She had not taken a holiday during the last week of the month in more than 10 years.

That week belonged to shift planning for the production plant: her task, her anchor, the thing she showed up for even when everything else competed for her time. Then her company deployed an AI solution. The shift plan that had structured her professional life for a decade was completed in milliseconds.

The technology worked perfectly. But nobody told her what she should do now in that last week of the month. Dr. Anja Konhäuser has seen this moment more times than she can count. As Founding Partner of OMMAX, a 300-person European AI consultancy, she has spent 15 years delivering technology and organizational change inside companies that thought they were ready for it.

Her argument is precise: the AI works. The adoption doesn't. And the gap between those two things is where most consulting engagements are currently losing.

TL;DR

• Most AI projects fail because firms treat them as tech projects. The failure is a classification error, not a technology problem.
• The budget has shifted: technology is now the smaller part of an AI engagement. Organizational change is the majority of the work.
• Before any rollout, map your client's workforce into 4 personas: Enthusiasts, Skeptics, Overwhelmed, and Rejectors. Each needs a different response.
• The methodology, not the technology, is what determines whether an AI deployment sticks.
• AI adoption work requires more seniors, not more juniors. The consulting pyramid is the wrong structure for this.

The Wrong Mental Model Is Costing Your Clients

For two decades, technology implementations inside client organizations followed a recognizable sequence.

A task force assembled, usually from IT. A vendor was selected. The system was configured and deployed. Training happened. A hyper-care phase absorbed the initial friction. Then the team moved on. That model worked for ERP systems, CRM platforms, and infrastructure upgrades. It worked because those implementations had a bounded scope: a defined system replacing a defined process, with a defined population of users who needed to learn a defined set of new behaviors.

AI does not work like that.

When your client deploys an AI solution, the scope is not bounded. The influence touches workflows, roles, and routines across the organization in ways that were not anticipated at the outset, and in ways that deepen over time as the team gets more familiar with what the solution can actually do. The breadth of impact is not comparable to anything in the prior history of enterprise technology.

"If you then make the mistake of parking it as a tech project, you just adopt the wrong expectations and the wrong mechanism, and the wrong rollout plan because it is something different than a tech project," Anja says.

When a leadership team approaches AI through the lens of an IT project, 3 things break immediately. They staff it wrong (an IT task force is not equipped to manage workforce redesign and behavioral change at scale). They measure it wrong (technical deployment milestones are not adoption milestones). And they sequence it wrong: training and change management get treated as a post-go-live activity rather than a pre-deployment prerequisite.

The table below maps the two models.

McKinsey's November 2025 global AI survey found that 88% of organizations were using AI in at least one function, yet only 39% reported any meaningful EBIT impact. The gap between those numbers is not a technology gap. It is the gap between deployment and adoption.

Your clients are living in that gap. The question is whether you are equipped to close it.

Why the Organizational Work Decides Whether AI Engagements Stick

OMMAX's sequence starts the same way every time: document the status quo before touching a solution. Headcount, time per task, ticket complexity, tech stack, satisfaction baseline. Every number recorded before any vendor is considered. That baseline is what later proves whether the engagement actually delivered.

"We were doing a status quo analysis of their work today. We documented everything," Anja says. "How many people, how many minutes? What is the average ticket size documented on which databases... How long it takes, and also the client satisfaction at the end."

The next move is the one most external consultants skip. OMMAX does not present its findings directly to the team. The leadership team does. OMMAX equips them with the language and the framing, then steps back while leadership runs the conversation with their own people. An external consultant using the wrong internal term for a workflow generates friction that has nothing to do with the technology. Leadership carries the trust that a consultant cannot borrow.

Once the matching is done, client pain points mapped against AI capability, the deployment is sequenced to minimize threat and maximize transparency. The AI runs in parallel with the existing team during training, with the team reviewing outputs together at the end of each day. Not a demonstration. A shared experience of watching the system improve under conditions the team controls. This is what generates trust before the AI ever takes on its own volume.

When the AI is ready, it does not start by replacing the people doing the work. It starts with the lowest-threat substitution available, typically overflow or peak-period capacity rather than core staff. Lower threat, higher acceptance. Confidence builds, and scope expands from there.

The Effort Split That Predicts Whether Your AI Project Will Work

Anja frames the shift in how AI engagement budgets are structured as a heuristic, not a universal. "I always tend to say it's an 80-20 rule, but it's not applying to any case that we are working on at the moment, but it gives you a very good idea of what we are talking about."

The direction is the signal, not the precise figure. Previously, the majority of an AI engagement budget went to technology: building, configuring, integrating, testing. The organizational work around it was the smaller part. Now that ratio has inverted. Technology is cheaper, faster to deploy, and increasingly commoditized. The organizational work (understanding the workforce, designing the rollout, managing adoption across 4 different types of people with 4 different relationships to change) is where the majority of the effort lives.

This has an immediate implication for how you scope and price an AI engagement. If your commercial model still treats technology delivery as the primary cost driver and organizational support as a line item, you are not pricing the work correctly. More critically, you are not staffing it correctly, and you will not deliver it correctly.

The diagnostic question Anja applies before any solution is introduced: where are the actual pain points? Not where does the technology fit. Where does the client's work break down? "Fall in love with your problems and not with your solutions," she says, and the warning behind it is specific: a lot of AI vendors have done effective marketing, and a lot of clients arrive at the engagement with a solution already in mind. The consultant's job is to go further upstream: map existing infrastructure, team workflows, and operational setup before any use case is defined.

In practice this means producing a business case for every use case candidate, not just a list of possibilities. For each of the 5 to 10 most relevant use cases per department, OMMAX builds the ROI case: investment required, expected output, timeline to positive return, alignment with the company's current situation (cost reduction, growth, market entry, scale). The business case is what enables a board to make a real decision rather than approve a pilot with unclear success criteria.

4 People Your AI Rollout Will Meet (and What Each One Needs From You)

Before any AI goes live, before a line of code is deployed, OMMAX maps the team. Not by name (in German companies with a Betriebsrat, or works council, individual-level profiling requires careful navigation) but by group. What percentage of the team falls into each of 4 categories? What does that distribution tell you about how to sequence the rollout, who should run the training, and where the resistance will come from?

The framework comes from Anja's PhD research on innovation adoption. Its second life, applied to AI implementation, is the most operationally useful thing in this article. Here are the 4 types.

Enthusiasts

They have already tried the tools. They cannot wait to start. They are your co-designers, not your testers. Give them ownership of workstreams, not a seat in the back of a training session. The critical operational move: identify them across the whole organization, not just in the target team. An enthusiast in finance or operations can accelerate an AI initiative in HR. They are a resource that most firms systematically underuse by keeping them local to the pilot.

Skeptics

The largest group in most organizations, particularly in regulated sectors. They are not anti-technology. They are risk-sensitive. Their operating question is: if something goes wrong, am I accountable? They have heard the AI horror stories. They repeat them in meetings. What they need is not a technology demo. It is transparency, clear boundaries, a human in the loop they can trust, and evidence from peers.

That last point determines who runs their training. An external consultant presenting benchmarks and case studies is less persuasive to a skeptic than a trusted internal peer showing them what is possible. "If they are skeptical, they need someone from their company community and their crowd," Anja says. Your job is to identify who that person is and equip them, not to run the training yourself. When benchmarks are used (comparable companies of similar size and market position who have adopted this solution), skeptics move faster. They can relate. The abstraction disappears.

Fear of being obsolete (FOBO, by analogy with FOMO) sits largely in this group. The shift planner who had not taken a holiday during her planning week in more than 10 years was not resistant to technology. She was facing the disappearance of something that structured her professional identity. Without a clear answer to what fills her last week of the month, the adoption conversation is also a grief conversation. Address it as such.

Overwhelmed

Different from skeptics in a specific way that matters for rollout design. Skeptics are risk-sensitive. The overwhelmed are capacity-exhausted. They are already operating on top of legacy tools and workarounds from past implementations that were never properly resolved. They do not want another tool. They want relief.

Go 2 steps back. Diagnose what is overwhelming them today. If you can fix something in their existing daily workflow before the AI arrives, do it. You arrive as a problem-solver, not another source of complexity. The training model must then be continuous and embedded, not a full-day pullout session, which the overwhelmed team cannot absorb. Fifteen minutes, regularly, integrated into their actual workflow.

Rejectors

The smallest group. Openly resistant, questioning AI at a societal level, citing years of successful work without it as evidence it is not needed. The instinct is to convert them. Resist it. Partially. "You can also never, never turn around anyone," Anja says. The practical question is not whether you can convert them but whether they are acting as negative influencers, and how large that influence is. Work with leadership to understand why they reject, whether turnability is realistic, and whether existing company initiatives can be aligned with the AI rollout to reduce the additional burden they feel. Measure this group. Track their influence. Do not make them the center of gravity of your rollout strategy.

The reference table

Post-go-live, the work is not done. Hyper-care in the AI era means hyper-personalized care. Every individual on the team needs to find their new routine: what their role looks like now, what the AI handles, what they are freed to do instead. That conversation does not happen in a town hall. It happens one person at a time, or in small groups, over weeks and months after deployment. The firm that builds this into its engagement structure will have materially better adoption outcomes than the firm that treats go-live as the finish line.

5 Failure Modes That Keep Appearing

Five patterns. Name them. Use them.

The Tech Project Trap

The client's AI initiative is staffed, scoped, and measured like an ERP rollout. An IT task force runs it. Training is scheduled post-deployment. Success is defined as technical go-live. The organizational work (workforce mapping, behavioral change, post-implementation adoption) is either absent or treated as a support function. The technology works. The people do not change. The ROI does not arrive. The project is declared a success by IT and a failure by the business.

The Enthusiast Waste Pattern

The consultant identifies the team's enthusiasts and treats them as validators. They are put in the pilot group. They test the solution. They give positive feedback. The rollout is declared ready. Then the rollout meets the skeptics (the majority) who were not prepared, were not involved, and were not given a peer they trust to guide them through it. The enthusiasts were used as testers when they should have been used as change agents. The early momentum collapses on contact with the real population.

The Post-Go-Live Vacuum

Go-live is the milestone everyone builds toward. The project team celebrates. The handover happens. The external consultant exits. The team is left with a working AI solution and no clear understanding of what their individual role looks like now. The shift planner knows the AI does the scheduling. She does not know what she does instead. Nobody told her. This is not a training gap. It is a design gap. The engagement was built to deploy, not to land.

The IT Inheritance Trap

The overwhelmed team is carrying the weight of every previous technology project that was not properly finished. They have workarounds for workarounds. When AI arrives, it is not received as a solution. It is received as proof that the organization still does not understand what their daily work actually requires. The resistance is real, but it is not resistance to AI. It is resistance to being handed another problem to absorb. Go 2 steps back. Fix what is already broken before adding what is new.

The Management Credibility Gap

The leadership team announces the AI initiative. They present the vision. They ask the team to adopt tools and workflows that will change how the business operates. Then a team member asks how the AI makes its recommendations, and the leader cannot answer. Or the team discovers that leadership is not using the tools themselves. The initiative loses its authority before it gains its momentum. "If you have a management that is not utilizing it themselves, is not capable of explaining what an LLM is or never had built an agent themselves and wants the whole team to do it tomorrow — this kind of doesn't get together," Anja says. The fix is available before the project starts: run the leadership team through an AI masterclass before asking anything of the broader organization.

Why The Pyramid Is Wrong for This Work and What Needs to Change

Everything described in this article (the status quo analysis, the persona mapping, the leadership-facilitated matching session, the parallel running, the post-go-live individualized care) requires people who can do more than one thing well. They need tech depth. They need data fluency. They need enough emotional intelligence to sit with a shift planner and have an honest conversation about what her role looks like now. And they need the seniority to hold a room with a leadership team that is not yet confident on AI and bring them forward without making them feel exposed.

That is not a junior consultant. The traditional consulting pyramid (one senior partner directing a layer of mid-level managers directing a base of juniors) is not built to put enough of the right people in the room.

"When it comes to the staffing of the projects, we need more seniors there to deliver it," Anja says. OMMAX has already shifted its hiring strategy toward senior profiles specifically because of this. The AI adoption engagement cannot be delivered by a team where the seniors check in and the juniors execute. The seniors need to be present, because the work that determines success (the persona conversations, the leadership coaching, the post-implementation individual care) cannot be handed to someone who has not yet earned credibility in the client room.

The training implication compounds this. Your senior consultants need genuine working familiarity with what new AI tools can and cannot do, not a quarterly update session. OMMAX runs an internal function of around 10 people out of 300 whose job is to experiment with the newest AI tools, build institutional knowledge, and feed it back into client delivery. That structure is not typical for a consulting firm. It will become more so.

If your firm is still treating AI knowledge as a personal development initiative (something individuals opt into) rather than a delivery capability the firm is accountable for building systematically, you are behind the firms that have already decided otherwise.

7 Weeks After This Conversation, OMMAX and Singulier Combined — Here’s What That Signals

When this episode was recorded on March 30, 2026, Anja recommends Rémi, the CEO of Singulier, as a possible future guest on the podcast. She describes Singulier as worth hearing from because of France's more advanced position on AI openness relative to the German-speaking market.

On May 18, 2026, seven weeks later, OMMAX and Singulier announced they were combining operations.

The combined entity brings together more than 400 professionals across London, Paris, Munich, Berlin, Amsterdam, and Milan. Backed by Eurazeo, which invested in OMMAX in 2025 to support international expansion, the combined group is targeting revenues of €80 to €100 million and aims to triple in size within 5 years. Singulier, recognized in the 2026 Private Equity Wire European Awards as a leading AI due diligence provider, brings a complementary practice in digital strategy, execution, and transaction advisory to private equity firms.

The structural logic of that combination is the same logic this article has been making. AI adoption work requires senior practitioners, geographic breadth, and execution depth across the full engagement cycle: from AI readiness assessment through implementation through post-go-live care. No boutique builds all of that alone. The firms defining this market are the ones that have decided scale and specialization are both required.

That decision is one every consulting leader reading this needs to make. Not whether to build for AI. How fast, and at what scale.

Where Your Clients Stand Depends on Where They Operate

For consulting leaders operating across European markets, the persona framework does not apply uniformly. The distribution of Enthusiasts, Skeptics, Overwhelmed, and Rejectors shifts by geography, and so does the decision speed you can expect from leadership teams.

These are Anja's observed patterns across client engagements, not survey data. Apply them as calibration, not as benchmarks.

The Betriebsrat point deserves specific attention for Germany-active firms. Individual-level persona mapping requires careful legal and HR navigation where a works council is present. Work at the group level: what percentage of the team is skeptical, what percentage is overwhelmed, what does the distribution tell you about rollout design? You do not need the individual to design the response. You need the proportions.

Your Company Likely Has Gaps in This Model — Here Is Where to Find Them

Seven questions. Score each: 0 (not in place) / 1 (partially in place) / 2 (fully in place).

1. Do you scope AI engagements as organizational change projects or technology delivery projects?

Green flag: Your AI engagement proposals lead with workforce analysis, change management, and adoption planning. Technology delivery is a component, not the frame.

Red flag: Your AI proposals are structured around a tech stack, a deployment timeline, and a training phase. Organizational change is a line item.

2. Do you map employee personas (at minimum across the Enthusiast / Skeptic / Overwhelmed / Rejector spectrum) before designing the rollout?

Green flag: Persona mapping is a standard pre-deployment deliverable. The rollout plan is explicitly adjusted based on the distribution.

Red flag: You design a single rollout plan and apply it across the team.

3. Is the client's leadership team visibly and credibly using AI before you ask the broader organization to adopt it?

Score 0 if you have never assessed this.

Score 1 if you assess it but do not address gaps before rollout.

Score 2 if you run a leadership readiness intervention (masterclass, coaching, or equivalent) as a standard pre-deployment step.

4. Do you have a structured post-go-live adoption plan that operates at the individual or small-group level?

Green flag: Your engagement does not end at go-live. You have a defined post-deployment phase with individualized touchpoints for each team member to find their new routine.

Red flag: Your post-go-live plan is a standard hyper-care period and a feedback mechanism.

5. Do you produce a business case for each use case candidate before recommending where to start?

Score 0 if you produce a use case list.

Score 1 if you produce rough ROI estimates.

Score 2 if you produce a full business case per use case (investment, output, timeline to positive return, alignment with company situation) enabling board-level prioritization.

6. Are your senior practitioners on AI engagements equipped with emotional intelligence and change management capability alongside technical depth?

Green flag: You assess this explicitly in engagement staffing. Your senior AI practitioners have run adoption programs, not just technology deployments.

Red flag: You staff AI engagements by technical expertise alone.

7. Does your firm have an internal function dedicated to experimenting with new AI tools and feeding that knowledge into client delivery?

Score 0 if this does not exist.

Score 1 if it exists informally.

Score 2 if it is a defined team with a defined mandate.

Score interpretation

• 0–4: Structurally exposed. Your AI engagement model carries significant delivery risk. Address the scoping and staffing gaps before the next proposal.

• 5–8: Partially equipped. You have the right instincts but gaps in execution. Post-go-live and persona mapping are the highest-leverage areas to build.

• 9–14: Delivery-ready. You are building for where the market is going. The question is speed and scale.

How to Run an AI Engagement That Delivers Adoption: The 7-Step Method

The question most consulting leaders are asking right now is not whether AI implementation requires organizational change management. Most accept that it does. The question is: what is the actual sequence, and where does it break down?

Here is the sequence. Each step tells you what to do. The breakdown points tell you where firms skip.

Step 1: Document the status quo before you recommend anything

"We were doing a status quo analysis of their work today. We documented everything."

Before a use case is defined, before a vendor is considered, map the client's current reality: who does what, how long it takes, where it is recorded, what the quality output looks like, what satisfaction looks like at the end. This is not discovery as usual. It is a baseline you will return to when you need to prove whether adoption is actually working.

Common breakdown point: teams skip this when the client arrives with a solution already in mind. The pressure to move to implementation is immediate. Resist it. A solution built without a documented baseline has no mechanism for proving its own value.

Step 2: Match your observations with the team, through leadership not directly

"We equip the team as well, and the leadership team as well, with what it means to get from today's status quo to an integrated AI solution."

Present your findings to the leadership team. Equip them with the language and the framing. Then let them take it to their team. Do not run the matching session yourself. A well-briefed leader who walks their team through the findings and opens the floor to where AI could help will surface better information and generate more genuine buy-in than an external consultant presenting the same content.

Common breakdown point: the consultant presents directly to the team to accelerate the process. The team responds with polite acknowledgment and no real ownership. The implementation proceeds without genuine team input. The adoption gap starts here.

Step 3: Map the personas before designing the rollout

Identify the proportion of Enthusiasts, Skeptics, Overwhelmed, and Rejectors in the target team. For regulated environments or German companies with a Betriebsrat, work at the group level. Adjust the rollout design explicitly based on that distribution. If the team is predominantly skeptical, the training model and the choice of who delivers it change entirely.

Common breakdown point: firms design one rollout plan and apply it uniformly. The plan is calibrated for enthusiasts. It meets skeptics and stalls.

Step 4: Run the AI in parallel before going live

"This will run in parallel to your usual work, to make sure that the outcome of the AI solution is as good, if not even better, as the outcome of the manual team's work."

Do not flip the switch. Run the AI alongside the existing workflow during the training period. Have the team review outputs together. Make the quality trajectory visible. This is what builds trust: not a demonstration, but a shared experience of watching the system improve under conditions the team controls.

Common breakdown point: the parallel phase is shortened under client pressure to show results. The AI goes live before the team has had enough exposure to trust it. Adoption drops. The system is blamed.

Step 5: Deploy to peak capacity first, not the permanent team

When the AI is ready to take on volume, direct it to overflow and peak-period work currently handled by outsourced providers or contractors, not to tasks held by permanent staff. The permanent team watches the AI handle the peaks. They do not feel replaced. They feel supported. Scope expands as confidence builds.

Common breakdown point: deployment targets the most visible, most sensitive workflows first, because those are where the ROI calculation is largest. The team reads this as confirmation of their worst fears. Resistance spikes at exactly the moment adoption should be accelerating.

Step 6: Design post-go-live care at the individual level

Go-live is not the finish line. Every member of the team who worked with the previous workflow needs to find their new routine: what they do now, what the AI handles, what they are freed to do instead. That answer is different for every person. Build the post-go-live phase as a structured set of individual or small-group touchpoints, over weeks, until each person has settled into a new working pattern.

Common breakdown point: the engagement commercial model ends at go-live. The post-deployment work is handed to the client's internal team without the structure or expertise to run it. Adoption plateaus. The project is assessed on its technical delivery, not its organizational outcome.

Step 7: Equip leadership before you equip the team

"The top management is then the one who has to stand before the team and stand up for the topic of AI, and confidently discuss those topics together with the team."

Before the team sees a single slide about the AI initiative, make sure the leadership team can hold the room. They need to understand what the AI does, be able to answer basic questions, and be visibly using AI tools themselves. Run a leadership masterclass before the project kickoff. Not as a supplement. As a prerequisite.

Common breakdown point: leadership education is treated as optional or self-directed. A team member asks a question at the launch event. Leadership defers to the consultant. The team concludes that even leadership does not fully believe in this.

Closing Reflection

The company has a responsibility to train its people. The consultant has a responsibility to manage the adoption. But Anja names a third responsibility: the one nobody assigns.

"I also strongly believe that people themselves have to undertake responsibility... to understand what is going on."

The consultant waiting for their organization to hand them AI literacy is making the same mistake as the client waiting for a vendor to hand them AI transformation. You cannot turn around every rejector. But you can decide which side of that line your own firm sits on.

Frequently Asked Questions

1. Why do most AI projects fail to deliver measurable business value?

Most AI projects fail because the implementation is classified as a technology project. That classification imports the wrong expectations, the wrong staffing model, and the wrong rollout sequence. Technology delivery becomes the milestone. Adoption (the behavioral and workflow changes that produce business value) is treated as a support function. McKinsey's November 2025 global AI survey found that while 88% of enterprises use AI in at least one function, only 39% report any meaningful EBIT impact. The gap is organizational, not technical.

2. What is FOBO and why does it matter for AI rollouts?

FOBO (Fear of Being Obsolete) describes employees who fear the AI solution being deployed will make their role unnecessary. Unlike general change resistance, FOBO is identity-level: not 'this will be harder' but 'I will not be needed.' It concentrates in the Skeptic and Overwhelmed personas. Managing it requires a specific answer to the question every affected team member is asking privately: what does my role look like now? Without that answer, the adoption conversation is also a grief conversation, and training alone will not resolve it.

3. What are the four employee personas in an AI rollout?

The four personas, drawn from Anja Konhäuser's adoption research at OMMAX, are: Enthusiasts (open, already experimenting, best used as co-designers and cross-team change agents); Skeptics (risk-sensitive, the largest group in most organizations, best moved through trusted internal peer-led training and peer benchmarks); Overwhelmed (capacity-exhausted from existing legacy tool friction, need existing problems fixed before AI is added); and Rejectors (the smallest group, openly resistant, to be measured as potential negative influencers rather than converted at all costs).

4. How should the rollout strategy differ based on persona distribution?

Persona distribution directly determines rollout design. A predominantly skeptical team needs internal peer-led training, extended parallel running, and transparency around how the AI works and who remains accountable. A predominantly overwhelmed team needs existing workflow friction addressed before AI is introduced, then continuous embedded training rather than pullout sessions. An enthusiast-heavy team can move to co-design quickly. Rejectors should be tracked for negative influence but should not become the center of gravity of rollout planning.

5. How do you choose where to start with AI in a client organization?

Build a business case for each of the 5 to 10 most relevant use cases per department before recommending a starting point. Evaluate each on: investment required, expected output, timeline to positive return, and alignment with the company's current situation (cost reduction, growth, market entry, or scale). Use case selection should be driven by the deepest pain points in existing workflows, identified through direct documentation of the status quo, not by vendor marketing or internal enthusiasm.

6. What does the effort split shift mean for how consulting firms should price AI engagements?

Anja Konhäuser describes a directional shift (offered as a heuristic, not a universal rule) in how AI engagement effort is allocated. Previously, technology delivery was the majority of the work. Now, organizational work (persona mapping, change management, leadership preparation, post-go-live adoption care) is the majority. Firms that still price AI engagements around technology delivery as the primary cost driver are underpricing the organizational work and understaffing the team for what the engagement actually requires.

7. How do AI adoption patterns differ across European markets?

Based on Anja Konhäuser's observed patterns across client engagements: France shows higher AI openness at both leadership and team level, with shorter paths to pilot approval. The UK shows higher openness than DACH, with some skeptical movements present. DACH markets are more skeptical and regulation-sensitive; German companies with a Betriebsrat require group-level rather than individual-level persona analysis. The US shows the shortest decision cycles and highest willingness to run unvalidated pilots, correlated with stronger national AI infrastructure and regulatory posture.

8. What does an AI engagement's post-go-live phase need to include?

Post-go-live care for AI deployments must be individualized, not standardized. Each team member needs structured support to find their new routine: what their role looks like now, what the AI handles, what they are freed to do instead. This requires individual or small-group touchpoints over weeks, not a town hall and a feedback form. The engagement model that treats go-live as the finish line consistently produces lower adoption than the technical pilot suggested. The post-deployment phase is where adoption is either built or lost.

9. What does the OMMAX-Singulier combination signal for the AI consulting market?

The combination of OMMAX and Singulier, announced May 18, 2026, reflects a clear market direction: AI adoption work requires scale, geographic breadth, and end-to-end execution capability that most boutiques cannot build alone. The combined entity brings 400+ professionals across 6 European cities, targeting €80–100M revenue with ambitions to triple in 5 years. The consolidation of AI-native consulting firms is accelerating as enterprises move from pilot programs to organization-wide deployment, and seek partners who can manage both the strategy and the sustained organizational work that follows.

What senior profile does AI adoption work require from consulting practitioners?

AI adoption engagements require practitioners who combine technical literacy (understanding AI tools, data infrastructure, and integration requirements), commercial judgment (structuring business cases, measuring ROI), and emotional intelligence (managing FOBO, coaching leadership teams, holding individual-level post-go-live conversations). That combination is not typical of a junior or standard mid-level consultant. Firms staffing AI engagements with a senior-light model (one partner directing a junior-heavy team) are structurally misaligned with what delivery requires.

If you want to hear the full conversation behind this analysis with Anja, you can find the episode in the podcast section: https://www.leadersinconsulting.com/podcast

If you’d like to stay in the loop on future deep dives and community updates, you can subscribe to the newsletter here: https://www.leadersinconsulting.com/blog

For more perspectives on how leaders are navigating challenges like this, follow LEADERS IN CONSULTING on LinkedIn: https://www.linkedin.com/company/leadersinconsulting-en/

About the guest:

Dr. Anja Konhäuser is Founding Partner at OMMAX, where she leads AI-driven growth and digital transformation engagements for global and mid-sized brands. With over 15 years of experience and 200+ successful projects for clients including Amazon, MG Motor, Zentiva Pharma, Organon, and MEDIFOX DAN, she specializes in scalable go-to-market strategies, AI adoption, and the digitalization of internal processes. She received the WirtschaftsWoche Female Trailblazer award in November 2025 and is co-founder of Europe's largest Women in Finance network, with 700+ members. Connect with Anja on LinkedIn.

About OMMAX:

Founded in Munich in 2011, OMMAX is a leading AI-first management consultancy specializing in AI strategy, business transformation, and transaction advisory. Following its combination with Singulier in May 2026, the firm brings together more than 400 professionals across London, Paris, Munich, Berlin, Amsterdam, and Milan. OMMAX has delivered more than 4,000 digital value creation projects and supported more than 500 commercial and digital due diligence mandates, with a combined deal value exceeding €20 billion. The firm maintains a Net Promoter Score of 90 and a 50:50 gender ratio across all levels, including leadership.

This article is based on an episode of the LEADERS IN CONSULTING Podcast, hosted by Sammy Gebele, Founder of SAWOO.