From hype to reality: An enterprise AI playbook

Generative AI promises to reshape industries, but many organizations find themselves stuck. The path from initial excitement to tangible business value is often unclear, with a staggering 95% of GenAI projects failing to launch successfully.

How can enterprises navigate the noise and turn AI potential into a genuine competitive advantage?

In a special two-part series on The STEMINISTS Podcast, hosts Phoebe Goh and Mekka Williams sat down with Anju Mohan, NetApp’s Senior Director of IT Data & Analytics and head of our AI Center of Excellence.

Across two insightful episodes, Anju provides a masterclass in building practical, scalable, and secure enterprise AI. She offers a clear playbook for moving beyond trends to solve real-world problems and deliver measurable results.

The first discussion cuts through the hype to address the fundamental missteps many companies make. Anju argues that successful AI adoption doesn't start with technology; it starts with a deep understanding of a business problem. Chasing the latest model or shiny new tool without a clear purpose is a recipe for failure.

Instead of getting caught in endless debates over building a solution from scratch versus buying an off-the-shelf product, the focus should be on the desired outcome. Anju reframes the "build vs. buy" dilemma, suggesting a more nuanced approach that prioritizes flexibility and business alignment.

She also demystifies agentic AI, explaining that it's not a futuristic threat but a practical tool for automating complex workflows when governed correctly. The key is to ground every AI initiative in metrics that truly matter--user adoption, productivity gains, and business impact.

Key Takeaways:

• Problem-First Mindset: Don’t start with a technology and search for a problem. Identify a genuine business challenge and work backward to find the right AI-powered solution.
• Beyond Build vs. Buy: The most effective strategy often lies in the middle. Leveraging open-source tools and platforms can provide the customization of "build" with the speed of "buy."
• Agentic AI is Practical: When designed with human oversight and strong governance, agentic AI can automate complex, multi-step tasks to drive significant efficiency.
• Focus on Core Metrics: Success isn’t about the sophistication of your model. It’s measured by three key indicators: user adoption, tangible productivity improvements, and positive business outcomes. Everything else is just noise.

This episode serves as a critical reality check for any leader tasked with steering their company’s AI strategy. It provides a foundational framework for sidestepping common pitfalls and focusing resources where they can create the most value.

In the follow-up conversation, Anju Mohan shares the "how" behind the "why." She details NetApp's remarkable journey in developing Net AI Chat, a secure, private AI platform that now serves nearly 10,000 users and has processed over a million chats. This success wasn't achieved with a massive team or unlimited budget. It was the result of a small, nimble team that embraced rapid learning and iterative development.

Anju reveals that many on her team had no prior AI experience. They succeeded by fostering a culture of continuous upskilling and a shared commitment to a clear vision. This agility allowed them to build and deploy highly customized, department-specific bots for functions like HR, finance, and legal, each tailored to unique workflows and data sources.

A core principle of their success was an unwavering focus on data quality and security. "What you put in is what you’re going to get out," Anju emphasizes, highlighting the importance of a human-in-the-loop system and robust data governance from day one. Their story is a powerful testament that enterprise-grade AI is achievable without a colossal investment, provided the approach is smart, secure, and human-centric.

Key Takeaways:

• Small Teams, Big Impact: A small, motivated team with a clear vision can outperform larger, slower-moving groups. Agility and a passion for learning are more valuable than a long list of credentials.
• Rapid Upskilling is Possible: Team members can acquire new AI skills on the fly when the project provides a compelling purpose and a supportive environment.
• Data Quality is Everything: The performance and reliability of any AI system depend entirely on the quality and governance of the underlying data. A "human-in-the-loop" approach ensures continuous improvement and trust.
• Customize for Impact: Building department-specific bots that integrate with existing workflows and data ensures high adoption and solves concrete problems for different business units.
• Iterate and Integrate: Start with a minimum viable product and continuously integrate new models and capabilities based on user feedback and evolving business needs.

This episode offers a practical blueprint for execution, demonstrating how to translate strategy into a successful, scalable, and secure AI platform that employees love to use.

Together, these two episodes provide a comprehensive guide for any enterprise leader looking to make AI work. The lessons from Anju Mohan's experience distill into a clear, four-part playbook:

1. Problem-First: Anchor every initiative in a specific business need.
2. Human-in-the-Loop: Design systems with human oversight to ensure quality, build trust, and enable continuous learning.
3. Governance-by-Design: Prioritize data quality, privacy, and security from the very beginning.
4. Iterative Delivery: Start small, prove value quickly, and scale based on what works.

By shifting the focus from chasing trends to solving problems, organizations can join the 5% of companies that successfully turn AI hype into a powerful engine for innovation and growth. Learn how to streamline hybrid enterprise AI workflows with NetApp.