When AI disrupts Premium Consulting_The McKinsey Perspective

McKinsey faces a paradigm shift when PharmaCrest Global deploys AI to challenge their $18.5 million consulting proposal. Part two of our negotiation analysis examines how established consulting firms adapt when clients use AI tools that complete market analysis in two hours versus their traditional two-week timeline.

This episode explores McKinsey's strategic response to AI-empowered buyers who arrive prepared with multi-agent workflows, leverage analysis, and outcome-based pricing demands targeting 25-30% cost reduction. We analyze how consulting firms must evolve their value proposition when traditional information asymmetries disappear.

Critical lessons for consulting leaders navigating the AI transformation in professional services and maintaining competitive advantage against technologically sophisticated clients.

If you enjoyed this episode, please leave a review and check out our website: www.negoai.ai

I welcome any suggestions, questions, or comments at yrana@negoai.ai

--------------------------------------------------------------------------------------------------
Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing.

Transcript

Part two of our case study on the negotiations between McKinsey and Pharmacrest Global. The case, as we've seen last time, examines how artificial intelligence has challenged traditional consulting models.

Now, here is the McKinsey perspective. Last time we saw the Pharmacrest perspective, so today we'll dig into the point of view of McKinsey. Dedee Morrison, the head of United States operations for McKinsey & Company, is just hours from renegotiating the firm's 10-year $18.5 million annual engagement with Pharmachrist Global. The partnership is facing tougher client expectations, artificial intelligence tools that replicate basic analyst work, and steep price cuts from rival consultancies. Morrison is going to enter the negotiations to protect revenue and show that the firm can blend human insight with AI.

Now, the highlights. McKinsey assisted Pharmacrest in acquisitions valued at $8.2 billion. They designed operation reforms that saved millions of dollars. They helped Pharmacrest have a substantial share increase. The current snapshot is the following: annual fee of $18.5 million. There is a team of 25 consultants with two senior partners, four partners, eight senior consultants, and 12 consultants. Core work is strategy, mergers, acquisition, operational excellence, digital transformation, and we also have success measures.

Now, there are some trouble spots. Only 42% of McKinsey recommendations are fully implemented, far below industry average. Junior consultants handle most of the work, and senior partners only appear at the start and end of projects. There have not been many knowledge transfer sessions between McKinsey and the company. There are also external pressures. Boston Consulting Group, Bain & Company, Deloitte have offered steep discounts.

Artificial intelligence is widely used by Pharmacrest, and McKinsey has lost two large pharmaceutical clients in 2024 for similar reasons. So this is a very important client to retain.

What can McKinsey do? They are ready to cut price. They're also ready to guarantee a senior partner at every major decision point, reduce the team size, raising average seniority. Link part of the fee, not all, to clear outcomes. Offer a joint venture to co-develop drug discovery algorithms and share future licensing revenues. They also are ready to present a six-month talent exchange program between the company and McKinsey. License a digital toolbox that provides McKinsey analytics apps so Pharmacrest staff can run quick in-house analysis. These are all integrative options to enlarge the cake and to cover for some of the probable loss of revenue.

The last one is bringing in artificial intelligence tools for data crunching to speed delivery and cut costs without losing strategic depth.

So the negotiation playbook would be acknowledge reality, redefine values, show innovation, and offer accountability. What is McKinsey's leverage? Institutional knowledge, board relationship, cross-industry insights, and the risk of change, because switching advisors could delay critical programs. There are also some broader industry implications. The Pharmacrest result will become a benchmark for Fortune 500 companies reviewing advisor spending in the artificial intelligence era. This is very important, because a win would validate McKinsey's human plus machine model.

Now let's move to the practical application and see how we can use AI tools to prepare for this complex negotiation scenario.

So we are in Cassidy now, and this is our workflow, the strategic B2B sales McKinsey case. I gave a very simple user prompt: "Please analyze attached document," and I provided the Pharmacrest McKinsey contract renegotiation from McKinsey's perspective. What we saw earlier was just a summary. Now they have the full case. The full case was provided to Cassidy. They also provided Sarah Chan's LinkedIn profile. Sarah Chan, as you remember, is the chief procurement officer for Pharmacrest.

Here is the workflow that ran for around eight, nine minutes. Just finished now. You have four different agents part of the workflow. Jerry is the B2B sales agent. Daniel is the behavioral agent. Deepak is the negotiator, and Linus is the compiler, the one that provides a final report. So let's go through the final report. Then we'll run through each one of the agents to see how they're built.

There is an executive summary. The B2B sales analysis and context. This is a pivotal moment for both organizations, client company industry context, market segment analysis, and our differentiated value proposition. Pharmacrest operates in a highly competitive, innovation-driven market where regulatory complexity, R&D efficiency, and speed to market are critical success factors. McKinsey's value proposition has historically centered on deep industry expertise, client-specific institutional knowledge, and strategic judgment that goes beyond what AI or lower-cost competitors can offer. To maintain differentiation, McKinsey must now demonstrate its ability to integrate AI tools for efficiency while preserving the high-value, senior-level strategic guidance that Pharmacrest relies on.

Also important here that this just inferred from the case that emphasizing McKinsey's unique understanding of Pharmacrest culture, regulatory environment, and board-level dynamics is crucial to defend its premium positioning. Very interesting.

Key decision-makers and stakeholders mapping. Plausible needs, challenges, opportunities. The other party needs to reduce consulting costs in response to market-wide cost pressures, client dissatisfaction with the dilution of senior partner involvement, frustration over inadequate knowledge transfer, desire for outcome-based pricing model.

There is also opportunity to leverage McKinsey's unique institutional knowledge and long-term relationship as a barrier to competitor entry. Need for a more customized, client-specific approach to implementation. There is also an opportunity to position McKinsey as an innovation partner by integrating AI. Not easy to defend premium pricing in the face of aggressive competitor discounts and AI commoditization, for sure.

Competitive positioning and critical success factor metrics, we are fighting against Boston Consulting Group, Bain, Deloitte. This is the situation of McKinsey.

This is strategic consultative insight-selling approach. The approach should emphasize McKinsey's irreplaceable institutional knowledge, board-level relationships, and track record of delivering strategic value. Very important. At the same time, should also acknowledge that AI is disrupting the market, and there are Pharmacrest's legitimate cost concerns.

Framing outcome-based pricing as a shared risk could be very successful in this meeting. Anticipating potential objections: high consulting fees, insufficient senior partner involvement in day-to-day strategic work, frustration with knowledge transfer, skepticism about McKinsey's ability to integrate AI tools, doubts about the visibility and fairness of outcome-based pricing, and also a potential perception that proposed changes are reactive rather than proactive, risking confidence in McKinsey's leadership.

How to meet those objectives and propose next steps: secure an agreement on the strategic importance of the partnership, present and discuss revised service model, negotiate a mutually acceptable pricing structure, establishing also a joint working group to define measurable outcomes.

Then we have the counterpart behavioral profile and communication intelligence. So there's a confidence of 85%. The primary energy is likely cool blue. That is methodical and analytical approach. Very important. So Sarah is methodical and analytical, very logical. Structured presentation of information. Cool blues are driven by accuracy, understanding, and systematic thinking.

Her communication style reflects the cool blue preference for precision and thoroughness. Cool blue approach also works with a focus on quality over quantity. So this is important. They also require analysis, research, planning, and quality control.

The secondary influence is earth green. That is the more collaborative approach and emphasis on relationship building. Here, the emphasis on partnership, for example, could be very important. So they are motivated by harmony, stability, and genuine care for others.

The two conflict mode analyses, so Thomas-Kilmann, are compromising and avoiding. So what we can say is that with an avoiding negotiation style, you have to provide a safe environment where Sarah can negotiate without feeling the tension. At the same time, she will strive for a fair solution, even if it's quick. So moving her towards a more collaborative option could be not so easy because usually compromising individuals tend to strive for a fair but quick solution.

These are the pre-negotiation engagement: provide comprehensive background information, emphasize mutual benefits, demonstrate flexibility and openness to multiple solutions, use structured and collaborative language, and also allow time because the analytical side goes against the compromising. So she needs time, but once she has decided, she will be very quick to move.

Also maintain a professional and respectful tone. Relationship assessment, and this is a strategic negotiation framework we have. The interest analysis, and we have seen that our BATNA is move to other pharmaceutical companies or accept a revenue loss. For them, for sure, other consultancies are a strong BATNA.

The likely interest and scenarios of the other party, scenario number one is cost optimization, achieve a 15-20% cost reduction. The second is partnership evolution. Transform the consulting relationship into an AI-augmented strategic partnership with enhanced knowledge transfer. The third is relationship preservation with performance accountability. I think all three can be mixed and integrated.

Creative options: hybrid AI-augmented service model, outcome-based pricing with risk sharing, knowledge transfer, strategic innovation lab partnership, joint development of pharmaceutical industry AI tools and methodologies. Very interesting. Flexible engagement architecture: modular service structure allowing Pharmacrest to scale up McKinsey involvement based on project complexity and internal capability. Very interesting.

For sure, the keys are pricing, senior partner involvement, AI integration, implementation success metrics, knowledge transfer, and outcome-based pricing. Our arguments: proven strategic value, irreplaceable institutional knowledge, and board-level relationship value.

So there are some questions. These are the aspirations and the negotiation strategy. Acknowledge disruption, value demonstration with data, hybrid model presentation, and test a little bit, and also move towards a partnership evolution.

Anticipated objections, we've seen them, and here is a transparency note. We have seen that Cassidy provides a comprehensive preparation for McKinsey and Morrison for the upcoming meeting with Sarah Chan.

So let's go through the workflow. The first thing is the manual trigger. We have a user prompt that attaches the perspective of McKinsey on the case and the LinkedIn profile of Sarah.

Jerry is our general B2B sales assistant that has been completely customized for McKinsey and for this negotiation with this user prompt. It provides all the information Jerry needs to know of being part of a workflow as step one, and which is the input and which is the output that it will have to provide.

Then we move to Daniel, and Daniel is our behavioral assistant. Again we have a general behavioral assistant that is analyzing the LinkedIn and individuals. Here we have Daniel that is customized for the specific workflow, knowing that it is step two, which will be the input and which will be the output of the agent. The input is the LinkedIn profile, and that's it.

Then we move to Deepak, our negotiator. We have seen the negotiator agent many times in this video series, and Deepak has been again customized to be the number three step of this workflow. As you can see here, it also has the input data it will receive. That is the output from Jerry, the output from Daniel, the user prompt, and the context and information provided by the case study.

The last agent is Linus. Linus is our compiler that integrates the outputs of the previous three agents. It is provided with a user prompt of being the step four and the final step of the workflow, and also it is given the three inputs that it will receive. Jerry's output, Daniel's output, and Deepak's output. This is a very comprehensive workflow built with four agents and provides a full preparation report for the specific case.

Thank you.