Consultancy leaders today face a troubling ultimatum: embrace AI or risk being left behind.

Few have navigated this space more successfully than Ronny Fehling, Chief AI Officer, Partner, and VP of GenAI at BCG X. Over the past seven years, Ronny has helped BCG X grow from a not-so-humble unit of 300 to a powerhouse of over 3,000 tech experts delivering end-to-end AI solutions at scale.

Their goal is clear: harness the power of AI to redefine how consulting is done, unlock unparalleled value for their clients, and pave the way for sustainable growth.

So how can you do the same?

In this article, we’ll explore BCG X’s approach to integrating AI into consulting, drawing on Ronny’s insights and experience to provide actionable steps for leaders like you.

Mindset Shift: How to Approach AI for Maximum Business Impact

Let’s get one thing clear from the start: AI isn’t just another tool in your kit. It’s not there to automate mundane tasks or replace employees.

In fact, treating AI as a mere tool is the biggest mistake you can make.

As Ronny puts it: AI should boost your existing offers.

It should enhance what you’re already great at, allowing you to deliver even more value.

Automating tasks might sound appealing, but the real game-changing potential of AI lies in its ability to drive strategic growth and efficiency.

“I am convinced that it's important to think about AI, really, from an impact point of view. Don’t use it as a tool or something that can help automate tasks. These are commodities where tech players will offer them, and value capture is very complicated.” - Ronny Fehling

Look at BCG X’s approach. They didn’t focus on the superficial aspects of AI, like how it could automate small tasks.

Instead, they made AI part of their strategic framework—boosting their core offerings and aligning it with client needs. This is how BCG X went from 300 people to 3,000, transforming AI into a driver of growth, not just another piece of tech.

The key takeaway?

AI should never be an add-on. It must become a key part of your strategy, designed to solve big problems, scale your services, and boost what you already do best.

Automation? That’s table stakes. Strategic impact is where AI wins.

Building the Right AI Team: Key Talent and Organizational Structure

If you want AI to work in your consultancy, you need more than just tools and tech.

You need the right people, the right mindset, and the right structure to make it happen.

It’s not just about finding a talented data scientist; it’s about creating a culture where AI can drive real, measurable impact.

1. The Investment Mindset

Here’s the thing: the right AI people won’t pay off immediately. That’s normal.

You have to approach it with an investment mindset, much like you would a startup within your own company.

Ronny compares this phase to investing in a startup, where the initial costs are high, but the long-term returns are exponential.

“In the beginning, those people cost money and will not make money. You need to treat it like a startup within your company—with funding but a clear goal to profitability.”

If you’re only focused on immediate returns, you’ll miss out on the long-term potential AI can deliver.

Commit to the investment.

2. Senior AI Talent: The Game-Changer

At the core of any successful AI implementation is senior AI leadership.

Without a Project Leader (PL)-level or higher expert to guide the initiative, your AI efforts can quickly get stuck in technical complexities without ever translating into real business value.

Ronny highlights how crucial it is to invest in senior-level talent who understand both the technical side of AI and its broader business implications:

“In my thinking, you need a senior set of people, PL and up, to really have an understanding about the practical implications of such solutions.”

So don’t skimp on AI leadership.

If you want to succeed, you need experts who can connect AI to your business goals, not just build cool models.

3. Building the AI Team

Once you have your AI leader, you need to build a specialized AI team. But here’s the trick: AI experts aren’t like traditional consultants. You need a unique approach.

At BCG X, Ronny and his team developed clear career pathways for AI talent, making sure they learned core consulting skills to blend seamlessly into the broader consulting teams.

“We also make sure they learn consulting skills… It’s important that AI is part of consulting value creation, not separate from it.”

Tip: You’re not just hiring AI engineers. You’re creating a hybrid team that can think like consultants while delivering top-tier AI solutions.

4. Creating Career Paths for AI Talent

You can’t just drop AI experts into your consultancy and expect them to thrive.

They need clear career paths and a defined role within your organization. This isn’t the typical consultancy track—these are data scientists and AI engineers, and their career progression needs to reflect that.

Provide structured growth opportunities for your AI talent, and make sure they feel integrated within the broader consulting framework.

5. Integration is Everything

Think of your AI talent as part of your core consulting team, not a separate, isolated department.

The biggest mistake many firms make is keeping their AI specialists in a “tech bubble” that doesn’t interact with their consulting processes.

At BCG X, Ronny ensured that AI and consulting teams worked hand-in-hand, so AI wasn’t just an add-on—it was a core driver of value. This integrated approach allowed BCG X to embed AI into the DNA of their consulting offering.

“It’s crucial that AI is integrated into the consulting value creation process… Teams of consulting and AI need to work together, from problem identification to change management.”

Key takeaway: Make AI a core part of your consulting value chain—not just something you bolt on after the fact.

6. Getting Internal Stakeholders On Board

Implementing AI in a consultancy is not so much a technical challenge as it is a cultural and strategic one.

One of the most difficult aspects of this journey is convincing internal stakeholders, particularly partners and senior leadership, to take the leap and invest in AI. Many partners may be hesitant, as they are protective of client relationships and sceptical about unproven solutions.

The first step to overcoming resistance is to build trust and confidence with both internal and external stakeholders.

Ronny highlights that successful AI implementation requires careful management of expectations and, crucially, fall-back options in case the AI solutions don’t immediately deliver the desired results. Offering a safety net helps alleviate concerns and ensures that traditional methods are available if AI solutions underperform.

“We built confidence with fallback options… The client was confident that if AI didn’t work, we could still deliver value through other means.”

To help stakeholders embrace AI, consider these strategies:

• Develop contingency plans: Provide reassurance that if AI doesn’t achieve the desired impact, existing methods will remain in place.
• Showcase small, early wins: Demonstrate quick successes with AI to build momentum and trust.
• Leverage your strongest relationships: Focus on trusted clients and internal champions who are open to innovation.

Starting Small: A Step-by-Step Learning Phase for AI Implementation

Starting your AI journey is less about launching full-scale initiatives and more about learning from the first steps.

At BCG X, Ronny stresses the importance of using existing client relationships and starting with data-heavy processes that can yield measurable impact.

This is the phase where you learn what works and start creating the blueprint for future AI success.

1. Leverage Existing Relationships

Before jumping into AI, ask yourself: Where can AI deliver the most value for my existing clients? Instead of treating AI as an abstract tool, Ronny recommends identifying data-heavy processes within your current client base.

“We focus on entering the value chain at one specific point where we see the most feasible and provable impact.”

Look for these areas in your client relationships:

• Supply chain and logistics processes
• Sales and marketing operations
• Manufacturing workflows

These are ripe for AI-driven efficiency and transformation.

2. Three Buckets of AI Impact

According to Ronny, you can categorize AI into three key areas—each with its own potential and challenges.

1. Efficiency Boost – Automating routine tasks, like writing emails faster or processing information. Sounds great, but as Ronny notes, it’s difficult to scale and measure. The market will likely commoditize these types of solutions.
2. Process Reinvention – BCG X’s sweet spot. This involves reimagining processes entirely through AI—like revolutionizing marketing or supply chain management. This is where AI can drive the most sustainable value.
3. Black Swans – These are new businesses or entirely new functions created by AI, like launching an AI-powered product that competes with traditional offerings. Exciting but rare and very hard to implement.

3. Start Small and Learn Quickly

Your first AI projects should be focused and manageable.

This is where you start identifying proofs of value—those tangible wins that give you insights into what works.

At BCG X, Ronny and his team started with one or two cases, and used them as a platform to build their AI capabilities.

“You don’t need to build the fanciest models. Focus on using the client’s data in a smart way.”

4. Identify Patterns and Replicable Assets

Once you’ve proven value in one client relationship, it’s time to look for patterns that can be replicated across industries.

As Ronny explains, vertical and horizontal elements—the building blocks that work across different clients—can be leveraged to create scalable AI assets.

“We started to identify the parts that are not specific to the client but maybe to the industry. What are the vertical and horizontal elements that are replicable?”

This mindset allows you to take early learnings and turn them into assets that can be adapted for future projects.

5. The Next Step: Expand with Proof of Value

Once you’ve established proof of value with an initial client, it’s time to take that success to your broader client base.

Armed with real-world examples and measurable outcomes, you can position your AI offering as industry-leading, and build on your early successes to scale AI throughout your consultancy.

The lesson here: Prove it before you scale it.

Transitioning to Value-Based Pricing: Maximize Your ROI with AI

Tired of hourly billing? So are your clients.

Let’s face it, charging for AI solutions by the hour doesn’t capture the real value you’re delivering.

The shift toward value-based pricing allows you to align fees with measurable outcomes. This isn’t just about keeping clients happy—it’s about making your work pay off.

“We propose a value-based project where clients pay a portion of the captured value.”

Here’s how to make that transition.

1. Start with Outcomes, Not Hours

Instead of tracking time, track impact.

Value-based pricing ties your fees directly to the results AI delivers, whether it’s through cutting costs, boosting efficiency, or driving revenue. Clients see the benefit immediately, and you’re rewarded for delivering value—not clocking hours.

And the best part?

This model builds trust because it proves you’re invested in their success.

2. Deliver Quick Wins and Build Trust

You want clients to jump on board, right?

Start with small, high-impact wins that get them excited. Show them how AI can make a difference quickly.

“We look under every stone… finding the first $3 million in savings by identifying unnecessary costs, and doing this quickly can make a big difference.”

Ronny and his team proved AI’s potential by identifying shadow IT costs. In just a few hours, they uncovered millions in savings for clients. These early victories don’t just build trust—they set the stage for larger, more complex AI projects down the road.

3. Manage Risk with a Phased Approach

Clients hate risk. You hate it too. So, why not de-risk the AI conversation from the start?

Start with an assessment phase.

Charge a risk fee—a safety net for clients who are hesitant to jump in. This allows them to dip their toes into AI without feeling like they’re committing to the deep end of the pool.

“We do a first assessment phase with a risk fee… If we don’t find something we thought we would, there’s a safety net.”

The phased approach makes AI adoption more palatable. Once they see results, moving to larger projects becomes a no-brainer.

4. The Power of Repeatable Success

What’s better than one win? Many wins.

Once Ronny’s team showed success with shadow IT savings, they didn’t stop. They scaled that success across 30 different projects, consistently finding 20-30% in savings.

“We identified shadow IT costs across 30 projects… Knowing we could find 20–30% savings was a game changer.”

That’s how you turn quick wins into repeatable success stories.

And it’s exactly how value-based pricing becomes more than just a buzzword.

5. Why Value-Based Pricing is the Future

Outcome-driven pricing is becoming the new normal.

When your fees are tied to results—whether it’s cost savings, efficiency boosts, or revenue growth—both you and your client are working toward the same goal.

It’s not just a pricing model; it’s a partnership.

BCG X, is currently moving towards more value-based pricing, with plans to double the number of clients and projects using it in the next few years.

“Clients pay us a portion of the captured value… This model helps align our interests directly with the client’s success.”

Here’s why you should consider it:

• Align your goals: When both parties are working toward measurable outcomes, everyone wins.
• Build long-term trust: Clients are far more likely to see the partnership as fair when they’re only paying for success.
• Scale your impact: Once you start delivering results, replicating that success across clients becomes easier.

Creating Scalable AI Assets: From Proof of Concept to Real Value

Once you’ve established a strong foundation and tested AI’s potential in specific client projects, it’s time to build AI assets that can scale across multiple industries and functions.

This is where BCG X really shines: moving from one-off AI use cases to creating scalable, reusable assets that can transform entire industries.

1. Turning Use Cases into Assets

Early AI projects are proof-of-concept opportunities.

Ronny explains that their initial focus was on high-impact use cases—small, contained AI solutions that could demonstrate value quickly.

However, the real transformation comes when you convert these use cases into assets that can be deployed at scale.

"We’re now shifting towards bundles and suites for specific industry offerings… This allows us to focus on functions and deliver transformation."

The Zero-Based Budgeting Example

One of the most powerful real-world examples Ronny shared was BCG X’s work in the Zero-Based Budgeting (ZBB) market. In this case, they leveraged AI to analyze massive datasets and uncover hidden savings that traditional methods had overlooked.

"We reduced 100 million transactions into 15,000 clusters using AI, then worked with consultants and AI to identify savings opportunities."

The result? AI-driven insights allowed them to uncover 20–30% in savings in areas like shadow IT costs and operational inefficiencies.

By creating a customized AI solution, they were able to deliver measurable results, and then use those learnings to build a replicable asset.

This example highlights the importance of turning insights into assets—what starts as a one-off project can be scaled and adapted for future clients.

2. Create Bundles, Not One-offs

Single AI use cases are valuable, but they’re limited in scope.

The key to long-term value is bundling solutions together to create suites that serve multiple functions or industries.

For example, at BCG X, they developed AI bundles that focused on industry-specific needs, creating solutions that could be customized for each client, but scaled for broader applications.

"We started with isolated AI projects, but quickly moved to offering suites that could be adapted to various industries."

This method allows you to build once and scale multiple times, turning isolated wins into a replicable solution.

3. Keep Customization at the Core

No matter how scalable or efficient your AI solutions become, customization remains key.

Every client has unique needs, so the assets you build should be flexible enough to be tailored to each specific industry or business.

Ronny emphasizes the importance of custom-building solutions, ensuring that even scalable AI assets remain aligned with the client’s specific challenges.

To Summarize

To build scalable AI assets within your own consultancy, take these steps from BCG X’s playbook:

• Start small, think big: Test AI use cases in manageable, high-impact areas.
• Turn use cases into bundles: Consolidate your solutions into scalable assets that can be deployed across industries.
• Always customize: Keep your AI solutions flexible enough to adapt to each client’s specific needs.
• Leverage data insights: Use AI to uncover savings, inefficiencies, and opportunities that traditional methods might miss.

Remember: The key to long-term success is scalability with flexibility. By building reusable assets and customizing them for each client, you can maximize both the value AI delivers and your consultancy’s ability to scale.

10-Step Checklist: Integrating AI Into Your Consultancy Business

• 1. Commit to an investment mindset, treating AI like a startup within your company.
• 2. Hire a senior AI leader who understands both AI's technical and business impact.
• 3. Build a specialized AI team that collaborates closely with consulting teams.
• 4. Create clear career paths for AI talent to grow within your organization.
• 5. Integrate AI experts with your consulting teams from the start of every project.
• 6. Secure internal buy-in by showcasing small wins and offering fallback options.
• 7. Leverage existing client relationships to identify a data-heavy process for AI implementation.
• 8. Start with small, high-impact AI projects that deliver fast, measurable results.
• 9. Develop reusable AI assets that can be scaled across different clients and industries.
• 10. Transition to value-based pricing by tying fees directly to the results AI delivers.

Final Thoughts

AI isn't a magic wand. It's a tool that, when used right, can supercharge what you're already doing—but only if you have the right mindset and structure in place.

This isn’t about quick wins.

It’s about playing the long game, investing in the right people, and embedding AI into the DNA of your consulting firm.

Start small, build momentum, and watch as AI shifts from a buzzword to a core driver of value. The sooner you get started, the sooner you’ll see the payoff.

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