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Estd 4.22.2023
Traditional management consulting has long been associated with sky-high fees, labor-intensive projects, and bespoke advice delivered in thick slide decks. Top firms like McKinsey, BCG, and Bain operate on a people-intensive, project-based model that, while delivering customized insights, comes at a steep cost and scale limitation.
In a world increasingly dominated by scalable software and AI-driven solutions, a provocative question arises: Can the deep research and strategic insight of a McKinsey be transformed into a subscription-based product?
In other words, can “McKinsey” be installed next to my CRM? This post explores that possibility, examining why consulting is so expensive, how technology (LLMs, AI copilots, automation) is changing the game, real examples of “productized” consulting services, and the feasibility of delivering consulting-like value via a scalable subscription model.
The Traditional Consulting Model: Expensive and Human-Intensive
Management consulting’s classic model is high-touch and high-cost. Elite firms deploy teams of highly educated consultants to work on a client’s problem for weeks or months, often on-site. Clients essentially rent a brain-trust of analysts and MBAs who crunch data, conduct interviews, and produce recommendations. This model carries inherent cost drivers:
1. Labor and Overhead
Consulting teams are stacked in a pyramid, from junior analysts up to partners, all billing for their time. A typical strategy project can cost anywhere from $500,000 to over $1,000,000 for a few months of work. Clients are paying not just for expertise, but for the hours of a whole team (and expenses like travel and lodging for on-site work).
For example, a 12-week engagement might involve 5+ consultants on the ground, incurring over $200K in travel and overhead costs in addition to hefty salaries. This reliance on human brainpower means more clients require more consultants, scaling up is tied directly to increasing headcount. This approach limits economies of scale and keeps fees high.
Additionally, as the consulting product is the human expertise of the team, its analytical skills, frameworks, and experience, it’s hard to automate, making it inefficient in today’s tech-driven world.
2. Bespoke, Project-Based Work
Traditionally, consulting engagements are one-off projects tailored to a specific question (entering a new market, pricing a new product, etc.). The output is a customized analysis and set of recommendations.
Once delivered, the project ends. If the client needs ongoing support or a new study next quarter, that’s a new contract. This project-by-project approach means value is delivered in discrete, non-recurring chunks; there's no continuous service unless the client retains the firm for another project.
3. Human Expertise as the Product
In management consulting, the product is essentially human brainpower. The value lies in the consultants’ analytical skills, frameworks, and experience. This does not lend itself easily to automation or scalability, adding more clients traditionally requires adding more humans.
Economies of scale are limited. Firms are constrained by the number of consultants and their billable hours; more clients can only be served by hiring more consultants. This is lucrative but not very scalable, and it’s part of why fees remain high.
4. Complex, High-Stakes Issues
To be fair, the problems consultants tackle (corporate strategy, major operational changes) are often complex and high-impact, justifying a premium. Clients pay for an outside perspective and rigorous research that they may not have the time or skill to do internally. However, it has led to a reliance on brute force research, e.g. a team of junior analysts combining data and building models from scratch, which in today’s terms looks increasingly inefficient.
All these factors make traditional consulting prohibitively expensive for many companies, especially startups or mid-market firms. Even those who can afford it might question the ROI.
Why Change? Pressure to Scale and the Tech Revolution in Research
Several converging forces are challenging the traditional model and opening the door to a productized, subscription approach:
1. Client Demand for Value and Flexibility
In an era of lean and agile business, even large enterprises are seeking more cost-effective and flexible ways to get strategic insights. The idea of a $1M project with a big reveal at the end is less attractive when businesses need continuous guidance and faster iteration.
Many clients would prefer a longer-term partnership or on-demand advice model, rather than ad-hoc big projects. This creates an opening for subscription-style arrangements (steady advice for a steady fee) or outcome-based pricing. Indeed, industry observers note early signs of consulting engagements moving toward outcome-driven or subscription-based pricing models. Rather than pay purely for hours, clients want to pay for results or ongoing support.
2. The Need to Scale Insight Delivery
Consulting firms themselves face a scalability problem. Traditional work doesn’t scale easily, revenue grows linearly with headcount. This has become the consulting world’s “Achilles’ heel.” Leading firms recognize that productizing parts of their service can allow them to serve more clients without a proportional increase in headcount.
In other words, packaging repeatable research or analysis into a product can break the linear model of “more consultants for more clients.” Scalability is a key driver pushing firms to rethink their delivery model.
3. Technology Advancements
LLMs and AI to the Rescue: The biggest catalyst is technology, specifically advancements in artificial intelligence and large language models (LLMs) that can perform research and analysis tasks. We are now at a point where much of the heavy “grunt work” of consulting can be automated or accelerated by AI.
For example, generative AI (like GPT-4) can summarize and analyze vast amounts of data in seconds, something that used to require a team of analysts weeks of work. Need to comb through hundreds of market reports, earnings call transcripts, and academic papers? An AI can do that and spit out a coherent summary or initial hypotheses almost instantly.
In fact, major firms have piloted exactly this: automated summarization and even initial hypothesis generation using AI, dramatically shrinking task times.
4. AI Co-Pilots for Consultants
Beyond one-shot analysis, “AI agents” can perform iterative research loops. Early examples (IBM and others) show AI agents autonomously conducting research, drafting documents, and refining analysis continuously. What does this mean? The classic model of a junior consultant pulling data 14 hours a day is being upended, an AI co-pilot can handle a lot of that repetitive work.
A Harvard Business School and BCG experiment with 758 consultants quantifies this: consultants using GPT-4 were 25% faster and 40% more productive in producing high-quality outputs, compared to those without AI. That’s a staggering efficiency gain. Routine analytical tasks are no longer the exclusive realm of humans.
5. Integration into Workflows
Another tech trend is the integration of these AI capabilities directly into business software and workflows. Think of it as embedding a “consultant” in your everyday tools. A prime example is the McKinsey–Salesforce partnership announced in 2023, which explicitly aims to deliver McKinsey’s AI models and data insights within Salesforce’s CRM platform.
By bringing McKinsey’s proprietary knowledge into Salesforce’s Einstein AI and Data Cloud, the idea is to give sales and marketing teams AI-driven strategic insights during their normal course of work, essentially, McKinsey’s advice at the click of a button, inside your CRM. This highlights how consulting expertise can be productized as a software layer, integrated with the client’s systems. It’s literally putting some of “McKinsey” next to your CRM.
6. Market Research and Data at Your Fingertips
Relatedly, companies now have access to rich data sources and research tools that didn’t exist a decade or two ago. Where a consultant might have been needed to run a custom market survey or build a competitive analysis, today there are SaaS products that provide on-demand data and insights.
Services like Gartner Research (subscription access to analyst reports and inquiry calls) or fintech data platforms (e.g., PitchBook, CB Insights) deliver research as a product. They aren’t complete substitutes for custom consulting, but they certainly handle the “deep research” function in a scalable way, via subscription. This has conditioned clients to expect continuous insight delivery rather than one-time studies.
In short, the tools now exist to decouple consulting insight from consulting labor, at least for the research-heavy parts. The repetitive, data-heavy tasks can be automated; the insights can be delivered through software; and the human experts can be redeployed to higher-value activities (or to serve more clients). This technological shift underpins the possibility of a new model.
From Bespoke Projects to Scalable Products: Early Signs of a Hybrid Model
Can consulting be productized? Increasingly, the answer seems to be yes, at least in part. We’re already seeing real-world movement toward “consulting-as-a-product” or hybrid service models. Notably, the big consulting firms themselves are leading some of these changes, indicating that even at the top end the model is evolving.
In fact, the MBB firms (McKinsey, BCG, Bain) have been pioneering the productization trend. Rather than selling only reports and human hours, they are developing tech-enabled solutions, data platforms, and subscription services alongside traditional consulting. A few examples:
1. McKinsey Solutions & Periscope
McKinsey launched an entire suite called McKinsey Solutions a number of years ago, which includes tools and analytics platforms that clients can use (often on a subscription or license basis).
One flagship offering is Periscope® by McKinsey, a suite of analytics for pricing, promotions, and sales optimization. This is essentially a software product (with McKinsey’s expertise built-in) that companies subscribe to in order to continuously improve pricing strategy. In one case study, a global retailer using Periscope achieved a 1.5% revenue increase and 20% improvement in promo effectiveness, purely from the data-driven insights the product delivered.
Periscope’s success lies in merging high-end consulting IP with a scalable, tech-driven delivery, a McKinsey-caliber analytics brain that multiple clients can plug into at once.
This isn’t just “shoehorning” consulting into software; it’s a “fully baked, highly productized research offering” that delivers insight with a speed and cost efficiency you “wouldn’t normally associate with a high-end strategy consultancy.” In other words, McKinsey found a way to bottle its secret sauce (at least for certain domains like pricing analytics) and sell it as a recurring product.
2. BCG’s Digital Products (BCG X)
Boston Consulting Group took a slightly different route by heavily investing in BCG X, its tech build and design unit (formerly BCG Digital Ventures). BCG X helps clients build technology and even new businesses as part of consulting engagements, effectively blending product development with consulting.
But importantly, BCG also uses AI internally at scale, they reportedly developed an “Enterprise GPT” tool to synthesize information for teams, and their consultants have created thousands of internal AI “co-pilots” to automate tasks like document summarization.
This internal productization (building AI tools for their own use) hints at what they could offer to clients. If BCG can automate a large chunk of analysis with its in-house GPT, it’s not a stretch to imagine offering that capability to clients via a platform.
In essence, BCG is turning its consulting knowledge into code, an investment that could be repackaged as client-facing products. Their moves signal that strategy consulting can come with software attached.
3. Bain’s NPS Prism and Accelerate
Bain & Company has also embraced productization. A notable example is NPS Prism, a benchmarking tool for Net Promoter Score and customer loyalty metrics across industries. Rather than each client hiring Bain to conduct a custom customer loyalty study, Bain gathered the data into a subscription platform where clients can self-serve insights on how they stack up in customer experience.
Additionally, Bain’s Accelerate initiative focuses on building industry-specific solutions (tools, data benchmarks, etc.) for common challenges. This represents a shift from pure advice to packaged expertise. Clients get the benefit of Bain’s research in a more standardized (and likely lower-cost) format. It’s a classic example of turning tacit knowledge into a tangible product.
4. Big Four and Others
It’s not just the strategy of pure-plays. Firms like Deloitte, PwC, and Accenture have been investing in software and platforms for years. Accenture, for instance, often bundles proprietary tools in implementations. PwC in the US has reportedly invested $1 billion in AI to develop internal and client-facing AI solutions, including more subscription-like or outcome-based offerings in their consulting mix.
Even smaller boutique consultancies are offering “Consulting-as-a-Service” packages, for example, fractional CxO advisory on a monthly retainer, or ongoing Salesforce consulting for a flat monthly fee instead of a big SOW (statement of work).
These are essentially subscription consulting models, albeit usually still human-driven. The key shift is the focus on ongoing engagement and scalable deliverables.
5. Analyst Firms & Research Subscriptions
While not management consulting per se, it’s worth noting the success of firms like Gartner and Forrester in selling research and advisory services via subscription. Gartner’s core business is a subscription that gives clients continuous access to research reports and expert analysts on-call. This model has existed for decades, proving that clients will pay hefty recurring fees for ongoing insights (Gartner is effectively productized insight with a human touch, analysts available for inquiries). It addresses a similar need: organizations want on-demand answers and benchmarking, not just one-time studies. The popularity of these services underscores a market appetite for recurring insight delivery.
Collectively, these examples show a clear trend: deep research and consulting insight are being delivered via software or hybrid models. Major firms are now actively turning consulting knowledge into scalable products. The lines between a “service” and a “product” in the consulting realm are blurring.
Notably, generative AI is turbocharging this movement. The ability to capture a consultant’s analytical approach in an AI model means parts of the consulting process become reproducible at near-zero marginal cost.
McKinsey, BCG, and Bain wouldn’t be investing in these digital platforms if clients weren’t interested… but they are. Clients are seeing the value of quicker, tech-enabled insights: Why hire a big team to spend 8 weeks on market research when an AI-infused platform can give you initial answers in 8 minutes, and then you only spend human time on the most complex 20% of the problem?
It’s the same reason many industries have moved from bespoke to productized solutions - efficiency, consistency, and scalability. Let’s break down those drivers in the consulting context:
- Scalability: Productized services can be delivered to many clients simultaneously without linear cost growth. A software platform can serve 100 clients almost as easily as 10, which is not true for traditional consulting headcount.
- Consistency: A standardized product (e.g. a benchmarking tool or an AI analysis engine) ensures a consistent level of quality and methodology for all clients. It reduces the variability that comes with different project teams. Clients know what they’re getting each time.
- Efficiency and Speed: By leveraging repeatable solutions and automation, the time and resources for each engagement plummet. What might have taken a consulting team a month might be delivered in a day via a SaaS platform, dramatically lowering cost. This also allows for more competitive pricing or reaching smaller customers profitably.
- Data-Driven Insights: Productized solutions often involve big data and analytics that go beyond what a manual project could do. For example, a platform might continuously ingest market data, so it’s always up-to-date, something a one-time project cannot match. This can lead to deeper insights delivered faster, a win-win for client and provider.
These advantages explain why even the top firms are heading in this direction. However, delivering consulting via a subscription or product raises a host of questions: How exactly would such a service work for clients? How do you blend human expertise with software in a seamless way? And crucially, how do you price this kind of offering for mutual success?
Designing “Consulting-as-a-Subscription”: How It Might Work
Imagine subscribing to a strategy advisory platform that combines software and service. Instead of one-off projects, your company would gain access to a platform with AI-driven research capabilities, real-time industry data, and human expert guidance. On your dashboard (or within your CRM), you could ask strategic questions and receive immediate insights.
For example, “How is our market share trending versus competitors this quarter?” or “Simulate the revenue impact if we raise prices 5%”. Powered by AI and connected to both internal and external data, the system generates quick reports. If deeper expertise is required, an on-demand consultant could refine and discuss the analysis.
This platform would operate continuously, monitoring KPIs, news, and trends, providing timely alerts when a consulting team would typically present findings months later. It’s like having a virtual analyst 24/7, with real consultants available as needed. Over time, AI adapts to your business context, offering more tailored insights, and scheduled check-ins with human advisors ensure ongoing personalized support.
The pricing for this service would deviate from traditional time-and-materials models. It would operate on a subscription basis, similar to SaaS, offering predictable costs. Here are some potential pricing models:
1. Subscription Fee
A fixed monthly or annual price for the service, granting a certain level of access for your team. This might be tiered (e.g. Basic, Pro, Enterprise packages) depending on the depth of support or number of users. A flat fee provides predictability, much easier to budget for than variable consulting bills, and encourages clients to use the service more freely. It turns consulting into an OPEX item like any other software subscription.
2. Per-Seat (User) Pricing
Charging per user (or per “seat”) who accesses the platform or advisory service. For example, you pay for each team member who wants to query the system or consult the experts. This model might make sense if the platform is something many individuals in the organization will use (like how many SaaS tools are priced). It aligns price to the number of beneficiaries.
3. Usage-Based Pricing
Tying the fee to how much you use the service, for instance, number of AI analysis queries run, or number of hours of human consulting support consumed, or volume of data processed. A metered approach ensures you pay in proportion to value received. If the service heavily leverages AI, usage-based pricing could be analogous to paying for API calls or processing time (much like cloud platforms charge by usage).
Hybrid models might emerge too, e.g. a base subscription that includes X analyses per month, with overages charged per use. Notably, usage-based pricing has become popular in SaaS because it can align price to value and drive strong net retention; companies with usage-based models have seen superior net dollar retention and customer growth. A consulting product could benefit from the same dynamic if usage correlates with realized value.
4. Outcome or Value-Based Pricing
The holy grail (and toughest to do) is charging based on outcomes, for instance, a percentage of cost savings identified or revenue growth achieved via the platform’s recommendations. Traditional consulting sometimes uses success fees or gain-sharing, but it’s rare.
In a productized context, this could look like bonus charges if certain KPI improvements occur. While appealing to clients (“pay only if it works”), it’s complex to measure and attribute, so likely this would be a smaller component or offered in selective situations. That said, the mindset shift to shared risk/reward may become more common. Industry experts foresee more success-based contracts and outcome-driven models supplementing subscription fees.
The right pricing strategy is critical for success. As Monetizely philosophy suggests, pricing is a growth lever. Subscription revenue is more predictable and stable than project-based fees, encouraging continuous service improvements and renewals rather than just landing big projects. It also allows for organic growth by expanding usage, adding more teams or modules, without relying solely on acquiring new customers.
Switching to a subscription or hybrid model also requires a shift in a consulting firm’s operations. To succeed, firms must invest in product development, AI models, data engineering, and user interface design, while managing a product roadmap, customer support, and service uptime.
This represents a significant change for firms traditionally focused on bespoke work, as it introduces a product mindset. Top firms are already hiring engineers, data scientists, and product managers to support these new offerings, indicating a recognition of the shift towards AI-driven, hybrid models.
The Human Element: Can AI Fully Replace Consultants?
A key question is the role of humans in this new model. Can AI truly replace the McKinsey team, or is it just an augmentation? While AI excels at analysis and data processing, human consultants remain essential for judgment, creativity, and stakeholder management. Consulting isn’t just about data; it’s about navigating organizational dynamics, aligning leadership teams, and driving decisions to action. These elements, empathy, experience, and persuasion, are human strengths.
In a productized consulting model, AI handles data-heavy tasks and generates insights, while human consultants focus on interpreting and validating those insights, guiding clients to take action. Think of AI as an analyst and humans as strategists or coaches. AI may miss critical nuances, like understanding a CEO's bias or a demoralized sales team, which can affect the success of a recommendation.
As one consultant noted, “No matter how capable AI becomes, human insight and communication are indispensable in organizational transformation.”
For the subscription model, this means maintaining a hybrid approach. The platform could include a set number of hours with a consultant each month or access to a pool of experts for special queries. AI can draft reports, but a human reviews and finalizes them before sending them to the CEO. This structure might be 80% software, 20% human, balancing efficiency with the personal touch where it matters most.
This hybrid model remains far more cost-effective than traditional consulting. If one consultant, augmented by AI, can replace a five-person team, costs drop significantly. Providers could offer subscriptions (e.g., $10k/month) for continuous insights, which is much more affordable than one-off projects, while still maintaining profitability due to tech efficiencies.
The RocketBlocks analysis of a $1M engagement showed roughly 50% profit margin for the consulting firm; with a tech-driven approach, margins could potentially be maintained or improved through efficiency while charging the client less on a per-engagement basis. Thus, scalability and margin can go hand in hand if done right.
Pricing Implications: A Strategic Lever for Growth
Now let’s zero in on the pricing strategy. In transitioning deep consulting research to a subscription product, how you price it is as strategic as the technology behind it. Pricing will determine market adoption, competitive positioning, and long-term economics. A few key considerations:
1. Lowering the Barrier
Traditional consulting’s pricing is a barrier that keeps many potential customers out. A SaaS-like pricing approach (e.g. affordable monthly fees or freemium tiers for basic insights) could vastly expand the market. Small and mid-size businesses might tap into strategic insights they previously couldn’t afford. This could drive a new wave of growth for whoever offers such a model, analogous to how cloud software made sophisticated tools available to the masses.
However, the challenge is to price in a way that doesn’t undervalue the expertise. There’s a fine line between democratising access and eroding the premium aura of top-tier advice. The strategic move might be offering tiers: an entry-level automated insight service at a low price, and higher tiers that incorporate more expert time and customization for a premium. That way, you capture value across customer segments.
2. Aligning Price with Value Delivered
A core tenet is aligning pricing metrics to customer-perceived value. In a consulting product, this means identifying what the client really values, is it the number of insights, the outcomes achieved, the breadth of features, or simply the peace of mind of having an advisor on call? If, for example, the value is in continuous decision support, maybe a simple subscription makes sense (unlimited use for a flat fee, encouraging clients to utilize it fully).
If the value is tied to usage (say a client only uses it sporadically for big decisions), a usage-based model might resonate (“pay only when you need it”). The provider must choose a pricing model that encourages adoption but also scales with the value the client gets. This is why hybrid pricing is attractive, for instance, a base retainer (ensuring commitment) plus a variable fee for high usage or big outcome events.
We know from SaaS benchmarks that hybrid models can maximize revenue: a combination of subscription plus usage often yields better growth and retention. The same principle likely applies here to balance predictability and value-capture.
3. Outcome-Based Upside
While difficult to implement, outcome-based pricing could be the ultimate alignment of interests. If a subscription consulting product directly contributes to (or at least can credibly track) improvements in revenue, cost, or other KPIs, structuring a bonus or commission on those outcomes would turn pricing into a true win-win lever.
For example, the contract could stipulate that if the client’s revenue grows by X% attributable to the platform’s recommendations, the provider gets a bonus payment or an escalating renewal rate. This is analogous to how some software pricing now is tied to ROI metrics. It’s not common yet, but it’s been discussed as a way consulting might evolve, essentially putting “skin in the game.” We might see experiments here as a differentiator in the market.
4. Strategic Bundling and Packaging
The move to a product model also allows creative packaging of offerings. A consulting firm might bundle software access with a certain number of quarterly workshops or with membership in a peer forum (added value).
Packaging can create new value pools and justify pricing that is based on outcomes rather than hours. It also helps avoid direct price comparisons to traditional consulting, framing it as a different category (e.g., “Strategy Insights Platform - Enterprise Plan includes 5 user licenses + quarterly expert briefings, for $15k/month”). Effective packaging is a strategic lever to communicate value and expand revenue per customer.
5. Competitive Dynamics
If one firm successfully productizes consulting, others will follow. We could see a landscape where, say, McKinsey’s subscription competes with a SaaS startup’s AI consultant product, and perhaps with Deloitte’s version, etc.
Pricing strategy will be crucial in this competitive context, whether to undercut incumbents, use land-and-expand tactics, or maintain a premium positioning. Given pricing’s huge impact on profitability (recall that even a 1% price improvement can boost profits ~8-11% on average), these decisions are make-or-break.
Challenges and Feasibility Considerations
Is it truly feasible to deliver deep consulting research as a cost-effective, scalable subscription product? The trends and examples suggest yes, but there are challenges to acknowledge:
1. Data Security and Privacy
Deep consulting often requires diving into a client’s confidential data (financials, strategies, etc.). Offering an automated platform means handling sensitive data at scale. SaaS solutions will need robust security, possibly on-premise or private cloud options for cautious clients, and clarity on how client data is used (especially if LLMs are involved, ensuring no leakage of proprietary info into public models).
2. Scope Limitations
Not every aspect of consulting can be automated. The current sweet spot is research, analysis, and insights generation. Areas like organizational change, leadership coaching, or highly nuanced strategic judgment are harder to codify.
So the productized model will have scope boundaries; clients may still need traditional consulting for certain projects. The subscription might serve as a constant baseline service, with bespoke projects as add-ons for extraordinary needs. This hybrid approach (product + bespoke when needed) could actually strengthen consulting firm relationships.
3. Change Management for Adoption
Clients have to be ready to use these new tools. Some companies may still want a person in the room to debate with, rather than an AI interface. Part of the service might involve training client teams to trust and effectively use the platform.
Consulting firms will need to market the value of the new model, not just in cost savings, but in speed and agility. Success stories (like the retailer who gained revenue via Periscope) will help convince the skeptics.
4. Cannibalization Concerns
Big consultancies have to manage a classic innovator’s dilemma, by offering a cheaper, scalable product, do they cannibalize their own premium projects? The smart view is that productized services will capture a different segment (or meet different needs) and expand the pie, rather than simply undercut existing work.
In practice, firms will segment their offerings: Fortune 100 CEO transformations might still be high-touch, but mid-tier strategy support could shift to the subscription model. Over time, as trust in the model grows, even large clients might favor a retainer+platform instead of constant RFPs for projects.
5. Competition from New Entrants
Tech startups (unencumbered by legacy business models) may jump into this space with pure-play “AI consultant” products. We’re already seeing venture investment in tools that claim to be “GPT for enterprise strategy” or domain-specific AI advisors.
These entrants will push innovation and might force traditional firms to accelerate their own productization. Ultimately, the winners will be those who can combine the best of tech and human insight in a seamless offering.
Despite these challenges, the feasibility is bolstered by the fact that clients are already buying pieces of this concept today, whether it’s subscribing to data platforms, using GPT-4 on their own data, or signing ongoing advisory retainers. The puzzle pieces are there; it’s now about stitching them into a compelling, integrated product.
Conclusion: A New Era - Consulting in the Cloud
We are witnessing the early stages of a new era in consulting, one where high-end expertise, once reserved for elite firms, is becoming accessible through subscription-based models and AI-driven platforms. The question, “Can McKinsey be installed next to my CRM?” is no longer far-fetched. The deep research and insights that once required a team of consultants can now be delivered faster, cheaper, and continuously via algorithms and cloud solutions.
However, the human element remains essential. Consultants will increasingly focus on translating, validating, and facilitating AI-generated insights to drive business outcomes. The future of consulting will likely resemble a blend of tech companies and strategy firms, with product managers and data scientists collaborating with consultants to deliver high-value, scalable support.
This shift promises more accessible expertise for businesses of all sizes, from startups to enterprises, moving from episodic projects to continuous, on-demand support. The key to success will be in how firms design their pricing models, whether through subscription tiers, usage-based fees, or outcome-driven charges. By treating pricing as a strategic lever, firms can unlock new growth opportunities and ensure their model scales effectively.
In short, the evolution of consulting into a subscription-based, tech-enabled service is already underway. It won’t replace traditional consulting entirely, but it will create a new category, revolutionizing the way businesses consume strategic advice. The firms that successfully integrate AI and human expertise with a flexible pricing model will lead this transformation, shaping the future of consulting and unlocking vast growth potential.