Many firms believe in GenAI’s competitive potential yet remain unprepared
Generative AI is a turning point. According to new research by the BPI Network and the Growth Officer Council, 79% of corporate leaders expect GenAI to deliver a competitive advantage within the next 18 months. They’re not wrong. AI is moving fast, and it’s already reshaping how businesses sell, support, and build. But here’s where it gets real: 60% of companies surveyed don’t think they’re ready to use it effectively.
Failure here doesn’t come from a lack of ambition. It’s usually due to poor data infrastructure, unclear strategies, or slow execution. Most companies have data, but it’s scattered, outdated, or unstructured. When your foundation isn’t solid, adding AI won’t help. You just automate the chaos.
If your company wants to lead, not follow, two things need to happen. First, stop overthinking it. Second, move fast on aligning your data stack. You don’t need perfection to start, you need clarity and direction. GenAI’s potential is real, but it’s only realized by those who actually prepare for it.
Tom Kaneshige, Chief Content Officer at the BPI Network, summed it up well: “As organizations plot a path toward GenAI competitive advantage, they’ll need to make an honest assessment of their data-AI readiness and where they stand among their peers.” That’s just smart. No spin. No fluff.
High-quality data governance is essential for harvesting GenAI benefits
Everyone talks about AI, but not many talk about what powers it, data. If your data isn’t reliable, your AI doesn’t work. And to make it work at scale, you need governance. This means using the right data, in the right form, used legally and ethically.
There’s a clear performance gap between those who have structured, high-quality data and those who don’t. The numbers? Companies with solid data foundations are almost four times more likely (81% vs. 21%) to expect improvements in customer experience from AI. That’s a sharp contrast. And it shows one thing: AI starts with the data. Period.
This is where companies typically stall. They chase the shiny objects, large models, agent-based tools, but skip the basics. Your customer insights will get worse before they get better if AI is working off wrong or outdated input.
Executives need to prioritize investment in data governance frameworks. That includes accuracy, lifecycle management, and responsibility. Without it, it’s not just a missed opportunity, it’s a guaranteed failure at scale.
Jesse Todd, CEO at EncompaaS, put it directly: “GenAI projects are failing because they are not grounded in a foundation of well-prepared, high-quality data.” That’s the takeaway. You don’t scale with bad data.
GenAI readiness and satisfaction greatly vary by company size and geographic region
The gap between those seeing results with GenAI and those still struggling with deployment is widening fast, and it’s not random. Company size and geographic location play a big role in how prepared a business is to use AI effectively.
Larger firms, those bringing in over $5 billion annually, are far more confident and satisfied with their GenAI outcomes. 90% of them report that their projects are meeting expectations. Now compare that to firms earning between $500 million and $1 billion, where only 16% see comparable success. That’s a huge drop in satisfaction, and it highlights that scale brings more than just money. It brings experience, resources, and mature infrastructure that smaller companies haven’t always built yet.
Locally, there are differences that can’t be ignored. North American firms are ahead in GenAI maturity. They report the lowest dissatisfaction levels with current GenAI outcomes, 38%, compared to 45% in Europe and 84% in the Asia-Pacific region. This stems from differences in access to skilled talent, digital infrastructure, and executive-level commitment to AI integration.
There are also strategic differences between sectors. Business-to-business firms are pushing GenAI use cases faster and more effectively than business-to-consumer companies. B2B processes tend to be more structured and data-rich, which typically makes GenAI easier to deploy and scale efficiently.
Executives should track these trends closely. Understanding what impacts success and who is succeeding helps you better benchmark your own position, and make smarter moves, faster.
Key obstacles in realizing GenAI value
GenAI doesn’t fail because the models aren’t good. It fails because the execution falls short. The biggest barriers today are mundane but critical: poor data accuracy, weak system integration, and a lack of ethical policies guiding how AI is used.
The data side is the most pressing. 69% of executives report that inaccurate or unreliable data is holding back their GenAI outcomes. If the input isn’t solid, results won’t scale. You’ve got to trust your data to trust what GenAI gives back.
Then comes integration. 68% said that existing infrastructure isn’t ready to handle the demands of AI tools. Legacy systems, fragmented platforms, and disorganized workflows turn even the smartest AI into a stalled project.
Ethics and governance are rising fast as a concern. 58% flagged the need for stronger rules around how AI is deployed, how it’s monitored, audited, and explained. Trust is essential, especially as AI drives decisions in customer service, marketing, and operations.
Fixing these issues means aligning teams, processes, and safeguards so that GenAI tools can operate confidently, at scale. If you’re in the C-suite, this responsibility is yours. Delegating it is the quickest way to fall behind.
Strategic planning and investment in data maturity are vital to unlocking GenAI’s full potential
Most GenAI projects succeed or fail long before the technology is deployed. The difference lies in the strategic groundwork, specifically, how well an organization has developed its data capabilities. If the basics aren’t right, data quality, precision, privacy, cost models, then scaling AI across functions becomes difficult and expensive.
Data maturity comes from knowing what you have, where it lives, how accurate it is, how secure it is, and what it actually costs to store and use. The companies seeing real results are the ones treating these areas as strategic priorities. They’ve assessed their current state, defined their AI objectives, and made direct investments to close the gap between readiness and execution.
The report from BPI Network and Growth Officer Council outlines four capability pillars every company needs to evaluate: data quality, accuracy and reliability, security and privacy, and cost-to-ROI efficiency. These aren’t optional, they are the actual operational levers for GenAI success. When these get prioritized, everything downstream, from performance, to automation, to customer experience, improves.
This is where executive leadership matters. It’s not enough to approve budgets or bring in outside vendors. Leaders have to drive organizational focus around these core foundations. Without that level of direction, even the best GenAI tools won’t deliver sustained value.
Tom Kaneshige, Chief Content Officer at BPI Network, said it best: “As organizations plot a path toward GenAI competitive advantage, they’ll need to make an honest assessment of their data-AI readiness and where they stand among their peers.” That clarity, knowing where you are and what to fix, is what separates active transformation from passive experimentation.
Main highlights
- Firms are confident in GenAI’s potential but not execution-ready: 79% of leaders expect GenAI to drive competitive advantage, yet 60% admit they aren’t ready to deliver value due to data and integration gaps. Leadership must assess AI readiness now to avoid costly delays.
- Data governance determines GenAI success: Companies with strong data practices are nearly four times more likely to expect customer experience gains from AI. Leaders should prioritize data quality and governance early to maximize ROI.
- Size and geography shape GenAI readiness: Larger enterprises and North American firms show higher GenAI satisfaction, while APAC lags. Executives must factor revenue scale and regional capabilities into GenAI rollout strategies.
- Execution stalls without reliable data and strong governance: The most cited GenAI blockers are data integrity (69%), integration issues (68%), and ethical trust concerns (58%). C-suite leaders must lead corrective action on foundational enablers before scaling AI.
- GenAI strategy must be built on data maturity: AI initiatives succeed when grounded in mature data operations, quality, reliability, security, and ROI optimization. Executives should align investment and leadership focus around these pillars to unlock sustained GenAI value.
