For a decade, the sales technology industry has poured its engineering budget into execution: better CRMs, faster forecasting dashboards, sharper conversation intelligence, smarter sequencing tools. The implicit bet was that if reps executed well enough, revenue targets would follow. New research from four separate corners of the revenue stack now points at a different, less comfortable answer: most of the damage happens before a single rep makes a single call, in how the plan itself is built.
The Strongest Predictor Isn’t the One Anyone Measures
Varicent, a sales performance management (SPM) vendor, surveyed more than 1,000 senior revenue leaders for its 2026 Market Spotlight Report, “Nobody Outsells the Plan.” The company ran statistical analysis across dozens of variables tied to revenue performance, including seller experience, industry, prior growth, and company size. Plan quality beat all of them as the strongest predictor of whether a company hit its revenue target.
The scale of the miss is what stands out. For more than half of the companies surveyed, the estimated losses tied to poor planning exceeded their entire annual revenue growth. Only 4% of companies said they could identify which past planning decisions worked and which didn’t, and 96% had no system for applying those lessons to the next planning cycle. Companies that used AI in revenue planning were 46% more likely to exceed target, and those that scored well across five dimensions of plan quality, described as strategic grounding, connected-system design, results tracking, and adaptability, outperformed peers by 58%.
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“For years, SPM has focused on helping companies execute their revenue plans more efficiently. This research shows how true transformation comes from improving planning itself,” said Marc Altshuller, CEO of Varicent. “With AI, companies can finally learn from past decisions, improve how they plan, and turn those learnings into durable competitive advantage. The future of SPM is more than managing performance. It’s designing for it.”
That reframing matters because it contradicts how most revenue organizations diagnose a miss. When targets slip, leaders reach for execution explanations: pipeline coverage was thin, sellers underperformed, the market shifted. Varicent’s data suggests they are frequently looking downstream of the actual failure point. The plan structures what sellers do every day, according to 75% of the revenue leaders surveyed: which accounts they chase, where they spend their time, which targets they treat as priorities. A flawed plan doesn’t just under-forecast. It misdirects effort for an entire quarter before anyone notices.
Compensation Has the Same Speed Problem
The planning gap isn’t confined to territory and quota design. CaptivateIQ’s 2026 State of Incentive Compensation Management Report, based on a survey of more than 200 compensation leaders, found that plan design has become more strategic and more visible at the executive level, with 97% of respondents now tying incentives directly to strategic goals and 43% including C-suite leaders in plan design. But visibility hasn’t translated into speed. Weekly plan adjusters are nearly five times more likely to report being well prepared for economic volatility than teams still running annual cycles, yet only 12% of organizations said they could implement a plan change in under two weeks.
The report also found that AI adoption in incentive compensation is wide but shallow: more than eight in ten organizations use AI in some capacity, but only 28% report using it extensively, and just over a third have connected it to plan design or quota setting rather than summarizing reports and dashboards. Nearly two-thirds of organizations logged at least one payout error in the past year.
“Things change, from accounts to account teams, big structural drivers and individual exception requests. Having a comp plan framework that allows for mid-year adjustments with strong business justification is necessary,” said Jordan Wong, Senior Manager of Sales Commissions at Snowflake, in CaptivateIQ’s report. The point echoes Varicent’s finding from the opposite side of the same problem: it isn’t that comp teams lack visibility into their own plans, it’s that the infrastructure underneath those plans wasn’t built to move at the pace the plans themselves now change.
The Friction Also Lives Inside the Deal
A third data point locates the same failure mode further downstream, inside live deals rather than annual planning cycles. In an SAP blog post published this week, Jan Gilg argued that in most enterprise deals, the seller isn’t the bottleneck, the system around the seller is: approvals, pricing sign-off, quote generation, and contract routing are where time gets lost while deals stall. As an example, Gilg pointed to Amadeus, which worked with SAP to deploy an autonomous agent that reconciled unstructured payment data, clearing around 40,000 incorrect transactions that had previously required manual intervention.
That same friction is visible in how CPQ vendors are now positioning their products. Impartner this week launched what it calls the first configure-price-quote tool purpose-built for channel partners rather than direct sales reps, arguing that traditional CPQ was never designed for the partner motion. “Quoting should not be the point where a partner opportunity slows down,” said Robert Harris, Impartner’s VP of Product Management. “In a direct sales model, sellers have the tools and authority to move an opportunity from one stage to the next. Vendor companies have historically had to bridge the partner quoting gap through manual processes and internal team intervention. We’re changing that dynamic by giving partners a direct path to act while preserving the controls vendors need to run their business.” Impartner’s own framing lines up with Varicent’s central claim: the constraint isn’t seller effort, it’s process design that was never built for how revenue actually moves today.
SalesTech has covered this same consolidation pressure building across the go-to-market stack from a different angle: Gartner’s redrawn SPM leaderboard shows the vendor landscape itself reorganizing around exactly the planning-versus-execution split these four reports describe.
And the Week-to-Week Reality Backs It Up
If the planning layer is where the losses originate, field data shows where the hours actually go. SPOTIO’s 2026 State of Field Sales report, based on a survey of 336 field sales leaders, found that B2B field reps spend just 33% of their working week on active selling, versus 49% for B2C and hybrid reps, a 16-point gap the report attributes largely to administrative burden. Of the 213 respondents who gave a definitive answer about manual CRM data entry, 65% spend five or more hours a week on it, 19% spend a full workday or more, and only a handful reported having fully automated it. SPOTIO’s researchers calculated that for a rep handling 10 to 25 visits a week, losing a full day to data entry costs four to five opportunities every single week.
The report also flagged an adoption gap specific to field sales: 33% of the teams surveyed said they use no AI in any capacity, for email, CRM entry, or forecasting, compared with the 87% AI usage rate Salesforce reported across the broader sales organization in its own 2026 State of Sales research. Field teams, the group closest to the actual selling motion, are also the group furthest behind on the tools meant to remove the administrative weight that is eating their selling time.
SPOTIO’s researchers also asked managers how many face-to-face activities they expect from reps each week versus how many actually happen. The gap was consistent across the sample: the largest cohort of managers expected 10 to 25 in-person activities per rep per week, but actual completion rates skewed noticeably toward the low end of that range. On the automation side specifically, only 3% of respondents said their CRM data entry was fully automated, about 13% had no automation at all, and roughly a third sat in a partially automated middle where the tooling exists but the workflow around it doesn’t use it consistently. That middle group is instructive: it is not an adoption problem in the sense of missing technology, it is the same design gap Varicent and CaptivateIQ describe at the planning layer, just showing up at the level of a single rep’s Tuesday afternoon.
What It Means for the Revenue Leader
Read together, these four reports describe one shift rather than four unrelated ones. Revenue organizations have spent years instrumenting execution, tracking calls, meetings, pipeline stages, and forecast accuracy, while leaving the upstream decisions that shape all of that activity comparatively unmeasured and slow to change. Plan design, comp design, quote and approval workflows, and admin load are not separate problems competing for budget. They are four instances of the same underinvestment.
For a revenue leader building next year’s operating plan, the actionable read is not “add more AI.” Varicent’s own data undercuts that shortcut: companies using AI in revenue planning outperformed, but the report is explicit that the advantage came from where AI was applied, not merely from using it. CaptivateIQ found the same pattern in compensation, where AI usage is nearly universal but strategic application is rare. SalesTech’s earlier reporting on why most enterprises missed their 2025 revenue targets reached a related conclusion from the forecasting side: the fix wasn’t better prediction, it was fixing what the prediction was built on.
The practical starting point these four reports converge on is auditing plan velocity before buying another execution tool. How long does it actually take to change a territory, adjust a quota, or revise a comp plan once the market has already moved? Who has to approve a quote before a partner or rep can act on it? How many hours a week does the team lose to manually keeping the system of record current? None of those questions require new technology to answer. They require revenue leaders to measure the layer they have spent a decade not measuring.
The vendors are already repositioning around that answer. Varicent is selling AI applied to plan design rather than plan execution. CaptivateIQ is selling agility in compensation, not just accuracy. Impartner is selling quoting speed for a channel motion CPQ ignored. SAP is selling agents that remove the invisible friction around the deal itself. The product category chasing “planning,” not “performance,” is where the next round of sales tech spending is likely to concentrate, and the leaders who audit their own planning velocity now will be buying from a position of knowing exactly what they are trying to fix.
Source: Varicent
