Anjan Lahiri, the founder & CEO of Navikenz, an AI consulting startup, explained that AI companies are fundamentally horizontal technology providers, meaning their relationship with enterprises will likely resemble the role that Microsoft, Google, or other infrastructure providers have played. They provide the foundation—but building a usable system requires designing the structure, defining its purpose, and shaping how it will be used. The translation of foundational technology into business value is where consulting firms operate.
“The narrative that AI would make consulting redundant was always an overstatement”, according to Gaurav Vasu, Founder & CEO, UnearthInsight. “There are various partnerships between AI and consulting players — Anthropic has tied up with Deloitte, BCG, and McKinsey, while OpenAI has partnered with Bain, Accenture, and PwC. AI firms seek consulting partnerships because they need the institutional knowledge, client relationships, and sectoral expertise these firms have built over decades.”
The primary fear was that LLMs would automate core deliverables like data cleaning, content generation, and market research, rendering large portions of the billable workforce obsolete. However, enterprises are currently stalled by AI projects stuck in pilots, where AI projects fail to scale due to fragmented data silos, legacy infrastructure, inadequate governance, and a lack of clear ROI.
Arun Chandrasekaran, Distinguished VP Analyst at Gartner, observed that consulting firms bridge this gap by providing the “last-mile” integration, handling the change management and process redesign that AI models themselves can’t. These partnerships accelerate deployment by transforming raw model access into customized, governed workflows that align with specific industry regulations and business goals.
“There is a gap between AI capability and realized value. Despite rapid advances in model performance and significant investment, many organizations struggle to translate AI into measurable business outcomes. This is consistent with patterns observed across AI applications, where the challenge lies in contextual integration, scaling, and production deployment rather than in core algorithmic capability,” Kashyap Kompella, AI industry analyst, echoed.
Issues like reliability, explainability, and regulatory compliance continue to limit deployment in high-stakes environments. So, AI monetisation is constrained by a combination of execution capacity and technological maturity. Meanwhile, partnerships between AI vendors and consulting firms can bridge the gap between experimentation and scaled deployment.
“Consulting firms have become the primary ‘feet on the ground’ implementation arm for AI providers, acting as intermediaries for risk-averse CIOs. While this helps with AI scaling, it creates a wall that could prevent AI firms from developing deep, direct operational knowledge with their enterprise clients. However, for companies like OpenAI and Anthropic, the trade-off is necessary to bypass the slow, complex enterprise sales cycles, as they aren’t yet fully equipped,” Chandrasekaran said.
Garry Singh, President, IIRIS Consulting, noted that consulting is meant to mutate to advise in new environments. This evolution has happened over many eras, and will happen even with AI, maybe with more vigor. A consultant’s ability to understand specific client needs, coupled with solution-creating ability, makes a consultant a high-value entity. Consulting firms are reviewing new AI availability and even designing new age needs to remain a high-value entity.
Published on March 22, 2026