Still Investing in Fax Machines or Already in Gen-AI?

A Guide for Smart Business Decisions

Time and again, the question arises: how do you identify the right use cases for generative AI and, more importantly, whether these cases are worth the investment? That’s exactly what I want to get to the bottom of in this post – to give companies more clarity in their approach and facilitate well-founded decisions.

Generative AI isn’t some niche product that only finds specialized applications. No, we’re talking about a comprehensive trend that affects large parts of the currently manual processes in companies. This breadth requires a deep engagement with the technology before you can even think about making investment decisions. Naturally, many companies wish for simple, quick answers – an understandable desire given that they’re usually confronted with less complex technology decisions. But here’s where the danger lurks: hasty decisions often lead to unsatisfactory results because the complexity and the multitude of stakeholders are simply underestimated.

Gen AI is different
than most tech

With generative AI, however, things look different for various reasons.

This technology is so fundamental that it integrates flexibly and surprisingly easily into different workflows and task areas. This leads to a certain lack of clarity at the stakeholder level. Because a technology that can be used broadly and flexibly across the company inevitably attracts a multitude of stakeholders. Many companies have never learned to make quick decisions with a large number of legitimate stakeholders and competing requirements – and that in an area where there are no best practices. For many of our clients, decision-making processes are already lengthy and marked by significant compromises, even with just a few stakeholders.

And with generative AI, there’s another factor at play:

the use cases are notoriously granular and niche-specific, leading to an overwhelming flood of non-prioritizable options. Each individual use case might be valuable for the user, but only the sum of these cases brings economic benefit to the company. It’s a bit reminiscent of how we once had to teach employees to use computers or the internet. The real problem here is that the current leadership generation hasn’t actively shaped such scenarios themselves. Instead, those who are often overwhelmed by the speed of technological change and the increasing number of decisions that need to be made quickly and competently are at the helm. Staying up-to-date in the field of generative AI and making informed decisions is a real challenge even for experts, considering the speed at which new tools and models come to market.

You Must Know
Current Models

Let me be clear from the start:

anyone who believes they can make investment decisions without regular and in-depth engagement with the topic is seriously mistaken. The quality of such decisions depends on the quality of the underlying models and the speed at which they develop. Up until about ten years ago, progress in this field moved at a pace that was hardly worth mentioning. The time when it was sufficient to bring in an expert every few years to determine the current state of affairs and invest based on that is definitely a thing of the past.

Today, in an era where technological developments are accelerating rapidly, it’s no longer enough to maintain an overview only sporadically.

Since 2023, the dynamics have been so rapid that you need to get up to speed at least weekly to avoid losing touch. If we continue to rely exclusively on external consultants, we risk that so much happens during the appointment scheduling that any statement from yesterday is already obsolete today. What we need are internal experts who are continuously at the pulse of the times and not only know the current status quo but actively help shape it. External consultants should no longer be viewed as the sole source of knowledge, but rather as partners who work at eye level with internal staff to make well-founded investment decisions together.

Companies that rely on being enlightened by external consultants to derive their decisions should pause and ask themselves: Is this the right approach? Especially with this topic, that would be a fallacy. The development dynamics are too rapid, and the success of applying Gen-AI tools depends far too much on the individual processes and knowledge of one’s own teams

The bottom line:

Solid investment decisions can only be made when there’s a deep understanding within the company of the capabilities and limitations of current models – and a clear vision of how these will develop in the near future.

You Must Know
Current Tools

In today’s world, it’s no longer sufficient to understand only the theoretical foundations of Gen-AI tools.

What really advances companies is a solid overview of current solutions and the complementary, often non-Gen-AI-based features that make these tools truly successful.

Think about the integration of RAG systems that work behind the scenes to compile information from databases based on a user prompt. Such functions aren’t just gimmicks – they address one of the biggest weaknesses of LLMs: their tendency to hallucinate. RAG minimizes this weakness by unobtrusively injecting factual information into the prompt, making the models‘ responses more grounded.

But this is just one example of functionality that only reveals its true value when you think beyond Gen-AI models and look at the market landscape. Companies that understand these tools and their additional capabilities are better positioned to make informed decisions – whether for purchasing a product or for in-house development. Such a solid understanding enables them to recognize the actual added value and not blindly follow the hype around the next „cool“ Gen-AI tool. On the contrary, they become active shapers who know what technical requirements are needed for a tailored solution.

The bottom line:

Anyone who doesn’t engage with the possibilities and functional components of Gen-AI tools today is missing the chance for smarter investment decisions. It’s not just about understanding the technologies, but also about keeping an eye on developments in the coming years. Leaders who ignore this are setting a wobbly course in uncertain waters. It’s time to set sail and prepare for the future – or get left behind.

You Must Know
Your Workflows

Since we’ve been advising companies on the rollout of generative AI, it’s become increasingly clear that the real bottleneck isn’t in the speed of technical development.

It doesn’t matter how much money flows into model training or how complex the tools become. The real bottleneck lies in the processes, workflows, and mindsets within companies. The reality is that in many companies, most workflows are shockingly heterogeneous, opaque, and heavily person-dependent. These organically grown processes often haven’t been questioned for years because there simply isn’t time for it. And so tasks arise that appear simple at first glance but in reality require deep understanding and prior knowledge from employees to deliver the desired results.

This is where we start in our Gen-AI consulting. Before we deal with the introduction of AI tools, we first collect the existing tasks, roughly evaluate them, and transfer the associated workflows into a Value Stream Map. Only when this fundamental work is done does it often become clear how broadly applicable generative AI really is and what potential lies in thoughtful implementation.

This process not only brings the famous „aha moment“ to employees when they see how Gen-AI tools can be used in a structured way and in small steps. It also often leads to a first-time reflection on their own processes and a professional discussion about the meaningfulness of the current approach. Before we even start using AI tools, the processes within our consulting are usually first streamlined and accelerated. Because true innovation always begins with questioning the status quo.

In Summary:

If you master the art of understanding your own processes and refining them through lean methodology, then the gate opens to better investment decisions. Only then can you recognize the true value of Gen-AI tools and sensibly assess how much more efficiency they can bring to already lean workflows. Because only those who are already able to create lean processes will be able to unleash the full power of these tools.

You Must Know
Your Employees

Besides the usual Gen-AI topics like tools and models, which often lead to overwhelm, there’s another largely overlooked topic:

Do the employees actually want this? In our consulting work, we repeatedly encounter use cases that are technically feasible and could potentially have great business impact, but ultimately fall flat. Why? Not because of lacking resources, but because the people involved simply don’t want to participate. This isn’t always just about fear of job loss. Rather, it’s often a mixture of a corporate culture that has become sluggish, high stress levels among participants, and general skepticism toward new tools or trends. You can draft as many Gen-AI strategy plans as you want and choose the coolest tools – if no one is willing to try them out, none of it matters.

That’s why we often advise not to dive directly into the depths, but to start with impulse presentations that spark curiosity and create contexts focused on playful experimentation and discovery. Workshops are also excellent for this, but they require a certain level of motivated participation.

Pay attention

It’s essential that leaders and decision-makers don’t just bet on technological trends like Gen-AI, but take their teams along on this journey. The introduction of Gen-AI isn’t just a tech project – it changes a company’s foundation and is therefore a cultural issue. Similar to digitalization, agility, or user-centricity, this shows a challenge that companies have been struggling with for years. Therefore: It’s time to question the status quo, roll up your sleeves, and proactively tackle the change.

Scenarios to consider

When introducing Gen-AI, you must take the existing reality as a foundation:

1) Output frequency and workload are defined for each produced artifact in existing tasks and established processes. Deviations from this are handled through general process and estimation adjustments.

2) Expectations for output quality are defined and tested. Deviations from this are handled through adjusting employee behavior.

In complex decision-making processes, it’s crucial to define scenarios to derive clear action recommendations.

We should focus on four essential scenarios that are relevant in the Gen-AI context:

Scenario 1: Employees don’t deliver the required quality, but Gen-AI does. Here, companies should seize the opportunity to invest in AI. This technology not only increases productivity but also takes over repetitive tasks more efficiently. „Those who don’t move with the times, move with the times“ – a quote that fits perfectly here.
Scenario 2: Both employees and Gen-AI deliver the desired quality. The question of whether to invest in AI only arises if it works faster or more cost-effectively. Progress is only progress if it steers us toward a sustainable future.
Scenario 3: Neither employees nor Gen-AI achieve the necessary quality. Here it becomes clear: Before AI comes into play, the focus should be on recruiting specialists. „In calmness lies strength“ – sometimes patience is the key to success.
Scenario 4: Employees deliver the required quality, but Gen-AI doesn’t. In this case, waiting is the best strategy. But this doesn’t mean being inactive – continuous experimentation could reveal future potential of AI.

These scenarios give us clear indicators of when and how an investment in Gen-AI makes sense. You can find more about this in the article „Gen-AI: Your Investment Game-Changer or Just Hot Air? 4 Scenarios You Must Know!“

There's More Than Just the Obvious to Consider

The consideration remains incomplete if you focus solely on internal processes and the takeover of tasks by Gen-AI. Looking beyond your own boundaries is essential. The competitors? Yes, sometimes they offer us an impressive preview of how Gen-AI revolutionizes similar task areas. But the real challenge lies in changing user behavior. These changes aren’t mere trends but signal profound shifts that will challenge every business model. It’s becoming increasingly obvious that some business models won’t have a chance of survival in the upcoming Gen-AI-driven future. Here, it’s important to act with foresight and find the courage to make necessary course corrections early, before it’s too late.

Competitors Are Also
Playing the Game

It seems like many companies align their strategies with competitors – and that’s often sensible. But when it comes to implementing Gen-AI, there are some important points to consider:

The technology is so new that your competitors are also still in experimental mode – best practices simply don’t exist yet. Moreover, the changes are mainly internal and therefore hardly visible from the outside. The initial efficiency gains achieved through Gen-AI might initially appear as additional margins among competitors before new standards are established.

But once the price war really gets going, it’s too late to start with Gen-AI implementation. Because these processes are slow, detailed, and require profound changes. Therefore, it’s important to keep an eye on the competition but not copy their measures one-to-one. Acting too late could cause fatal delays in your own Gen-AI implementation that you simply can’t afford.

Not to be forgotten is the fact that increasing productivity per employee lowers market entry barriers. This opens the door not only for known competitors but also for smaller, more agile providers who could suddenly represent serious competition. Gen-AI-supported small teams could prove to be the toughest competition for large companies. The danger from such scenarios depends heavily on how much your business model is based on knowledge work and how high the initial investments in your industry are. Particularly in software development and creative services, where freelancers and small agencies dominate, the risk is especially high.

User Behavior Will Change Fundamentally

While Gen-AI revolutionizes internal company processes, user behavior won’t remain untouched either. The signs are already recognizable, and existing trends will be further accelerated by this technology. Just look at today’s reality: Between the user and the company, there’s often already an AI – for example, in the form of a search engine. This separation between consumer and provider will deepen even further in the coming years. This means the original information source is increasingly losing importance, and the direct connection between sender and receiver is changing radically.

What does this mean? Expectations are changing – and this change can profoundly influence a company’s business model: from offered touchpoints to interactions to products and services. And by the way, internal employees are basically nothing more than users to whom the company provides tools. So if you don’t hit the mark with Gen-AI for employees or customers, you quickly burn valuable resources.

You can find more about this in the webinar „The Future of Web“ on YouTube.

Some Business Models
Will Cease to Exist

As already indicated, these changes leave clear traces in companies and their business models. Internal processes accelerate, which reduces costs, and the market forces a downward price adjustment through this efficiency gain. But that’s just the tip of the iceberg: The changed expectations and behaviors of users force companies to readjust their portfolio and find the right talent. Predicting the future exactly would be presumptuous, but one thing becomes clear: These external influences could very quickly lead to entire business models becoming unprofitable and simply collapsing.

Who doesn’t remember the video stores that were helplessly exposed to the rise of cloud services? That’s exactly why it’s crucial to think in scenarios that cover the entire range of possible developments to understand the risk to which the business model is exposed.

Gen-AI Investment Decisions Are Not Obvious Decisions

As already indicated, Gen-AI is a topic that can’t simply be fully grasped through punctual external expertise. It requires systematic and continuous engagement within the company. Only in this way can you identify those task areas where Gen-AI adoption really makes sense. These task areas consist of a collection of use cases that, viewed individually, might not bring immediate ROI, but through their applicability to similar cases in the same area can achieve enormous efficiency gains.

Gen-AI should be viewed less as a „top-down“ process, as you know it from other technologies. Much more, it’s a „bottom-up“ change management process that’s comparable to approaches like Agile or user-centricity. It undoubtedly needs management support, but the real work happens at the employee level. Therefore, it’s crucial that employees are empowered broadly, get room for experimentation, and always stay at the pulse of technological possibilities.

Take-Away 1:
"No One Can Avoid
Staying in the Game"

For employees, decision-makers, and leaders, it’s essential to understand that Gen-AI isn’t a marginal phenomenon. Progress doesn’t stop at the threshold – it enters whether you welcome it or not. Each of us will inevitably have to engage with this technology. Even if you recognize today that current Gen-AI models don’t yet have the maturity for your own area, there’s no room for standstill. The next encounter with the future is already waiting in six months.

Take-Away 2:
"Simple Answers Are Nice
But Unrealistic"

The question of whether Gen-AI should be used isn’t one that can be answered with a simple yes or no. In reality, we’re at a point where there are countless nuances and possibilities that we simply can’t brush away. It’s no longer sensible to hope for general answers; rather, we should be aware that each department, each team, even each individual task requires its own answer to this question. It’s essential that we rely on the expertise of our employees – because they’re the ones who know best what works in their area. And if we rely on this assessment, then we must also have the courage to follow this judgment consistently, without ifs and buts.