Most sales leaders already know they’re supposed to have an AI strategy in 2026. The problem is that many of those strategies quietly translate to: “Let’s buy some tools so it looks like we’re doing something.”

But Jody Geiger, co-founder of AI Sales Studio at GTMShift, says that approach doesn’t just waste budget, it actively delays real transformation.

Don’t put AI on top of an unoptimized system in 2026. Here’s where sales leaders should invest instead.

1. Invest in workflow redesign before automation

If your sales workflows were designed more than five years ago, AI won’t save them.

“I don’t think we should be adding AI on top of a broken go-to-market,” Jody says.

Most teams are trying to “AI-enable” processes that were built for a completely different buyer reality – one with slower cycles, fewer signals, and less data. Automating those workflows just makes the wrong things happen faster.

The right place to invest will feel like the wrong one in a role that’s always looking to move quicker. You need to pause and redesign your system first.

“If the budget line item was ‘redesign our workflows,’ I’d say yes immediately,” Jody says.

What this looks like in practice:

• Map the entire customer lifecycle, not just lead → opportunity → closed-won. You need to know where buying signals first appear, how and when leads are routed/scored/accepted by sales, what actually happens between stages (handoffs, approvals, reviews, etc.), and what happens post-sale.
  
  
• Identify where deals slow down two or three steps into a workflow. Sit with reps, follow a deal step by step, and ask: Where does momentum break down once the deal is already “in motion”?
  
  
• Find out where sellers are compensating for bad process with manual effort. This might look like shadow spreadsheets, “tribal knowledge” that only lives in people’s heads, or rewriting information that already exists elsewhere.

Then you can decide where AI belongs.

2. Invest in clean-up, not adding more data

Most sales orgs don’t have a data problem, they have a cleanliness problem. There are too many fields, tools, and “sources of truth”.

“Where does data live? That’s enablement’s biggest complaint,” Jody says.

AI doesn’t perform well in a chaotic system. Before investing in more intelligence, leaders need to simplify the system that intelligence flows through, which might mean a few things:

• Consolidate your pipelines as much as possible. Multiple pipelines usually exist to accommodate internal complexity, not customer reality. Review why each pipeline exists and what customer behavior it represents, identify where you could consolidate, and standardize stage definitions so they reflect real buyer actions.
  
  
• Clean your CRM data so it could actually be trusted. You need reliability here, so set a standard for good data, identify which fields truly matter for decision-making and eliminate the rest, and automate data capture where possible instead of relying on seller memory.

“Sellers used to have to seek out tools to find data. Now AI needs to meet them where they live,” says Jody.

3. Invest in a single, high-leverage outcome

There is a lot AI can do for sales teams – and plenty of tools to explore  – but tool sprawl and data bloat is already a problem. So don’t ruin your progress by throwing AI at obvious but low-opportunity use cases.

“You could have AI write all your outbound emails, that speeds things up, but is that actually your biggest problem?” Jody says.

Do this instead:

• Choose one revenue outcome that truly matters (e.g., higher win rates in a core segment, better outbound conversion, faster renewals)
  
  
• Evaluate AI use cases only by whether they move that outcome
  
  
• Focus human effort where AI improves decision quality, not just speed

Then assign an owner that reports on that outcome and give your team air cover from leadership to let them experiment on their own timeline.

4. Invest in real feedback loops

AI systems only improve when they learn – but many sales orgs may not truly understand a critical part of their sales cycle: why they win and lose deals.

“AI doesn’t replace the need to understand your customer, it makes it more urgent,” Jody says.

The explanations tend to be lazy: prices, product, or timing. But AI needs structured reasoning to learn, and frankly, so do you.

To help AI help you:

• Capture details in the buyer’s words. What made them feel confident or uncertain and what alternatives did they consider?
  
  
• Feed win-loss insights back into marketing and targeting. Use these insights to refine ICP definitions and disqualify poor-fit segments, adjust outbound messaging to reflect real buyer priorities, and improve signal models by aligning targeting with what actually converts.
  
  
• Review stalled deals – not just closed-lost ones. Revisit people who disengaged or went dark despite strong early engagement.

If win-loss data doesn’t change targeting or messaging, it’s just documentation. But these patterns often reveal friction in your process or mismatches in timing – exactly the insights AI needs to learn where deals really break down.

5. Invest in signal quality and ruthless focus

Signal quality matters more than activity volume. The goal isn’t to do more outreach faster, it’s to concentrate effort where success is most likely.

“There’s endless data out there, but only some of it actually helps you make better decisions,” Jody says.

AI is now capable of surfacing far better signals than human intuition alone. So instead of chasing every inbound lead, use AI to qualify and disqualify who you work:

• Front the why. Whether it’s a recent signal, role change, or buying behavior, have AI highlight what need the buyer is likely trying to meet.
  
  
• Filter the noise. Using the same characteristics, AI can deprioritize low-fit inbound leads, route weak signals into nurture or self-serve paths, and focus sellers on accounts that resemble past wins.
  
  
• Realtime adaptations. AI can trigger outreach based on buying behavior changes, not just stage changes. It can also adjust recommended actions based on signal strength and pull in relevant context at the moment of engagement.

When AI takes on pipeline maintenance, sellers spend more time closing and less time qualifying.

AI doesn’t create advantage, systems do

Yes, AI can write emails faster, summarize calls, and report on quick insights. But those gains are table stakes.

Jody says the real winners in 2026 will be sales organizations that:

• Understand their systems deeply
• Remove friction before automating
• Treat AI as a learning engine, not a shortcut

“The advantage isn’t the tool. It’s knowing your system well enough to apply AI with precision,” she says.

If your AI strategy starts with a tools list, you need to go back to the drawing board. If it starts with system design, you’re in the 1% of sales leaders.