At enterprise scale, sales operations is where strategy either reaches the field or starts to fall apart.

Every growth priority has to move through forecasting, segmentation, territories, quotas, compensation, systems, and seller communications. Each handoff creates another opportunity for delay, rework, or misalignment.

Chinesh Gandhi, Director of Sales Operations at Microsoft, regularly navigates this complexity. With a background in mechanical engineering, supply chain, Six Sigma, and Lean Operations Management, he approaches sales operations as an interconnected system. A system where bottlenecks or disconnected decisions eventually surface in the seller experience.

Today, his team supports target delivery for several thousands of sellers across 140 countries, covering billions in revenue across multiple products. At this scale, quota setting becomes a mechanism for aligning a global sales organization around business priorities.

In this episode of The Sales Compensation Show, Chinesh joins Forma.ai CEO Nabeil Alazzam to discuss how leaders can methodically build this alignment.

Their conversation explores why process discipline should precede technology, where AI can best help teams navigate complex constraints, and why a red attainment dashboard may not always be an altogether negative thing.

We’ve captured our top takeaways below. You can also watch or listen to the full episode on Spotify, Apple Podcasts, or YouTube.

Episode resources

• Connect with Chinesh on LinkedIn  

• Book recommendations: How to Win Friends and Influence People by Dale Carnegie., and How to Stop Worrying and Start Living, by Dale Carnegie

Engineer the operation for effectiveness and efficiency

As Chinesh shared, operational teams naturally track what is easiest to measure: cycle time, automation, errors, and post-launch tickets.

These metrics reveal how efficiently the work gets done. Chinesh argues, however, that leaders also need to measure effectiveness (or whether the operation delivered the right outcome for the business).

At Microsoft, his team supports target delivery for 37,000+ sellers across 140 countries, accounting for multiple products. Automation is clearly essential at this scale. But speed alone cannot show whether changing business priorities have been translated clearly across different markets and economic conditions.

Which is why Chinesh views quota setting as an exercise in strategy alignment.

This distinction changes how quota setting should be evaluated.

Effectiveness asks whether targets direct the right sellers toward the right products, customers, and opportunities.

Efficiency asks whether targets were delivered quickly, accurately, and with enough clarity that sellers could begin account planning instead of questioning how their numbers were produced.

Clearly both matter. A process can finish on time and still fail to land the strategy. It can also produce strategically sound targets through so much rework and confusion that the field loses valuable selling time.

The downstream consequences often obscure where the problem began. A weak segmentation assumption becomes a territory exception, for example. And then that exception turns into a quota dispute and eventually appears as a compensation escalation.

Mapping the full operating flow helps address the source rather than the symptom.

Strategy, forecasting, segmentation, territory design, quotas, and compensation may sit with different specialists. But it's sellers who experience one combined output. According to Chinesh, the strongest sales operations teams align these functions around the shared outcome, then make the process as proactive, accurate, and frictionless as possible.

Make the process earn the technology investment

Similar to other highlights we've heard on the podcast prior, one of Chinesh’s most influential professional lessons came from a large sales operations platform transformation that failed.

The experience taught him something that feels particularly urgent in the current race toward AI: business process has to come before technology.

Technology can change the speed and mechanics of a process. It can automate steps, improve controls, and make sophisticated analysis possible at scale. But it simply cannot provide an outcome the organization has never clearly defined.

When leaders haven't aligned on what the process should produce, who will consume the result, or how decisions should move between teams, technology tends to institutionalize the ambiguity.

Then it distributes it faster.

For Chinesh, preventing this outcome requires more than a well-written project charter. It requires a culture in which people close to the work can question the direction of the project.

He reflects candidly on being new in a role, seeing reasons for concern, and not speaking up early enough. In hindsight, greater curiosity and a safer environment for challenging the status quo might have helped the team avoid failure.

The lesson is that frontline expertise should pressure-test strategic direction.

Executives see the ambition, investment, and intended future state. But critically, operators understand the exceptions, informal workarounds, data limitations, and dependencies that may prevent that vision from functioning in practice. Transformation gets stronger when these perspectives meet before deployment.

Revisit the charter once the team knows enough to challenge it

Chinesh recommends returning to the project charter around the midpoint of a major initiative.

At the outset, a team may have only 15-20% of the clarity it will eventually gain. That is not necessarily a failure of planning. Some realities only become visible once teams begin working through actual requirements, stakeholder needs, data, and exceptions. By the midpoint, clarity may have risen into the 80-90% range.

Yet many organizations continue executing against the assumptions captured when they knew the least.

A midpoint reset gives leaders a structured opportunity to ask:

• What have we learned that could not have been known at the beginning?
• Which original assumptions no longer hold?
• Has the intended business outcome become more specific?
• What are the people closest to the work warning us about?
• Which requirements should change?
• What work should stop entirely?

Reopening the charter shows that the organization is willing to replace early assumptions with better evidence.

That discipline also protects AI initiatives from becoming technology projects in search of a business problem. Before introducing intelligence, leaders should be able to explain the decision being improved, the user receiving the output, and the friction the investment is expected to remove. Otherwise the organization may gain an impressive new capability without becoming meaningfully more effective.

Will AI have a large role in quota setting?

Chinesh is bullish about potential for AI in quota setting, particularly because much of the work is structured, repeatable, and governed by clear rules.

Automation can already handle defined operational tasks faster and more consistently than manual effort. And AI adds value when it evaluates several interacting constraints and identify viable options.

Chinesh illustrates this via a simple example aligned to constraints and reasoning:

Ultimately, enterprise quota setting presents the same type of challenge as Chinesh's birthday cake scenario at a far greater scale.

As he shares, a global organization may need to account for products that cannot be sold in certain markets, workers’ council requirements that limit later changes, regional competitive conditions, and complex rules for global customers and revenue allocation.

Planning teams can work through these variables manually. The difficulty is doing so quickly, consistently, and across every relevant scenario.

AI can now help teams evaluate more possibilities, surface conflicts, and flag exceptions that require human review. This gives planners more capacity to interpret the results and consider the tradeoffs, rather than spending most of their time assembling the analysis.

Importantly the final decision still belongs to the business. Quotas and compensation outputs must remain accurate, explainable, and defensible. Overall, the opportunity is to give AI the defined complexity it handles well, while keeping leaders accountable for deciding which outcome best serves the strategy.

Not everyone should hit 100% attainment...

Chinesh closes the conversation with a view that may feel uncomfortable in an executive review: a healthy quota system doesn't necessarily put every seller above 100% attainment.

As Chinesh sees it, some red on the dashboard may reflect intentional stretch and meaningful performance differentiation. If everyone's delivering green status, leaders should be willing to ask whether the targets were ambitious enough in the first place.

Attainment alone cannot tell leaders whether the plan worked. They need to understand the shape of the distribution and what produced it.

A deliberate pay-for-performance model will concentrate more payout among top performers. Sellers on the other side of the curve may need coaching, development, or a closer examination of whether their territories and quotas offered credible opportunity.

The goal is a distribution that can be explained: enough stretch to differentiate performance, meaningful upside for exceptional results, and clear evidence of where the organization should coach, redesign, or investigate further.

Build a sales ops system that can learn

Continuous improvement is the thread running through Chinesh’s approach.

You need to:

• Make the operating flow visible
• Measure both the outcome and the quality of execution
• Let frontline expertise challenge assumptions before they become embedded in technology.
• Give AI clear constraints, then
• Keep leaders accountable for the final decision.

The opportunity extends far beyond faster quota setting, too. You ultimately want to build a sales operations system that learns from each planning cycle, detects problems earlier, and adapts as the strategy and market change.

That is how sales operations moves beyond merely administering strategy and begins deliberately engineering how it reaches the field.

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