Amit Shivpuja is the director of data product and AI enablement at Walmart.
In a previous article, I argued that organizations cannot build advanced AI capabilities on foundations designed for a different era. Venice is beautiful and iconic, but it cannot support skyscrapers—not because skyscrapers are impossible, but because the underlying structure was never meant to bear that kind of load.
AI magnifies patterns—even organizational ones. When foundations are strong, AI amplifies strength. When foundations are weak, AI amplifies fragility. From what I’ve seen in the industry, countless businesses are adopting AI, and the stakes are rising quickly. According to McKinsey, 40% of large organizations are scaling AI agents. Even still, according to Deloitte, many organizations still need 12 months or more to see resolve or observe adoption and ROI challenges when it comes to agentic AI. This is because many AI initiatives still fail to scale, largely due to organizational barriers rather than technical limitations.
The good news? Readiness is buildable. In this article, I’ll lay out my AI-readiness playbook—a roadmap for organizations that want to build skyscrapers responsibly and at scale.
1. Start With Honesty: Acknowledge The Readiness Gap
AI reflects an organization’s existing patterns. Strong collaboration can become stronger. Clear workflows can become more efficient. Disciplined data practices can translate directly into reliable AI outputs.
This step is not about dwelling on deficiencies. It’s about recognizing strengths and designing AI systems that amplify them. Organizations that begin with honesty can gain clarity on where AI will thrive and where it will create compounding benefits. That clarity can then become your first structural advantage.
2. Redefine Success Beyond Technology
Organizations redefine success around capability, not tools. This shift aligns AI with how the business actually operates.
A retailer that focuses on alignment rather than model selection can use AI to unify forecasting across merchandising, supply chain and store operations. A financial institution that prioritizes workflow clarity can use AI to streamline credit decisioning, reducing cycle time and improving consistency.
By shifting the definition of success from “deploying technology” to “strengthening capability,” organizations can unlock value through coherence and shared outcomes.
3. Build The Data Foundation
A strong data foundation is one of the most powerful value creators in the enterprise. When data is governed, consistent and contextualized, AI can, in my experience, become dramatically more effective.
A healthcare system with unified patient data can identify care gaps earlier. A logistics company with standardized delivery data can optimize routes. A manufacturer with consistent equipment data can predict failures before they occur.
This matters because, from what I’ve seen in the industry, data quality, fragmentation and governance are some of the top barriers to scaling AI. A strong data foundation can transform AI from an experiment into an operational advantage.
4. Curate Context: The Meaning Layer
Curating context—policies, relationships, definitions and business logic—can unlock some of the highest‑value AI outcomes. When meaning is made explicit, AI can operate with precision rather than approximation.
A bank that documents its credit policies can build AI assistants that help loan officers make faster, more consistent decisions. A manufacturer that captures operator knowledge can build AI systems that diagnose equipment issues with the same nuance as its most experienced technicians.
Organizations that want to implement successful AI pilots must invest heavily in contextual modeling and workflow integration. Context can elevate AI from a tool to a capability.
5. Adopt Platform Thinking
Platform thinking can transform AI from isolated wins into durable capability. When organizations build shared foundations—reusable components, governed patterns, centralized stewardship—every new use case can strengthen the whole.
A global retailer that builds a unified AI platform for forecasting, inventory optimization and pricing can gain compounding value. A healthcare network with a centralized clinical AI platform can deploy new models faster—with higher trust and lower risk.
According to Gartner, half of GenAI projects fail due to “poor data quality, inadequate risk controls, escalating costs or unclear business value.” Platform thinking can address this.
6. Invest In Literacy And Culture
AI literacy and culture are hard drivers of value. When employees understand how AI works and how decisions are made, adoption can accelerate and outcomes can improve.
A logistics company that invests in literacy can help drivers understand routing algorithms, increasing trust and efficiency. A financial institution that reframes AI as a partner can help analysts use GenAI assistants to accelerate research and reduce manual effort.
Culture determines trust. Literacy determines effectiveness. Together, they can form the human operating system of AI.
7. Establish Ownership And Accountability
Clear ownership can unlock clarity, trust and measurable outcomes. When every AI initiative has a defined business owner, data steward and governance partner, alignment can accelerate.
A telecom company that assigns ownership for customer retention can use AI signals to trigger coordinated interventions across teams. A hospital that assigns clinical ownership for AI triage can ensure thresholds and workflows evolve responsibly.
Ownership creates accountability. Accountability creates trust. Trust creates adoption.
8. Start Now: Readiness Compounds
If you want to succeed with AI, you must start deliberately. Readiness compounds. Small improvements in data discipline, literacy, context curation and workflow integration can accumulate into a structural advantage.
The skyscraper is not built in a day—but every day invested in readiness can strengthen your foundation.
This Is How You Build The Skyscraper
Every organization carries data debt—inconsistent definitions, fragmented systems and undocumented logic. As AI becomes embedded in workflows, a new form of debt is emerging: context debt. This is the gap between what the organization knows and what it has made explicit enough for AI to use responsibly.
Enterprises also face an AI-readiness gap—a widening gap between the pace of AI innovation and the pace at which organizations can absorb, govern and operationalize it.
The way forward is to focus on the common denominators that do not change: Data, governance and culture. These are non‑negotiable, foundational and strategic. If you use this playbook as your blueprint, your organization can prepare for what AI will demand next. This is how you can build a skyscraper.
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