In the first seven years of my career, I worked at some of the top consulting firms, including McKinsey in London. I travelled the world and collaborated with brilliant colleagues and top executives. It was an exciting yet intense time, with many 60-hour weeks spent meeting tight deadlines—like building a profitability model for a new business in a single night…

These big consulting firms, especially McKinsey, have received mixed reviews recently, and I can see why. It’s rare for clients to see a strong return on their investment. In my experience, only around one in four or five projects truly delivered great value, and much of that success depended on how well the client managed the project’s scope and goals. With the increasing appeal of start-ups and specialised firms, the traditional consulting industry is changing.

Would I hire McKinsey today? Probably not. But there are still a few scenarios where a firm like McKinsey can add real value.

Scenario 1: Navigating Exec/Board Politics to Reach a Decision

Imagine your organisation is grappling with strategic choices—like prioritising international expansion over core market growth or deciding whether to acquire a company. In these high-stakes situations, McKinsey excels at three things:

1) Deep Analysis using your internal data along with industry and competitor insights

2) Executive-Level Communication that distils complex findings into clear slides

3) Objective Recommendations with a structured, unbiased approach

By tapping into McKinsey’s neutral, data-driven perspective, you can bring in an independent viewpoint to help guide a decision. Just remember: they may not always support your preferred approach, so be open to their perspective if you want to get the most out of the engagement.

Scenario 2: Limited Internal Data or Analytics Capability

If you need top-tier analysis but lack internal data capabilities, McKinsey’s analysts can be invaluable. They’re experts at building accurate models and quickly uncovering unique insights, especially with a well-defined problem.

Here’s what they can provide:

* Data Cleaning, Structuring, and Supplementation

* Benchmarking and Predictive Models with flexible inputs and assumptions

Their analysts also work closely with specialised “knowledge centres” (often based in India or Eastern Europe) to gather external reports and competitor data to strengthen your assumptions.

Important Caveats:

1) McKinsey analysts aren’t data scientists. If your needs include complex coding or advanced machine learning, you’re better off hiring a data scientist or engineer.

2) Make sure you fully understand the models they build. Reviewing the structure, assumptions, and inputs will allow you to explain and adapt the model within your organisation.

Scenario 3: Collaborate with a Trusted Expert

Sometimes, it’s worth engaging McKinsey if you know someone there with deep expertise in an area you need, like organisational design, restructuring, or cybersecurity. In this case:

* Keep the team small—just the expert you trust plus one or two analysts

* Limit the project duration to around 4–6 weeks to stay focused

* Work closely with them to ensure alignment with your needs, possibly through a detailed statement of work

This lean approach helps keep costs down while still delivering high-impact results.

In classic McKinsey style, I’ve kept this list to just three scenarios, but I’m sure there are a few more. Feel free to get in touch with your thoughts.