows, focusing on bottlenecks that people already noticed, instead of chasing broad “art of the possible” projects.
Decide before you build. Govern what you build well.
The research is consistent: the AI strategy gap is a decision-making problem, not a technology problem. Companies that define clear business objectives before selecting tools and implement governance frameworks to protect value throughout the project lifecycle are significantly more likely to see meaningful returns.
For mid-market companies, this matters more than it does for anyone else. Mid-market organisations face real constraints: tighter budgets, smaller talent pools, and less room for expensive failures. But they also have structural advantages that large enterprises lack: faster decision-making, less bureaucratic resistance, and leaders who are closer to actual operations. The World Economic Forum estimates that $2 trillion of potential AI value lies in the mid-market segment. (Weinberg, 2026)
The question is no longer whether to invest in AI; that decision has been made. The question is whether your organisation is set up to get value from it. In our experience, the companies that do are not necessarily the ones with the biggest budgets or the most sophisticated tools. They are the ones that slow down long enough to get the strategy right before they build.
At Warp, our team of specialists and experts brings over two decades of experience helping mid-market organisations navigate this challenge. We work with leadership teams to assess AI maturity, build roadmaps aligned to real business objectives, and put the governance and change management structures in place that make implementation stick. We have seen what happens when companies skip these steps, and what becomes possible when they get them right.
Rudi Mostert, CTO of Warp Development, emphasises this point: “The foundations most companies set a decade or two ago weren’t built for what AI demands; different data architectures, different workflows, different ways of making decisions. That’s not a criticism; it’s just reality. The organisations we see getting real value from AI are the ones willing to re-evaluate those foundations honestly, not just layer new technology on top of old assumptions. When leadership engages early, aligns on strategy, and commits to governance before committing to platforms, results come faster and compound. In a rapidly evolving AI landscape, clarity on where you’re starting from matters as much as clarity on where you’re heading.” The compounding cost of delay
The last insight from the research is the most urgent. The strategy gap is not staying the same; it is getting wider. McKinsey found that the maturity gap between AI leaders and laggards grew by 60% in just three years. BCG’s data shows that “future-built” companies are moving further ahead on every financial measure. This is a compounding effect. Companies that invest in strategy, governance, and people now build skills that help every future AI project, while those stuck in pilot mode fall further behind each quarter.
The first step isn’t building. It’s deciding.
If your organisation is using AI but not yet seeing the value, the starting point is not another tool; it is an honest assessment of where you are, where you want to go, and what is standing in the way. That is exactly what Warp’s AI strategy advisory sessions are designed to do. Book a session with our team to evaluate your position and determine what it will take to close the gap.
Key Takeaways
- Most mid-market companies use AI, but very few have integrated it into core operations. Only 25% have moved beyond experimentation, despite 91% adoption.
- The technology is rarely the problem. BCG’s research shows 70% of AI success depends on people, process, and culture, yet most investment goes elsewhere.
- Leadership engagement is the single strongest predictor of AI outcomes. Companies with deeply engaged executives are 12 times more likely to be among the top performers.
FAQs
What is the AI strategy gap?
The AI strategy gap is the disconnect between AI adoption and the value it creates. Most organisations invest in AI tools without first making strategic decisions about where AI will create value, how workflows need to change, and what governance structures are needed to sustain results.
Why is leadership so critical to AI success?
BCG research found that companies with deeply engaged C-level executives are 12 times more likely to be among the top 5% generating real value. AI is fundamentally a leadership and strategy challenge; without executive alignment, investments consistently stall at the pilot stage.
How can mid-market companies close the AI strategy gap?
Mid-market companies can close the AI strategy gap by taking three steps before investing in AI tools: defining clear business objectives and identifying where AI creates genuine leverage, redesigning workflows and investing in people and change management, and establishing governance frameworks that build confidence and manage risk. Companies that take this strategy-first approach are twice as likely to report meaningful financial returns from AI.
Sources
- RSM US Middle Market Business Index, AI Special Report, June 2025.
- BCG, “Where’s the Value in AI?” Global AI Survey, October 2024.
- McKinsey & Company, “The State of AI,” Global Survey, November 2025.
- Deloitte, “State of AI in the Enterprise,” 6th Edition, 2026.
- Gallup, U.S. Workplace Survey on AI Integration, Late 2024.
- BCG, “The Widening AI Value Gap,” September 2025 (1,250 executives).
- RAND Corporation, “Why AI Projects Fail,” 2024.
- S&P Global Market Intelligence, AI Adoption Tracking Survey, 2025.
- BCG, 10-20-70 Principle,validated by research 2020–2026; reaffirmed in “Scaling AI Requires New Processes, Not Just New Tools,” 2026.
- BCG, AI Radar 2026 (2,360+ leaders, including 640 CEOs), January 2026.