Last Updated on 6. October 2026
A department formulates its new requirements with the help of AI in a single afternoon. In the past, it took several days or weeks to draft the requirements and coordinate them with the stakeholders. Nevertheless, this need will not be prioritized until the next portfolio board meeting, which is held only quarterly.
This example illustrates a pattern that many companies are currently experiencing. The use of AI makes individual work steps noticeably faster, but the organization does not automatically become faster as a result. Often, the bottleneck simply shifts: away from creation and toward coordination, handoffs, and decisions.
This is the blind spot of many AI initiatives. The focus is on measuring how much faster people complete their tasks. Whether a need is thereby turned into a usable result more quickly is rarely the focus. So what happens to an organization when upstream work is suddenly completed much faster, but the downstream decisions remain unchanged?
The bottleneck shifts
It has long been known from bottleneck theory that accelerating a single step barely changes the overall flow as long as the bottleneck lies elsewhere. When AI is used, two additional mechanisms come into play that can systematically shift bottlenecks.
An implicit filter is eliminated: Until now, thoroughly developing a requirement involved considerable effort. This effort also acted as a filter, since not every idea was fleshed out and not every analysis was conducted. If this effort is drastically reduced through AI support, not only does the speed increase, but so does—in many cases—the volume of work that must subsequently be prioritized, reviewed, or decided upon. As a result, the portfolio committee has no additional decision-making capacity and meets at the same frequency as before.
The review burden increases. Depending on its impact and context of use, AI-generated output must be validated or reviewed. In many knowledge-intensive activities, AI reduces the cost of creation significantly more than it reduces the effort required for review and verification. If this leads to an increase in the volume of generated artifacts, work may shift from creation to review, validation, and approval.
Both mechanisms lead to the same outcome. The new bottleneck therefore often lies where human capacity for review, approval, or decision-making is limited. Traditional bottleneck management attempts, among other things, to better utilize the bottleneck, increase its capacity, or limit the load placed on it. With AI, another option comes more to the fore: Certain decisions can be structured in such a way that, within defined limits, they no longer require individual human judgment on a case-by-case basis.
This does not mean that every bottleneck should disappear. Some human decision points are wasteful—such as approvals that exist out of habit and whose outcome is a foregone conclusion anyway. Others are deliberate control mechanisms, such as the dual-control principle for authorizations or the approval of changes to critical systems. Treating both the same way accelerates the process in the wrong place.
Two reflexes that fall short
When prioritization or approval becomes the new bottleneck, organizations usually react in one of two ways. Often, the first attempt is to increase existing decision-making capacity: through additional meetings, more preliminary reviews, or additional roles. This can be useful, but it does not answer the underlying question of whether each of these decisions must continue to be made on a case-by-case basis at all.
The second reflex is a one-size-fits-all rule. An assistant who creates technical documentation from approved information needs different guidelines than an agent who can modify user permissions. If both are treated the same, one of two undesirable extremes results. Either harmless use becomes so cumbersome that it migrates to the “shadow AI,” or autonomous systems are granted freedoms that are disproportionate to their potential impact.
Three steps to the decision space
Step 1: Identify the bottleneck. The starting point is a point where AI has already accelerated upstream work. What happens next is crucial. Where do wait times, follow-up questions, prioritization conflicts, or approval loops arise? This doesn’t require a comprehensive process analysis. Often, it’s enough to follow a typical process from request to result and ask those involved what they’re waiting for.
Step 2: Categorize decisions. Every decision at the bottleneck is put to the test and ends up in one of four groups: The decision is eliminated; standardized through fixed rules; automated within defined limits and escalated in the case of exceptions; or the decision is deliberately left to a human because it requires careful consideration and responsibility. The group of fixed rules—or those with defined rules—warrants the closest scrutiny, as this is where opportunities for automation arise—whether rule-based or, where context and variability require it, through AI.
Step 3: Define the decision-making scope. For everything that becomes a rule, the question arises: What is the system allowed to decide or execute on its own? Four factors determine the answer:
- Scope of action: What can the system access, and what can it change?
- Impact: How significant are the consequences of its decisions?
- Reversibility: How easily can an incorrect action be corrected?
- Unambiguity: How clear are the rules, context, and decision-making situation?
The greater the scope of action and impact, and the harder it is to reverse an action or the less clear it is, the narrower the scope for autonomous decision-making becomes, and the sooner a human takes over.
This changes the very meaning of control. Instead of retroactively monitoring every individual case, the organization defines in advance:
- What is the system allowed to do on its own?
- Within what limits?
- When must a human take over without exception?
A larger portion of control thus shifts from individual processes to the design and monitoring of the decision-making space.
What this means for CIOs
Using AI does not automatically translate to business productivity. Licenses, active users, the number of use cases, and pilot projects indicate whether AI is being adopted. They do not show whether AI deployment improves the organization. Another question is key here: How long does it take to go from identifying a need to achieving a productive result, and how much of that time is spent working versus waiting?
If AI shortens a work step from five days to five hours, but the result then has to wait three weeks for a decision, local productivity has increased. However, little of that benefit reaches the business unit.
For AI transformation, this means: The focus must be on the entire journey from need to result. Optimizing individual use cases is only one part of that. Three questions can help you get started:
- Where has your bottleneck shifted since AI has accelerated the upstream work?
- Which decisions can be brought forward as a rule?
- Where does human decision-making remain deliberately necessary?
The crucial question of AI transformation is therefore not just which tasks AI can take over. CIOs must agree with the business units on which decisions their company still wants to make on a case-by-case basis in the future.
How We Support You
The question of which decisions will remain in human hands in the future, which can be standardized, and which can be automated within clear boundaries is part of a larger challenge: structuring the IT organization in such a way that new technological possibilities not only speed up individual work steps but actually improve the end-to-end workflow.
To this end, we support leadership teams with three complementary formats:
- IT Evolution: The Impulse is a free presentation for strategy retreats and executive meetings. It creates a shared understanding of how AI, platform logic, and new forms of division of labor are transforming the IT organization.
- IT Evolution: The Assessment is a half-day workshop with the CIO and the IT leadership team. Together, we identify where bottlenecks, dependencies, and missing prerequisites are currently limiting the impact of AI and automation and where the greatest need for action lies for further development.
- IT Evolution: The Check is a customized analysis conducted over three to four weeks, resulting in an executive-level assessment and roadmap. It identifies the necessary changes to the organization, governance, and operating model.
To get started, our white paper Are CIOs Becoming Obsolete? and our CIO Study are available for download. Both explore in depth how the role, governance, and organizational model of IT are changing under the new technological conditions.
The best way to determine which format suits your situation is to discuss it directly with us. Please reach out. We’d be happy to explore this question further with you.
