Sponsored Content: The content below is sponsored byMarlabs and does not represent the viewpoint of Inside AI News.Enterprise AI has an execution problem. McKinsey reports that 88% of organizations now deploy AI in at least one business function. Yet PwC’s
__29th Global CEO Survey__Marlabs’ research team documented this in its 2026 Enterprise AI Adoption Playbook, which synthesized 10 major surveys representing more than 30,000 leaders in over 100 countries. Marlabs decided the most credible response to that gap was to close it inside the company first. To credibly advise Fortune 500 clients on AI transformation, the team needed to live one themselves. Realizing the value of AI doesn’t come from one off projects or individual usage, it comes from integrating it throughout your organization and processes.
Microsoft recently documented that journey in one of its customer story, * "Marlabs becomes an AI-first firm with Copilot 365, GitHub Copilot and Dynamics 365*,
” describing the company as “
leading from experience by becoming AI-first itself.” The numbers in it were earned in Marlabs’ own workflows, and they are the basis for the company’s confidence in the playbook it hands to clients.
Marlabs Started With HR and Built Outward From There #
Marlabs’ first major win was deliberately unglamorous: HR. Two years ago, the company used Microsoft Copilot Studio to build a virtual HR agent that unified multiple HR systems into a single conversational experience.
Employees now apply for leave, explore policies, and resolve questions in seconds rather than tickets. Response times dropped by more than 60%, and the previous help desk system was eliminated in favor of the Copilot agent that now handles 80% of queries.
Marlabs built outward from there. An onboarding agent now saves three days per new hire. Additional agents handle approvals, contract reviews, and proposal buildouts, and surface financial and operational data on demand. The pattern matters as much as the tools: a short list of focused workflows tied to measurable outcomes.
BCG’s research found the same pattern across the market. Leading firms concentrate on roughly 3.5 AI use cases on average, while lagging firms spread across more than six and earn about half the ROI. Focus is not a constraint. It is the strategy.
GitHub Copilot Gave Marlabs a Developers Boost #
Software engineering is the core of Marlabs’ business, so GitHub Copilot was a strategic investment rather than a perk. GitHub Copilot has become central to Marlabs’ engineering lifecycle. Developers now work 20% faster, and the quality uplift is driving entirely new solution offerings for clients. That experience has since become a client-facing service in its own right.
And for clients in regulated industries such as financial services and life sciences, Copilot’s built-in security and data loss prevention capabilities have been a notable differentiator in those engagements.
Why Marlabs Trains Across All Major AI Model Families? #
Tools alone do not close the value gap. People do. Across the surveys in Marlabs’ playbook, 62% of executives cite talent and AI skills shortages as the top barrier to enterprise AI value, ahead of data quality, governance, and security.
Marlabs made training the backbone of the transformation. Employees and engineers complete structured AI training that spans all of the major model families, not a single vendor’s stack, covering prompt design, agent development, governance, and secure deployment.
That breadth matters more every quarter. According to a 16z’s third annual CIO survey, 81% of Global 2000 firms now use three or more model families in test or production, up from 68% a year earlier. An engineer fluent in only one model is already behind the market.
The investment shows up in adoption. After workshops co-delivered with Microsoft India, Microsoft 365 Copilot usage accelerated rapidly across the company, producing a 56% productivity gain across the organization. Accenture’s 2026 research** points **to a 24-point expectation gap between leaders and employees on AI-driven change. Sustained, hands-on training is how Marlabs keeps that gap from opening internally.
Marlabs Bakes Cost Saving Into Every AI-Powered Proposal #
Efficiency we keep is a cost saving. Efficiency we share is a business model. Marlabs now includes a 20% cost savings in proposals for work that uses AI, and encourages clients to reinvest those gains in their next strategic AI initiatives. Marlabs’ internal agent portfolio also seeded AgilityAI, the company’s full-lifecycle enterprise AI transformation suite built on proven, reusable agentic accelerators and organized around a simple operating frame: Align, Build, Control.
Next: A Single AI Interface Connecting Every Agent and Data Source #
Marlabs is modernizing its delivery engine with Microsoft Dynamics 365 Project Operations, creating a unified, AI-powered system of record for project timelines, resources, financials, and performance. With Dynamics 365 Project Operations strengthened by Copilot, the company will have a more unified, AI-powered system of record that centralizes delivery data and surfaces deeper operational insights.
The longer-term vision is what Raghunathan calls "one AI as the UI" — or, as Microsoft's story put it, "a vision for a single conversational interface that connects multiple agents," where specialized agents work together to surface data and insight from across the company.
Marlabs’ 2026 playbook found that 79% of enterprises report significant challenges moving AI initiatives into production and measurable ROI. The firms pulling ahead are not the ones with the most pilots. They are the ones that redesign work, integrate AI into the systems where work happens, govern it, and measure it against the P&L. Marlabs knows, because it did it to itself first.
When Marlabs sits down with a client, the company is not presenting a theory of AI transformation. It is presenting its own operating history.
Sriraman Raghunathan is Senior Vice President of Global AI Transformation at Marlabs, a New York-based AI consulting and transformation partner that helps Fortune 500 organizations operationalize AI and deliver sustained, measurable value across industries.