Ramakrishnan Venkatasubramanian, CTO at Galent, leads AI-native transformation for global enterprises.
Enterprises are pouring billions into AI pilots, co-pilots and automation platforms.
Despite these investments, only a fraction are seeing measurable business impact. Both BCG and IBM independently found that only about 1 in 4 organizations can achieve ROI from their AI initiatives based on their current infrastructure or capabilities. The rest remain trapped in what many now call pilot purgatory.
Often, the problem isn’t AI itself. It’s whether organizations are solving the right problems, with the right integration environment.
The Problem: Fragmented AI Efforts
A marketing model predicting churn, a finance model forecasting spend, a supply chain model optimizing inventory. Enterprises today are not short on AI capability. They’re short on connection.
From data lakes to model libraries, innovation often thrives in silos isolated by business units, budgets or geography. Well-governed data and cutting-edge tools still sit apart if teams don’t share or collaborate across boundaries. This lack of integration can stem from culture, geography or historical growth patterns—the natural result of scale.
But unless enterprises bring these capabilities into a common kitchen, the AI potential remains undercooked. As Deloitte has pointed out, organizations need connected systems, strong governance and integrated data to move beyond isolated pilots into enterprise-scale value creation.
Integration: The New Enterprise Superpower
Integration is both technical and organizational. It isn’t just about linking APIs—it’s about aligning systems, processes and people around shared goals. It’s about turning fragmented gains into compounded outcomes.
Think of it as connected intelligence: an enterprise nervous system that orchestrates action across departments and drives unified outcomes.
Critically, integration isn’t only a systems challenge. It’s also a people challenge.
AI adoption demands a cultural shift. Teams must learn to collaborate differently, moving from siloed automation to shared augmentation. This shift requires deliberate change management:
• Redefine how problems are prioritized. Instead of isolated use cases, the focus should be on enterprise outcomes. When teams co-own the problem, they co-own the solution—and that’s where scale begins.
• Encourage cross-functional experimentation. The best ideas emerge where business, data and engineering collide. Small, empowered pods can test, learn and scale fast. Shared innovation zones help ideas travel further.
• Align incentives across business units. If success is measured in silos, collaboration dies in silos. Shift KPIs to shared metrics like productivity gains or customer impact. When rewards reflect collective success, integration becomes natural.
When integration is paired with cultural alignment, enterprises move from “doing AI” to “thinking with AI.”
The Solution: A Platform-Driven Approach
Becoming AI-native doesn’t happen organically. It needs structure. Here is a blueprint that connects vision to execution:
1. Assess readiness before scaling. Before deploying a platform or tool set, organizations need to audit their data, workflows and decision systems. Where are the silos? Which processes already generate value? A readiness assessment helps pinpoint integration pain points and prioritize AI investments where they’ll drive measurable impact.
2. Build shared governance and ownership. Integration fails when AI sits within a single department. Establishing a cross-functional AI council—spanning IT, business and operations—can help to ensure alignment on data standards, ethical guardrails and outcome metrics. Governance is not bureaucracy; it’s the connective tissue that keeps scaling sustainable.
3. Integrate continuously. Enterprises that treat integration as an ongoing discipline (through feedback loops, versioning and AI operations) sustain momentum and avoid “pilot fatigue.”
4. Adopt a modular, platform-driven foundation. AI platforms can act as flexible scaffolds—connecting data, models and workflows across systems. They’re not meant to replace enterprise applications but to unify them. But they should also be agnostic, flexible and composable, as experimentation requires allowing teams to plug in new models, agents or APIs as the ecosystem evolves.
When done right, a platform-driven approach becomes a living architecture that amplifies what already exists and keeps the enterprise adaptive as technology evolves.
What’s Next: AI-Native Enterprises
The road ahead belongs to AI-native enterprises who treat integration as a strategy, not an afterthought. Their edge will come from how seamlessly intelligence flows across teams, platforms and decisions. They’ll turn fragmented data into shared insight, pilots into platforms and collaboration into a competitive advantage.
Because in this new era, the question isn’t whether you’re using AI. It’s whether your enterprise is built to work with it.
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