We’ve seen this story before: disruptive technology captures the imagination of business leaders across the industry and is expected to transform at a large scale. In the early 2010s, it was Robotic Process Automation (RPA). Soon after, cloud computing is coming. Today, generated AI (AI) is taking the spotlight – organizations first jump into pilots without a clear path forward.
result? The tide that can be called Generated AI pilot fatigue. When there is no structure, purpose, or measurable goal to initiate too many AI plans, it is a state of exhaustion, frustration and reduced motivation. Companies run dozens of pilots simultaneously, with the intentions overlapping, but no clear criteria for success. They are pursuing potential in various departments, but instead of unlocking efficiency or ROI, they cause chaos, redundancy and stagnant innovation.
Define AI Gen Pilot Fatigue
Generated AI pilot fatigue reflects a broader organizational challenge: infinite ambitions for infinite structures. Anyone who witnessed the tech wave in the past is familiar with the root cause:
- Unlimited possibilities: Gen AI can be applied in every feature (marketing, operations, human resources, finance), which allows multiple use cases to be launched without clear boundaries.
- Easy to deploy: Tools like OpenAI’s GPT model and Google’s Gemini allow teams to quickly spin pilots without engineering dependencies – sometimes within a few hours.
- Lack of maintenance plans: AI Gen requires high-quality data to be effective. In many cases, data may become stale without the need to implement the process to ensure that the data remains correct and up-to-date.
- Poor measurability: Unlike traditional IT deployments, it is difficult to determine when AI generation tools will be “good enough” to transition from pilot to production. ROI is often blurred or delayed.
- Integration barriers: Many organizations have difficulty inserting Gen Gen tools into existing systems, data pipelines, or workflows, adding time, complexity, and frustration.
- High resource demand: Pilots usually require a lot of time, money and manpower investment – especially around training and maintenance of cleaning available data sets.
In short, AI fatigue occurs when experiments exceed strategies.
Why does this continue to happen?
In many cases, this is because the organization skips basic work. Before deploying any advanced technology, you must first optimize the process you want to improve. On Accruent, we have seen that by simplifying workflows and ensuring data quality, companies can improve efficiency improvements by up to 50% before introducing AI. On top layer of well-adjusted system AI, the improvement can be twice as good. But without this foundation, even the most impressive AI models will not bring meaningful value.
Another trap is the lack of a clear guardrail. AI-generation pilots should not be considered infinite experiments. Success should be measured in defined results – saving time, reducing costs or extending functionality. Must be based on data-driven evaluation, and there must be a gate to progress, pivot or end the project. Ultimately, half of the AI generation’s idea might be more suitable for other technologies, such as RPA or codeless tools – that’s OK. The purpose is not to implement AI, but to effectively solve business problems.
Lessons from RPA and cloud migration
This is not the first time that technology enthusiasm has been swept away. RPA promises to eliminate duplicate tasks; the flexibility and scale of cloud migration promises. Both were eventually delivered, but only for those who applied discipline deployment.
A major gain? Don’t skip foundation. We’ve seen firsthand the organization can drive to reach 50% efficiency improvement Improve data hygiene only by simplifying existing workflows and by introducing AI. When AI is applied to an optimized system, the gain can be doubled. However, when AI stratification is at the top of the rupture process, the impact is negligible.
The same is true for data. Gen AI models are only as good as the data they consume. Dirty, outdated or inconsistent data can lead to poor results or, worse, biased and misleading. That’s why companies have to invest in strong Data governance frameworkthis view has been supported by industry experts and highlighted in McKinsey’s report.
“Easy” AI’s temptation
One of the double-edged swords that generate AI is its barrier to entry. With pre-built models and user-friendly interfaces, anyone in the organization can spin a pilot in a few days (and sometimes even minutes). Despite this accessibility, it also opens the floodgates. Suddenly, you have teams from various departments experimenting in silos with little oversight or coordination. It’s not uncommon to see dozens of AI plans running simultaneously, each with different stakeholders, data sets, and a definition of success or lack of success.
This fragmented approach leads to fatigue – not only from a resource perspective, but also from an increasingly frustrated view of obvious rewards. Without centralized governance and a clear vision, even the most promising use cases can get stuck in an endless cycle of iteration, improvement and reevaluation.
Breaking the cycle: Intentionally establishing
Treat Gen AI first like any other enterprise technology investment – based on strategy, governance and process optimization. Here are some of the principles I found to be crucial:
- Start with the problem, not the technology. Organizations often chase AI Gen use cases because they are exciting – not because they solve defined business challenges. First determine the friction points or inefficiency in the workflow, and then ask: Is AI Gen Gen the best tool for work?
- Optimize before innovation. Fix the process before layering AI into the damaged process. Simplifying operations can unlock significant gains on your own and make it easier to measure the addition of AI. As Bain & Company noted in a recent report, businesses focused on basic ready will see faster time for AI Gen Gen.
- Verify your data. Make sure your model is trained in accurate, relevant and ethical procurement data. According to Gartner, poor data quality is one of the main reasons why pilots cannot scale.
- Define a “good” look. Every pilot should be relevant to business objectives. Whether it’s reducing the time spent on daily tasks or cutting operational costs, success must be measurable – the pilot must have a decision door to continue, pivot or sunset.
- Keep an extensive toolkit. AI Gen is not the answer to every question. In some cases, automation via RPA, low-code applications, or machine learning may be faster, cheaper, or more sustainable. If the return on investment is not a pencil, you are willing to refuse AI.
Looking to the future: What will help and what may hurt
Pilot fatigue may get worse and then get better in the next few years. The speed of innovation is only accelerating, especially among emerging technologies such as Agesic AI. The pressure of “doing something with AI” is huge – without the right guardrail, the organization can be overwhelmed by a lot of possibilities.
However, there are reasons for optimism. Development practice is maturing. The team started to treat AI with rigor that works for traditional software projects. We also see improvements in the tool. Advances in AI integration platforms and API orchestration have made it easier to insert Gen Gen Gen Gen into existing technology stacks. Pre-training models from providers such as OpenAI, Meta and Mistral ease the burden on internal teams. Like those supported by AI’s current institutes, the framework around ethics and person-in-charge AI is helping to reduce ambiguity and risk. Perhaps most importantly, we are seeing an improvement in cross-functional AI literacy – business and technology leaders are increasingly understanding what AI can (and can’t) do.
Final Thought: It’s about purpose, not pilots
Ultimately, AI’s success comes down to intention. Generated AI has the potential to drive tremendous efficiency gains, unlock new capabilities and transform the industry – but only if it is guided by strategy, supported by clean data and measured by results.
Without these anchors, this is just another technical fashion that is doomed to exhaust your team and disappoint your board.
If you want to avoid AI Gen Pilot fatigue, don’t start with technology. Start with the purpose. And build from there.
