A Practitioner's Honest Take on AI Across Compensation, Planning, and Workforce Decisions
A little over a year ago, I was still skeptical of artificial intelligence. Not because I doubted its potential. After nearly thirty years in enterprise technology, I had simply seen too many innovations arrive with promises that ran ahead of what was actually production-ready. Cloud, analytics, and machine learning all delivered meaningful value, but often a few steps behind where expectations had been set.
In compensation, revenue operations, and workforce planning, that gap carries even more weight. These are environments shaped by fragmented data, competing objectives, and decisions that affect people's careers and livelihoods. A polished demonstration is not the same as an enterprise-grade capability.
I was not willing to recommend something I could not explain, measure, or stand behind.
Some of that skepticism was warranted. The data challenges are still real. Adoption requires more effort than many organizations anticipate. Governance, integration, and change management cannot be treated as afterthoughts. What changed was not my standard. It was what I started seeing in practice, and the measurable results that followed.
From Skeptic to Pragmatic Adopter
My perspective began to shift while watching a close friend, an editor in commercial post-production, navigate what AI was doing in his industry. AI was not replacing his creative judgment. His experience, taste, and instinct for what made a story work remained essential. What changed was the work underneath that judgment. A process that once required producing several rough cuts, evaluating each one, and methodically eliminating alternatives could now happen much faster. AI reduced the time and investment required to generate the options, but it did not decide which option was best. The expert still made that decision.
That distinction helped me understand the opportunity in my own work. The value was not limited to writing emails or generating images. AI could help me explore scenarios, compare alternatives, identify gaps, and prepare recommendations. It could accelerate the work leading up to a decision without removing accountability for the decision itself.
When I began seeing measurable improvements in productivity, quality, and time saved, it became difficult to remain skeptical. The shift toward pragmatic adoption followed naturally. It also changed the questions I bring into client conversations. In my work at Argano, I spend less time discussing whether organizations should engage with AI and more time helping them determine where to begin, how to sequence adoption, and how to measure whether it is working.
Start With Capacity, Not a Percentage
Most sales performance management leaders have now attended a meeting where a CRO or CFO points to peers reporting significant AI-driven efficiency gains and asks: "What is our plan to achieve the same result?" My advice to the compensation or RevOps leader in that room is simple: do not borrow someone else's percentage and promise to reproduce it inside your organization.
The more credible conversation is about operational drag. Where is manual effort slowing the organization down? Which repetitive activities consume capacity without improving the quality of the outcome? Where could AI make work faster, more consistent, or easier to review?
Then ask the more important question: What could the organization accomplish with the capacity that becomes available?
The objective should not be framed simply as doing the same work with fewer resources. The opportunity is to redirect capacity from administrative activity toward higher-value planning, analysis, and decision support. For organizations that want to begin applying AI now rather than wait for the next platform cycle, I see four practical areas of opportunity across the performance management lifecycle.
Where to Begin
1. Build
For many organizations, Build is the most practical starting point. AI can support requirements gathering, testing, documentation, configuration, and quality assurance. These activities are time-consuming, but their outputs can be reviewed before they affect the sales force. That makes Build a relatively controlled environment in which teams can learn how to work with AI, establish governance, and measure value without transferring decision authority to the technology.
2. Run
Run is closely connected to Build and is where operational friction accumulates every day. Compensation and RevOps teams respond to recurring questions, investigate data issues, resolve exceptions, reconcile results, and explain plan mechanics. AI can help organize inquiries, surface relevant information, identify patterns, and prepare initial responses or recommendations. Reducing this friction creates capacity that can be redirected toward more strategic work. It can also improve the experience of the sales organization by providing faster and more consistent support.
3. Analyze
Once the operational foundation is in place, the analytical opportunity expands. Analysts have traditionally spent a disproportionate amount of time gathering, cleaning, validating, and preparing data. AI can begin to change that ratio by helping identify patterns, investigate anomalies, compare performance scenarios, and summarize findings. The greatest benefit is not simply producing analysis faster. It is enabling analysts to spend more time interpreting the results, challenging assumptions, and helping leaders make better decisions.
4. Plan
Plan may represent the largest long-term opportunity, but it also requires the strongest foundation. Potential applications include territory and quota allocation, capacity planning, compensation design optimization, scenario modeling, and workforce decisions. These use cases can create significant value, but they depend on reliable data, clear objectives, appropriate governance, and organizational trust.
That is why sequencing matters. Organizations should build confidence through applications where outputs are reviewable before expanding into decisions with greater financial and human consequences. Not every organization will begin in the same phase. The right entry point depends on its business priorities, operational maturity, data readiness, and current level of AI adoption. What matters is starting with a use case that is measurable, governable, and connected to a meaningful business outcome.
Where Accountability Stays
As organizations move further into AI, leaders increasingly ask where to draw the line. I do not view that primarily as a trust question. I view it as an accountability question. The closer a decision gets to someone's compensation, career trajectory, or livelihood, the more important it becomes to have a human accountable for the outcome.
AI can identify patterns, surface alternatives, flag exceptions, model scenarios, and prepare recommendations. But there is a meaningful difference between AI informing a human decision and AI owning that decision. Compensation decisions, workforce actions, and quota allocations require human accountability. AI can help orchestrate the process and improve the quality of the information available, but the people responsible for the business should retain the final say.
The principle is straightforward: AI can accelerate the path to a decision. It should not eliminate accountability for that decision.
What I Am Watching Next
After everything I have worked through over the past year, I keep returning to the idea that this is not fundamentally an AI story. AI is the latest technology being applied to what remains a performance conversation. As I become more invested in its potential, I also believe it is increasingly important to ask whether the value is real. I use three questions to evaluate that:
- Does the use case move beyond an impressive demonstration and succeed in a real enterprise production environment?
- Do the economics still hold after accounting for integration, governance, change management, review effort, and the ongoing cost of operating the technology?
- Are people genuinely being freed to perform higher-value work, or are they spending as much time reviewing and correcting AI output as they previously spent completing the task themselves?
If any of those tests fail, the approach needs to be adjusted. The technology may not be ready, the organization may not be ready, or the use case may simply not justify the investment yet. That is the standard I continue to hold myself to. I recommend a capability only when I can see where the value comes from, explain how it works, measure the outcome, and understand the risks involved.
Applied to AI, that standard has not changed. What has changed is how often I am seeing the technology clear it.
The teams creating real advantage right now, both inside Argano and across the organizations we support, are the ones already building that muscle. They are experimenting, measuring, and developing governance as they go. That is what makes this such an interesting moment for compensation, planning, and workforce leaders. The opportunity is no longer theoretical, but realizing it will require pragmatic adoption, disciplined measurement, and continued human accountability.
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