How to Build a Growth Engine That Scales in the AI Era with Andrew Silard

Exploration is the work of finding a repeatable growth motion: testing positioning, channels, onboarding approaches, and customer segments. Exploitation begins once a company has enough evidence that a motion works. At that point, fractional head of growth Andrew Silard recommends concentrating on one or two bets—often just one—and building the capabilities to scale it well. That involves more than increasing marketing spend: teams may need to improve activation, lifetime value, product experience, or viral loops. The challenge is not to abandon experimentation altogether, but to deliberately separate it from the focused work of scaling a proven lever.

A spike test increases investment in a promising channel by roughly 5–10x for a short period to see whether results rise in a meaningful, roughly proportional way. For paid channels, that may mean a significant temporary budget increase. For organic channels, it could mean launching more partnerships or activating a larger referral effort at once. If signups or revenue respond clearly, the team has stronger evidence that the channel can scale. If results barely move, the issue may be weak channel fit or gaps in onboarding, conversion, or monetization that need attention before more investment makes sense.

When deciding whether to scale a channel, marketers should look beyond blended CAC and focus on marginal CAC: the cost of acquiring the next customers generated by additional spend. A channel can appear efficient in aggregate but yield little incremental value once investment rises. Comparing normal performance with the results of a spike test helps reveal whether the extra investment is actually producing more customers. The inverse, a pause test, can also be informative: temporarily turning off a channel can show whether the outcomes it appears to drive truly disappear. These tests help teams make a more credible case for budgets, forecasts, and growth targets.

Segmentation helps a company identify the customer for whom it solves the most material problem, then sharpen the positioning and experience around that audience. Andrew Silard suggests starting with customer data to find the most engaged, retained, or valuable groups, then talking directly with those customers to understand their motivations, buying journey, and unmet needs. Those insights can shape messaging, integrations, product decisions, and even payment timing. The goal is to turn broad but unfocused traction into tighter product-market fit. Teams should retain some flexibility, however, because an overly narrow segment may prove too small or difficult to scale.

Marketing teams should focus AI adoption on clearer decisions, better context, and more consistent execution—not on building every tool themselves. Andrew recommends giving teams space to experiment, while prioritizing a shared “context brain” that makes company knowledge and data easier to access. AI agents in tools such as Slack can help democratize reporting and answer questions while work is in progress. Point solutions can also improve specific workflows, such as content, lifecycle marketing, performance marketing, or website operations. For more complex needs, teams should rank the highest-value automation opportunities and evaluate build versus buy deliberately; in most cases, Andrew expects buying to be the better choice.