Ready for Agentic AI? Why Scalability Demands a New Foundation
Scaling Agentic AI with Lessons from McKinsey.
Shen Pandi
September 17, 2025
Is Your AI Working for You, or Are You Working for Your AI?
You’ve invested in AI, but the results feel… static. You get answers, not actions. Reports, not results. You’re left managing the technology, not leveraging it to manage your business.
What if your AI could actually do the work? Not just suggest a strategy, but execute it. Not just analyze a market, but draft the campaign and deploy it.
This isn't a future fantasy. It’s called Agentic AI, and it’s reshaping how leading companies operate. But making the leap from promising pilots to enterprise-wide transformation requires a new playbook and a fundamentally new foundation.
The lessons from the front lines are in, and they all point to one undeniable truth: to win with autonomous AI, you must first master the workflow.
Building Your Agentic AI Future: Three Scalable Lessons from the Front Lines
Recent insights from McKinsey & Company, particularly in their article, provide a crucial reality check from the trenches. The authors highlight that success isn't about finding the most powerful model, but about designing intelligent systems.
The key lessons? Start with a clear, valuable problem. Break work into clear steps. Expect the unexpected and build in safeguards.
And most importantly, think in workflows, not one-off prompts.
This is the core of the challenge. How do you operationalize these lessons across an entire organization, with its complex data silos and stringent compliance needs?
The answer lies in moving from a project-based mindset to a platform-based one.
A specialized tool like DataManagement.AI becomes not just beneficial but essential for implementation at scale.
Lesson 1: Anchor on a High-Value, Specific Problem
McKinsey's real estate experts Katy McLaughlin and Jules Barker state that the breakthrough starts with a precise question.
Instead of "optimize our portfolio," think "create an agent that continuously analyzes local permit data, zoning law changes, and construction material costs to identify the top three value-add acquisition opportunities in the Southeast region this quarter."
"The real estate industry sits on a treasure trove of data but has often struggled to ask the right questions that unlock its value. The key is to move from generic analytics to precise, problem-specific inquiries."
To build this, your agents need instant, governed access to disparate data sources, county databases, financial records, and supply chain APIs. A scattered data landscape dooms agentic AI to failure.
This moves you beyond a paradigm where data leaders are solving the wrong problem.
For decades, the obsession has been: “How do we move data faster? Store it better? Transform it cleaner?” Billions have been spent on ETL pipelines, data warehouses, data lakes, and now lakehouses, endlessly moving data from one box to another.
But here’s the truth: enterprises solely solving the data movement problem are backward-looking.
They are still fixing leaks with plaster, while you have a unique opportunity to solve business problems using data in a new way. I.e., Fix the business context problem by building a Context Cloud.
Because…..
AI doesn’t fail from a lack of data.
AI fails from a lack of context.
That’s why hallucinations happen. That’s why 80% of AI pilots stall. That’s why CFOs are questioning every dollar spent. The new question for you isn’t “Where should data live?” It’s “How do we make data useful for AI to solve business problems?”
That’s where the Context Cloud comes in. Powered by Chain-of-Data, it eliminates the endless data shuffling and gives your AI agents the context layer they need to deliver ROI, with zero ETL, zero copy, and zero waste.
The future of data isn’t in warehouses or lakes; it’s in the Context Cloud.
A powerhouse like DataManagement.AI provides the critical foundation: a unified, clean, and secure data layer that your AI agents can reliably act upon, turning a high-value idea into an executable workflow.
Learn how you can transform your data into actionable intelligence for your AI ambitions and power your Context Cloud.
It’s time to shift from moving data to solving business problems with context. The future of data isn’t in warehouses or lakes; it’s in the Context Cloud.
This is the strategic advantage a dedicated platform provides. DataManagement.AI is engineered specifically for this new paradigm.
Lesson 2: Architecting the Workflow is the Real Work
As Mark Birkhead, Chief Data Officer at JPMorganChase, astutely noted in his McKinsey conversation, the focus is shifting "from model-centric to data-centric AI." This is amplified tenfold with agents.
The magic isn't the LLM; it's the orchestration around it.
Imagine a workflow for dynamic pricing in real estate:
Agent 1 gathers real-time data: competitor listings, mortgage rate feeds, and local event calendars.
Agent 2 analyzes this data against historical performance and recommends a pricing strategy.
Agent 3 drafts the new listing description and marketing copy based on that strategy.
A human-in-the-loop approves the final changes.
Agent 4 executes the update across all relevant property portals.
Manually coding and connecting these steps for every use case is unsustainable. You need a platform that allows you to visually design, test, and deploy these complex, multi-agent workflows without getting bogged down in endless integration code.
“We’re shifting from model-centric AI to data-centric AI. The focus is less on building the perfect model and more on ensuring we have high-quality, well-governed data for the models to use.”
This orchestration layer is what allows you to implement McKinsey's lessons on reliability and safety at scale, ensuring each agent performs its specific task within guardrails before handing off to the next.
Lesson 3: Scale Requires Governance and Guardrails
The McKinsey consistently underscores that agentic AI introduces new risks. An agent with the ability to act autonomously can amplify errors just as quickly as it can amplify value.
Birkhead’s point on the need for robust data governance is paramount here. You cannot have agents making decisions on stale, incorrect, or non-compliant data.
“As we use data for more advanced AI and analytics, governance becomes even more important. We need to ensure responsible use of data and AI, which includes transparency, fairness, and ethical considerations.”
Scaling agentic AI means building governance directly into the fabric of your operations.
This includes:
Automated Data Quality Checks: Ensuring agents only work with trusted data.
Permissioned Access: An agent working on an HR compliance workflow should have no access to financial trading data.
Audit Trails: A complete log of every action taken by every agent, for every workflow, to ensure transparency and compliance.
Trying to bolt this on after the fact is a recipe for disaster. A purpose-built platform bakes these guardrails in from the start, providing the security and governance framework that allows you to deploy agentic workflows with confidence across the enterprise.
This is the only way to truly operationalize the lessons learned from early initiators.
Your Strategic Advantage: The Platform Approach
The collective wisdom from McKinsey’s experts and practitioners is clear: the winner in the age of AI won't be the company with the best individual model, but the one with the most robust and scalable AI operational architecture.
You need a system designed for the entire lifecycle of agentic work, from data foundation to workflow orchestration to governance.
This is the strategic advantage a dedicated platform provides. DataManagement.AI is engineered specifically for this new paradigm.
It integrates the critical pillars of data management, workflow automation, and AI governance into a single environment, enabling your teams to build, manage, and scale agentic solutions that are both powerful and trustworthy.
By adopting a platform approach, you stop building isolated AI projects and start building an integrated AI capability.
You empower your domain experts, in real estate, finance, marketing, or logistics, to design solutions based on their deep knowledge, using a framework that handles the underlying complexity.
The future of work is agentic. The lessons from the front lines are in. The question is no longer if you will implement this technology, but how.
The most effective, secure, and scalable way to bring these lessons to life across your entire organization is to build on a foundation designed for the task. Your journey to building a truly intelligent enterprise begins with architecting the workflows that power it.
Warm regards,
Shen and Team