Diagnose Your GTM Engine. Then Hit the Gas with AI.

The goal is productive growth

I think about this through two connected goals: growth and productivity. Growth means winning customers, expanding relationships and retaining the recurring business you already have. Productivity means improving what your people and agents can accomplish with the time and resources available. Productive growth brings the two together: a larger business with better economics.

Of course, you sometimes trade one for the other. Entering a new market may mean hiring ahead of revenue and accepting lower near-term efficiency. Protecting cash may mean slowing expansion. Make the tradeoff explicit: what should the investment achieve, and when will you reassess it?

That gives each improvement a purpose. More meetings should produce worthwhile opportunities. Faster proposals should help customers receive the right offer and reach a decision. Lower support effort should preserve the quality of the customer’s experience. The operating metric tells you whether the work changed; the business outcome tells you whether the change was useful.

To connect the two, you need to see how the work fits together. Start with the path from generating demand to delivering value and earning the next purchase.

Map the GTM value chain

The GTM value chain connects attracting customers, winning their business, delivering value and growing the relationship. Each stage depends on the previous one. What happens after the sale should also inform whom you pursue and what you promise next.

At each stage, follow the same sequence: understand the work, measure its performance, investigate the friction and choose an improvement. Include the handoffs between people, agents and teams. The map below gives you questions and evidence to start with:

For the stage you are investigating, establish a baseline using the relevant measures above. Record their definitions, the period covered and the customer group. If evidence is missing, collect enough to locate the problem; a sample of recent deals can be a useful starting point.

Look at the pattern by customer segment and over time. Strong lead volume with weak qualification points you toward targeting and fit. Healthy opportunity creation with a growing queue of proposals points you toward the close. Good win rates followed by difficult implementations may mean sales is promising work the delivery team cannot readily fulfill.

Take the growing proposal queue as our running example. The value chain locates the delay in the close; it does not yet explain the cause. Slow proposals might reflect incomplete discovery, an overloaded specialist or unclear rules for discounts. The next step is to distinguish those possibilities.

Diagnose the cause through five lenses

The value chain tells you where to look. Five diagnostic lenses help you work out why the work is breaking down there: People/Agents, Data, Process, Metrics and Technology. The questions and situations below are examples to investigate. Use them to form hypotheses, then check which explanations fit your business. Several areas may contribute to the same problem.

For the proposal queue, review a sample of deals with the reps, solutions engineers and commercial reviewers who handled them, alongside records of any agent tasks. Separate time spent preparing the offer from time waiting for information or a decision. Compare standard deals with those requiring special terms.

Suppose the review finds reps assembling proposals from scattered discovery notes, while unusual discount requests wait without a clear decision-maker. You have two candidate improvements: reduce preparation effort and clarify how commercial exceptions are handled. Their costs, benefits and timing will help determine where to start.

How do you prioritize what to work on improving first?

Start by estimating three things for each improvement, then weigh them against your business goals, risk appetite and available resources:

ROI: Is the expected benefit worth the full cost? Include implementation, tools, agent usage and ongoing review. Compare costs with spending avoided or incremental contribution from new business over the same period. Hours released become financial value only when put to productive use or converted into savings.

Time to execute: How long from starting the work until it functions in daily operations? Include data preparation, integration, testing, training people and configuring agents.

Time to value: How long from starting the initiative until a meaningful benefit appears? This includes execution and adoption. Less proposal effort may show up quickly; higher win rates may take a sales cycle. First value can arrive before full payback.

Make the expected benefit concrete before you build. For a qualification improvement, model how additional qualified opportunities could turn into wins at existing downstream conversion rates and average deal value. For our proposal pilot, estimate the hours released and how the team would use them. Use comparable internal baselines and test a range of assumptions. These scenarios help size the opportunity; the pilot must establish what actually changes.

The weighting depends on the business. Protecting cash may favor a quick, credible cost reduction. Entering a new market may justify a longer investment. Consider how much uncertainty, cost and customer disruption you can accept. Then order the work around dependencies: reliable data first, a focused pilot second, broader automation third if results justify it.

Apply that logic to our proposal problem. If approval delays dominate, clarify discount ownership first. Suppose instead that preparation consumes most staff effort, the goal is to release sales capacity and the source material needs no major rebuild. I would start with a limited preparation pilot, reviewing proposals before they reach customers and including a clear route for discount exceptions. That gives us a specific priority to plan and test.

Turn the chosen priority into a plan

We chose proposal preparation because it consumes sales capacity. The plan should address the cause we found: reps assembling offers from scattered information. Agree on the customer requirements, product information and approved pricing each proposal needs, and make those inputs accessible to the people and agents doing the work.

Next, define how the draft becomes an offer. The rep checks it; the designated commercial or legal owner reviews discount and contract exceptions. Confirm that reviewers have capacity and requests reach them with complete information. That gives us a workflow in which to assign AI a useful role.

Give AI a job in the improved process

With those inputs and responsibilities clear, test an agent on assembling the first draft, flagging gaps and showing its sources. The rep can review the offer without reconstructing every input. Use the agreed rules to route exceptions to the authorized reviewer.

This targets the preparation effort that justified the pilot while keeping commercial decisions with their owners. To see whether it works, measure the whole People/Agents workflow, including corrections and handoffs. A faster first draft is useful if it reduces the total effort and cost of producing an accurate offer.

Measure what the improvement is worth

Build the test into the pilot. Before implementation, record the baseline and expected dates for deployment and first benefit. Track drafting and review effort, approval delays, turnaround and corrections. Then follow the deal: does faster drafting move the customer forward or shift the queue?

Consider a hypothetical team producing 40 proposals a month at four hours each: 160 hours. Reducing drafting and review to 1.5 hours per proposal, including corrections, releases 100 hours a month. Measure waiting time separately.

Suppose tools, agent usage and administration cost $4,000 monthly, plus $6,000 for setup. Assuming twelve full operating months at those costs and capacity gains, the total is $54,000, or $45 per hour released. That prices the capacity; its return depends on how you use it.

Measure avoided overtime or external spending, or incremental contribution from additional business after the costs of winning and serving it. Benefits must exceed $54,000 over that period for positive financial ROI on the stated costs. Do not count the same hours as both savings and capacity used for growth.

Track quality and customer experience too. Compare similar deals or use a staged rollout, allowing time for outcomes to appear. Changes in pricing, customer mix or demand may also affect results.

Compare actual costs, benefits and timing with the original estimates. Continue, change or stop the pilot accordingly. Productive growth means improving the economics of earning and retaining revenue; the test should show whether this change helps.

Scale the improvement, then revisit the value chain

When results justify expansion, roll out to teams or segments with similar conditions. Train people, configure agents and check that those receiving more work have the capacity to handle it.

The next constraint may now become visible. Faster proposals could expose slow contract review; faster acquisition could strain implementation. Return to the value-chain map and diagnostic lenses. Use the results to update processes, training and agent instructions, with someone accountable for that review.

Return to the weekly GTM meeting. The team can explain where deals stalled, why proposal preparation became the first pilot and whether it released useful capacity at a justified cost. Those results guide the next investment. Diagnose the engine, demonstrate the improvement, then hit the gas.