From Consulting to Code: Establishing the Right Approach for the AI Age

As generative AI continues to advance, it’s raising questions about the future of many sectors. One such question concerns the role of consulting in the age of artificial intelligence. While I see no real risk of AI replacing consulting completely, the domain is certainly set to undergo far-reaching transformation.

Even today, artificial intelligence is automating routine diagnostic workstreams, slide decks, and some parts of strategic analysis. Looking ahead, Gartner predicts that, by 2026, 60% of consulting growth will be generated by tech products – up from 38% in 2020. Against this background, consultancies are increasingly asking whether they’ll continue to operate as service-based advisors or morph into providers of scalable software-centric platforms?

A Shift from Human to Digital Value Drivers

The changing face of consultancy is reflected in a recent IBM survey of global executives of who purchase consulting services. A massive 86% of respondents state that they’re actively seeking services that incorporate AI and technology assets. And around 66% say they’ll no longer work with consulting providers who don’t incorporate AI into their services.

These attitudes represent a radical departure from conventional consulting models. Not all that long ago, the key value drivers in the sector were the human workforce and a relationship-driven market. By contrast, today’s clients have more control and a greater number of options, enabling them to choose between boutique specialists, consulting platforms, and global players, depending on their specific project needs.

What we’re witnessing is a move away from human leverage and toward digital leverage, with traditional value drivers giving way to code, data, and reusable systems. One reason for this is that software products offer greater scalability and higher gross margins than service-only models.

Impact on Operating Models and Hiring Strategies

Unsurprisingly, these changes affect consulting providers’ operating models. Large firms have long relied on people-intensive approaches. Generally, this involves a large base of junior consultants, who tackle routine work, with a smaller number of senior leaders handling strategy and client relationships. Structures of this kind are now being challenged as a result of AI’s ability to perform repetitive tasks.

As a result, the “pyramid” model is now breaking down. Automation and greatly accelerated task management are reducing the number of juniors required. So, it’s now essential to ensure that these workers are reskilled so that they can make a greater value contribution.

However, these developments also bring a new structural risk: If AI takes on the tasks traditionally handled by juniors, who will train the next generation of consultants? Unless consultancies radically rethink their approach to junior development, they could well end up losing their internal pipeline of future senior advisors.

Five Steps to Redefining Consulting

To master these challenges, consultancies must adopt the following five-step approach:

• Define the “product” based on repeatable value

• Establish product development capabilities and align talent with associated new requirements

• Monetize software-led models (service plus software approach)

• Prioritize strategic action instead of delivery alone

• Target specific sectors and vertical

  
  

Let’s look at each of these in turn.

Pinpoint Recurring Issues and Attract New Talent

Rather than focusing exclusively on custom projects, consulting firms need to identify recurrent client issues – for example, risk modelling and cloud-migration analytics – and create modular service and software platforms designed to address them.

In addition, they must develop the capability to design, build, and maintain software. This calls for a change in hiring strategies, with a shift in focus toward software engineers and data scientists. Even more importantly, it entails redefining the role of junior consultants, who should no longer act merely as task executors but as early-stage strategists.

Put Software Center Stage and Refocus on Strategy

It goes without saying that the increased emphasis on software must be monetized. This can be achieved by introducing software as a service (SaaS) subscriptions, usage-based models, outcome-based contracts, and embedded analytics modules in client systems. For example, rather than bill daily rates, a consulting-turned-software firm could license predictive-AI modules to clients.

Delivering these capabilities to clients is important, but consultancies must also focus on integrating their solutions into client processes – for instance, in the form of workflow triggers and decision systems –, and enabling clients to get the most out of their solutions.

Target the Right Industries

The greatest opportunities for these new consulting approaches are in data-heavy, decision-intensive sectors with regulated environments – for example, manufacturing and energy. As discussed in my most recent blog post on digital twins, these sectors offer considerable opportunities for transforming asset management and operational risk by deploying innovative tech.

Master the Challenges of Large-Scale Change

However, involving software in projects and building the necessary solutions poses a host of new challenges. For one thing, it entails higher up-front costs and different investment horizons than service projects – and carries associated risk.

Adopting new software-driven consulting models can also necessitate change management initiatives since clients may be more comfortable with traditional advisor-in-the-room models.

What’s more, attracting the necessary talent could prove difficult. Engineers and data analysts, for instance, are currently in high demand. So, finding the right people to implement the shift in consulting focus may not be easy.

From KT to IP: New Challenges for Consultancies

As mentioned above, as the classic model of knowledge transfer from senior to junior breaks down, training future consultants will present structural challenges. To avoid talent erosion, it will be imperative to actively invest in new mentorship models, simulation-based learning, and real-case exposure.

Consultancies also face the two-fold challenge of intellectual property and productization, which necessarily arises when they turn knowledge and their own AI implementations into reproducible, reliable, and scalable software modules for clients.

And last but not least, firms must be prepared to tackle major changes in the areas of go-to-market and monetization. In short: Sales, marketing, and positioning must evolve from an hours-billed to a platform-value-sold model.

Appreciating the Nature and Scale of AI-Fueled Change

It’s important to realize that AI is much more than just a new tool in the consulting toolbox; it’s a large-scale structural shift. That being said, human-centric tasks will remain essential. However, the way that humans are trained, deployed, and developed will have to fundamentally change.

In my view, the major risk facing consultancies is not loss of market relevance; riskier still is the potential loss of ability to cultivate future consultants before other companies with the necessary skillsets make the move into the advisory space.

It will be firms that can successfully redesign both their business model and their talent development pipeline now that will ensure scale, independence, and long-term relevance in the software-centric consulting future.

What Do You Think?

Interested in taking a deeper dive into consulting in the AI age? Then feel free to reach out to me. Do you have thoughts of your own about this hot topic? If so, please share them in the comments below.