What It Takes to Build a Wired Comp Program: An Interview with Brandon Farb

Our Experts on the State of Incentive Compensation Management Q&A series features leaders across compensation, operations, and broader Go-to-Market (GTM) teams discussing the trends, challenges, and opportunities shaping incentive compensation today. In this edition, we dive into key findings from CaptivateIQ’s 2026 State of Incentive Compensation Management Report.

For this conversation, we spoke with Brandon Farb, Director, Sales Planning, Analytics, and Compensation for Allstate Canada and National General. Brandon and his team focus on designing compensation programs that drive growth while keeping pace with rapidly changing business priorities and market conditions.

Brandon joined CaptivateIQ’s recent webinar on the report findings, where he shared perspectives on accelerating planning cycles, operationalizing AI, and building compensation processes that can adapt quickly as business needs evolve. Watch the webinar recording for more insights and analysis.

Here’s Brandon’s perspective on what it takes to move from reactive compensation processes to more wired, agile operations.

While many organizations review and adjust plans quarterly, 39% report that it takes one to two months to implement these changes. Can you walk us through the last time a strategic change took longer to show up in comp than you would've wanted?

We actually led quite a large compensation design change for the start of 2026. It probably took anywhere between six to nine months total of planning, strategy, modeling, design changes, and communications rollout to our sales channel.

It was a lot of work, with a lot of moving parts and a lot of sign-offs required. But when you're moving big rocks, making big strategic bets for the organization, and trying to change behavior, it takes time to execute.

Six-plus months on a comp plan change makes sense if you’re moving a big rock and trying to change behavior. But sales planning operates on a much faster cadence. In that world, execution has to happen within a month — and I’d even argue one to two weeks — or things start to get stale.

When you think about a comp plan change versus a sales plan change, we’re planning on a quarterly — and sometimes monthly — basis. I’d say the biggest lag, or visibility gap, comes from a lot of the modeling that we do.

It’s great in the moment, but if you can’t execute on those changes within one to two weeks, your targets become outdated. Your managers are coaching to the wrong numbers, and your comp team can get stuck analyzing or reconciling against old targets instead of where people should actually be focused. 

Agents and reps want to stay motivated, and you’ve got to do what you can to get your plans into market as soon as possible.

Our report found that 81% of GTM professionals use AI, but only 28% mean it. What are the questions you hear repeatedly from your teams where you think AI should be helping them?

There are a few questions that we constantly hear from our leadership group, and the common thread is uncertainty.

If we pushed quotas differently, what would that have done to earnings? Why does this plan behave differently in practice than it did on paper? Why is a certain territory or sales rep an outlier? Are they underperforming or overperforming our assumptions? Was the plan misaligned? Can we scenario model or test assumptions and have more sanity checks on our changes, plans, or comp designs before we roll them out?

For me, the biggest opportunity from today’s discussion is that organizations, Allstate included, aren’t split between AI users and non-users anymore. The real divide is between people who are dabbling in AI and those who are actually operationalizing it.

Overall, I see the largest opportunity in using AI to impact what is traditionally a slow, manual, labor-intensive planning process. AI will enable planning decisions to happen faster, give you the ability to test and validate assumptions, and confidently roll out decisions to the field in ways you maybe didn’t have before. It also helps build trust and alignment.

I think AI is going to change the game for a lot of us in this field by compressing planning cycle times — from decision-making to modeling to field execution.

If I could close out this topic, for me at Allstate, and I think for my team as well, improving cycle time is critical. It goes back to what I spoke about earlier: getting those changes into market within one to two weeks instead of a four- to six-week period. You can see such a different reaction from your sales rep group. They have more relevant direction, and guidance that’s not stale. Then you tell them to go run with it.

Sales reps are very reactive. They want to make money, and they want to make money against targets that are achievable — not ones that were thought of four to six weeks ago that are no longer relevant based on today’s market.

Payee trust in accurate compensation is the third-highest predictor of organizational preparedness. Among organizations where payees have “very high” trust, 55% are very prepared for market volatility. How do you think about maintaining trust at scale?

I think of trust as a leadership and system design issue first. If you have trust issues, you need better communication. Errors just expose where your system isn’t keeping up. But if you don’t have the right communication to your sellers and reps on a consistent basis around your process and payment accuracy, it’s no wonder there’s a lot of shadow accounting.

When I think about trust at scale, it comes down to three things. 

1. The first thing is predictability. Can a rep accurately estimate how much they’re going to earn before the payout? If they can’t, they build their own model.
   
   A great way to avoid that at scale is to have earnings visible in a comp tool or provide a calculator that reps can leverage. There are a ton of calculators that my team and I are producing for all of our distribution channels. We want to put information out there. We have scorecards and dashboards around how everyone is trending.
   
   People are confident in the data if they can see it and replicate it themselves, but you have to push that information out instead of making them pull it from you.
   
   
2. The second thing is transparency. It’s not just about the number — it’s about the detail behind how the number is being built. The best systems let a rep trace a dollar from the deal to their payout and understand why they got paid.
   
   When visibility is missing, questions spike immediately, which burdens your comp execution function. People get bogged down in questions, inquiries, and dispute resolution. That cycle time could be eliminated if there were more transparency, allowing people to move on and focus on selling.
   
   
3. The third thing is responsiveness. Errors happen. What matters is how quickly and clearly they are resolved. Slow resolution is what actually erodes trust — not the existence of the error itself.
   
   I always say to our sellers  that we want the inquiries and we love the questions. If there were no questions and everyone said, ‘Yeah, this is perfect every month,’ maybe there’s something we’re missing.
   
   We love that two-way dialogue. It’s normal, but it needs to be manageable. It comes down to having the right SLAs to get back to your reps, making sure they know what those SLAs are, and making sure they trust that you’re getting back to them with the right information in the right timeframe.

When reps stop asking, ‘Is this right?’ they start asking how they can earn more. Managers spend more time coaching, and you start to see a drop in the types of inquiries coming in. They become less transactional and more strategic.

That’s an area for both AI and trust overall. We want people to stop saying, ‘Hey, I didn’t get paid correctly,’ and instead ask, ‘How can I make more money? What do I need to do to drive a certain result or behavior to earn more?’

It’s that continuous focus and push to eliminate the “why” and get to the “how.”

Many organizations are moving from “reactive” compensation operations built on manual workflows and spreadsheets toward more “wired” environments where planning, compensation, and execution are connected in real time. How have you seen this evolution happen in real-time?

We’ve definitely lived through the reactive stage, and we’ve been very intentional about moving out of it as quickly as possible.

A big focus for us has been getting much closer to our comp and planning teams and really working inside the system that we have — not around it. That includes eliminating Excel and manual tracking as much as possible. We’re huge proponents of eliminating that kind of work.

What I mean by that is we’re really pushing to use our current ICM tool the way it was designed: standardizing logic, reducing one-off adjustments, stripping out manual work, and using our inquiry and dispute function.

The goal is to shift people from holding a system together to using a system that drives accuracy, speed, trackability, and better data management — one that people can be trained on as they move in and out of the business and have information they can easily reference.

I would say we’re not perfect yet, but the difference is we’re actively closing the gap.

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For anybody listening [or reading], I’d say spend a lot of time working to close that gap accurately and eliminate as much manual tracking and manual process as possible.

If you’re interested in participating in one of the Multiplier Q&A features, or have burning questions to ask today’s ICM leaders, let us know at multiplier@captivateiq.com.