Industry transformation: The digital online caravan moves on – From print to online media agency to AI integrator agency

More than just consulting: The $558 billion business of AI integration

The caravan of digital transformation moves on, leaving behind an industry in flux. While the German consulting market surpasses the impressive €50 billion mark, the overall figures conceal a profound tectonic shift: growth is slowing across the board, while a new gold rush mentality prevails in one specific segment. Leading this movement is a new type of service provider redefining the rules of the game: the AI ​​integrator agency. It represents the logical evolution from the traditional media agency to a technology implementation partner and marks a fundamental shift – away from pure creativity and towards operational excellence.

This shift is more than just a trend; it's a response to changing demand. Companies today no longer demand strategic PowerPoint slides, but rather functional, scalable AI solutions that integrate directly into their business processes. This is precisely where the new integrators come in. They don't develop their own AI models, but instead orchestrate existing technologies like GPT-4, Llama 3, or Claude into customized systems. Their value lies not in proprietary technology, but in the speed, reliability, and domain expertise they bring to implementation.

➡️ But beware: Where there are experts, there are also charlatans who promise a lot for the sake of media attention and see the quick money, but cannot demonstrate any real AI expertise.

When “old” agency structures transform into technology integrators: The market for AI integrator agencies – Structural reassessment and the transformation of the German consulting business

The German consulting market is undergoing a subtle yet profound structural transformation. In 2024, the total volume of the consulting industry in Germany exceeded €50 billion for the first time, reaching €50.1 billion. This not only marks a quantitative milestone but also indicates a qualitative reorganization, the dynamics of which are manifested in the emergence of entirely new business categories. The consulting industry grew by 5.9 percent in 2024, which, within the context of the overall economy, must be described as solid but significantly more moderate growth than in previous years. By comparison, growth was 16.0 percent in 2022 and 7.3 percent in 2023. This flattening of the growth curve is not an expression of weakening industry strength but rather an indicator of market segmentation, in which certain specialized areas are growing exponentially while others are stagnating or shrinking. The phenomenon of so-called AI integration should not be considered marginal in this context, but rather a structuring force of the coming decade.

Among the consulting fields, firms are forecasting particularly strong growth in AI consulting for 2025, with an expected increase of 13.9 percent. This is a clear signal: the economic demand for expertise in artificial intelligence far exceeds the overall growth of the industry and has established itself as a strategic multiplier. At the same time, IT consulting is experiencing the strongest growth among the traditional consulting fields at 5.9 percent, while strategy consulting (4.0 percent) and organizational and process consulting (3.5 percent) lag significantly behind.

This divergence is not accidental. It signals a fundamental shift in what companies expect from their consulting partners: not abstract strategy documents or organizational restructurings, but the concrete implementation, integration, and operational scaling of new technologies, particularly in the field of generative artificial intelligence. The global market for enterprise AI solutions is estimated at $98 billion in 2025 and is projected to reach $558 billion by the end of 2035. This represents an average annual growth rate of 19 percent. This is not just a growing slice of an already large pie, but the emergence of an entirely new market segment alongside the existing one.

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Who are the players who are serving this new demand?

The question is crucial because the answer reveals a classic scenario of market dynamics: It is not the established, large consulting firms that dominate this sector—at least not in its early stages—but rather a new generation of specialized integrators and hybrid agency models that attempt to combine traditional agency structures with technological depth. These players often emerge from three origins: formerly pure digital or performance agencies that have moved upmarket into consulting; specialized tech boutiques that have expanded their systems integration capabilities to include business enablement; or traditional management consulting firms that have had to significantly expand their operational implementation capabilities.

The economic logic behind this transformation is elegant and compelling. While the traditional management consultant presents a strategic concept and then leaves the implementation to the client or an implementation partner, and while the classic digital agency sells its services at daily rates and maximizes its margins by billing by the hour through increased staffing, new hybrid models are emerging that are neither purely hourly nor purely strategic. These integrated models combine multiple revenue streams in an architecture organized around three poles: consulting fees for strategy and capability (initially based on daily rates), implementation and project fees for concrete execution in time-defined sprints (fixed fees based on deliverables), and long-term retainers for support, maintenance, and iterative optimization of existing systems (subscription-like models). This triangle is crucial because it explains why such companies are able to maintain higher margins and, at the same time—at least theoretically—grow more stably and predictably than purely hourly agencies.

The core of this new industry: capital resources, not creativity

The conceptual shift is fundamental. While traditional agencies (in marketing, design, or traditional consulting) based their fees on creative output and strategic originality, these new AI integrators operate on a completely different value logic: the operationalization of existing technological building blocks. The term "integration" is precisely chosen here. Such a company does not develop its own language model or proprietary AI infrastructure. It utilizes existing, publicly available, or licensed models—typically OpenAI models like GPT-4 and GPT-4o, Anthropic Claude, Google Gemini, or, for cases with strict data privacy requirements, open-source models like Meta Llama 3, Mistral, or DeepSeek. Building upon this foundation, it orchestrates a specialized technological architecture consisting of a combination of frameworks and infrastructure layers.

The typical tech stack of such a company follows a proven pattern: In the backend, Python with FastAPI is often used for providing APIs, as FastAPI offers high asynchronicity and concurrency in handling parallel AI requests. Frameworks like LangChain or LlamaIndex are used for orchestrating complex workflows—chaining multiple AI calls, routing requests, and managing conversational memory. Vector databases such as Pinecone, Weaviate, or the open-source equivalent FAISS are used for storing vectors and performing semantic searches in large knowledge bases. PostgreSQL or similar relational databases are used for persisting business data and managing conversation histories. For scaling in the cloud market, Azure, AWS, or Google Cloud are used, leveraging these providers' AI services as a fallback or primary option depending on the requirements. The frontend layers are often implemented using Streamlit, React, or similar frameworks to provide user-friendly interfaces for customers.

This may sound technical, but it's the economically crucial detail: These stacks are not proprietary, they are not secret, and they are not subject to patents or other intellectual property rights. Rather, they are de facto industry standards, readily available everywhere. Those who are competent in assembling them can deliver faster, work more cheaply, and scale more effectively than those who try to develop their own core technologies. This structurally lowers the barrier to market entry, but it doesn't reduce the barrier to genuine competitive differentiation—it merely shifts it: from technological proprietaryness to domain knowledge, implementation excellence, and the ability to drive organizational change.

This is precisely why established agencies (such as those originating from traditional media agency networks or digital boutiques) are more likely to penetrate this space than others: They possess skills that are often lacking in the technology industry. They understand organizations, change management, internal resistance, and the psychology of innovation adoption. They can communicate. They have client relationships. They have brand trust. What they lack—and what they must learn or acquire—is the ability to assemble the technological components quickly and robustly.

This explains the curious inversion emerging in some parts of the market: While traditional management consultants are trying to learn how to write code and deploy systems, traditional agencies are trying to shift their positioning from "creativity and brand building" to "business transformation through technology integration." Some of them are very successful at this. Some—and the next decade will show this—will fail.

Market consolidation and the private equity invasion

One phenomenon that cannot be overlooked is the increasing wave of consolidation in the consulting and agency market. Private equity investors have been heavily active in this sector since 2023. The latest Lünendonk analyses show that private equity currently represents a strategically relevant option for 30 percent of the surveyed consulting firms. This is not a trivial matter. It means that a large proportion of medium-sized consulting firms in Germany are explicitly considering, or are actively engaged in discussions about acquiring equity stakes or divesting part of their business.

Private equity-driven consolidation follows an established playbook: Private equity investors identify a platform company with an established customer base and market position. This company is then expanded through several add-on acquisitions – typically, specialists in specific areas (such as AI consulting, cloud migration, cybersecurity) are acquired. Synergies are leveraged through standardization, resource pooling, and cross-selling. After a typical period of four to seven years, an exit occurs, either to a strategic buyer or a larger private equity investor.

The consequences are multifaceted. Firstly, this leads to increased capitalization: Mid-sized consulting firms that have traditionally bootstrapped or grown with small investor groups gain access to growth capital, enabling them to acquire specialized expertise. This should bring new services to market more quickly. Secondly, it creates consolidation pressure: Those who don't become part of a private equity portfolio face growing competitors who are significantly better capitalized. This leads to a two-tier market structure: large, well-funded platforms on the one hand, and specialized small boutiques on the other. The middle class is under pressure.

At the same time, it's important to understand that this private equity-driven consolidation has so far been primarily active in traditional management consulting or established IT consultancies. In the segment of new AI integrators, this process has progressed less far. Many of these companies are still relatively young, small, and organized in a traditional way – either as a limited liability company (GmbH) with a majority shareholder or as a classic partnership. The reason is simple: they are too new a category. Private equity investors invest in categories they understand, with business models they can evaluate. The AI ​​integrator category is too young to have attracted private equity investment on a large scale. However, this will likely change.

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The wage structure and the skills shortage paradox

A key economic challenge for this new category of companies is the availability and retention of skilled workers. The German job market for specialized AI developers is extremely tight. An experienced machine learning engineer or specialized AI developer costs between €80,000 and €120,000 per year – if you can find one. On top of that, there are social security contributions, training allowances, and attractiveness bonuses. The IT job market as a whole is overheated; 41 percent of IT professionals plan to change jobs in 2025, the majority of them in the first quarter of that year.

This creates a conceptual dilemma: On the one hand, these integrator companies need to attract highly specialized talent to remain technologically competitive. On the other hand, a mid-sized integrator cannot compete with the salaries offered by large tech companies (Google, Meta, Microsoft). Some of these firms are attempting to solve this problem through several strategies. First, they position themselves as learning spaces and innovation labs for developers seeking a kind of professional adventure. Second, they build partnerships with universities and coding bootcamps to cultivate early-career talent before it realizes its full market power. Third, they implement highly model-driven ways of working, where junior talent is quickly able to produce high-quality deliverables under supervision. Fourth, they utilize freelance and contractor models to reduce the overall payroll burden.

This last model—the use of freelancers and contractors—is very common in this industry. An AI integrator might employ a core team of five to ten full-time staff (often the partners or founders and a few senior employees). Beyond that, they work with a network of specialists who are brought in as needed for specific projects. This is economically rational because AI projects rarely have a stable, regular workload—a period of intensive implementation is followed by less intensive optimization and maintenance phases. Keeping the fixed cost structure low is therefore rational. The problem, however, is that this model makes it harder to create organizational continuity and knowledge accumulation. If the best people leave after each project, deep expertise cannot be built up. Many of these firms struggle with precisely this problem.

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The business model trilemma: Between daily rates, project fixed fees, and retainers

The revenue logic of these new integrators has proven surprisingly complex. There are three basic models for determining fees in the consulting business, and each has advantages and disadvantages:

The first model is the classic daily rate billing. The consultant or agency bills for hours or days, multiplied by an hourly or daily rate. This is simple, transparent, and gives the client clear control over the cost unit: per hour or per day, I see exactly what I'm paying. The problem: it creates perverse incentives. The more inefficient the consultant is, the higher their earnings. There is no economic incentive to work faster or smarter. This leads to a classic principal-agent divergence.

The second model is a project fee or fixed price based on deliverables. The customer and the provider agree on a service package: for example, "AI chatbot implementation for customer service," fixed price €50,000, delivery date in 8 weeks. This creates real incentives – the provider is motivated to work efficiently because the margin increases with efficiency. The problem: It's difficult to calculate. If the requirements are unclear, or if the scope changes during implementation, losses can quickly accumulate. This leads to two problems: Either the provider builds in massive safety margins (and the prices become unattractive to customers), or they end up with a project that is more expensive than planned. Many mid-sized integrators report projects that were completed with a 15-20 percent loss because reality was more complex than the specifications.

The third model is the retainer – the subscription model. The customer pays a fixed monthly fee in exchange for a specific level of service or guaranteed availability. This creates unparalleled planning security: The provider can reliably factor these revenues into their budget. At the same time, it incentivizes efficiency and customer focus, as dissatisfied customers are more likely to cancel. The problem: Retainers are difficult to sell. They require a high degree of trust from the customer and a strategic conviction that the collaboration will be valuable in the long term. Many customers (especially in the SME sector) think in terms of projects, not subscriptions. Furthermore, the retainer model only works if it leads to standardization – if the monthly services remain roughly the same. This is not the case for highly customized, complex projects.

Most successful AI integrators have learned to operate a hybrid model: They often start with a consulting engagement on a daily rate basis to truly understand the requirements. This then evolves into a defined project with a fixed fee (usually 6-week sprints). After successful implementation, a retainer model is offered. This creates several advantages: The initial daily rates finance the in-depth analysis. The pressure of the project phase leads to faster delivery. Finally, the retainer secures the client's long-term commitment and stabilizes revenue. This is also attractive for the client: They pay first for analysis, then for implementation, then for continuous optimization – all phases are economically viable.

Data protection and regulatory complexity

A key differentiator between AI integrators is their ability to handle stringent data privacy requirements. Many clients—particularly in the public, financial, and healthcare sectors—cannot simply upload their sensitive data to cloud services. In these cases, integrators must be able to deploy AI systems locally or operate them in closed, managed environments.

This leads to a clear distinction. Many of the cheaper, faster integrators primarily work with cloud APIs (OpenAI, Google, Anthropic). They can deliver MVP prototypes quickly and cost-effectively. This is often not feasible for regulated industries. Here, specialized providers with expertise in on-premises deployments must step in – for example, using open-source models like Llama 3 or Mistral, or locally hosting models with frameworks like vLLM or llama.cpp.

The GDPR and the new European AI Act (AI Regulation) have also led many of these integrators to develop specialized expertise in addressing compliance risks. This has proven to be a competitive differentiator: companies that understand how to set up GDPR-compliant AI systems, meet the requirements of the AI ​​Act, and translate these complex requirements into concrete technical implementations systematically achieve higher prices and greater customer acceptance.

The paradoxes of growth: scalability versus quality

There's a classic paradox in the consulting industry: The best firms are often small and highly specialized. They have a well-established, highly talented core team. They can make quick, quality-oriented decisions. They can turn down projects if they're not a good fit. The problematic firms are often large, bureaucratic organizations that lose their best talent to vast matrices where no one truly owns the business.

This leads to an investment dilemma: If such an integrator company becomes successful, if demand increases, if it has the opportunity to scale – then it must decide: Does it want to remain small and high-quality, or does it want to become large and scalable? Historically, many of these decisions have ended badly. The company tried to scale, went through an inefficient recruiting process, hired people who didn't fit the culture, quality suffered, better people left, and the downward spiral became self-perpetuating.

Some of the more successful players in this category have taken a different approach: They have consciously chosen not to scale widely. They remain small (20-30 people) instead of trying to grow to 200. They build a strong partner network – other smaller integrators specializing in specific verticals or use cases. They take on the role of an orchestrator rather than a one-stop shop. This is no less scalable in terms of revenue and customer impact, but it has a different structure – it's more of a network play than hierarchical growth.

The Industrial Structural Shift: The Transition from the Creative to the Tech Paradigm

Historically, agencies – whether marketing agencies, design agencies, or traditional management consultancies – were essentially structures of creative and intellectual work. The differentiation arose from:

1. Creativity: Who would have the most original idea, the best design concept, the most innovative strategy?
2. Reputation: Who was known for excellence in specific domains?
3. Talent acquisition: Who was able to attract the best creative talent?

In classical economic terms, these agencies were markets for credence goods – the customer could not really evaluate the quality ex-ante; they bought based on references and reputation.

The new generation of AI integrators operates according to a different paradigm. The differentiation stems from:

1. Technical robustness: Who can bring a system into production faster, more scalably, and with a lower error rate?
2. Domain knowledge: Who understands the specific industry – banking, insurance, manufacturing, public sector – so well that they know where the critical use cases are?
3. Change management skills: Who understands how to guide companies through organizational resistance to truly implement these systems?

This is no less a trust-based industry. But the criteria for trust have shifted. It's no longer primarily about "Do you have a great, creative idea?" – but about "Can you really do it, reliably, within budget, and on time?"

This shift from the creative to the tech paradigm has implied that traditional agencies, which have built their identity too heavily on "creativity and innovation," are not automatically competitive in this new category. Some of the established, large digital agencies have precisely this problem: They excel at ideation and conception. But when it comes to rough-and-ready implementation, technical depth, and operational excellence, they are less strong. They need to reinvent themselves or acquire specialists.

The economic conclusion: The structure of the new industry

In summary, the following can be said about the economic structure of these new integrator agencies:

It's a still very fragile industry in its early growth phase. It's experiencing double-digit growth, but from a still small base. Available data shows that AI consulting as a whole is growing at 13.9 percent – ​​but this figure includes large, established management consulting firms that have built AI consulting arms. The specialized, new integrator boutiques are likely growing even faster, but are still statistically too small to be tracked separately.

Margins are better than those of traditional hourly-selling agencies, but worse than those of traditional tech companies. A project margin of 20-35 percent is realistic, and a retainer margin of 40-60 percent. This is significantly better than traditional digital agencies (which often had profit margins of 8-15 percent), but significantly worse than software companies (which often operate with EBITDA margins of 60-80 percent).

The market will consolidate. The next 3-5 years will show who is viable in this category. Many of the current players will either be consolidated, acquired by larger consulting firms, or they will fail. Only the highly focused specialists and a select few of the most intellectually gifted generalist boutiques are likely to still exist as independent players in 2030.

The dynamics of the skilled labor market will continue to increase. This is likely the biggest structural constraint of the coming years. If these integrators truly want to scale, they must develop and retain talent faster than the general labor market. This will force them to invest in systematic learning programs, organizational development, and cultural differentiation.

Regulatory complexity is becoming a moat. Companies that build expertise early in data protection, AI Act compliance, and local deployment architectures will have a structural advantage over later-arriving competitors. This will be particularly relevant in Germany and Europe.

Hybrid models are becoming the standard structure. Neither pure project billing nor pure retainers, but a combination of both will become the norm. This will be more attractive for customers and more stable for providers.

The transition from the creative to the tech paradigm is structurally irreversible. Companies that fail to understand this and adapt their infrastructure and culture accordingly will be selected out of this market.

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