Free like a puppy, the cognitive overhead of "easy" AI projects.
Adopting AI feels fun and low-effort at first. Then comes the feeding, the walks, the vet bills—and the quiet realization that your new "pet" is now running your life.
Everyone loves puppies. They’re cute, they’re playful, and the upfront cost seems trivial. You bring one home, and for the first few weeks its all fun and games. Gradually, reality sets in and you’re budgeting for kibble, shots, grooming, and the inevitable chewed-up shoes. Six months later you realize this “easy” addition to the family is now a big part of your life, maybe thats OK but you should know that going in.
AI projects work exactly the same way. Most organizations still treat new AI initiatives like a one-time purchase: spin up a quick pilot, ship something impressive, and move on. What they rarely price in is the cognitive overhead—the ongoing operations, security reviews, monitoring, compliance, model retraining, alert fatigue, integration glue, and the constant low-level mental tax of “is this still working?”
In my experience of launching real, work-related and pro-bono projects, even fun project “light” maintenance adds up fast when you’re running a portfolio. Cognitive overhead must be treated as a first-class constraint when launching any new AI effort. If you ignore it, you end up with a menagerie of half-maintained codebases that quietly drain time, budget, and attention.
To make better decisions, I use a simple three-bucket framework for every AI proposal that lands on my desk:
Building a new capability or adding features
Making something you already have work better
Getting rid of something you don’t have to do anymore
The first two buckets get almost all the oxygen in boardrooms and strategy decks. The third is woefully underrated—and where the biggest leverage usually hides.
Bucket 1: Building a new capability or adding features
Dangerous. Introduces fresh overhead.
This is the classic “shiny new toy” bucket.
Someone pitches an AI-powered recommendation engine, a generative content tool, a predictive analytics dashboard, or a customer-support chatbot. In the pilot it feels lightweight and magical.
Then get into production. You now own new data pipelines, monitoring dashboards, familiarization calls, and drift detection. If you launched it you own it and are L1 tech support, like it or not. Then you may need security reviews, compliance checks (think EU AI Act, SOC 2, HIPAA, or whatever applies to you).
And thats for the deterministic projects! When the project includes generative AI features they carry hidden weight: prompt libraries that must be maintained, output-quality monitoring, PII leakage risks, and the slow creep of model drift as real-world data changes.
Industry numbers are sobering. Many AI projects cost 3–5× more from PoC to production than originally estimated, with annual maintenance alone consuming 15–30% of the infrastructure budget. Models are not “set and forget.”
New capabilities are puppies that grow into large, energetic dogs. Fun at first. Expensive forever.
Bucket 2: Making something you already have work better
Interesting. Potentially neutral—but be careful.
This bucket feels safer. You’re not bolting on something brand new; you’re improving an existing process. AI-enhanced search, smarter forecasting, automated triage, or better fraud detection.
Because you’re optimizing rather than expanding, the added overhead is often lower. The AI can sometimes slot cleanly into current tooling and data flows.
But “often” is not “always.” You still inherit monitoring, drift detection, security patches, and the subtle new dependencies that come with any model. Sometimes the “improvement” quietly increases data requirements or creates new context-switching costs for the team.
Net effect is usually positive, but rarely zero. Treat these projects with cautious optimism and run a quick cognitive-overhead audit before you green-light them.
Bucket 3: Getting rid of something you don’t have to do anymore
Woefully underrated. This is where the real prize lives.
Here’s the part almost no one talks about—and the part we should spend the most time on.
AI doesn’t just automate tasks. It can make entire legacy processes, systems, reports, approval chains, and manual workflows completely obsolete. Yet very few organizations ever do a thorough, top-to-bottom survey of their processes with the explicit goal of deletion.
Why? Deletion feels like loss, not progress. Stakeholders defend their turf. No one gets promoted for sunsetting a system. Leadership bias is toward “more” instead of “less.” And cognitive overhead compounds when you keep carrying dead weight—maintenance contracts, tribal knowledge, duplicate dashboards, outdated compliance checklists, and the mental load of managing seventeen different tools.
Flip the script. Instead of asking “What cool new thing can AI do?” we first ask: “What can we stop doing because AI now makes it unnecessary?”
This bucket can deliver negative overhead: you remove systems, reduce alerts, shrink your attack surface, cut licensing costs, and free up human bandwidth for genuinely high-value work. It is the only bucket that can shrink your total cognitive load.
Stratechi.com provides several key areas to find legacy processes to delete, data visualization, multiple CRMs, business automation platforms, even there in the examples provides, it requires a project to consolidate platforms into one. It would be even better to retire systems that resist agentic, require large amounts of cognitive overhead, can be replaced or is already replaced by an AI function.
Expanding on this, Communications overload is a prime target for Bucket 3. Newsletters, webinars, seminars, all-hands emails, monthly updates, and “quick sync” recordings consume enormous time on both ends. Creators spend hours (or days) drafting, designing, recording, and chasing metrics. Consumers spend scattered minutes or hours reading, watching, or attending—often with low actual value. The result is inbox clutter, calendar bloat, and a constant low-level guilt of “I should probably know this.”
Ask the hard question: Is this communication truly providing unique value, or is it mostly noise? In most cases, the answer is the latter.
The better alternative is to lean into AI-powered, on-demand targeted knowledge. Instead of pushing one-size-fits-all content to everyone, build (or buy) internal systems—RAG-powered chatbots, agentic AI assistants, or enterprise search tools—where employees can ask specific questions and get precise, contextual answers instantly.
No more mandatory webinars. No more skimming irrelevant newsletters. Just “tell me what’s new in Q3 product roadmap for my team” or “what changed in our benefits policy since last month?”Gartner predicts that by 2028, 75% of employees will rely on chatbots for relevant internal communications, replacing traditional push channels with frictionless pull-based access. Newsletters, mass emails, and static intranet pages become secondary. The cognitive overhead drops dramatically for everyone.
Practical ways to start doing this in your own organization:
Run an AI-powered process-mining sweep, even a simple analysis of emails, slacks and calendars, can surface processes where >80% of the work is now doable by AI.
Zero-based budgeting for processes. Every quarter, force every team to justify why a particular report, dashboard, approval step, or legacy application still needs to exist. If it can’t be justified, it gets sunset.
Sunset sprints. Pick one legacy system or manual workflow. Build the AI replacement in parallel, run both for 4–6 weeks, measure outcomes side-by-side, then flip the switch and turn the old one off. The psychological safety of the parallel run makes deletion far less scary.
Measure cognitive overhead explicitly. Track hours spent on maintenance, number of monitoring dashboards, alert fatigue incidents, and context-switching between tools. Deletion shows up immediately as lower numbers across all of these.
The organizations that will win in the next 3–5 years won’t be the ones with the most AI puppies running around the office. They’ll be the ones who had the discipline to house-train them—and then had the wisdom to retire the legacy processes that no longer earned their keep, creating lighter, happier teams with far more bandwidth for what matters most.
Conclusion
Cognitive overhead is real, it compounds quickly, and it is rarely visible on the shiny pitch decks that sell AI projects. Treat it as a first-class constraint from day one.
When the next “easy” AI proposal lands on your desk, run it through the three-bucket test. Ask the deletion question first: What does this let us stop doing?
The answer might be more valuable than the new capability itself.
The future belongs to the teams that end up with fewer systems, fewer alerts, fewer meetings about model drift, and far more mental bandwidth for the work that actually matters.Further reading / citations: