How to Build an AI-Driven Enterprise: Moving Beyond Siloed Projects

The AI era is officially here, but for many organizations, the promise of transformation remains just out of reach. While pilots and prototypes are popping up in every department, a staggering 70% to 90% of AI projects fail to ever get off the ground.

To bridge this "production gap," we are thrilled to announce our new 5-part podcast series, “The AI Advantage: Navigating Risk, Reward, and Real-World Deployment”, created in collaboration with CIO.com. Our goal is to provide a strategic blueprint for moving past siloed experiments to create scalable, integrated business models that deliver measurable ROI.

In our inaugural episode, "Beyond the Singular Project: Building an AI-Driven Enterprise," host Barbara Call, Global Director of Content Strategy at CIO.com, sits down with some of the most respected minds in technology and business to discuss why the one-off AI projects are failing and so difficult to scale across the organization.

Our panel brings decades of deep technical expertise and front-line leadership experience:

• Dr. Abel Sanchez: Research Scientist at MIT with over 20 years of experience in applied computation and organizational technology implementation.
• Professor John Williams: Professor of Information Engineering at MIT and a pioneer at the Auto ID Lab, where the Internet of Things (IoT) was invented.
• Keith Schlosser: A veteran multi-time CIO with 35 years of experience leading transformations in the insurance industry.

The 4-step framework for scalable enterprise AI adoption

1. AI is an operating model, not a standalone tool

The panelists agree that the biggest mistake companies make is treating AI as a "singular problem" solver. Dr. Abel Sanchez notes that just as the cloud era fundamentally changed IT spend and infrastructure, AI represents a "fundamental shift in your company's operating model". “This is not a moderate technology. This is something we haven’t seen before—it’s at the level of the Industrial Revolution.” Success requires a vision that integrates AI across the entire business rather than pigeonholing it into siloed projects.

2. The power of “human + machine”

A recurring theme of the discussion was the "human part" of the AI equation. Dr. Sanchez shares a compelling analogy from the chess world: while machines can beat grandmasters, an amateur with a laptop (human + machine) can beat both.

• The power user: We are entering an era of "self-serve IT," where non-technical "power users" can build agents to simplify their workflows and summarize massive data sets that previously crashed standard systems.
• The strategic CIO: CIOs must pivot from being support-focused to strategy-focused, educating their teams and the C-suite to overcome "imposter syndrome" regarding AI.

3. Data can be the "make or break" of AI technology success

Every speaker emphasized that data is the foundation of any successful AI strategy. Companies often realize too late that they lack the "good data" needed to answer their most critical business questions. As Keith Schlosser points out, identifying your data gaps during early pilots is crucial for preparing for an "enterprise-class, scalable solution".

4. Emulate the "ambidextrous" organization

Professor John Williams argues that the primary challenge of AI adoption isn't just the technology itself, but the internal friction between different organizational "zones." Many companies successfully launch a pilot in one department, only to hit a wall when they try to scale. They encounter the next department—one that is inherently more traditional, risk-averse, and "stuck in the middle."

To survive this, Williams suggests companies must become ambidextrous:

• The innovation zone: One part of the organization must run at high speed—experimenting, breaking things, and failing fast to master AI.
• The core zone: The other part maintains the traditional business, ensuring stability and managing risk.

The goal is to bridge these zones so that the "traditional" side doesn't stifle the "innovative" side before it can scale. As Williams notes, the luxury of a slow transition simply doesn't exist:

"This wave is traveling at something like five or six years... it's moving really fast. You can see the changes occurring every month." — Professor John Williams

Future-proofing your IT charter for the generative AI era

As discussed in the episode, the "DIY approach" to AI is often too expensive, too slow, and nearly impossible to scale. Vertesia offers a high-speed, low-code way to build and deploy agentic AI apps and agents at scale. By abstracting the complexity of infrastructure and security, Vertesia allows your team to focus on solving actual business problems rather than wrestling with the "plumbing" of AI.

Stay tuned for Episode 2, where we’ll explore how to bridge the communication gap between IT and the C-Suite to keep AI adoption from stalling.