AI Isn’t Coming for Your Job. It’s Coming for Your Excuses

By Brad Nicolaisen - SVP Strategic Growth and AI Innovation

The most popular story about AI is also the laziest: “AI is going to replace people.”

It’s a headline built to jolt your stress response, not deliver understanding. The more uncomfortable truth is this:

AI will not primarily replace humans. It will replace organizations that refuse to redesign work.

And yes, some jobs will shrink. Some roles will disappear. Pretending otherwise is dishonest and frankly unhelpful. But the replacement narrative misses what is actually happening inside high-performing teams right now.

AI is turning into a force multiplier. It is the turbocharger, not the engine. And if your engine is a mess, you are about to go faster into a wall.

The Real Threat Is Not AI. It’s Organizational Self-Deception.

Watch what many companies are doing:

• They hold a “prompt-a-thon.”

• They spin up an innovation lab.

• They run a dozen pilots.

• They proudly announce they are “AI-forward.”

• Then… nothing changes.

The same meetings. The same rework. The same spreadsheets duct-taped to systems that no one trusts. The same approvals that exist only because nobody wants to own a decision. The same “we’ve always done it this way,” except now it has a chatbot in the corner like a decorative plant.

If your AI program is mostly demos and deckware, you do not have an AI strategy. You have AI theater. And AI theater is expensive. It burns credibility, distracts leaders, and gives your teams a new tool to ignore when deadlines hit.

Here is a simple test:

If AI is not embedded where work actually happens, it is not transformation. It is entertainment.

AI Replaces Fragments of Work, Not Whole Humans

Jobs are not neat checklists. Jobs are messy systems of judgment, coordination, accountability, context, and relationships.

AI is excellent at compressing time on work fragments like:

• searching for information
• summarizing and drafting
• translating between formats
• pattern recognition in large volumes
• triage and routing
• reconciliation and exception detection

That is not “the whole job.” It is often the annoying part of the job.

Which is exactly why AI creates such a strange effect inside organizations:

• It can make a great employee feel superhuman.
• It can make a mediocre process fail faster.
• It can make a weak manager look better for a quarter, then worse for the next year.

AI does not level the playing field. It tilts it.

The winners are not the companies with the flashiest model. The winners are the companies that redesign work so their people stop spending talent on nonsense.

The Force Multiplier: People, Processes, Platforms

If you want to understand why some organizations are pulling away, stop obsessing over models and start looking at where AI multiplies the real system.

People

AI does not eliminate the need for strong talent. It makes strong talent more scalable.

It shifts humans up the value chain:

• From compiling to interpreting.
• From drafting to deciding.
• From chasing updates to shaping outcomes.

But it also exposes a brutal reality: judgment is now the bottleneck.

If someone cannot frame a problem, evaluate quality, spot risk, or make tradeoffs, AI will not save them. It will simply output confident nonsense faster.

This is the part nobody wants to say out loud in a town hall:

AI will reward the people who can think and punish the people who coast.

That is not cruelty. That is physics.

So if your “AI enablement” plan is mostly prompt tips, you are training people to press buttons, not to produce better outcomes.

What organizations actually need is a higher bar for:

• critical thinking
• data literacy
• domain judgment
• communication
• ethical and risk awareness

In other words, the “human” part gets more valuable, not less. The easy part gets automated. The hard part gets revealed.

Processes

AI accelerates whatever you built, including the dysfunction.

If your processes are unclear, inconsistent, and full of institutional knowledge, AI will not fix that. It will amplify it.

Think of AI like a microphone. If the singer is off-key, turning up the volume does not make it music.

Bad processes create:

• vague definitions
• inconsistent inputs
• undocumented exceptions
• handoffs with no ownership
• decisions made because “that’s how we do it”

AI in that environment produces:

• inconsistent outputs
• hallucinations that look plausible
• decisions nobody can explain
• risk that scales faster than value

And then leadership panics and blames “the technology,” when the real culprit is the same one they have been ignoring for years: process rot.

If you want AI to multiply value, you have to do the unglamorous work first:

• clarify decision rights
• standardize inputs where possible
• define what “good” looks like
• document exceptions
• build feedback loops

AI does not replace operational discipline. It demands it.

Platforms

If AI lives outside your systems, it will stay optional. Optional tools get abandoned.

The biggest wins happen when AI is embedded inside the platforms where the business already runs:

• ERP and finance systems for anomaly detection, faster close narratives, guided exception handling
• CRM for deal risk signals, pipeline hygiene, next-best actions
• service platforms for knowledge retrieval, case summarization, coaching feedback
• supply chain systems for exception triage, supplier risk, scenario reasoning

Not because platforms are trendy. Because platforms create the things AI needs to be real:

• operational context
• governed data
• traceability
• adoption inside the workflow
• measurable outcomes

Let’s be blunt:

If your AI is not connected to a system of record, it is usually a pilot looking for a purpose.

The Dirty Secret: Most “AI Transformations” Are Just Cost-Cutting Fantasies

A lot of executives hear “force multiplier” and translate it as “headcount reduction.”

Sometimes there are legitimate efficiency gains. Sometimes you should take them. But when cost-cutting becomes the primary story, two things happen:

• People hide problems, because honesty feels like volunteering for elimination.
• Adoption collapses, because why would anyone help build the machine that deletes them?

If you want AI to multiply the organization, you need to be honest about workforce impact and equally honest about reinvestment.

The healthiest AI strategies do three things at the same time:

• Remove low-value work
• Redesign roles around higher-value work
• Reinvest capacity into growth, quality, customer outcomes, and risk reduction

If you only do the first, you will get a short-term bump and a long-term talent drain.

Force multipliers do not work if the force you are multiplying is disengaged, anxious, or cynical.

Governance Is Not Bureaucracy. It’s How You Scale Without Getting Burned.
AI introduces real risks. Not hypothetical risks. Real ones.

• Confident errors
• Bias and unfair outcomes
• Data leakage and IP exposure
• Compliance failures
• Decisions nobody can explain
• Automation that breaks under edge cases

So the goal is not “move fast and break things.” That worked when you were breaking UI elements. It does not work when you are breaking trust, compliance, or customer outcomes.

The winning play is speed with control.

Non-negotiables for scaling responsibly:

• Clear decision rights: advisory vs authoritative
• Traceability: explainable recommendations, reproducible outputs
• Guardrails: allowed data, restricted data, brand and compliance rules
• Feedback loops: learning from corrections, measuring real outcomes
• Ownership: business owners accountable for value, not just IT owners accountable for uptime

If your AI rollout does not include these, you are not innovating. You are gambling.

A Provocative Starting Point: Stop Asking “Where Can We Use AI?”

That question leads to scattered use cases, novelty, and wasted time.

Ask two better questions:

What are our top 10 time sinks?

The high-volume, repeatable work that drains capacity:

• Report compilation
• Information retrieval
• Meeting summaries
• Reformatting and deck production
• Reconciliation and data cleanup
• Routine triage

Use AI here for automation. Measure cycle time reduction, rework reduction, hours returned to the business.

What are our top 10 judgment calls?

The high-impact decisions with messy input:

• Prioritization
• Risk assessment
• Pricing exceptions
• Escalations
• Forecasting under uncertainty
• Root cause analysis

Use AI here for augmentation. Measure better outcomes, fewer errors, faster decisions, reduced risk.

Then do the part most companies skip:

Redesign the role and the workflow so the “saved time” turns into better results, not just more meetings.

If you remove 30 percent of the busy work and reinvest nothing, all you did was create a new capacity vacuum that bureaucracy will happily fill.

The Future Is Not Human vs Machine. It’s Human Judgment at Machine Scale.

This is what is actually happening:

• Machines will generate drafts, options, and patterns.
• Humans will decide what matters, what is true, what is fair, and what is worth doing.
• The companies that operationalize this pairing will compound advantage.
• The companies that treat AI as a bolt-on tool will keep hosting pilots while competitors rewrite the rules.

AI is not a silver bullet. It is a mirror and a multiplier.

It will reflect your clarity or your confusion.

It will amplify your discipline or your dysfunction.

It will increase your speed, but it will not choose your direction.

So if you are looking for the real op-ed conclusion, it is this:

AI will not replace your people. It will replace your tolerance for broken work.

And if that feels threatening, it is probably because you already know where the broken work is.