Moving Beyond Experimental Pilots: What It Actually Takes to Get Value from GenAI

If your organization has run a GenAI pilot that never quite made it to production, you’re not alone. Despite $30–40 billion in enterprise investment, MIT research finds that 95% of organizations still

report no return on investment from GenAI. Many are stuck in experimentation mode, and a surprisingly small number have achieved meaningful business transformation.

When organizations treat GenAI as a cost-cutting shortcut rather than a tool for solving their priority business and operational challenges, they miss what it can do. McKinsey’s research finds that the companies realizing the most value from AI aren’t the ones moving fastest, but those redesigning workflows, scaling what works, and investing intentionally.

That gap between pilot and impact is often more of a strategy challenge than a technology one. Ultimately, the organizations moving beyond ad hoc pilots are those that apply a more strategic, value-aligned approach to AI, first taking an inward look at what the business needs.

Avoid the FOMO Factor

Fear of missing out, or FOMO, has driven a surprising amount of AI adoption decisions. Unfortunately, this approach often results in a solution in search of a problem: a chatbot nobody asked for, an automation tool without a workflow to improve, a pilot that stalls because no one defined what success looked like.

The fix requires a mindset shift that starts with the problem, not the technology. Instead of asking “Do we need a chatbot?”, start by noting that you want to improve the customer experience, then ask “How can GenAI help us respond more effectively to our customers?” The answer to that question tells you which tool belongs in the conversation because it prioritizes identifying a valid business need and then selecting the right solution to address it.

A strategic roadmap makes it easier to keep AI projects on a value-driven path. That means:

• Identifying your high-value, pre-existing challenges that GenAI is actually suited to address
• Setting clear objectives before selecting a solution or tool
• Defining milestones, timelines, and data readiness criteria upfront

Also Read: AiThority Interview with Matej Bukovinski, Chief Technology Officer at Nutrient

Embrace the Experience Factor

Generative AI is changing how humans interact with technology. LLMs enable two-way, natural-language interaction, leading users to expect AI to feel less like a search bar and more like a knowledgeable collaborator.

Regardless of the use case, AI investments are more likely to succeed when you design for experience and outcomes, not just output. Building a GenAI experience that balances user satisfaction with business outcomes requires a few intentional choices:

• Build for resilience, and know which situations still require human judgment and intervention
• Prioritize clean, organized, and centralized data as a critical foundation for actionable AI insights
• Use governance frameworks to enable innovation, not obstruct it
• Treat AI development as iterative, not a linear process
• Define KPIs specific to GenAI and track metrics that demonstrate tangible business impact

Minimize Risk, Maximize Trust

Trust is an outcome of a well-designed AI system, not a feature that can be added at the end.

Beyond creating bad press, incidents like privacy violations, algorithmic bias, hallucinations, and misuse erode the user confidence that makes AI systems worth building in the first place.

Minimizing that risk requires both investment and commitment to core practices. As your data, tools, decisions, and usage scenarios become more complex, the surface area for errors and failures grows, and your ability to detect issues diminishes. Greater complexity also tends to entail higher risk and greater investment in oversight, governance, and guardrails to manage it responsibly.

Regardless of whether your jurisdiction mandates it, global AI legislation offers a useful baseline:

• Inventory and conduct a risk assessment of your AI systems
• Build in continuous testing and monitoring processes
• Include human-in-the-loop for high-stakes decisions
• Introduce failsafes to pause or shut down a system if something goes wrong

Prioritize Your Value Factors

The best practices above apply across all industries, from healthcare, pharma, and manufacturing to retail, finance, and professional services. Since solutions will vary across sectors, organizations, and business functions, your organization-specific lens helps determine the customization needed to achieve specific results.

For one leading paint manufacturer, this approach meant identifying the need for a custom tool to address a core problem: prospective buyers who struggled to choose the right paint color and experienced decision fatigue, which often led to cart abandonment.

The solution was a conversational AI trained on the company’s color data and designed for expert-informed, natural-language interaction. By guiding prospective paint buyers to the perfect color, the solution makes choosing a paint color simpler, more personal, and less overwhelming for consumers. The purpose-built system now averages more than 10,000 consumer sessions per month, driving stronger consumer engagement and informing faster decision-making at scale.

The Bottom Line: Invest with Intention

Organizations that win will be the ones that recognize moving from pilot to impact isn’t about moving faster. It’s about doing the upfront work to maintain focus on priorities: identifying the right problem, designing for the user, building trust into the system from the start, and measuring outcomes, not just outputs.

GenAI isn’t a quick fix. But for organizations willing to approach it strategically, it’s a genuine

opportunity for innovation and growth. The question isn’t whether to invest in it, it’s whether you’re prepared to invest in the right opportunities in the right way.

Also Read: ​​AI systems – Interoperable AI systems: Connecting models across platforms

[To share your insights with us, please write to psen@itechseries.com]

About the Author of this Article

Lauren Burke-McCarthy is an Associate Director of Data Science, AI Strategy & Product Strategy at Further, a leading data, cloud, and AI company focused on helping companies turn raw data into the right decisions. She supports organizations with the enablement, implementation, and risk management of responsible AI solutions and enterprise transformations. Throughout her career, Lauren has built systems in highly regulated and consumer-facing sectors to address user needs, improve decision-making, strengthen products, and deliver measurable value to organizations and end users.

About Further

Further, is a leading data, cloud, and AI company focused on helping companies turn raw data into the right decisions.