Gone are the days when consulting engagements were only defined by fat Gantt charts, weeks of data collection, and teams of analysts working through slide decks. I have lived through that phase of consulting and, in many ways, helped operate within it. Today, Artificial Intelligence (AI) is restructuring the consulting industry in ways that extend well beyond digitalization and automation. 

What we are witnessing is deeper. AI is transforming how firms are structured, how projects are executed, how outcomes are delivered, and how value is measured. A profession historically tied to billable hours, layered hierarchies, and sequential analysis is now being engineered for speed, precision, and tangible business impact. 

This shift mirrors a broader macroeconomic reality: Governments and industry bodies increasingly view AI as a core lever of productivity. NITI Aayog estimates that AI adoption could add $500–600 billion to India’s GDP by 2035 through efficiency gains, operational accuracy, and faster decision cycles across sectors. The implication is clear: consulting must evolve alongside the systems it seeks to improve.

From Billable-Hours to Value-Based Pricing Model 

Through decades, consulting economics were straightforward: more hours billed implied more value delivered. AI disrupts that equation. When data ingestion, benchmarking, scenario modeling, and variance analysis can occur in minutes, time-based pricing becomes misaligned with the outcomes clients seek. 

In our own engagements, we are increasingly seeing value shift from effort expended to impact delivered. Clients don’t ask how long something takes; they ask what changes because it was done.

This is reflected in how transformation engagements are being structured. Instead of open-ended timelines, expectations centre on defined outcomes, faster close cycles, improved forecasting accuracy, reduced compliance risk, or accelerated digital rollouts. In practical terms, this would mean moving from billing $50,000 for 400 hours of tax analysis to billing the same amount for identifying and delivering $1 million in tax savings.  

That subtle difference is powerful. Value is tied to the business advantage delivered, not time consumed.

The Decline of Layered Consulting 

The traditional consulting pyramid, with many junior analysts supporting a few senior experts, is flattening. Much of the work that justified that structure has been automated. Data cleansing, reconciliation, benchmarking, and initial diagnostics are now largely machine-led.

What remains distinctly human is interpretation, prioritization, and judgment in execution. 

Consider an ERP optimization. Previously, large teams spent months mapping processes and testing configurations.  AI can now analyze transactional data, identify configuration gaps, and simulate process changes upfront. Smaller pods combining experienced practitioners with AI-enabled tools can bring focus on design decisions, stakeholder alignment, and value creation. 

For clients, this means faster stabilization and earlier benefits realization. For consulting firms, it implies higher-impact delivery with clearer ownership. And for many of us leading these engagements, it reinforces a shift we have long anticipated: depth over scale, insight over manpower.

From Weeks of Analysis to Real-Time Decision Support

Across disciplines, AI is accelerating the generation and implementation of insights. Continuous data ingestion, real-time dashboards, and predictive models are replacing linear analysis cycles that once took weeks. Consulting is moving from episodic assessments to ongoing decision support. 

Static presentations give way to living intelligence systems embedded into workflows.  

This is particularly consequential in financial transformation, risk, and compliance domains where timing matters as much as accuracy. In finance transformation engagements, we are increasingly seeing AI-driven maturity assessments continuously evaluate forecasting reliability and control effectiveness using live data. Senior advisors then interpret these insights to design implementation roadmaps.

The result is earlier visibility, faster course correction, and sustained relevance beyond project closure.

How AI Is Raising the Bar on Consulting Capability

A recurring question I hear is whether AI replaces junior consultants.  My experience suggests otherwise. It reshapes expectations, but does not remove relevance. 

Technology has always raised the bar, and this moment is no different. Tasks that once consumed disproportionate time are now automated. Entry-level consultants must focus on framing problems, interpreting insights, and engaging clients meaningfully. 

Interestingly, AI also accelerates learning. Instant access to benchmarks, patterns, and scenarios allows younger professionals to contribute sooner and more consistently. Quality becomes less dependent on tenure and more on how effectively tools are integrated into delivery. 

For clients, this reduces the risk of paying for learning curves. For firms, this strengthens capability development without expanding headcount. And for individuals, it encourages early ownership, something I see as a positive evolution.

The Human Core of Consulting

As AI embeds itself deeper into consulting, it becomes equally important to define its limits. Machines analyze patterns and generate scenarios, but transformation success remains human-led.

Trust remains fundamentally personal. Senior leaders place confidence in advisors who understand context, navigate internal dynamics, and stand accountable for outcomes. Algorithms cannot build credibility in boardrooms or regulatory conversations.

Judgment also resists automation. Consulting rarely operates with neat data boundaries. Trade-offs between speed and control, growth and risk, or competitiveness and compliance demand contextual understanding and experience.

Finally, ethics and empathy require human stewardship. AI reflects its inputs and design choices. Responsible consulting requires professionals who question assumptions, validate outputs, and ensure recommendations remain fair and defensible.

In an AI-backed model, the role of people evolves from information processors to custodians of judgment, trust, and responsibility.

Consulting in the AI Era

The future of consulting is neither purely technological nor nostalgically human. It is AI-native and human-led. 

As service delivery models evolve, organisations should evaluate consulting partners on substance: the maturity of AI integration, governance discipline, and the depth of senior engagement.  Efficient relationships balance machine-enabled precision with human accountability for outcomes.

From my vantage point, AI has not diminished consulting’s relevance; it has clarified it. It removes mechanical effort and amplifies what truly matters: understanding context, shaping decisions, and delivering results that endure.

The tools may change, but responsibility for value creation continues to rest with people.

Deepak Narayanan