Beyond the Numbers: How Storytelling Drives AI Deployment

Leadership in the Loop: Edition 20

Amir Hartman | Managing Director, Dasteel Consulting | Director AI Strategy Research Experience Alliance, Fidere.ai, Praxis AI

Venkataraman Lakshminarayanan | Chief Revenue Office & President Cron AI |

Turning an emergent AI strategy into a purposeful one is about more than just crunching numbers or drafting cost-benefit analyses. It’s about navigating challenges, crafting a compelling story, and demonstrating real value to stakeholders. Storytelling isn’t just a communication tool—it’s the thread that ties every stage of this journey together. Moving from exploration to execution requires showing promise, building momentum, and avoiding common pitfalls like getting stuck in pilot mode. As Nachiket Jeurkar , Principal at Exavalu, puts it, "Making the case for AI requires storytelling that illustrates tangible benefits."

Start by Showing Promise

The first step in turning an AI idea into reality is proving its potential to solve meaningful problems. For the insurance industry, AI isn’t entirely new. “The insurance industry has been using advanced predictive models for a long time,” Jeurkar explains. AI models are already being deployed in areas like risk assessment, where they analyze images to detect fire or trip hazards. Starting small with targeted use cases like these helps organizations build confidence in AI's capabilities.

Creating a Proof of Concept (POC) is key. A POC doesn’t need to be polished—it’s simply a way to test whether the idea works. For example, insurers might use AI to summarize underwriting documents or flag high-risk claims. Use storytelling here to frame your POC as a glimpse of what’s possible: share how it aligns with organizational goals, improves efficiency, or solves a problem that resonates with your audience. The goal is to validate the concept in a controlled environment, iterating quickly and keeping timelines short—3 to 4 months—so momentum isn’t lost. As Jeurkar emphasizes, “It’s the cost of a mistake we want to avoid,” which is why cautious, incremental adoption often works better than diving into fully autonomous solutions.

Move from Promise to Proven Value

Once a POC has shown promise, the next step is proving value in real-world conditions. This is where the Proof of Value (POV) or Minimum Viable Product (MVP) comes in. For insurers, this might mean deploying AI models in claims processing or customer support to enhance productivity rather than replace human workers. "AI is an assistive tool," Jeurkar notes. It helps flag risks, suggest actions, and summarize complex data, but human oversight remains critical.

Storytelling becomes even more important at this stage. Use the results from the POV to tell a story about impact: how the AI tool saved time, reduced errors, or improved decision-making. Link these outcomes to organizational priorities like customer satisfaction or cost reduction. By focusing on small wins in live environments, organizations can demonstrate that AI delivers measurable benefits. This approach avoids the trap of “pilot paralysis” and ensures the initiative continues moving forward.

Share the Story and Results

Storytelling is essential at every stage of an AI journey, but especially when it comes to sharing results. Data and outcomes alone won’t win over stakeholders—you need a compelling narrative that connects AI achievements to the organization’s broader vision. Jeurkar highlights the importance of storytelling in building momentum: “Making the case for AI requires illustrating tangible benefits.”

For insurers, this could mean showing how AI improves productivity, reduces manual errors, or speeds up claims processing. Just as importantly, organizations must communicate how AI can scale beyond its initial use case to address challenges across the business. Demonstrating scalability inspires confidence and sets the stage for broader adoption.

This table reinforces how storytelling evolves through the journey and ties directly to measurable outcomes.

Build a Business Case and Prioritize

With attention captured and value proven, the next step is formalizing the plan with a business case. This document should clearly outline the problem being solved, the proposed solution, required resources, and expected outcomes. For example, insurers might highlight how AI can automate repetitive tasks while maintaining human oversight to avoid costly errors.

Storytelling remains central here: frame the business case as a roadmap to transformation, not just a request for resources. Highlight the human benefits, like freeing up employees to focus on higher-value tasks, and align these outcomes with the organization’s mission. Organizations should also prioritize achievable milestones over a 12-month period, focusing on delivering incremental value while minimizing risk. As Jeurkar points out, "You can never plan enough change management." Effective planning ensures teams have the training and support they need to adopt AI with confidence.

Secure Buy-In

Securing organizational buy-in is about more than just numbers—it’s about alignment and trust. This often involves an internal roadshow to present the plan to decision-makers across the organization. Tailor your pitch to their priorities, whether it’s improving efficiency, reducing costs, or driving innovation. For insurers, addressing concerns around accuracy, privacy, and risk is critical to winning stakeholder support.

Training and upskilling employees are equally important. "We need to ensure staff can effectively interact with AI systems," Jeurkar emphasizes. Helping teams validate AI insights and critically assess automated processes builds trust in the technology and its outputs. Storytelling plays a role here too: show employees how AI tools can make their work easier and more impactful, fostering a sense of empowerment.

Prepare for Launch

With buy-in secured, it’s time to operationalize. Assemble the necessary resources, including the right people and technologies, to deliver on your initial use cases. Start small but design with scalability in mind, ensuring solutions can grow alongside organizational needs. Risk mitigation should also be a priority—especially in industries like insurance, where the stakes are high.

The launch is a chance to tell the most important story yet: the beginning of transformation. Use this moment to connect the dots between early successes and the larger vision, inspiring teams and stakeholders to see AI not as a tool but as a strategic enabler.

From Theory to Action

The journey from emergent to purposeful AI strategy requires balancing exploration with execution. Storytelling is the glue that holds this journey together, transforming data and results into a compelling vision that inspires action. At the same time, organizations must avoid pilot paralysis by moving quickly from concept to production, ideally within 3 to 4 months. As the insurance industry demonstrates, careful adoption paired with a clear focus on value can make AI a cornerstone of long-term success.

But the role of storytelling doesn’t end with the launch. After deploying AI, organizations must quantify the outcomes and continue communicating the impact. Share the improvements AI has delivered—whether it’s higher efficiency, better customer satisfaction, or reduced costs—using clear, relatable metrics and narratives. This keeps stakeholders engaged, builds trust, and ensures alignment for future initiatives. Storytelling after launch also helps embed AI into the organizational culture. By showing how AI contributes to strategic goals and everyday successes, you inspire teams to embrace its potential fully. In the end, effective storytelling transforms AI from a technology initiative into a driver of meaningful change and lasting value.

How have you used storytelling to drive alignment or success in your AI projects? Share your storytelling stories -- we'd love to hear ow it's made an impact in your work!

#AI #GenAI #Exavalu #Storytelling