Taking AI projects from the experimental stage towards working solutions

Artificial intelligence is everywhere. Global corporate investment in these technologies hit a record $252 billion in 2024, and 78% of organizations now report using them in some form. In industries from finance and law to manufacturing and logistics, executives are exploring new tools to streamline operations and gain an edge. The promise is real, as early adopters have achieved up to 2.4 times productivity gains and 13% cost reductions. High-growth companies are seeing cost-efficiency ratios 4.5% higher than their peers.

Yet amid this enthusiasm, a sober reality comes up. Many projects never move beyond the pilot stage. Studies show that nearly 75% fail to deliver expected results. Some stall. Others are scrapped. The key challenge for leadership is bridging this gap and turning promising experiments into sustained results aligned with strategic goals.

Projects often falter due to a familiar set of causes:

• No clear objective. Too many teams launch initiatives driven by hype rather than business need. Without defined success criteria, experiments drift and ultimately fizzle.
• Weak data foundations. Incomplete, siloed, or low-quality data undermines even the most advanced tools. Infrastructure limitations only add to the challenge.
• Lack of skills. Many organizations lack in-house technical talent and rely heavily on vendors. Without strong internal knowledge, adoption is slow and shallow.
• Poor collaboration. When tech teams operate in isolation from business units, solutions don’t align with real workflows or user needs.
• Unrealistic expectations. Some leaders expect instant transformation. When early results don’t match the promise, support evaporates. Others face resistance from employees who don’t trust or understand the tools.

Taken from: “Why Most AI Projects Fail” – McKinsey & Co.

If business leaders want these technologies to deliver value, they must treat them not as experiments but as strategic capabilities. That means creating the right conditions across the organization.

Every project should begin with a clear problem to solve. A legal team might want to reduce contract review time. A factory might aim to lower error rates in quality control. Anchoring efforts to specific, measurable goals keeps teams focused and outcomes visible.

Take British Airways, for example. They invested £7 billion in operational technology and analytics. By using real-time weather forecasting and optimizing aircraft gate assignments based on onward travel plans, they significantly improved punctuality, raising their on-time departure rate to 86% in early 2025 – up from just 46% in 2008. This initiative, built on clearly defined goals, eliminated approximately 403,000 minutes of delay.

Taken from: “The AI Playbook” – Harvard Business Review

Bring the Right People Together
Projects work best when business leaders, technical experts, and end-users collaborate from the beginning. Cross-functional teams ensure solutions fit real needs and drive adoption. Leadership from business units is especially important. When department heads sponsor the work, momentum follows.

Deutsche Bahn’s Long-Distance division exemplified this by forming an AI Competence Center to evaluate over 200 projects. Their success with the Railmate customer platform came from deep collaboration across customer service, IT, and operations – demonstrating how structured teamwork scales promising pilots into enterprise systems.

Taken from: “Cross-Functional AI Teams” – World Economic Forum Case Study

Strengthen Your Data Foundation
Technology is only as good as the data behind it. That means investing early in improving data quality, connecting siloed systems, and ensuring teams can access what they need. A strong digital core – clean data pipelines and modern infrastructure – is essential for success.

Ford Motor Company approached their predictive maintenance efforts by first centralizing vehicle data from thousands of sensors. Once their data platform matured, they gradually introduced analytics and automation – reducing downtime, improving fleet efficiency, and expanding the system across manufacturing lines. The structured foundation enabled long-term scale.

Taken from: “The State of Data Infrastructure for AI” – Accenture

Demonstrate Early Wins
Big transformations rarely happen all at once. The better approach is to start small. Pilot a project in one region, on one product line, or with one process. Measure the outcome. If it works, scale it up. This step-by-step method builds confidence and helps teams learn quickly.

A logistics company, for instance, tested a route optimization tool in one urban hub. The trial resulted in a 12% increase in on-time deliveries and reduced fuel consumption by 9%. Encouraged by the success, the company rolled out the tool across five cities, then nationwide. Expansion involved regular debriefs with drivers, redesigned routing dashboards, and incentives for using the system correctly. By aligning outcomes with everyday workflows, adoption stuck.

Another example comes from a financial services firm that used automation to streamline loan application reviews. A three-week pilot reduced approval time from 5 days to under 48 hours. Based on measurable impact, the firm expanded the model to six product lines, saving over 1,000 work hours per month and increasing customer satisfaction scores.

Taken from: “Scaling AI for Business” – Deloitte Insights

Make New Tools Part of the Routine
To create lasting impact, new technologies must be embedded into daily work. That may mean redesigning tasks, training users, or writing new playbooks. Just as important, leaders must establish governance. Guardrails around privacy, bias, and quality are essential to build trust.

For example, a European legal services firm that introduced automation for document review also developed standard templates and review checklists. These adjustments helped employees trust and use the new system more confidently.

Check: “Operationalizing AI at Scale” – BCG

What Every Business Leader Should Prioritize

1. Define a business goal. Choose a problem worth solving and clarify how you’ll measure success.
2. Get your data ready. Ensure it’s clean, integrated, and accessible across teams.
3. Involve the right people. Cross-functional teams with executive support will move the fastest.
4. Start small. A pilot with a tight scope and clear feedback loops is your best path to scale.
5. Integrate and govern. Make it part of the job—and protect its use with smart policies.

Technology can create value, but only when it is led with purpose. The real advantage goes to companies that treat innovation not as an experiment, but as an opportunity to rethink how they work, solve problems, and grow. When anchored in business outcomes and built through iteration, pilot projects become platforms for transformation.

Across interviews and business forums like Hacker News and Reddit’s r/MachineLearning and r/consulting, one sentiment is echoed repeatedly by transformation leaders: “The technology isn’t the hard part, change is.” Business leaders report that successful transformation hinges not on algorithms, but on sponsorship, culture, and iteration. One Head of Operations in the logistics sector shared that success came only after three cycles of feedback and process adjustments. Another CTO emphasized that “the real breakthrough was when the business stopped viewing the model as ‘the product’ and started asking how the product should change to use the model.”

What stands out from these conversations is a shared recognition. These tools only amplify what’s already working, or expose what’s broken. Leaders who foster a culture of curiosity, engage teams early, and focus on sustained value rather than short-term wins, see the biggest results.

Innovation is no longer about moonshots. It’s about steady, repeatable progress.