What Has Changed in Consulting Recently

In 2023, Infosys packaged generative AI into Infosys Topaz - not as a side tool, but as a service layer across consulting, engineering, operations and enterprise transformation. That one move captures the new consulting reality: clients no longer pay only for smart advice; they expect speed, implementation, data, AI and measurable business impact.

Big Picture: The Consulting Model Has Moved Downstream

The simplest way to understand what has changed is this: consulting has moved from “tell the client what to do” toward “help the client make it happen.” If you need a refresher on the basic business model first, revise what management consulting actually is before going deeper.

Older consulting was often remembered as boardroom strategy plus polished slides. That still exists, but the centre of gravity has shifted. Clients now ask: Can you redesign the process? Can you build the dashboard? Can you manage adoption? Can you show value in weeks, not months?

Core Explanation: The Six Recent Shifts You Must Know

1. From Strategy Advice to Execution Ownership

Clients still buy strategic clarity, but they increasingly expect consultants to stay through implementation. A market-entry recommendation is incomplete if the consultant cannot help with operating model, channel setup, technology stack, capability building and tracking.

Why it changed: leadership teams are under pressure to show visible business impact. A 100-slide strategy deck that never gets executed is now much harder to defend.

2. From Pure People Leverage to People Plus AI Leverage

The classic consulting pyramid depended on senior partners selling trust, managers structuring work, and analysts doing research-heavy execution. AI does not remove that structure, but it changes the work inside each layer.

Research summaries, market scans, transcript analysis, code generation, slide drafting and knowledge retrieval are increasingly AI-assisted. But the valuable human work remains: framing the right question, checking assumptions, handling messy politics, making trade-offs and persuading stakeholders.

3. From Generalist Decks to Specialist, Data-Rich Solutions

A consultant can no longer survive on structure alone. Many engagements now require domain depth plus technical fluency - for example, pricing analytics in retail, cloud migration in banking, route optimisation in logistics, or working-capital analytics in manufacturing.

This is why consulting teams increasingly mix profiles: strategy consultants, data scientists, product managers, engineers, designers, industry experts and change managers.

4. From Time-and-Material Comfort to Value Scrutiny

Clients are more careful about consulting spend. They question team composition, senior involvement, deliverables, expected savings, implementation responsibility and whether work can be done by internal teams or lower-cost providers.

To understand why this matters for firms, connect it to consulting firm economics: leverage, rates and utilisation. A change in how clients buy directly changes how firms staff, price and protect margins.

5. From Static Projects to Continuous Transformation

Many consulting problems no longer end neatly at “final presentation Friday.” Digital transformation, analytics adoption, operating model redesign and cost transformation require multiple waves: diagnose, build, test, train, refine and scale.

6. From Confidential Advice to Higher Risk Governance

Consultants now work with sensitive client data, AI models, cyber systems, cloud platforms, employee data and regulatory constraints. That makes ethics, confidentiality, conflicts of interest and data governance more central than before.

A good consultant must ask: What data can we use? Who owns the model output? Are recommendations explainable? Could the same firm advise two competing clients in a way that creates conflict?

Definitions You Can Say in One Breath

The New Consulting Map: What Changed Where

Not every change affects every consulting firm equally. A strategy boutique, a Big Four advisory practice, an IT services firm and a deal advisory team will experience the shift differently. But most recent changes can be mapped on two questions: Is the work more human-judgment-heavy or more technology-enabled? Is the client buying advice or execution?

This is why “consulting” now covers a wider spectrum: CEO agenda work, cost transformation, M&A support, digital product building, cloud migration, analytics implementation, process automation and organisation change.

Case Study: Infosys Topaz and the Productisation of Consulting

Situation: Large enterprises wanted to experiment with generative AI, but most did not only need a chatbot demo. They needed use-case selection, data readiness, governance, integration with existing systems, change management and measurable value.

The move: Infosys positioned Infosys Topaz as an AI-first set of services, solutions and platforms. The strategic logic was not simply “use AI.” The primary driver was to productise repeatable AI-enabled consulting capabilities. Supporting drivers included Infosys’s enterprise client base, delivery talent, cloud and data engineering capability, and its ability to combine advisory with implementation.

The lesson: This is what has changed in consulting. Firms are not just selling expertise hour by hour; they are packaging methods, assets, accelerators and platforms so that engagements can be delivered faster and more consistently.

So what: The real consulting change is not that AI tools exist. It is that consulting firms are redesigning their offerings, delivery model and economics around reusable technology-enabled expertise.

How AI Changes What Has Changed in Consulting Recently

AI is not just another trend inside consulting. It amplifies almost every other recent shift because it changes the cost, speed and structure of consulting work.

Practical student workflow: Use ChatGPT or Claude to compare two consulting firm service pages - for example, AI transformation and operations transformation - and ask: “What problem is each firm really selling against: cost, growth, risk, speed or capability?” Then refine the answer yourself into a 60-second interview response.

Interview Relevance

Common Mistake