Consulting Goals in Age of AI: What MBA Applicants Must Know

How is AI changing consulting, and what should MBA applicants do about it? This guide on consulting in the age of AI covers six things to understand before you apply: how clients pay, how healthy a firm really is, why industry depth matters, how to choose an MBA program, how to challenge AI output and what AI costs a business. Together they describe the AI skills for consultants that firms are starting to look for, and they shape how you build a consulting career after MBA in the AI era.

Consulting is changing, and the skills that made someone a good consulting candidate five years ago are not necessarily enough today.

AI is taking over more of the research and analysis. Clients are becoming more focused on measurable outcomes rather than hours billed. Consulting firms are putting more money into technology, cybersecurity and industry-specific capabilities. You don’t need to become a consulting industry expert before applying to business school. But you should understand where the industry is going and what that means for the skills you need to build.

Everything below applies to consulting in the AI era, whether you are targeting McKinsey, Bain, BCG, a Big Four firm or a technology-led firm. The common thread is judgment: knowing what clients will pay for, and where AI helps and where it doesn’t.
Here is what you should understand.

Stop Describing Your Work as Activities. Talk About Outcomes.

McKinsey is estimated to earn about a quarter of its global fees from arrangements where clients start with the result they want and pay mostly against delivery. When fees ride on results, things outside your scope, like a late data feed or an uncooperative client team, can sink your outcome. So learn the vocabulary (baselines, KPI verification, retention triggers, shared-risk fees) and pay attention to payment terms and working capital.

If you pitch “I led a cost review”, that is an activity. You may have a new approach by saying that “I protected USD4 Mn, measured against a baseline we agreed upfront…”. This is a tangible outcome. Rewrite your resume or speak in the interview that way, with the measurement method in the bullet, to show how you are well researched and up to date with the expectations.

This is how outcome-based consulting changes your resume. Consulting resume bullets that state the result and how it was measured read as more credible than a list of activities, and they show you understand how clients now judge consulting work.

Read the Firm’s Health Before its Brand

A famous consulting brand does not automatically mean that every practice within that firm is growing equally. Look at what firms are actually selling, where clients are spending, and where the firms themselves are investing.

For example, Accenture’s Q3 FY26 bookings were $19.3 billion against $18.7 billion of revenue. But its bookings were down 2% year over year, while revenue grew 6% in reported currency.

Cognizant shows how lumpy bookings can be. Its Q1 2026 bookings grew 21% year over year, with a trailing book-to-bill of about 1.4x. In Q2, bookings fell 6% year over year, and the trailing ratio eased to about 1.3x, still healthy but a reminder to read more than one quarter.

The lesson is that you should understand what is happening underneath the brand name.

Look at:

• What industries are growing?
• What types of projects are clients still willing to spend on?
• Is the firm investing in AI, cybersecurity, supply chain, operations or another growth area?
• How diversified is its client and government exposure?
• What is happening to bookings, rather than just last year’s revenue?

Accenture has launched Accenture Edge for mid-market companies with $300 million to $3 billion in revenue, which it sizes at $240 billion. It has also committed about $4.2 billion to operational-technology cybersecurity, taking a majority stake in Dragos and acquiring runZero and NetRise outright. Together, these show where the firm sees growth.

Look past the brand when you pick a company target. The firm’s internal AI capability shapes how fast you will learn and how well you will be trained to work at AI speed. PwC, for example, counts close to 200,000 regular users of its internal assistant. But most large firms now have something similar, so the useful question in an interview is how deeply juniors use the tools day to day, and whether you’ll be trained on them.

If you are working out how to choose a consulting firm, treat these questions as your checklist. Bookings, growth areas and AI training tell you more about what your first years at a firm will look like than its name does.

Specialise in a Regulated Sector.

I think industry depth is becoming increasingly useful in consulting. Healthcare, financial services, energy, supply chain and cybersecurity are all spaces where understanding the underlying business is needed to do well as a professional.

A consultant who understands how a bank makes money, how regulation affects its products, or where credit losses actually come from is bringing something different from someone who only knows how to build a PowerPoint deck. The same applies to healthcare, or energy, or supply chain, or cybersecurity.

Ask yourself, whether you understand the economics and operating model of the industry you claim to be interested in.
If you have spent three years working in pharma, for example, don’t write an MBA essay saying you want to become a “strategic problem solver across industries.” You already have something more valuable because you understand a specific industry.
Build on that.

This is why industry specialization in consulting matters for MBA applicants. If AI handles more of the routine analysis, your knowledge of a specific sector is part of what a firm still needs from you.

Choose Your MBA Program for the Skills You Need

When you compare MBA programs, don’t stop at rankings and consulting placement numbers. Look at what you will actually learn.

If you want to work in consulting, I would pay particular attention to:

• Strategy and corporate finance
• Operations and supply chain
• Analytics linked to business decisions
• Pricing and commercial strategy
• Technology and AI
• Industry-specific electives
• Experiential learning and consulting projects

And increasingly, ask a very simple question: Where does AI show up in the curriculum?

Don’t ask “Do you have an AI elective?”

AI has to be integrated into how students actually learn, analyse cases, work on projects and make decisions, because that is increasingly how consulting itself is changing.

That question is also a useful filter when you look for the best MBA for consulting. Check whether the MBA programs for consulting on your list teach AI inside their cases and projects, not only in a standalone elective.

Learn How to Challenge AI.

McKinsey has been piloting an interview exercise in selected U.S. offices where candidates use its internal AI tool, Lilli, to work through a business problem. The exercise is designed around how candidates interact with and assess AI output, rather than simply accepting whatever the tool produces.

The valuable skill now is knowing when the AI answer is wrong

If AI gives you three reasons why a company’s margins are falling, can you identify the missing fourth reason?
If it recommends entering a new market, can you identify the assumption that makes the recommendation fragile?
If it gives you a beautifully structured answer based on bad data, can you catch it?

Build proof

Do a small multi-agent project on at least two platforms so you aren’t tied to one vendor, measure cost per completed task, and publish the before and after. Consider a governance credential such as IAPP’s AIGP, ISACA’s AI audit certification or an ISO/IEC 42001 auditor course.

This kind of exercise is what applicants now call the McKinsey Lilli interview. It has reportedly been piloted in select final rounds in the U.S., so it is not a standard step everywhere yet, but it signals that AI fluency in consulting is becoming a core expectation. To prepare for the McKinsey Lilli interview, practise asking the AI focused questions, checking its output and refining the answer, while keeping ownership of the final recommendation. The most common mistake is leaning on the tool too heavily.

Learn the economics of AI

Understand what AI actually costs a business. Everyone wants to talk about how much AI can automate. Fewer people are asking whether the economics actually work.

KPMG’s Q2 2026 U.S. AI Pulse found that 53% of organisations were deploying AI agents, but only 26% had full, real-time visibility into their AI operating costs. Its global research also found that organisations with full visibility into AI operating costs were five times as likely to report established ROI as those without that visibility: 15% versus 3%.

A client doesn’t need another presentation saying, “AI can transform your business.” but they need someone to help answer Which processes should we automate? What will it cost? What will we save? What risks are we taking on? And which AI initiatives should we stop because the economics don’t work?

If you are preparing for consulting, learn to think about AI as a business investment rather than a technology trend.
Understanding AI ROI in consulting is one of the clearest ways to show you can treat AI as a business decision. A client who asks for an AI business case wants to see costs, savings and risks, not a general promise of transformation.

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