For a middle-market, PE-backed software company, GTM efficiency is not just a margin initiative. It is a way to fund the product development that keeps the business worth buying.
The question is no longer simply, “How much should we spend on sales and marketing?” It is, “How little can we spend to run a reliable system that acquires the right customers, converts demand, and retains and grows the accounts we already have?”
That is not an argument for starving growth. It is an argument for refusing to fund commercial complexity at the expense of product competitiveness.
Multiple compression changes the operating question
SaaS Capital reported a peak median public B2B SaaS valuation of 16.9 times annualized run-rate revenue in August 2021 (SaaS Capital’s historical analysis). Its October 31, 2026 index data puts the median at 4.97 times, or approximately 5.0 times, annualized run-rate revenue (SaaS Capital’s October index data).
These are public-market trading multiples, not middle-market private transaction multiples. The index uses market capitalization rather than enterprise value, so it should not become a shortcut for marking an individual portfolio company (SaaS Capital’s methodology).
The operating implication still matters. A value-creation plan should not depend on a return to peak-era pricing to compensate for an expensive revenue model.
Growth remains essential. But a company that needs a proportionate increase in commercial cost every time it adds revenue has a different investment problem from one that can grow through a repeatable, increasingly efficient system.
The objective is not merely to produce more revenue. It is to produce durable revenue while preserving the capital needed to keep the offering competitive.
AI makes product investment harder to postpone
The next product threat may not come from a familiar competitor with a larger sales team. It may come from a buyer deciding that part of the product no longer needs to be purchased.
In McKinsey’s 2026 global AI survey, 32% of respondents said their organizations had decided against buying at least one software product or feature because they could build the functionality internally with agentic coding tools (McKinsey’s State of AI survey). That is not evidence that every software category faces the same risk, but it is a reason to test which parts of an offering remain differentiated.
For a portfolio company, that means asking uncomfortable questions. Which features will buyers still pay for? Where does the product deliver an outcome that a cheaper tool, an internal workflow, or a different service model cannot match?
Answering those questions can require changes to architecture, data, integrations, security, implementation, and the product itself. Adding an AI feature to the existing roadmap may not address the underlying competitive problem.
AI can also reduce development costs. The argument is not that every business must increase total R&D spending; it is that management needs the financial room to redirect investment quickly when the evidence demands it.
GTM efficiency is not a percentage-of-revenue target
There is no universal percentage of revenue that makes a commercial organization efficient. A familiar budget ratio can describe the cost of the current model without proving that the model deserves to survive.
Look across the commercial P&L: compensation, programs, agencies, paid media, data, platforms, sales operations, and customer success. Then ask what each cost contributes to acquiring, converting, retaining, or expanding the right accounts.
A company can spend less than its peers and still waste money on weak targeting, duplicate work, poor handoffs, and opportunities that should never have entered the pipeline. Another can spend more because an attractive segment supports a deliberate investment with strong economics.
Benchmarks help identify where to investigate. They do not replace the investigation.
The better standard is the lowest sustainable cost of producing the required commercial outcomes, subject to clear growth, retention, service-quality, and risk requirements. That standard leaves room to invest aggressively where the economics work and demands discipline where they do not.
Engineer demand generation, pipeline management, and customer success together
An efficient revenue system is not a collection of departments that each report improving activity. It is a connected program in which market selection, opportunity progression, customer delivery, and expansion reinforce one another.
Start with demand generation. Define which accounts the business can win, serve profitably, and retain, then organize research, targeting, messaging, and activation around those accounts. More leads are not progress if they create expensive work for sales without improving qualified demand.
Apply the same discipline to pipeline management. Set qualification standards, ownership, next steps, stage evidence, and exit criteria. A CRM full of opportunities is not an asset if management cannot distinguish real buying activity from stalled evaluations and optimistic close dates.
Then connect customer success to the promises made during acquisition. Onboarding, adoption, risk detection, renewals, and expansion need clear triggers and accountable owners. Customer success should not have to repair an account-fit problem that the acquisition process could have prevented.
In this model, each motion produces evidence that improves the others. Retention informs targeting, delivery informs positioning, and win/loss findings inform both product priorities and pipeline qualification.
The unit of improvement is the full customer journey, not the departmental budget. Otherwise, a savings initiative can simply move work and cost from one team to another.
Use AI to change the work, not preserve the dysfunction
Buying a tool is not the same as engineering a program. Automating a weak process can make the process faster without making it more valuable.
Before introducing another AI tool, define the work, the required outcome, the necessary data, the decision rules, and the points where human judgment matters. Then decide which technology should perform which part of the job.
That is different from giving each team a new set of subscriptions and asking it to become more productive. The test is whether the redesigned workflow removes unnecessary work, improves a commercial outcome, or reduces the sustainable cost of delivering it.
The operating model also has to survive changes in the tools. Define the program around its outcomes and controls, not around a vendor’s current feature set.
Research, routing, follow-up preparation, account monitoring, and reporting can all become candidates for redesign. But faster output is not enough: the business still needs accurate data, appropriate review, clear ownership, and a feedback loop that catches failure.
Turn efficiency into deployable product capital
An hour saved is not automatically a dollar available for R&D. If an initiative frees capacity but does not reduce an expense, avoid a planned hire, or change how that capacity gets used, it has not yet funded product development.
The CFO and commercial leadership team need to distinguish genuine financial capacity from theoretical productivity. That means accounting for implementation costs, ongoing technology costs, oversight, transition risk, and any work that moves elsewhere in the organization.
It also means protecting the revenue base while changing the model. A cheaper acquisition program that brings in poor-fit customers, or a lower-cost success motion that increases churn, can destroy the economics it was meant to improve.
The reallocation decision should be explicit. Which commercial costs can the company remove or avoid? When does that cash become available, and which product priorities should receive it?
Not every dollar belongs in R&D, and not every product initiative deserves funding. But when product competitiveness is the binding constraint, commercial efficiency should create room to address it rather than become only a short-term margin story.
Do not ask future growth to fund today’s inefficiency
Incremental revenue can help fund innovation. It should not serve as the only funding plan for fixing the product while the company continues to carry an inefficient commercial model.
Benchmarkit’s 2026 report puts median CY2025 ARR growth at 20% across its SaaS and AI-native software benchmark population (Benchmarkit’s annual report). That is a broad benchmark, not a forecast for PE-backed middle-market companies, and it does not tell any particular business how fast it can grow.
The point is not that growth has disappeared. It is that management should earn the right to underwrite growth through evidence about demand, conversion, retention, capacity, and cost.
For PE owners, the stronger question is not, “Are we spending the right percentage on GTM?” It is, “Have we engineered the least costly reliable way to grow and retain this business, and are we putting the resulting capital where future competitiveness requires it?”
GTM efficiency is not the destination. It is a funding mechanism for the next product advantage. Build the commercial system to release capital, then invest that capital in an offering customers have a compelling reason to keep buying.