Be the Answer

At the Austin stop on the Spotlight on the Road tour, a room full of B2B marketing, AR, and customer engagement leaders landed on some genuinely good news: AI can produce anything, but it still can't decide what's worth hearing. That job still belongs to humans.

Every stop on in the Spotlight on the Road tour has emphasized the same point: what you say about yourself (owned content) carries less weight than what everyone else says about you (earned authority). No stop made that case more forcefully than Austin — the speakers, the content, and the room itself all pointed to the same conclusion. Once machines start writing the answers, the risk isn't just that buyers stop listening to your own pitch. It's that they lose the ability to tell you apart from anyone else in your category at all.

That risk compounds because most B2B teams are still pouring the bulk of their budget into the owned side of the ledger — the polished decks, the campaigns, the messaging they fully control — even as the "robots" (ChatGPT, Gemini, Copilot, and the rest) quietly build their answers out of something else entirely: authentic customers, credible analysts, and real community consensus. The fastest-adopted technology in human history (ChatGPT) has made that preference impossible to ignore.

The numbers back it up:

• 94 percent of B2B buyers now leverage generative AI at some point in their journey (Forrester, Buyers' Journey Survey, 2025).
• Four out of five deals are won by the buyer's own predetermined favorite — decided through "dark research" long before they ever officially talk to a seller (6sense, 2025 B2B Buyer Experience Report).

Which leaves B2B teams with three uncomfortable truths to build a strategy around:

• The buyer journey isn't something you steer anymore. It's something you inherit.
• The deal is usually decided before anyone ever picks up the phone.
• The robots trust exactly what your buyers already trust: real people, not polished copy.

I gave the audience a challenge — be more than a result, be the answer — at every stop on this tour. What made Austin distinct was where the day went next: straight at the question of what's actually worth saying once you've earned the right to be heard.

Winning the Selection Phase

Austin's second session was, by design, close to a repeat: Kerry Cunningham, Head of Research and Thought Leadership at 6sense, delivered largely the same "Winning the Selection Phase" talk he gave a few days earlier in Chicago, and it landed just as hard the second time.

The short version: most B2B revenue systems — the MQL, BDR sequences, funnel-stage thinking — were built about 20 years ago, for a world where buyers had to talk to a seller to get basic information. That world is gone in most established categories. Buyers put four or five vendors on their shortlist on day one, based on prior experience and reputation, and 95 percent of purchases end up coming from that day-one list. First contact with a seller happens, on average, 61 percent of the way through a roughly 10-month buying cycle — and 94 percent of buyers say they'd already ranked that shortlist by preference before ever talking to a seller. The six months in between isn't buyers ignoring vendors; it's buying groups working toward internal consensus, which is also the single biggest reason (40 percent of the time, per LinkedIn's B2B Institute) that buying processes stall out entirely.

Kerry's recommended posture — "aggressively enable" rather than aggressively pursue a meeting — means identifying everyone in a buying group who needs to be influenced and delivering them useful information without expecting a response, since the data shows outreach timing has almost nothing to do with when a buyer actually reaches out. He was also careful to complicate the popular LLM narrative: buying journeys have compressed, but it's largely because vendor products now embed AI themselves (adding new evaluation steps), not because buyers are using LLMs to discover new vendors earlier. Where LLMs show up, it's more often mid-journey, reinforcing a preference buyers already hold. 

For more on Kerry’s presentation, see the Chicago recap.

Analyst Relations Isn't Enough

Katie Nafius, Director, Analyst Relations at Visa, was brilliant and blunt with the audience: analyst relations on its own no longer covers what the function actually needs to deliver. Her north star for the program she's built — what she calls AR+ — is "to be the leading source of strategic insight and influence: empowering internal teams with actionable intelligence while shaping external perception through trusted analyst and influencer partnerships, thought leadership, and industry presence."

She grounded that ambition in the same shift in buyer behavior the rest of the tour kept surfacing, citing research from IDC, G2, Forrester, and Profound: 54 percent of B2B buyers now start their journey with analysts, and 77 percent interact with analysts at some point — analysts remain essential. 89 percent consult peer review sites like G2, PeerSpot, Gartner Peer Insights, and TrustRadius during evaluation — peer review sites have surged. 78 percent of buyers now routinely use AI tools for recommendations and comparative analysis — AI-driven tools heavily influence. And 64 percent of GenAI outputs cite analyst research or user reviews when buyers ask for vendor recommendations — LLMs are sourcing analyst and peer proof together. Her point: none of those four forces operates in isolation anymore, which is exactly why a program built only around traditional analyst relations falls short of what's needed.

At Visa, AR+ sits at the intersection of three inputs — analysts and influencers, competitive intelligence, and customers — with market advantage in the middle. In practice, that means active relationships across a long list of known research firms, plus a growing roster of independent influencers.

To show how deliberately that's been built rather than assumed, Katie walked the room through Visa's own AR maturity year by year, mapped against a three-phase model — Educating, Learning, Leading — that she uses to plan and evaluate the program annually:

• 2020–2021: One product line, with a goal simply to change perception — early in the Educating phase.
• 2022–2023: Still one product line, but the goal shifted to improving perception and awareness.
• 2024: Two very different product lines, each running on its own separate maturity track.
• 2025: Four-plus product lines, plus the launch of a G2 customer reviews program, scaling across business units.
• 2026 (AR+): Two full business units fully supported, multiple customer review programs running, a new influencer program launched, and the function working cross-functionally rather than in its own lane.

That last point — the influencer program — got its own maturity curve. Visa currently has two influencer programs running, at the "Getting Started" and "Foundational" stages respectively: one has built a target influencer list, begun one-off collaborations, briefed senior stakeholders, and put a KPI framework in place; the other has moved into repeat work with top influencers, folded influencer content into planned moments, and started bringing subject-matter experts into co-created content as stakeholder support grows. The stages still ahead — Scale & Accelerate and Leading Edge — call for shifting top influencers into always-on partnerships, making executive pairings standard practice, and eventually operating a fully integrated influencer calendar measured on awareness, engagement, traffic, and demand.

She closed by tying it together with a framework adapted from Gartner: customers and sales feed market feedback; industry analysts and the AR function generate contextualized market and buyer-trend insight; media, influencers, and PR generate storytelling. All three feed the same center. That, in her framing, is the real argument behind "analyst relations isn't enough" — the function only creates real influence when it's deliberately connected to what customers and sales are hearing on one side, and how the story actually gets told publicly on the other.

Why Storytelling Matters More Than Ever

Jennifer Trou, Head of Marketing at Kung Fu AI, led Austin's storytelling session, bringing a vantage point few in the room could match. Jennifer joined Kung Fu AI, an AI management consulting and engineering firm, back in 2019 — years before ChatGPT existed — after starting her career in film and at agencies including Edelman and Ketchum. That timeline gave her session an unusual vantage point on how much has changed.

Her core argument: AI tools are collapsing differentiation at scale. She showed the room two completely unrelated pieces of content — one about surviving a layoff, one from Y Combinator — found five minutes apart on LinkedIn, with identical structure: the same beige layout, the same eyebrow header, the same tone. She didn't spare herself from the critique. A few months ago she built an entire client presentation using Claude's new slide-generation feature; a week later, a colleague asked her what the point of it had been. The information hadn't stuck, because she'd let the tool skip past the substantive middle of the story. In a second, sharper example, she'd drafted a line of website copy in Claude — only to read a New York Times article days later about Meta's layoffs that used nearly identical phrasing. The same AI-generated language had surfaced for a small AI consultancy's messaging and for coverage of a mass layoff at one of the world's largest companies. "Our differentiation is collapsing in real time," she said.

The underlying mechanism, in her framing: AI is probabilistic. It predicts the statistically most likely next word, which by design produces the average — sanding off the specific, distinctive edges that make a brand memorable. Her antidote was three examples she draws personal inspiration from:

• Dolly Parton — decades of consistent, intentional voice and image, essentially unchanged from age 18 to 80.
• Bad Bunny's Super Bowl halftime show — deliberate specificity for a defined audience rather than an attempt to appeal to everyone, which is precisely what made it powerful.
• MailChimp's 2014 "Male Kimp" campaign — after a Serial podcast ad mispronounced the company's name, MailChimp leaned all the way into the mistake with a full creative campaign (Mail Shrimp, Jail Blimp, Kale Limp, and more) instead of quietly correcting it. The campaign won a Cannes Lion and helped define the brand for more than a decade.

Her practical recommendation: build a documented, intentional narrative first — banned phrases, key messages, specific human moments worth preserving — and use AI against that narrative as a spine, rather than starting with the tool. She keeps a running "inspiration folder" and has Claude look for patterns and connections across it, always explaining the why behind an example rather than just feeding the tool raw examples to imitate. Her closing line, aimed squarely at the room: AI can produce anything. Only you can decide what's worth hearing.

Advocacy in the Answer Engine Era

Austin closed with Liz Richardson and Deena Zenyk, Co-Founders of Captivate Collective (now part of Spotlight), who've spent more than two decades in customer advocacy together and used their session to connect Jennifer's point about distinctiveness directly to their own discipline.

Their framing: customer advocacy has moved through one-to-one sales acceleration, then scaled advocacy built for volume and retention, and has now arrived at a moment where the real question isn't "how do we get noticed," it's "how do we get into the answer." From a London workshop, they shared a moment that landed hard with their own audience: a customer marketer ran a set of basic prompts against his own company for the first time — describe my company, what is it best known for, compare it to top competitors, who's the best customer fit — and discovered that a single, obscure competitor page designed to sow doubt was the top-cited source shaping ChatGPT's answer about his own business. In a second, more encouraging example, a prospect told them he'd never heard of Spotlight before his research began, but ChatGPT had already surfaced Spotlight as one of three finalists on his shortlist — sourced from Spotlight's G2 reviews and press coverage of the Captivate Collective acquisition.

Their central argument: customer marketers sit at the intersection of nearly every source an LLM pulls from, because customers feed all of them — reviews, community forums, analyst conversations, even influencer content. Software can be copied. Marketing campaigns can be copied. Years of genuine customer trust cannot, which makes it one of the few durable competitive advantages left. Their practical framework — precision advocacy — starts with knowing which AI tools your buyers actually use, running basic audit prompts against your own company and competitors, and using tools like Profound to identify which sources carry the most weight and where customer voice is missing, then closing those specific gaps surgically rather than chasing volume. They shared one fast-turnaround example: a client traced a negative AI-generated narrative to a single overlooked review, sourced counter-reviews addressing the same point, and saw a measurable shift in AI outputs within three days.

On detractors, their advice was direct: a detractor turned advocate is one of the strongest customer stories a brand can have, precisely because it's more credible. The hard part isn't identifying unhappy customers — NPS, CSM feedback, and call monitoring make that relatively easy — it's having a real process to close the loop and convert them, which most organizations lack. And on the broader question of countering inaccurate AI narratives, they drew a line between reactive correction (valid, but limited) and a more strategic approach: define your ICP narrative clearly, understand what your best customers actually need to hear, and make sure that consistent story is present everywhere LLMs index — rather than chasing down every inaccuracy one at a time. They named an emerging role for the discipline: the customer influence orchestrator, someone who connects business narrative strategy to how it actually shows up in AI-generated answers.

Closing Thoughts

Austin closed out six cities on this year's tour, and the day built to a fitting final note: the tools are the same for everyone, so the advantage goes to whoever has the discipline — and the courage — not to sound like everyone else.

• Kerry carried the selection-phase data from Chicago into Austin and it held up just as well the second time: the deal is largely decided before a seller is ever contacted.
• Katie showed what it actually looks like to outgrow "analyst relations" as a standalone function — building AR+ into a program that connects customer feedback, analyst insight, and storytelling into one system, matured deliberately over six years.
• Jennifer made the case, with her own mistakes as evidence, that AI's pull toward the average is real and constant — and that avoiding it takes a documented narrative and real intention, not just a good prompt.
• Liz and Deena showed exactly how that plays out in customer advocacy: the brands winning the answer are the ones who've spent years building trust that can't be copied, and are now using it with precision instead of volume.

My challenge to the room was the same one I've made in five cities before this: be more than a result. Be the answer. In Austin, that challenge came with a sharper edge, because being the answer isn't worth much if the answer sounds exactly like everyone else's. The brands that win from here are the ones with the discipline to stay specific — a documented narrative instead of a generic one, a customer voice that's genuinely theirs, an analyst relationship built for what the business is today rather than what it used to be. That kind of distinction isn't something AI can hand you. It's still, stubbornly, a human decision.

Spotlight on the Road 2026

San Francisco: April 1 — recap here

New York City: May 6 — recap here

Seattle: May 13 — recap here

Boston: June 3 — recap here

Chicago: June 24 — recap here

Austin: June 26

Spotlight Summit 2026

Influence + Advocacy + Visibility = Trust

September 14–16 | Kansas City, Missouri

Registration is open!