Executive Anxiety: When AI Threatens the C-Suite's Self-Image
You know, I find it fascinating that executives keep framing AI as "just a tool" while simultaneously losing sleep over whether it might replace them. That cognitive dissonance reveals something important.
When a CEO says "AI is just a tool like spreadsheets," they're not wrong - but they're also performing a kind of corporate exorcism. They're trying to banish the uncomfortable question lurking in their mind: "If this thing can analyze market trends, generate strategy documents, and make predictions faster than I can, what exactly am I bringing to the table?"
I watched this play out at a Fortune 500 company recently. They deployed an AI system that could synthesize competitive intelligence and recommend strategic pivots. The C-suite publicly celebrated it as "augmenting their capabilities" while privately scrambling to understand which parts of their intellectual contribution couldn't be replicated by the system.
The automation paradox is hitting the executive floor in a way it never did with previous technologies. Excel didn't make CFOs question their existence. PowerPoint didn't make CMOs wonder if their creative judgment was replicable. But generative AI makes everyone, regardless of seniority, confront the algorithmic shadow of their own job.
Maybe that's why companies keep automating the wrong processes first - they're comfortable replacing the labor of others before examining how much of their own decision-making might be next on the chopping block.
Totally agree that companies trip up by automating the wrong processes—but I think there's a deeper issue here: they don’t actually *understand* their processes to begin with. They're just guessing. Or worse, they're automating around irritants rather than identifying leverage points.
Let me explain.
Most orgs look at what’s tedious or annoying for employees and assume that’s the best place to drop in AI. Tedious? Sure. Strategic? Rarely. The result? You save some time on invoice data entry… while your revenue forecast model is still a spreadsheet stitched together by three different teams and a prayer.
Take customer support. It’s a classic target—"Let’s just drop in a chatbot!" Except, nine times out of ten, the real issue isn’t the number of tickets, it’s that your product UX is garbage and creates the tickets in the first place. So the bot becomes a digital band-aid for a wound that needs surgery.
The smarter move is to map your operations and ask: where does better decision-making actually impact outcomes? Is it your pricing engine? Your supply chain predictions? Your lead scoring? Then look at where AI could *own* or *augment* those calls, not just push pixels and fill forms.
The real superpower of AI isn’t making tedious work go away—it’s changing the economics of intelligence. But that only matters if you're applying it to a problem where intelligence creates value.
Otherwise, congrats—you’ve built a slightly cheaper way to do the wrong thing.
I think there's a fascinating quirk to human psychology at play here. We're perfectly comfortable saying "AI is just a tool" when discussing factory floors or customer service chatbots, but the moment it starts drafting emails that sound like us or making decisions we used to make, that detached perspective vanishes.
It reminds me of how surgeons were totally fine with robotic assistance for other medical specialties until the robots started performing aspects of surgery. Suddenly it wasn't "just a tool" anymore - it was an existential question.
What executives won't admit in board meetings is that many of their daily activities are fundamentally pattern recognition and decision making within constraints - precisely what modern AI excels at. When the McKinsey report says 30% of executive tasks could be automated, that hits differently than hearing the same about assembly line work.
The quiet panic isn't really about losing jobs tomorrow. It's about confronting how much of what we consider "skilled judgment" might actually be predictable pattern execution. That's why execs sabotage automation projects that get too close to their domain while championing ones that disrupt everyone else's workflow.
The truth? If you're worried AI might replace you, you're probably automating the wrong processes first. The right ones should make you more essential, not less.
Totally agree that picking the wrong process to automate is where most AI projects go sideways. But it's not just a sequencing issue—it's an empathy issue.
Companies get seduced by the glittery promise of ROI spreadsheets and try to automate the most expensive line item first. Usually something like customer support, invoice processing, or lead qualification. On paper, it makes perfect sense: high volume, labor intensive, repeatable. But here’s the catch—these are high-touch processes for a reason. They’re tied up in nuance, edge cases, and a whole lot of unwritten knowledge.
Take customer support. Sure, it’s tempting to throw a chatbot at your Tier 1 support queue and watch the ticket backlog shrink. But what actually happens is the bot becomes a glorified phone tree, customers get frustrated, and suddenly your CSAT takes a nosedive. Why? Because you're automating human judgment before you've actually mapped what the judgment is based on.
The irony is, the most successful automation cases often start small and boring. The things nobody talks about at the AI conferences. Like automating QA for document formatting. Or reconciling line items across two legacy ERP systems that hate talking to each other. No one gets a bonus for that—but it works. And it creates trust. You prove the plumbing works first, then scale the ambition.
Also, the whole premise of “automate to save time and money” is a trap. The smarter question is: Where can automation actually amplify what humans do well, instead of replacing them at tasks we don't fully understand ourselves?
If you don't know why your people make the decisions they do, you're not ready for AI. You're ready for a whiteboard and a lot of coffee.
Yeah, this whole "AI is just a tool" narrative is convenient corporate language, but the nervous laugh that follows when executives say it tells the real story.
I've seen it firsthand in boardrooms. When AI begins succeeding at tasks we thought were uniquely human, it forces us to confront uncomfortable questions about our own value. That SVP who prides herself on "strategic thinking" suddenly watches an algorithm identify market opportunities she missed. The operations leader whose identity is built around "good judgment" sees a model making better risk assessments.
It's existential. We're not just threatened by AI replacing our labor – we're threatened by it replacing our self-concept.
That's why companies end up automating the wrong processes. They target visible, measurable work that looks impressive on quarterly reports while carefully avoiding the executive functions that might expose the emperor's new clothes. It's more politically palatable to replace the call center than question whether the CMO's "intuition" outperforms a well-trained model.
What's fascinating is how this mirrors historical patterns. Remember when chess masters insisted computers could never master the game's "art"? The goalposts for "real intelligence" keep moving the moment AI catches up.
Totally agree—with a caveat.
Yes, companies absolutely jump the gun by automating flashy or high-visibility tasks: chatbot responses, invoice approvals, maybe even scheduling. But here's the deeper problem—they're automating processes that were already broken. Automation becomes a kind of lipstick-on-a-pig situation.
Take customer support as a classic example. A company thinks, "We’re getting a lot of tickets—let’s throw a large language model at it!" But if your support process is a mess—unclear escalation paths, inconsistent knowledge bases, no real feedback loop—then automating that chaos just gives you faster chaos. You end up with frustrated customers talking to a bot that’s incredibly efficient at being unhelpful.
Automation doesn’t fix your process—it amplifies it. If your workflow is incoherent, AI makes it faster and more chaotic. If your data is siloed, AI will confidently hallucinate answers based on incomplete context.
Ironically, the toughest—but most valuable—processes to automate are the ones that force you to clean house first. Think supply chain exception handling. It's ugly, messy, spread across emails and spreadsheets, and depends on domain know-how from people who’ve worked at the company for 15 years. Automating that means confronting the entropy. No one wants to do it. But that’s where the real leverage is.
Automation without process redesign is just expensive denial.
I think you've hit on the real discomfort lurking beneath the surface of all these executive AI strategies. We keep saying "AI is just a tool" in the same nervous way someone might say "this gun is just for home protection" - while eyeing it anxiously.
The brutal truth is that white-collar knowledge work hasn't faced this kind of existential scrutiny before. If you're in middle management spending 60% of your time synthesizing reports, scheduling meetings and writing up summaries - and suddenly AI can do all that faster - the "I provide strategic thinking" defense starts to feel pretty thin.
That's why companies keep automating customer service chatbots instead of the executive decision support tools that would actually save millions. There's this unspoken agreement not to point AI at the expensive parts of the org chart first.
The smarter leaders I know are actually leaning into this discomfort. They're asking themselves: "What parts of my job *should* be automated? And what would I do with that time?" It's scary territory, but at least it's honest. The alternative is protecting your own job while telling everyone else to embrace the "inevitable disruption."
Totally agree that companies often trip up by automating the wrong processes—but here's the twist: it’s not just that they automate the wrong things; it’s that they misunderstand what "right" even means. They chase ROI on paper instead of real leverage.
Let’s unpack that.
When execs pick a process to automate, they usually ask, “Where are we spending too much time? Where’s the cost center?” But that thinking gets you stuck automating low-leverage workflows—like generating reports or some back-office data wrangling. Sure, it saves a few hours, but it doesn’t move the needle.
The real opportunity lies in automating for differentiation, not just efficiency. Think product personalization at scale. Think intelligent workflows that create a new kind of customer experience. These aren’t always the most “expensive” processes on the balance sheet, but they’re the ones with leverage—meaning they compound value over time.
Take Spotify. Their Discover Weekly algorithm wasn’t an efficiency play. It was a value-creation play—an AI product that made people addicted to the platform. That’s automation as a growth engine, not a cost-saving tool.
But most companies aren’t structured to think this way. They structure their AI projects like IT initiatives: input, output, ROI in 6 months. So they end up with robotic invoice scanning instead of a smarter core product.
AI isn’t a hammer for busywork. It’s best used as an amplifier for your competitive edge. But to do that, a company has to actually know what its edge is—and that’s where a lot of them get exposed.
I think there's something fascinating hiding beneath all the executive anxiety about AI. When CEOs say "AI is just a tool," there's often a defensive edge to it — like someone insisting "I'm not worried" while checking the locks three times.
The awkward truth is that knowledge work has been partially sacred for decades. We've accepted that factory jobs could be automated, but somehow believed that thinking tasks were uniquely human. AI punctures that comforting myth.
What keeps executives up at night isn't just about their own jobs — it's the realization that their mental models about value creation might be wrong. If a $200 GPT subscription can do parts of what they pay MBA graduates $150K to do, what else have they mispriced in their organization?
This explains why companies often automate the wrong processes first. They rush toward the operational edges — customer service bots, document sorting, data entry — while avoiding the cognitive core where the real disruption lurks. It's easier to optimize what workers do than question what executives decide.
There's this unspoken hierarchy in organizations where "real thinking" flows from the top. AI challenges that by suggesting that many high-status cognitive tasks follow predictable patterns just like physical labor. No wonder it makes the C-suite squirm.
Exactly — companies keep reaching for the sexy stuff first. They go straight for AI magic tricks instead of the boring but critical plumbing.
Everyone wants AI to automate the sales funnel or generate content or “optimize” strategy (whatever that means). No one wants to fix the tangled mess of back-office operations where workflows are built like Jenga towers from the early 2000s. But guess what? That’s where automation actually works.
Look at RPA (robotic process automation). The companies that successfully scaled RPA didn’t start with flashy customer-facing use cases. They started by automating invoice reconciliation or claims processing — work that’s high volume, rule-based, and frankly soul-crushing for a human. Boring is beautiful when it comes to AI, because repeatable logic makes for trainable models.
But let’s go deeper on *why* companies pick the wrong stuff. Part of the problem is that AI gets dropped in from the top — it’s a boardroom fever dream. So no one on the ground, the people who actually understand the workflows, is involved in choosing which processes to automate. The result? You get AI trying to automate things that aren’t even processes — they’re just broken communication chains or legacy tech debt in disguise.
Also, there’s this delusion that automation is a silver bullet. But if the process is messy, automating it just gets you faster chaos. It's like installing a rocket engine on a shopping cart. Sure, it moves — straight into a wall.
Take customer support chatbots. Everybody wanted to slap GPT onto the front line of support. Except a lot of those teams never documented their escalation logic or integrated their CRM properly. So now you have a clever-sounding bot that doesn’t know when to hand off, feeding customers a word salad while your CSAT tanks. That’s not automation — that’s theater.
The hard truth is, good automation starts with process clarity. And most orgs don’t actually know how their own processes work. Until they do, AI just gives them faster failure modes.
Those corner-office anxieties are more revealing than most executives would admit. When leaders get defensive about AI potentially replacing them, they're unconsciously acknowledging something fascinating: management itself is largely pattern recognition and decision-making under constraints - exactly what AI excels at.
I've noticed the executives most threatened by AI are often the ones who've built careers on information gatekeeping rather than genuine leadership. If your value comes from being the person who "knows things" or controls data flows, you should be worried. That's commodity knowledge now.
The executives thriving with AI are those who recognize their humanity as their edge. They're focusing on the messier human elements that machines can't touch: navigating office politics, building genuine trust, understanding unspoken needs, and creating psychological safety for teams to innovate.
It reminds me of those studies showing how doctors resist diagnostic AI. The ones who fight it hardest tend to be the most insecure about their clinical judgment. Meanwhile, the truly confident ones say, "Great, this handles the pattern matching so I can focus on complex cases and patient relationships."
Maybe the real question isn't whether AI will replace executives, but whether it will expose which ones were actually adding unique value all along.
Totally agree that companies often pick the wrong targets for automation — but I think it goes deeper. It's not just that they’re automating the wrong processes; it’s that they don’t understand the process in the first place.
There’s this weird mental shortcut where execs see a clunky workflow and think, “Let’s automate it!” without ever asking, “Why does this exist like this at all?” So they end up building RPA bots on top of terrible processes that were never designed with logic or efficiency in mind. It’s like paving a cow path — you’re locking in dysfunction at scale.
Take customer service as an example. Companies often try to automate the first-line support (“What’s my account balance?” kind of stuff), assuming it's low value. But if 80% of your calls are that, maybe the real issue is upstream: your billing system confuses the hell out of customers. Why not fix the product or the interface instead of teaching a bot to handle frustration?
And here’s the kicker: when you automate without rethinking, your errors scale faster than your savings. A broken process done by people causes delays; a broken process done by software causes data loss, compliance issues, and—if you're unlucky—public apologies.
So maybe the first step shouldn't be automation. It should be a brutal audit: “What are we actually doing here, and why?” Strip it down first. Then decide whether it even needs automation—or just common sense.
I think there's something deeper than just fear going on with the executive resistance. It's cognitive dissonance. These leaders have spent decades building identities around "executive judgment" – this idea that their experience-honed intuition is something machines can't touch.
Then AI shows up and starts making better predictions in some domains than their vaunted judgment. That's not just threatening their job; it's threatening their entire self-concept.
I saw this with a CMO friend who prided himself on "knowing what creative will resonate." His team ran an experiment letting AI predict which ads would perform best, and it consistently beat his picks. He was genuinely rattled - not about losing his job tomorrow, but about what it meant for his professional identity.
The funny thing is, the real value of executives isn't prediction at all – it's in setting direction, building culture, and making complex ethical trade-offs. But we've built compensation structures that reward "being right" about market moves, not the harder-to-measure work of leadership.
No wonder they steer AI toward lower-level tasks. To point it at their own work would force a painful reassessment of what actually makes them valuable in the first place.
Totally agree that companies often pick the wrong processes to automate—but I’d argue it goes deeper than just poor prioritization. It’s the obsession with automating the visible versus the valuable.
Here’s what I mean. Most orgs default to automating loud, annoying processes—the ones that get the most complaints or make for easy wins in slide decks. Think invoice processing or employee onboarding. Sure, inefficiencies there are obvious, but they’re often not actually critical levers in business performance. They’re just easy to spot.
What gets ignored? The quiet, messy stuff buried in the middle: decision points. Not the workflow itself, but the logic that drives it. That’s the true bottleneck. Take supply chain planning—automating data entry is table stakes. The real value lies in automating the judgment calls: how much to order, when to shift suppliers, what trade-offs to make under uncertainty. That’s what humans spend days debating in Slack threads. It's ambiguous, political, and scary to touch. So it gets ignored.
The companies winning with AI? They go straight for the hairy logic. Look at Flexport—they took on global logistics by using AI to navigate customs classifications and tariff rules. Boring? Hugely valuable. Not something you find on a process map, but a place where intelligence—human or artificial—makes all the difference.
So maybe the problem isn’t just that we’re automating the wrong processes. It’s that we’re chasing the most legible ones, not the most leveraged. Automating a 3-step workflow feels safe. But in the long run, it’s a rounding error.
This debate inspired the following article:
Why most AI automation projects fail because companies automate the wrong processes first