Nexus vs McKinsey QuantumBlack: Platform vs AI Strategy
McKinsey's QuantumBlack delivers AI strategy, but strategy and building are different disciplines. Nexus deploys production agents in weeks with FDEs. Insider perspective.
Last updated: February 2026
Quick honest summary
McKinsey is the most respected strategy consulting firm in the world, and QuantumBlack is its dedicated AI arm. Acquired in 2015 as a 45-person London startup, QuantumBlack has grown to roughly 1,700 people across 40+ offices, and now drives approximately 40% of McKinsey's entire business. They employ exceptionally talented people, bring deep industry expertise, and carry the credibility that comes with the McKinsey name. QuantumBlack Labs has built 20+ AI products and 140+ use case accelerators across life sciences, retail, mining, financial services, and other sectors. When a board needs an AI transformation roadmap or a C-suite needs to align on strategy, McKinsey is the firm they call.
But it is worth understanding two structural realities about McKinsey that shape everything the firm delivers.
The first is the business model. McKinsey charges by the day, by the partner, by the phase. The longer an engagement runs, the more phases it includes, the more consultants it requires, the more the firm earns. This is not a criticism of their people; it is a structural reality of the consulting business model. The firm is incentivized to bill time, not to deliver outcomes. The client pays for effort, not results.
The second is the firm's DNA. McKinsey is fundamentally a strategy firm. The senior partners who control the firm are advisors, not builders. They believe frameworks, analysis, and strategic thinking are what matter. This creates a specific problem when it comes to AI implementation: consultants who claim they "implemented" AI typically project-managed developers. They did not touch a line of code themselves. They sit between the client and the technical team, adding a coordination layer that slows things down and inflates cost without adding technical value. McKinsey has tried for decades to build genuine technology capabilities through acquisitions, joint ventures, and in-house efforts (QuantumBlack being the most prominent example), but the results speak for themselves. The people in control see technology as something to be managed, not something to be built. The firm's DNA is advisory, and bolting on technology capabilities does not change that DNA.
This is not an outsider's critique. Nexus's CEO, Assem, is a former McKinsey consultant. He has seen from the inside how the firm operates, how engagements are structured, and why the advisory model structurally cannot deliver AI implementation at the speed enterprises need.
Nexus is an enterprise AI agent platform paired with white-glove service: Forward Deployed Engineers embedded with your team, change management support, and ongoing optimization. It is not just software you license and figure out on your own. Nexus is built for enterprises that need AI agents completing real business workflows in production, with business teams owning the outcome, deployed in weeks rather than quarters. Critically, you do not pay separately for the service. FDEs are included. Nexus is structurally incentivized to deliver results, because that is what earns renewals. And the people who advise you are the same people who build the solution. There is no coordination layer between strategy and implementation.
These are not the same thing. They solve different problems at different points in the AI journey. But they also operate under fundamentally different incentive structures and fundamentally different mindsets: advisory versus builder.
McKinsey helps you think about AI: what your strategy should be, where AI fits in your organization, how to build a roadmap, how to align stakeholders. This is valuable work. But that thinking often takes the form of extensive upfront analysis: AI maturity assessments, operating model redesigns, transformation roadmaps, all before a single agent is built. The advisory mindset treats AI as a strategic question to be analyzed. The builder mindset treats AI as a system to be deployed and measured. Nexus deploys AI agents that complete work: customer onboarding, sales intelligence, proposal generation, compliance automation, support triage. The agents operate inside your existing systems and produce measurable financial outcomes. Building and measuring beats planning for 12 months.
If your challenge is "we need an AI strategy and executive alignment," McKinsey is the right partner. If your challenge is "we need AI agents producing results in the next 60 days," that is where Nexus fits.
Many enterprises need both at different stages. Some have already done the McKinsey engagement and are now asking: "We have the strategy. We spent months on the roadmap. How do we actually deploy?" That transition from strategy to deployment is where the advisory mindset hits its limit. Deploying requires builders, and builders need to be in control of the solution, not managed by advisors.
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When McKinsey / QuantumBlack is the better choice
McKinsey employs exceptionally talented people, and there are scenarios where they are exactly the right partner. The structural incentive questions and advisory-versus-builder distinction outlined above do not invalidate the work; they simply mean you should go in with clear expectations about scope, timeline, and what "done" looks like. McKinsey has brilliant strategists. Strategy and building are different disciplines, and the firm is structured around strategy.
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You need a C-suite AI strategy and organizational alignment. If your board has mandated AI transformation and your executive team is not aligned on what that means, where to start, or how to prioritize, McKinsey excels here. Their strategic frameworks, industry benchmarks, and executive credibility are hard to match. Getting leadership aligned on an AI vision before doing anything else can save millions in misdirected investment. Just be clear upfront about what the engagement should deliver and when, so that strategic alignment does not expand into an open-ended planning exercise.
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You need an enterprise-wide transformation roadmap. If the question is "how should AI reshape our entire organization over the next 3-5 years," McKinsey has the analytical depth and cross-industry pattern recognition to build that roadmap. QuantumBlack's 140+ use case accelerators across industries give them a broad view of where AI creates value. The caveat: roadmaps can become ends in themselves. The most effective approach is to pair the strategic roadmap with early deployment of a few high-impact agents, so the organization sees results while the broader plan takes shape.
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Board credibility matters as much as the work itself. For public companies and large enterprises, the McKinsey name carries weight in boardrooms. If your AI initiative needs board approval and executive sponsorship, a McKinsey-backed strategy can provide the institutional credibility to secure budget and mandate. This is real and pragmatic. Just recognize that you are paying, in part, for the brand on the cover page.
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You need complex data science and advanced analytics at scale. QuantumBlack's roots are in data science and advanced analytics. If your challenge requires building custom machine learning models for supply chain optimization, pricing engines, or predictive analytics on massive datasets, QuantumBlack has deep expertise. Products like OptimusAI (for plant productivity optimization in materials, energy, and food production) and LifeSciences.AI demonstrate this capability. Note that analytics and data science are closer to the advisory mindset than AI agent deployment; they involve modeling and analysis rather than building production systems that complete work autonomously. This is where QuantumBlack is most natural, because it aligns with the firm's advisory DNA.
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You are starting from zero on AI maturity. If your organization has no AI strategy, no data infrastructure, and no internal alignment on where AI fits, McKinsey can help build that foundation. Some strategy work genuinely needs to happen before deployment makes sense. The question is how much planning is necessary before you start building. Nexus's view is that a 3-month POC teaches you more about AI readiness than a 6-month assessment, but not every organization is ready to move that fast.
When Nexus is the better choice
Enterprises that partner with Nexus tend to share a specific pattern: they already know AI should deliver value (they may have already engaged a strategy consultant), but they need agents in production delivering measurable outcomes, not another roadmap. They are also, often, looking for a vendor whose incentives are structurally aligned with their own and whose team has the builder mindset to actually ship: Nexus earns renewals by delivering results, not by extending timelines. The people who build the solution are the same people who advise on it. There is no coordination layer.
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You already have an AI strategy. Now you need execution. Many Nexus customers have already completed strategy engagements with firms like McKinsey, BCG, or Bain. They have the roadmap. What they need now is AI that actually works in production: agents completing real business workflows, integrated into existing systems, delivering financial results. Nexus bridges the gap between "strategy" and "working system." One client had an outsourcing firm on-site in "project management mode" for a full year; after 12 months, they had only finalized planning for a first knowledge assistant and had only begun to consolidate the knowledge base. Nexus came in, scraped the data, implemented the agent, and pushed it to production in 4 weeks.
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You need measurable results in weeks, not months. A typical McKinsey AI strategy engagement takes 3-6 months. Implementation afterward takes another 6-18 months. The full cycle from engagement start to working AI in production can be 9-24 months. Part of this is genuine complexity; part of it is a business model that does not penalize slowness. With Nexus, most agents go live within 2-6 weeks. Orange deployed customer onboarding agents in 4 weeks and generated $4M+ in incremental yearly revenue. The speed difference is not marginal; it is structural, because Nexus only earns renewals by proving value fast.
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Day rates are unsustainable for ongoing AI operations. McKinsey's pricing model works for discrete strategy engagements. But AI deployment is not a one-time project; it requires continuous optimization, iteration, and expansion. At $350-$1,000+ per hour, keeping a McKinsey team engaged for ongoing AI operations is prohibitively expensive for most enterprises. And the incentive structure compounds the problem: the firm benefits when you need more hours, more consultants, more follow-on phases. Nexus's per-agent pricing scales with value delivered, not with consultant headcount. FDEs are included in the platform cost. You do not pay separately for the service.
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Business teams need to own the AI, not depend on external consultants or IT. When McKinsey's team transitions off the engagement, your organization needs to sustain what was built. This often creates a capability gap: the strategy was designed by McKinsey's team, but your team did not build it and may not fully understand it. The advisory mindset compounds this problem: advisors create knowledge artifacts (strategies, roadmaps, frameworks) that require builders to execute, and the builders were never embedded in the engagement. There is also a structural dynamic at play: a consulting firm that leaves you fully self-sufficient has eliminated its own future revenue. Nexus takes the opposite approach. Forward Deployed Engineers work alongside your team, but your business team owns the agents. FDEs implement directly with your team and train them to operate independently. No IT dependency. No handoff to a separate implementation team. They build, iterate, and expand without filing tickets with external consultants. Nexus is structurally incentivized to make your team self-sufficient, because that is what drives adoption, expansion, and renewal. When Lambda's Head of Sales Intelligence needed to adjust data sources, he did it himself. No consulting engagement required.
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You want a proof of concept before a multi-million-dollar commitment. This is at the heart of the build vs buy decision. McKinsey engagements typically require significant upfront investment before any AI is in production. The consulting model front-loads cost and back-loads results. Nexus inverts this: you start with a 3-month proof of concept tied to specific, measurable outcomes. You see working agents, measure the impact, and decide whether to continue. Every Nexus POC has converted to an annual contract, because value is demonstrated before commitment. Nexus can only earn the annual deal by delivering during the POC. That is structural alignment.
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You need builders who implement directly, not advisors who project-manage developers. McKinsey consultants are brilliant strategists and analysts. But deploying AI agents into production requires a fundamentally different discipline: integration engineering, agent design, system architecture, change management at the workflow level. When McKinsey takes on an AI implementation, consultants typically sit between the client and the technical team, project-managing developers without touching the code themselves. They cannot challenge the technical decisions developers make, because they are not builders. Nexus FDEs are builders. They implement directly with your team. They handle configuration, integration, testing, deployment, and ongoing optimization. There is no coordination layer between the advice and the build. The entire process has no IT dependency. Nexus is full-stack: it develops its own framework, solution, and platform. The people who advise you are the same people who build the solution. And because FDEs are included in the platform, not billed by the hour, they are incentivized to deploy fast and move on to the next use case, not to extend the current one.
What enterprises experienced
Orange: $4M+ incremental revenue in 4 weeks
Orange Group is a multi-billion euro telecom operator with 130,000+ employees across Europe and Africa. They have the resources to engage any consulting firm and the internal engineering capacity to build AI themselves.
They chose Nexus. Where a typical strategy-led engagement starts with months of analysis before any agent is built, Orange went straight to production.
Their business team (not engineering) built customer onboarding agents deployed across multiple European markets. Timeline: 4 weeks from start to production. Result: 50% conversion improvement, $4M+ incremental yearly revenue, 100% adoption by sales teams, 100% compliance.
The agents did not require a 6-month strategy phase. They did not require a separate implementation partner. There were no AI maturity assessments, no operating model redesigns, no multi-phase transformation roadmaps. There was no coordination layer between advisors and builders. Business teams built and own the agents, supported by Forward Deployed Engineers who implemented directly. The structural difference is both incentive and mindset: Nexus could only earn the long-term contract by delivering results during those 4 weeks, and the builders who designed the solution were the same people who shipped it. No project managers sitting between the client and the technical work. A consulting firm billing by the day, staffed with advisors who project-manage developers, has neither the incentive nor the DNA for that kind of speed.
Lambda: a $4B+ AI company chose to buy instead of build
Lambda is a $4B+ AI infrastructure company. They provide GPU cloud services and build AI computing infrastructure for training and inference. They employ world-class AI engineers. If any company could build internal AI agents, or engage a consultancy to custom-build solutions, it was Lambda.
Lambda chose a platform approach over building with consultants or in-house engineering. Not a strategy firm. Not a systems integrator. A platform built by builders, with builders embedded in the engagement.
Joaquin Paz, Lambda's Head of Sales Intelligence, built an autonomous research agent that monitors 12,000+ enterprise accounts annually, surfaces buying signals, and synthesizes competitive intelligence. Joaquin is not an engineer. He built this in days, working directly with Nexus FDEs. No coordination layer. No IT dependency. No consultants project-managing the process.
The results:
- $4B+ in cumulative pipeline identified across accounts Lambda was not actively monitoring
- 24,000+ research hours added annually (equivalent to 12 full-time analysts)
- 12,000+ enterprise accounts analyzed with deep intelligence
- Deployed in weeks, not the months it would have taken to build internally or through a consulting engagement
Lambda has since expanded from a single agent to a fleet across sales and marketing. Anticipated value: more than $7M by 2026.
"I'm not an engineer. I built this in days. With the automation tools we looked at before, I would have needed to spec everything out and wait months for development."
Joaquin Paz, Head of Sales Intelligence, Lambda
Enterprise client: 1 year of planning vs. 4 weeks to production
This story captures both the incentive problem and the advisory-versus-builder problem in a single engagement. An outsourcing firm was embedded at one of Nexus's enterprise clients in "project management mode." After a full year, they had only finalized planning for a first knowledge assistant and had only begun to consolidate the knowledge base. Twelve months of billing. No working product. The consultants on-site were coordinating and planning, not building. They project-managed the process without ever implementing anything themselves.
Nexus came in. Within 4 weeks, the FDE team scraped the data, implemented the agent, and pushed it to production. Same problem. Same client. Same data. The difference was not talent; the outsourcing firm had capable people. The difference was twofold: structural incentives (the outsourcing firm earned revenue by staying; Nexus earned the contract by shipping) and mindset (the outsourcing firm's people were coordinators sitting between the client and the technical work; Nexus's FDEs were builders who implemented directly, with no coordination layer and no IT dependency).
Multi-billion euro telecom operator: 40% support capacity freed
A multi-billion euro European telecom operator (13,000+ FTE) deployed a multi-agent suite for support, compliance, and customer registration. 40% of support capacity freed. 12-week deployment. 100% compliance assurance. Millions of customer interactions handled. No multi-phase consulting engagement preceded the deployment.
Key differences explained
Strategy consulting vs. platform + service: different delivery models, different incentives, different mindsets
This is the core distinction, and it is important to understand clearly, because it is not just about delivery models. It is about incentive structures and, at a deeper level, about the difference between an advisory mindset and a builder mindset.
McKinsey's delivery model is traditional consulting: assess the current state, develop a strategy, present recommendations, guide implementation planning, transition to the client. The deliverable is intellectual capital: frameworks, roadmaps, models, organizational recommendations. The work is done by consultants who are on-site for the engagement duration and then move on. Crucially, the firm earns more when the assessment phase is longer, the strategy more comprehensive, the implementation plan more phased. This is not because the people are not talented; they are. It is because the business model rewards thoroughness over speed, and the firm's DNA is advisory. The senior partners who control McKinsey are strategists. They believe ideas, frameworks, and analysis are what create value. When it comes to AI implementation, this advisory mindset means the firm naturally gravitates toward planning, assessment, and coordination rather than building.
Nexus's delivery model is platform + service: identify the highest-impact use cases, design and build agents, deploy into production, measure outcomes, optimize continuously. The deliverable is working AI agents integrated into your business systems. The work is done by Forward Deployed Engineers who remain embedded with your team. Nexus earns renewals by demonstrating measurable outcomes during the POC. The incentive is to deploy fast and prove value. Nexus is full-stack: it develops its own framework, solution, and platform. The people who advise are the same people who build. The builder mindset means Nexus naturally gravitates toward shipping, measuring, and iterating.
These are not competing approaches. They operate at different layers. McKinsey answers the question "what should our AI strategy be?" Nexus answers the question "how do we get AI agents producing results this quarter?" The advisory mindset is excellent for the first question. The builder mindset is essential for the second.
The risk, however, is spending 12-18 months on strategy and implementation planning before any AI is in production. McKinsey is known for extensive upfront analysis: AI maturity assessments, operating model redesigns, transformation roadmaps, all before building anything. This is the natural output of an advisory mindset applied to a building problem. Markets move fast. Competitors are deploying. Every quarter spent in planning mode is a quarter without results, and a quarter of additional consulting fees.
The cost structure: day rates vs. per-agent pricing (and why it matters more than you think)
McKinsey's pricing reflects the caliber of talent they deploy. A typical AI strategy engagement starts at $500K-$1M+. Complex, multi-phase transformation programs can run into the tens of millions. Hourly rates range from $350 at the associate level to $1,000+ for senior partners. Costs scale linearly: more scope, more consultants, more duration, more cost. And here is the structural problem: the firm profits when engagements take longer, require more partners and associates, and involve more "phases" and "workstreams." The client pays for effort, not outcomes. This does not mean every engagement is artificially extended. It means the incentive structure does not penalize slowness.
Nexus uses per-agent pricing tied to value delivered. You pay for agents that complete work, not for consultant hours. FDEs are included; you do not pay separately for the service. The 3-month POC lets you validate ROI before committing. When you add more agents, you do not proportionally increase your spend the way you would with consulting headcount. Nexus's revenue depends on proving value fast enough to earn the annual contract.
For a discrete strategy engagement with a clear end point, McKinsey's model works. For ongoing AI operations that require continuous optimization and expansion, day-rate consulting becomes unsustainable, and the incentive misalignment compounds over time.
Forward Deployed Engineers vs. management consultants: builders vs. advisors
McKinsey consultants are among the most talented analytical minds in the world. They excel at strategic analysis, stakeholder management, and organizational design. QuantumBlack adds deep data science capability. The talent is not in question.
But three things are different.
First, deploying AI agents into enterprise production systems is a different discipline from advising on AI strategy. It requires integration engineering (connecting to CRMs, ERPs, communication tools), agent architecture design, real-time system testing, change management at the team level (not the boardroom level), and ongoing optimization based on production data. Strategy and building are different disciplines, and the people who excel at one are not necessarily equipped for the other.
Second, the incentive structures are different. A management consultant billing $700/hour is, structurally, not incentivized to finish fast. An FDE included in the platform cost is incentivized to deploy, prove value, and move to the next use case. This is not about individual motivation; both groups have talented, driven people. It is about what the business model rewards.
Third, and this is the point that often goes unspoken: McKinsey consultants who claim they "implemented" AI typically project-managed developers. They coordinated between the client and a technical team (often a separate systems integrator or QuantumBlack's data scientists), but they did not build the solution themselves. They cannot challenge the technical decisions developers make, because they are not builders. This coordination layer adds cost, adds time, and does not add technical value. It is the natural result of an advisory firm trying to deliver building work: the advisors manage the builders, and the builders are one step removed from the client.
Nexus Forward Deployed Engineers are builders who are in control of the solution. They embed with your team. They handle the complexity of connecting to your actual systems, designing agents that fit your specific workflows, running pilots with real data, and training business teams to own the outcome. FDEs implement directly with the client. There is no coordination layer between the advice and the build. The entire process has no IT dependency, because Nexus is full-stack: it develops its own framework, solution, and platform.
The work product is not a presentation. It is a production agent completing work.
The handoff problem: what happens when the consultants leave
This is the underappreciated risk of consulting-driven AI initiatives, and it connects directly to the incentive question.
McKinsey delivers exceptional work during the engagement. But when the team transitions out, the enterprise must sustain what was built. If the strategy recommended custom AI solutions, someone needs to build and maintain them. If the analytics models were built by QuantumBlack's data scientists, someone needs to operate and improve them. The knowledge transfer is never perfect, and the capability gap is real.
There is also a subtle structural dynamic: a consulting firm that leaves you fully self-sufficient has eliminated its own future revenue from that account. Follow-on engagements are a core part of the consulting business model. This does not mean firms intentionally create dependency, but the incentive structure does not reward making the client independent.
Nexus is designed around the opposite incentive. Business teams build and own the agents from day one. FDEs work alongside your team, not instead of them. Nexus earns renewals when your team is self-sufficient, expanding to new use cases, and seeing measurable outcomes. Making you independent is how Nexus grows the account. When agents are in production, your team understands how they work, how to modify them, and how to expand. There is no "handoff" because your team was always in the driver's seat.
The QuantumBlack question: can a strategy firm become a builder?
This is worth examining directly, because QuantumBlack is McKinsey's most serious attempt to build genuine technology capabilities, and it reveals the limits of the advisory model.
McKinsey acquired QuantumBlack in 2015 as a 45-person data science startup. It has grown to roughly 1,700 people across 40+ offices. QuantumBlack Labs has built 20+ AI products and 140+ use case accelerators. On paper, this looks like a firm that has successfully added building capabilities to its advisory core. In practice, the story is more nuanced.
QuantumBlack's strengths are real: data science, advanced analytics, machine learning modeling. These are closer to the advisory mindset than to the builder mindset. Analytics involves modeling, analyzing, and recommending. It is intellectually rigorous work that fits naturally within a firm of strategists. But deploying autonomous AI agents that complete business workflows end-to-end is a different discipline. It requires building production systems, integrating with enterprise infrastructure, shipping iteratively, and optimizing based on real-world usage data. This is builder work, and it requires a builder culture.
McKinsey has tried for decades to build technology capabilities through acquisitions, joint ventures, and in-house efforts. QuantumBlack is the most prominent example, but the pattern repeats. The reason the results have been mixed is structural: the people who control the firm (senior partners) do not have a builder mindset. They see technology as something to be managed, resourced, and coordinated. Not something to be built, shipped, and iterated. The firm's DNA is advisory. Bolting on a technology arm does not change the DNA of the parent organization. The advisory culture shapes how QuantumBlack products are sold (through consulting engagements, at consulting rates), how they are implemented (with consultants project-managing the technical work), and how they are supported (through managed services billed at consulting rates).
Nexus is different at the structural level. It was built as a technology company from the start. Nexus develops its own framework, its own solution, its own platform. The people in control of the product are builders. FDEs who implement directly with clients are the same people who shape the platform. There is no parent advisory firm whose culture overrides the building culture. This is not a criticism of QuantumBlack's talent; their data scientists and engineers are excellent. It is an observation about what happens when a builder organization operates inside an advisory firm: the advisory culture wins, because the advisory partners control the firm.
Time to impact: 9-24 months vs. 2-6 weeks (and the economics of delay)
A typical McKinsey AI transformation journey:
- Phase 1 (months 1-3): AI strategy and opportunity assessment
- Phase 2 (months 3-6): Roadmap development and prioritization
- Phase 3 (months 6-12): Implementation planning and vendor selection
- Phase 4 (months 12-18+): Build and deploy (often with a systems integrator)
Total time to first AI agent in production: 12-18+ months. Each phase generates billings. The firm has no structural incentive to compress the timeline, and every incentive to be thorough in a way that extends it.
This is the pattern sometimes called "complexity inflation": making problems feel more complex than they need to be, adding layers of analysis and planning that may be intellectually rigorous but delay the moment anything is actually built and measured. At one Nexus client, an outsourcing firm spent a full year in planning mode for a single knowledge assistant. Twelve months of project management before a single agent reached production.
Nexus compresses this dramatically:
- Weeks 1-2: FDE embeds with your team, identifies highest-impact use case, begins agent design
- Weeks 2-4: Agent built, integrated with existing systems, tested with real data
- Weeks 4-6: Agent in production, completing work, delivering measurable outcomes
Orange went from kickoff to production agents in 4 weeks. Lambda deployed in days. The client with the year-long planning exercise saw Nexus deploy the same agent in 4 weeks. The 3-month POC includes time for measurement and optimization, with most agents live well before the POC ends.
This is not about cutting corners. It is about a platform purpose-built for deployment speed, combined with engineers who are structurally incentivized to ship, not to plan. Builders ship. Advisors plan. Both have their place, but when the goal is production AI, the builder mindset determines the timeline.
Frequently asked questions
We already engaged McKinsey for our AI strategy. Can Nexus help with execution?
Yes, and this is one of the most common patterns we see. Several Nexus customers came to us after completing strategy engagements with top-tier consulting firms. The strategy identified where AI should create value. Nexus deploys the agents that actually deliver that value. This is also where the advisory-to-builder transition happens: the strategy firm did what strategy firms do well (align stakeholders, prioritize opportunities, build the roadmap), and now you need builders to actually ship. Having a clear strategy makes the Nexus engagement more effective, because the highest-impact use cases are already identified. Your FDE can focus on agent design and deployment from day one, implementing directly with your team, with no coordination layer between strategy and build. The important thing is not to let strategy become an indefinite planning phase. One client spent a year with an outsourcing firm in planning mode; Nexus deployed the same agent in 4 weeks.
McKinsey has QuantumBlack Labs with 20+ AI products. How is Nexus different?
QuantumBlack Labs products (like OptimusAI, Brix, and industry-specific accelerators) are designed primarily for data science and analytics use cases: supply chain optimization, molecule design, data quality frameworks. They are powerful tools for those specific problems, and they reflect what happens when an advisory firm builds technology: the products align with the advisory mindset (analytics, modeling, prediction) rather than the builder mindset (autonomous systems that complete work in production).
Nexus is purpose-built for a different category: autonomous AI agents that complete business workflows end-to-end. Customer onboarding, sales intelligence, compliance automation, support triage, proposal generation. These are operational processes where agents replace manual work, not analytical models where data scientists build predictions. Different problems, different architectures.
It is also worth understanding QuantumBlack in context. McKinsey has tried for decades to build genuine technology capabilities through acquisitions and in-house efforts. QuantumBlack is the most successful example, but it still operates within the advisory firm's DNA: products are sold through consulting engagements, implemented with consultants project-managing the technical work, and supported through managed services billed at consulting rates. The advisory culture shapes the technology, not the other way around. Nexus is full-stack: it develops its own framework, solution, and platform, and the people who advise are the same people who build.
Is Nexus really comparable to McKinsey? They are one of the largest firms in the world.
They are not directly comparable, and that is the point. McKinsey is a global consulting firm with 40,000+ employees and decades of institutional knowledge. Nexus is a focused enterprise AI platform backed by Y Combinator and General Catalyst, with a $4M seed round, founded by a former McKinsey consultant who understands from the inside why the advisory model cannot deliver AI implementation at the speed enterprises need. The comparison is not "which firm is bigger." It is "which approach gets AI agents into production faster and delivers measurable outcomes," and, critically, "which vendor's incentives and mindset are aligned with yours." McKinsey has the advisory mindset and the incentive structure of a consulting firm. Nexus has the builder mindset and the incentive structure of a platform company. Orange, Lambda, and other enterprise customers chose Nexus for deployment speed, business team ownership, embedded engineering support, and a pricing model that rewards outcomes over hours. McKinsey may have helped them define the strategy. Nexus helps them execute it.
McKinsey offers managed services through QuantumBlack. Is that similar to Nexus FDEs?
QuantumBlack's managed services team provides ongoing support, maintenance, and optimization post-deployment for their data science solutions. This is valuable for analytics models. But the model is still built around QuantumBlack's proprietary tools and consulting engagements, billed at consulting rates. The longer the managed service runs, the more the firm earns. There is also a mindset difference: QuantumBlack managed services are staffed by people operating within an advisory firm's culture and processes. They manage technology; they do not build alongside you.
Nexus FDEs are fundamentally different: they are builders embedded in your team who help your business users build, deploy, and own agents on the Nexus platform. FDEs implement directly with your team, train your team to operate independently, and work on a platform Nexus builds and controls end-to-end. FDEs are included in the platform cost; there is no separate billing for service hours. The entire process has no IT dependency. The goal is to build your team's capability, not to create a permanent dependency on external consultants. Your team owns the agents. Your team iterates. Your team scales. Nexus grows by helping you expand to more use cases, not by billing more hours on the same one.
What if we need both AI strategy and AI deployment?
Many enterprises do, and the distinction between advisory and builder mindsets actually clarifies how to structure this. Engage McKinsey for the strategic layer: organizational alignment, multi-year roadmap, board-level credibility. These are advisory problems, and McKinsey's advisory mindset is a strength here. Engage Nexus for the execution layer: agents in production within weeks, business team ownership, measurable outcomes. These are building problems, and they require builders. The two are complementary precisely because they require different mindsets. McKinsey defines where AI should go. Nexus makes it work. Our recommendation: run them in parallel rather than sequentially. Do not wait for a 6-month strategy phase to finish before deploying a single agent. Nexus starts with a 3-month POC precisely because building and measuring teaches you more than planning alone. Some enterprises start with Nexus first to get quick wins and build internal conviction, then engage a strategy firm for the broader transformation roadmap. Others do it in reverse. Either sequence works, but the worst outcome is spending 12+ months on strategy while competitors deploy.
How does pricing compare?
A McKinsey AI strategy engagement typically starts at $500K-$1M+ for a single engagement phase. Multi-phase transformation programs can run into the tens of millions over 12-24 months. The structural reality: McKinsey earns more when the engagement is longer, the scope broader, and more consultants are staffed. The client pays for effort. Nexus starts with a 3-month proof of concept at a fraction of that cost, with agents in production typically within the first 2-6 weeks. You measure the impact before committing to an annual contract. FDEs are included in the platform; there is no separate bill for service hours. The pricing models are fundamentally different: McKinsey charges for consultant time (which scales linearly with scope and creates an incentive to expand scope); Nexus charges per-agent (which scales with value delivered and creates an incentive to prove outcomes fast).
You say McKinsey consultants "project-manage developers" instead of building. Is that fair?
It is a generalization, but it reflects a structural reality. McKinsey consultants are trained in strategic analysis, stakeholder management, and problem structuring. When an AI engagement moves from strategy to implementation, the consultants typically coordinate between the client and a technical team (either QuantumBlack's data scientists, an external systems integrator, or the client's own IT department). They scope, plan, review, and present. They do not write the code, design the agent architecture, or debug the integration. The consultant sits between the client and the builder, adding a coordination layer. This is not a failure of the individuals; it is the natural result of an advisory firm staffing advisory-trained people on building projects. At Nexus, the FDE who advises you on agent design is the same person who builds it. There is no coordination layer. The builder is in direct contact with the client, implementing directly, with no IT dependency.
How does Nexus's CEO bring an insider perspective on McKinsey?
Nexus's CEO, Assem, is a former McKinsey consultant. He has seen from the inside how the firm structures engagements, how incentives shape behavior, and why the advisory model creates specific limitations when it comes to AI implementation. This is not an outsider's critique of a firm he does not understand. It is an insider's understanding of why a fundamentally advisory organization, no matter how talented its people, is structurally limited in its ability to build and deploy AI at the speed enterprises need. The incentive misalignment and the advisory mindset are not separate problems; they reinforce each other. The business model rewards thoroughness and duration. The advisory mindset naturally gravitates toward analysis and planning. Together, they create a system optimized for strategic thinking, not for shipping production AI.
What does the 3-month POC look like?
Every Nexus engagement starts with a 3-month proof of concept tied to specific, measurable outcomes defined upfront. A Forward Deployed Engineer embeds with your team from day one. Most agents are in production within the first 2-6 weeks. You see the results, measure the impact, and decide whether to continue. You can exit anytime. Compare this to a consulting engagement where you may invest $500K+ before a single agent is built. Nexus's structure forces us to prove value before asking for a long-term commitment. This is why our POC-to-contract conversion rate is 100%: we do not move forward unless the value is clear, and the incentive structure ensures we are focused on making it clear as fast as possible.
Worth exploring?
If your organization has already invested in AI strategy, or if you have been evaluating consulting engagements and wondering whether the 12-18 month timeline to production is the only option, it might be worth seeing how companies like Orange and Lambda approached this differently. It might also be worth asking two questions: are your vendor's incentives aligned with yours (do they earn more when you get results faster, or when the engagement runs longer?), and does your vendor have the builder mindset to actually ship (are the people advising you the same people building the solution, or is there a coordination layer between you and the technical work?).
Orange is a multi-billion euro telecom operator that deployed customer onboarding agents in 4 weeks. $4M+ incremental yearly revenue. 100% adoption. Lambda is a $4B+ AI company that chose to buy instead of build. $4B+ in pipeline identified. 24,000+ research hours added annually. One enterprise client watched an outsourcing firm spend a full year planning a knowledge assistant; Nexus deployed it in 4 weeks. All deployed with Nexus, with Forward Deployed Engineers embedded from day one. FDEs included. No separate service fees. No day rates. No coordination layer. No IT dependency. Builders implementing directly.
Every Nexus engagement starts with a 3-month proof of concept tied to specific outcomes. You see working agents before committing. You can exit anytime. Nexus earns the annual contract by proving value during the POC. That is both the incentive difference and the mindset difference. Nexus was built by builders, for building.
[Read the Orange case study -->]
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Every engagement starts with a 3-month proof of concept tied to specific, measurable business outcomes. Forward Deployed Engineers embed with your team from day one.