How Richard Wood is adapting GTM for the AI era

For years, GTM teams have optimized for predictability. Build a plan, lock a forecast, and scale volume until the numbers work. That model held when channels were stable and change happened slowly.

Richard Wood doesn’t believe that model holds anymore.

As CEO of Six & Flow Group, a global strategic GTM and HubSpot consultancy, Richard helps teams unify RevOps, marketing, data, and tech into revenue-driven systems. He sees how fast tools and buyer expectations are changing today, and how often teams fall behind trying to plan for an uncertain future.

“If you’re pinning your hat on predictability in a GTM space these days,” he says, “it’s probably a losing battle.”

Instead of chasing certainty, Richard focuses on resilience: building GTM systems that can adapt quickly without breaking.

Here’s how Richard approaches resilient GTM execution.

1. Architect your GTM for change

Richard believes the biggest mistake GTM leaders make today is anchoring their plans to long-range predictability. Six- or twelve-month plans can feel reassuring, but they break down quickly in a market where tools, channels, and workflows are constantly changing.

“We’re living through an industrial revolution that’s moving at hyper-speed,” he says. “If you’re trying to predict what GTM is going to look like in six months, good luck.”

Rather than planning around a single future state, Richard encourages teams to assume change and design for it. That means building GTM systems that can adjust without needing to be rebuilt every time a new tool, channel, or strategy emerges.

In practice, that shifts teams away from rigid forecasts and toward shorter feedback loops. Teams test ideas, see what holds up, and adapt before making heavy commitments. The ability to adjust quickly becomes more valuable than getting a long-term plan exactly right.

Richard also stresses the importance of staying tool-agnostic. When GTM systems are too tightly tied to one platform or workflow, adapting becomes slow and expensive. Flexible systems make it easier to change direction without stalling execution.

The goal isn’t to remove uncertainty. It’s to build GTM motions that keep working as assumptions change.

2. Treat AI as an operating model, not a feature

Richard sees a consistent pattern with AI adoption. Most teams know they should be using it, but very few are getting meaningful ROI from it.

“Everybody knows there are use cases that could transform their business,” he says. “What most people can’t do is actually generate real ROI or adoption across their teams.”

For Richard, the gap isn’t tooling. It’s approach.

Rather than layering AI onto existing processes, he treats it as part of the underlying GTM system. Before implementing anything, his team starts by understanding what’s already in place: data structure, existing workflows, and how ready teams actually are to adopt new ways of working. That assessment shapes where AI should be applied and, just as importantly, where it shouldn’t.

From there, the focus shifts to use cases that teams will actually use. Quick wins matter, but only if they create real uptake. If a workflow looks impressive but doesn’t change how people work day to day, it doesn’t compound.

That’s where governance comes in. AI workflows need clear ownership, defined review cycles, and the expectation that they will evolve. Prompts, logic, and models are treated as living assets. Without that discipline, teams end up with dead prompts, outdated assumptions, or hallucinations that quietly erode trust.

Richard also stays intentionally tool-agnostic. Platforms matter only insofar as they support the system being built. In some cases, that means using native CRM capabilities. In others, it means introducing custom tooling, APIs, or more advanced data infrastructure. The system is designed around the problem first, not the platform.

For Richard, ROI isn’t about how advanced an implementation looks. It’s about what teams actually use and keep using as tools and workflows change.

“AI is never finished,” he says. “It’s an iterative process from now on.”

For GTM leaders, the takeaway is simple: if AI isn’t embedded into everyday workflows, actively governed, and continuously refined, it won’t scale. Treat it like a feature and it stalls. Treat it like an operating model and it compounds.

3. Rebuild inbound around context

Richard has been a HubSpot partner since 2015. He’s watched inbound shift from a competitive advantage to a baseline expectation.

“Inbound used to be the differentiator,” he says. “Now it’s just the cost of entry.”

That shift has accelerated as buyer behavior has changed. “My buying model has totally changed,” Richard says. Like many buyers, he now starts his evaluation with tools like ChatGPT and Gemini.

Inbound still matters, but not in the way it used to. Blogs and SEO alone no longer drive differentiation. Instead, inbound needs to function as reliable infrastructure: clear, accurate, and aligned with how buyers actually discover and evaluate solutions today. If AI tools return vague or incorrect answers, the first impression is already off before a buyer ever visits the site.

Where inbound now creates leverage is in how context is applied once a buyer engages.

Richard sees the limits of traditional personalization quickly. Swapping in a name or company logo doesn’t make an experience feel relevant. It just signals a lack of understanding.

“For years, personalization meant token-based changes,” he says. “Now we can move beyond that.”

What works instead is context. Using known data to shape experiences that reflect what the buyer is actually dealing with in that moment.

Richard points to an insurance client as an example. The company already knows details like vehicle type, location, and life stage. Rather than letting that data sit idle, his team uses it to dynamically generate content that feels natural and useful, delivered through the right channel and tied to the buyer’s situation.

Inbound doesn’t win deals on its own anymore. But inbound that lacks clarity and context quietly loses them.

4. Define clear boundaries between AI and human judgment

Richard doesn’t believe in full automation by default. Whether AI should act alone or support a human depends on risk, cost, and brand impact.

“It depends,” he says. “If this goes wrong, is it catastrophic? Is it damaging to your brand?”

AI excels at processing data humans can’t realistically analyze at scale. Richard’s team uses it to surface signal, not make final decisions.

In customer success, for example, every account is analyzed weekly across three dimensions:

• Churn risk

• Expansion potential

• Estimated NPS

AI reviews product usage, email, call transcripts, and sentiment to flag risks and opportunities early.

“I personally believe NPS is bullshit,” Richard says. “You miss the huge middle.”

Traditional NPS scores tend to capture only the extremes, the customers who are either very happy or actively frustrated. What gets lost is the much larger group in between, where risk and opportunity often show up first.

AI helps surface that middle ground by analyzing ongoing signals like product usage, communication patterns, and sentiment across conversations. Humans still decide how to act on those insights, but AI makes it possible to see changes early instead of waiting for a score to spike or drop.

The same principle applies in sales. AI accelerates research, highlights relevant signals, and prepares context. Humans own the conversation, judgment, and trust.

Final takeaway

For Richard, good GTM today is less about prediction and more about adaptability. Teams that win are the ones that learn faster than the market changes. They test ideas quickly, adjust workflows without friction, and move on when something no longer works.

“I’d rather hang my hat on resilience than chase predictability,” he says.

That resilience comes from systems that can respond in real time. When teams can recognize buyer intent as it happens, enrich context instantly, and engage across chat, voice, and email, they stop relying on rigid plans and start acting on what buyers are actually doing.

This is the shift Spara is built for. Turning inbound demand into momentum requires speed, relevance, and coordination across the funnel. In a market that will not sit still, the advantage belongs to teams that can adapt quickly and convert intent into pipeline when it matters most.