Remember those mysterious farm implements from the Shelburne Museum I wrote about last week AI Is Not Always the Answer? Tools that clearly served important purposes in their time, but whose actual utility has been lost? That same disconnect between sophistication and usefulness is playing across the country as companies rush to deploy workplace AI.
“We need to leverage AI to boost productivity,” executives announce, like archaeologists picking up an ancient tool and declaring it must be valuable because it looks complex. They’re so focused on having cutting-edge technology that they forget the most fundamental question every organization should ask: “Will this actually help our people do their jobs better?”
With workplace AI spending projected to reach $79 billion by 2025, companies feel pressure to implement AI solutions—not necessarily because employees are struggling with specific problems, but because they think competitors, investors, or industry analysts expect them to.
The “Productivity Theater” That’s Killing Real Efficiency
Here’s the uncomfortable truth most organizations need to hear: Your employees might not want your AI tools.
While tech giants can afford to experiment with AI workplace initiatives for competitive positioning, most companies operate under different constraints. You’re not optimizing a smoothly running operation—you’re trying to solve real workflow problems, reduce genuine friction, and help people accomplish their actual work goals.
I see this constantly in corporate AI rollouts:
“Let’s deploy AI writing assistants across all teams!” (Are your people actually struggling with writing, or do they need clearer processes and better communication training?)
“We should implement AI-powered meeting summaries!” (Are employees asking for more meeting documentation, or are they asking for fewer, more focused meetings?)
“Everyone’s talking about AI productivity dashboards!” (Everyone’s also talking about the basic workflow problems your organization still hasn’t solved.)
This approach—what I call “productivity theater”—leads to sophisticated-sounding tools that employees ignore or actively resist, while the real sources of workplace inefficiency remain unaddressed.
The Employee Value Test Every AI Initiative Should Pass
Ironically, most companies will spend a fair bit of time making sure what they are building or selling is what their customers actually want. Those same companies, though, will all too often just plunge in. A better way is to follow some form of the “5 Whys” approach, which is really a root cause analysis. Ask “What work problems are our people actually trying to solve, and would AI genuinely help them solve those problems better?”
Can employees accomplish their goals with current tools? Fix the basics first.
Are they struggling with specific tasks that AI could meaningfully improve? Prove it with actual employee feedback, not assumptions.
Are they asking for AI, or are they asking for better outcomes that you think AI might deliver?
The fundamental principle here is that employees don’t want tools—they want to get their work done effectively and go home. Your constraint isn’t that you lack sophisticated algorithms; it’s that you need to remove the friction that prevents people from doing their best work.
The Real Cost of Workplace AI Distraction
Every organization has the same limiting resources: employee attention, IT capacity, and change management bandwidth. When you choose to deploy workplace AI, you’re choosing not to fix something else that might be more critical to actual productivity.
That AI initiative means:
- Your IT team isn’t addressing the core system integrations that cause daily frustration
- Your managers are learning new tools instead of developing better leadership skills
Your employees are adapting to complex interfaces instead of mastering their core competencies. - Your limited change management capacity is funding impressive demos instead of workflow improvements
The opportunity cost isn’t just the implementation time—it’s the momentum you lose while deploying tools that don’t improve your actual work outcomes.
What Employees Actually Need Instead
Instead of asking “How can we add AI to our workplace?” organizations should be laser-focused on work fundamentals:
Core Processes: Do your existing workflows actually work? Can employees complete routine tasks without unnecessary steps, approvals, or system switching?
Information Access: Can people find the information they need when they need it? Or do they spend significant time hunting through systems, asking colleagues, or recreating work that already exists?
Decision-Making: Are employees empowered to make appropriate decisions, or do bottlenecks and unclear authority slow everything down?
Communication: Can teams collaborate effectively, or do misaligned priorities and poor information flow create constant friction?
While AI could and maybe would improve some or even all of these things, it’s important to confirm the approach before diving into the AI pool. These may well be good old-fashioned organizational problems that require process design, change management, and disciplined execution—and yes, maybe even AI.
The “Shiny Tool” Reality Check
Here’s a framework to put employee value first:
Step 1: Identify work struggles, not tool gaps. What specific tasks do employees find difficult, time-consuming, or frustrating? Where do they get stuck or work around broken processes?
Step 2: Apply the simplicity test. For each work problem, what’s the simplest possible solution? Can better training, clearer processes, or system integration solve this without AI? I’m a firm believer in applying Occam’s Razor.
Step 3: Measure work impact, not tool adoption. Will solving this problem help employees achieve their goals faster, more accurately, or with less stress? Can you quantify the improvement?
Step 4: Count the complexity cost. Every AI tool adds learning burden, potential failure points, and change management overhead. Is the work benefit worth the added complexity?
If you can’t clearly show that an AI tool will meaningfully improve work outcomes—not just make your organization sound more innovative—you shouldn’t deploy it.
OK, So When CAN I Use AI
I know, I know. You may think I’ve been bashing AI use but I’m really not. There are lots of excellent uses for AI that can bring real business value. Here are some of the top ones, in my view:
AI Is Solving a Genuine Automation Problem
Some workplace AI genuinely solves problems that simpler approaches can’t address:
- Automating truly repetitive work: If employees spend significant time on manual, rule-based tasks that AI can handle more accurately and consistently.
- Processing information at impossible scale: If employees need to analyze large datasets or handle high-volume, similar requests that overwhelm human capacity.
- Providing 24/7 availability: If employees or customers need support outside normal business hours for routine questions.
But notice the pattern—these are cases where AI solves a real work constraint, not where AI makes the organization appear more sophisticated.
You Have a Competitive Constraint That Could Kill You
Sometimes organizations face specific operational problems that threaten their ability to compete effectively, and those problems genuinely require AI to solve.
Notice I said “threaten your ability to compete.” Not “would be nice to optimize” or “could make us 5% more efficient.” We’re talking about constraints that put you at such a disadvantage that clients will choose competitors, or inefficiencies will spiral beyond what your economics can handle.
Customer Service: When Volume Overwhelms Human Response
AI makes sense when:
- You’re losing customers because response times are unacceptable compared to competitors
- Manual triage and routing create inconsistent service quality that damages retention
- You have enough historical data to train systems that actually understand customer intent better than your current processes
Knowledge Work: When Information Overload Blocks Decision-Making
AI makes sense when:
- Employees can’t synthesize relevant information quickly enough to make competitive decisions
- Manual research and analysis create bottlenecks that slow critical business processes
- You have structured data that can genuinely improve decision quality beyond what skilled employees can achieve
The False Exception: Making Working Processes “Smarter”
Here’s where most organizations get trapped. You want to add AI to processes that already work fine, hoping to make them sound more sophisticated, efficient, or future-ready.
“Our expense reporting works fine, but AI categorization would seem more advanced.”
“We handle scheduling well manually, but AI optimization would look better to leadership.”
“Our document management works, but AI search would make us seem more data-driven.”
Here’s the brutal test: If you removed the AI tomorrow and went back to your previous processes, would work performance actually suffer, or would operations continue just fine?
If operations would continue just fine, you’re not solving a work constraint—you’re solving a perception problem. And for most organizations, perception problems are expensive distractions from the real work of building competitive advantages that customers and employees actually value.
A Practical 6 Step Workplace AI Framework
If you think you might actually need workplace AI, here’s how to approach it without disrupting your core operations:
- Step 1: Prove the constraint is real and costly. Can you quantify exactly how this work problem is limiting performance, increasing costs, or hurting competitiveness? “Better insights would be nice” doesn’t qualify.
- Step 2: Exhaust the simple solutions first. What’s the most straightforward way to address this constraint? Can you train people differently? Redesign the process? Use existing tools better? Only move to AI if simpler approaches genuinely won’t work.
- Step 3: Check your data reality. Do you have enough clean, relevant operational data to train useful models? Be brutally honest—most organizations overestimate both data quality and the predictive value of their historical information.
- Step 4: Calculate total cost of complexity. Include training time, ongoing maintenance, change management overhead, and the opportunity cost of not working on employee-facing improvements. What else could your organization accomplish with that energy?
- Step 5: Define success in work terms. How will you know the AI is working? What work metrics need to improve, and by how much, to justify the complexity and cost?
- Step 6: Plan for the maintenance reality. AI systems need constant care and feeding. Do you have the organizational capacity to maintain and optimize these systems while also running your core business and serving customers?
The Bottom Line: Tools Follow Work (Not the Other Way Around)
Workplace AI can be powerful for organizations—but only in specific circumstances. The key is being brutally honest about whether you’re solving a constraint that affects your ability to compete and serve customers or chasing a solution that makes your operations sound more sophisticated than they need to be.
Most organizations find their real productivity constraints are much simpler: they need clearer decision-making authority, better process documentation, more effective communication channels, or streamlined approval workflows. These aren’t AI problems, they’re management problems that require focus, discipline, and employee insight.
But for the organization facing a genuine operational constraint that threatens competitiveness, and where simpler solutions won’t work, workplace AI can be transformative. The trick is knowing the difference between operational necessity and technological vanity.
Remember those mysterious farm implements? They were useful because they solved specific, important problems for the people who used them. Your workplace AI should do the same—solve real work constraints that matter to your ability to compete and grow.
Everything else is just expensive curiosity that makes your employees’ jobs harder, not easier.