How CIOs Can Successfully Scale Gen AI Pilots
Key Takeaways
- Few companies scale gen AI pilots successfully.
- Pilots fail because of a combination of four factors.
- Successfully scaling AI delivers greater ROI.
- Five priorities determine pilot success.
AI has become one of the most heavily funded areas in business strategy. Companies are pouring resources into pilots, hoping to uncover the next big breakthrough.
But are they seeing results?
Probably not.
According to McKinsey and Company, only 11% of companies have adopted gen AI at scale.
The other 89% are stuck. They’re learning it’s pretty easy to build an impressive gen AI pilot, but getting it production-ready takes far more work than anticipated.
So how do you get unstuck? How do you ensure your AI pilot is a successful one?
It depends on the approach you take and your organization’s preparedness for implementing gen AI.
What’s Your Approach to AI?
McKinsey defines three AI approaches or stages businesses fall into: Takers, Shapers, and Makers.
Takers use off-the-shelf, gen AI–powered software to achieve their goals. Examples include GitHub Copilot, Salesforce Einstein, and Zapier.
Shapers integrate custom gen AI capabilities by engineering prompts, data sets, and connections to internal systems.
Makers create their own LLMs. Their names are almost synonymous with AI: OpenAI, Anthropic, Cohere, Mistral AI, and so on.
“The highest-value gen AI initiatives,” the McKinsey authors said, “generally rely on the Shaper approach.” Shapers often have specific use cases that off-the-shelf AI tools can’t handle.
Most of Taazaa’s AI clients fall into the Shaper category.
Why AI Pilots Fail to Scale
Promising pilots rarely fail because of the technology alone.
More often, a combination of organizational, strategic, and cultural roadblocks prevents enterprise-wide adoption of an AI project.
Four patterns appear consistently across companies that struggle to move beyond experimentation.
1. No Roadmap
To achieve success, planning needs to start by clearly identifying how AI aligns with the overall business strategy in ways that create meaningful value.
Part of this effort is identifying use cases, but that shouldn’t be where it ends. It also involves establishing a definition of success and evaluating project constraints.
Leaders need to raise and answer questions about feasibility, timeline, accountability, and measurable impact.
Many AI pilots begin as isolated projects within individual business units or IT teams. They remain siloed; disconnected from broader enterprise priorities without clear ownership or governance. They look impressive in isolation, but they aren’t designed to scale.
In some cases, boards and executives push for quick demonstrations of AI capability without a roadmap for long-term adoption. The result is momentum at the start but little staying power.
Learn more: Building an AI Roadmap to Take You from Vision to Execution
2. Low Data Quality
AI runs on data. To deliver reliable output, it must have access to data that has been prepared for AI consumption.
In a survey of business leaders, 87% said they believe their data ecosystem is ready to support AI at scale. However, 70% of technical teams report spending hours every day to fix data issues.
It would seem, then, that leaders often overestimate their data quality and readiness.
The disconnect often stems from misaligned views on data quality. Clean dashboards and central repositories don’t necessarily mean an organization’s data is easily consumable by AI. Siloed data alone can hinder enterprise-wide AI adoption.
When evaluating data readiness, the project team needs to consider several factors, including:
- Accessibility and organization
- Ownership and governance
- Data domains
- Data definition, glossary, and cataloging
- Optimization and correction
- The potential for biased data
Learn more: How to Make Your Data AI-Ready
3. Technology Hurdles
According to Ernst & Young, 97% of leaders see positive returns from their AI investments, but say the organization’s infrastructure limits ROI. 83% said AI adoption would be faster if they had a stronger data infrastructure, and 67% said a lack of infrastructure actively hinders AI adoption.
Even the best AI models cannot deliver without the right infrastructure. Many organizations face fragmented data architectures, limited governance, and an absence of production-grade pipelines such as MLOps.
Learn more: AI Readiness Checklist for Your Tech Stack
4. Talent and Skills Gaps
Then there’s the people challenge. Scaling AI requires teams that combine technical expertise with business understanding—and these people are increasingly hard to find.
Too often, pilots are driven by small groups of data scientists or IT teams who lack the cross-functional support needed to integrate solutions across the enterprise.
At the same time, much of the workforce is resistant and even fearful of using AI. Promising projects struggle to find traction at scale without investment in change management, reskilling, and vocal support from leadership.
Learn more: How to Build an AI-Ready Culture: Upskilling, Mindset, and Communication
The Business Impact
When successful, scaling AI translates directly into stronger business performance.
Companies that successfully scale AI are more likely to exceed ROI expectations. Scaled adoption drives higher revenue growth, delivering efficiency and workforce productivity gains, reducing costs, and accelerating delivery.
Conversely, each pilot that fails not only wastes resources and budget but also misses an opportunity to capture market advantage. These setbacks make it harder to close the gap with competitors.
How to Join the Successful 11%
If most AI pilots stall, the natural question is: what are the 11% doing differently?
The answer lies in how leaders build foundations and mobilize their organizations. Five priorities consistently separate companies that scale from those that do not.
1. Leadership Alignment
Scaling AI does not occur from the bottom up. It starts when the C-suite takes ownership and communicates the importance of AI to the company’s future. It requires aligning AI with the corporate strategy, treating it as a core initiative rather than a mere experiment, and ensuring that business leaders, not just IT, are held accountable for the results.
2. Prioritize High-Impact Use Cases
Organizations often dilute their efforts by running numerous pilot projects across different functions. This leads to a lot of activity but few quick wins. Therefore, it is vital to concentrate on a few key use cases that directly contribute to value creation. These can include reducing costs in critical processes, increasing revenue in essential markets, or enhancing the customer experience in measurable ways.
3. Invest in Data and Infrastructure
A promising pilot project can falter when faced with messy data or fragmented systems. That’s why scaling requires a significant investment in foundational elements: unified data platforms, governance frameworks that ensure quality and compliance, and MLOps pipelines that enable the monitoring and updating of models in production.
Cloud architectures provide the flexibility to expand quickly and securely. Without these essential components, AI projects remain in the prototype stage. With them, however, there is ample room for growth.
4. Focus on People and Processes
Technology may spark change, but people and processes sustain it. Scaling AI requires preparing the workforce to adopt new tools and workflows. It also calls for cross-functional teams that bring together data scientists, engineers, and business owners so that AI solutions are designed for impact.
5. Governance and Metrics
Each AI initiative needs clear KPIs tied to business outcomes, not just technical accuracy. Leaders should track progress, measure impact, and shut down pilots that fail to deliver, while supporting those that prove value. Success comes from decisive resource allocation.
What It Takes to Win with AI
Pilots prove that technology works, but they do not move the business forward on their own.
What separates success from failure is the ability to take those pilots out of the lab and scale them across the enterprise. That shift demands clear priorities, strong foundations, and leadership commitment.
The cost of hesitation is real, as every stalled pilot represents wasted investment and lost ground to competitors who move faster.
The upside is equally clear: Organizations that close the scale gap turn AI from a showcase into a source of measurable growth and efficiency.
Taazaa helps organizations overcome the roadblocks to scaling AI successfully. Our teams partner with you to assess your AI readiness, plan your AI pilot strategy, and successfully scale your custom solution across your entire business.
We deliver AI with real ROI and impact. Contact us today to get started!
FAQs
How many companies scale gen AI pilots successfully?
Only 11% of companies successfully adopt gen AI at scale, according to a survey by McKinsey and Company. Most companies fail to scale their gen AI pilots because getting them production-ready is more complex than they anticipated.
Why do most gen AI pilots fail?
A combination of technical, organizational, strategic, and cultural roadblocks often prevents enterprise-wide adoption of an AI project. These issues often manifest as a lack of proper roadmapping, low-quality data, insufficient technical infrastructures, and a shortage of in-house AI expertise.
How does successfully scaling AI deliver greater ROI?
Successfully scaling AI is more likely to deliver a greater ROI than expected because it drives higher revenue growth, delivers efficiency and workforce productivity gains, reduces costs, and accelerates delivery.
What are the five priorities that determine AI pilot success?
The five priorities that lead to successful AI pilots are aligning leadership with AI goals; prioritizing high-impact use cases; investing in data preparation and the supporting infrastructure required; preparing the workforce to adopt new tools and workflows; and establishing clear KPIs tied to business outcomes.
