The best AI use case may not be the most ambitious. It may be the one that gives your pricing experts hours back every week. Here is a more practical way to think about AI in pricing.
“What can AI do for pricing?” It is the wrong question. And it is why most automated pricing optimization projects stall before delivering measurable results.
Ask what AI can do and the list grows quickly: predict prices, analyze customers, detect anomalies, optimize discounts, automate approvals, summarize performance, answer operational questions. All technically true. None of it constitutes a business case.
The right question is: “Where is friction preventing your people from making better commercial decisions?” That question has a specific answer. Once you have it, automated pricing optimization has a specific job to do and a measurable outcome to hit.
According to Copperberg and Vendavo research (2026), pricing teams can reclaim more than 15 hours per week through AI-driven reduction of repetitive analysis. Quote turnaround can improve by up to 40% when guidance becomes contextual and approval paths clearer. These are documented results from identifying the right friction first, not projections.
Now AI has something specific to solve.
Look at where your experts spend their time
Pricing teams contain some of the most commercially valuable judgment in an enterprise. The challenge is that much of a pricing analyst’s working week is not spent exercising that judgment. It is spent on tasks that do not require it:
- Gathering information from disconnected platforms
- Investigating margin anomalies that have the same root cause every time
- Rebuilding reports that existed last quarter but need to be recreated
- Processing approval requests that follow predictable patterns
- Diagnosing performance changes that take hours to trace and minutes to explain
- Answering operational questions that require querying five systems to produce a four-sentence answer
Copperberg and Vendavo research (2026) finds that automated pricing optimization and AI-assisted analysis can reclaim more than 15 hours per week per pricing team member. At scale, that is not a productivity gain. It is a reallocation of expert capacity from data assembly to commercial strategy.
Consider what 15 hours returned to a pricing team every week could mean: more time for strategy, understanding customer behavior, working with sales, evaluating performance, and finding the next margin opportunity instead of reconstructing the last one.
That is AI multiplying human brilliance.
Start with friction you can see
Automated pricing optimization does not have to begin with a transformation program. It can begin with a recurring operational question, one the pricing team already asks, but currently takes too long to answer:
- Which customers have shifted toward less profitable products, and when did the shift begin?
- Which sales reps are consistently overriding recommended pricing ranges, and what is the margin cost?
- Where are discounts increasing without corresponding volume growth?
- Which agreements are creating unusual discount variance across similar customers?
- Which approval patterns are the leading drivers of margin erosion?
Copperberg and Vendavo research (2026) identifies these as examples of operational questions that pricing analytics and embedded AI can now answer through natural-language queries, without requiring an analyst to pull reports from five different systems first.
The analyst’s role shifts from gathering information to interpreting it. That is the difference between pricing analytics as infrastructure and pricing analytics as strategic insight.
Remove the search for answers
Commercial friction is not confined to the pricing team. Salespeople experience a version of it on every deal that involves an approval or a non-standard price request.
The sequence is familiar: the seller needs a price, then context for why that price is right, then an approval, then an explanation for why the approval was rejected, then a revised price. Each step consumes time. Each transition is a point where the intended pricing strategy can break down and deal momentum is at risk.
Automated pricing optimization removes transitions, not decisions. With guidance embedded in the quoting workflow, the seller has the price, the context, the margin risk flag, and the approval parameters — in the system where they are already working. What required five steps now requires one.
This is why quote turnaround can improve by up to 40% through pricing workflow optimization (Copperberg/Vendavo, 2026). Not because sellers are moving faster, but because they are not stopping as often.
Faster does not have to mean less controlled. Designed well, the same intelligence can support both.
Find margin leakage while you can still do something about it
Some of the most expensive pricing friction is temporal. The organization discovers the problem after the window to change the outcome has closed.
Margin leakage is the clearest example. Quarterly reviews surface excessive discounting, inconsistent execution, and problematic agreements. The analysis is accurate. But the transactions are already closed, the margin is already gone, and the only remaining question is how to prevent it from happening again next quarter.
Automated pricing optimization changes the timing. With AI embedded in commercial workflows, margin risk surfaces while deals are still active. Pricing anomalies appear before the quote is submitted. Pricing teams can intervene while there is still something to intervene in. Finance can flag margin exposure while value can still be protected.
This is the shift from pricing analytics as a diagnostic tool to pricing analytics as an operational one. Diagnostics tell you what happened. Operational pricing intelligence tells you what is happening and gives the commercial team time to act.
The difference is not simply better analytics but earlier agency, and that’s where AI starts to become commercially consequential.
Do not automate friction
The most important qualification for any automated pricing optimization initiative: adding AI to a poorly designed workflow does not fix it. It makes the broken process run faster.
Copperberg and Vendavo research (2026) shows exactly how prevalent this problem is: 46% of surveyed organizations remain only partially automated, 39% cite platform integration as a quoting bottleneck, and 38% identify manual effort as a primary constraint. These are not technology problems. They are workflow problems that AI cannot solve by itself.
Before deploying pricing automation, map the friction first:
- Where does work stop and wait? (approval queues, missing data, unclear ownership)
- Where does someone switch platforms to get an answer they should already have?
- Where is information re-entered manually because systems do not communicate?
- Where do approvals accumulate and which of them genuinely required human review?
- Where do people abandon the intended process and build their own workaround?
Those are the locations where pricing automation and better workflow integration can have a measurable impact. Fix those first. Then build AI on a foundation that is worth automating..
Give AI a job, then measure whether it does it
A practical automated pricing optimization strategy requires one thing before anything else: a defined outcome. Not a defined capability, but a defined outcome.
Match the problem to the metric:
- Repetitive analysis: measure analyst hours reclaimed per week
- Slow quote turnaround: measure days from quote initiation to submission
- Unnecessary approval escalations: measure escalation rate and approval cycle time
- Poor pricing recommendation adoption: measure recommendation usage rate and override frequency
- Margin leakage: measure deal-level margin vs. target, discount variance, and realized vs. quoted margin
This problem-to-metric structure creates a healthier path to scale. Identify one friction point. Embed pricing automation to address it. Measure the behavioral and financial change. Document what you learned. Then move to the next friction point.
AI pricing ROI becomes traceable rather than assumed. Traceable outcomes build internal confidence faster than any proof-of-concept presentation.
The best AI frees people to be more human
The most compelling vision for automated pricing optimization is not one where AI makes every pricing decision. It is one where AI and pricing professionals stop doing each other’s jobs.
Machine intelligence and human expertise are mutual multipliers. AI processes data at scale, detects patterns, and compresses investigation cycles. Pricing professionals exercise judgment, navigate customer relationships, challenge assumptions, and build commercial strategy. When each does what it does best, the result is not just efficiency. It is a more capable commercial organization.
That is a more ambitious outcome than automation. Automation asks how much work technology can absorb. Automated pricing optimization asks how much more effective the people doing the work can become.
Start with the friction. Find the specific points where commercial decisions slow down, where margin risk goes undetected, where expert time disappears into data assembly. That is where AI pricing delivers measurable value: not as a transformation program, but as a series of targeted interventions that turn commercial complexity into competitive advantage.
Frequently Asked Questions (FAQ)
What is automated pricing optimization?
Automated pricing optimization is the use of AI and rules-based logic to generate, surface, and enforce optimal pricing guidance in real time, without requiring manual analysis at every decision point. It differs from manual pricing in that guidance is embedded in quoting and sales workflows, not generated in a separate environment and then consulted.
How does AI reduce friction in pricing workflows?
AI reduces pricing friction by: (1) surfacing recommendations inside the quoting tool, eliminating platform switching; (2) flagging margin risk before quotes are submitted; (3) automating repetitive analysis tasks, freeing pricing analysts for strategic work; and (4) clarifying approval guardrails, reducing unnecessary escalations. Copperberg and Vendavo research (2026) finds that pricing teams can reclaim 15+ hours per week through these mechanisms.
What pricing problems should AI solve first?
Start with the most visible friction: slow quote turnaround, repetitive margin anomaly investigations, excessive approval escalations, and late discovery of margin leakage. Each has a measurable baseline, so AI’s impact can be tracked. Address one, establish the outcome, and then expand to the next.
How do you prevent automating bad processes with AI?
Before deploying pricing automation, map the workflow to find where work stops, where platforms are switched, where information is re-entered, and where people build workarounds. These are signs of a broken process, and AI will make a broken process run faster, not better. Fix the workflow design first, then embed AI on a foundation worth automating.
Find the friction worth removing first
Where can embedded AI make an immediate difference in your pricing organization?
Download Closing the Pricing Execution Gap with AI from Copperberg and Vendavo for the research, practical use cases, and an eight-point checklist designed to help identify where pricing execution is still being held back.
And to see how much friction embedded AI can actually remove, watch the Meet the AI Pricing Assistant demo on demand.