The first useful thing to know about Jen Snell is that she arrived in artificial intelligence before the crowd, but without the theatrical fog that would later settle over it. There were no daily prophecies about machines swallowing professions. There was a young company in Spokane trying to make software understand what people meant, and there was a marketer who had wandered in curious enough to help explain it.
Snell had grown up near LaCrosse, Washington, a wheat town small enough that the horizon does most of the decorating. She went to Washington State University, changed majors more than once, settled on advertising, and graduated in 2002. An internship with the Seattle SuperSonics gave her an early look at the machinery behind a public-facing brand. Then she returned east to Spokane and worked in production at an advertising agency.
The decisive turn was almost comically direct. She heard about Next IT, visited its offices, and was hired on the spot. It was 2006. The company was building conversational software years before Siri and Alexa became domestic nouns. The technology needed language models, engineering, integrations, and patience. The business needed someone who could make the strange thing legible.
Snell noticed that marketing lacked a clear owner, so she developed the role herself. This is the first stealable lesson in her career: a job description can be a lagging indicator. The valuable work often exists before an organization has named it. She gave the gap a name, occupied it, and spent more than a decade expanding what it meant.
“I’m trying something new every day.”
The category had to be invented twice
Marketing an established product is partly an exercise in choice. Marketing an emerging technology is an exercise in translation. Buyers first have to understand the problem, then believe the new category can solve it, then trust one particular company to do the work. Next IT was not merely selling a feature. It was asking large organizations to let software conduct consequential conversations in their name.
Snell learned to talk about personality, tone, context, and outcomes because those were not decorative qualities. They were the product. A virtual assistant that answered the literal question while missing the human intention had failed. A polished interface without enough domain knowledge was a well-dressed shrug. The machine had to understand, clarify, and know when the conversation required something else.
The translator’s loop
Her writing from that period reads less like futurism than product discipline. She argued that enterprise AI was not magic, that a bot should carry values as well as create value, and that trust was part of the interface. In 2017, she made a case that remains unusually sturdy: “AI is as much about art as it is engineering.” Humanities graduates, in her view, were not bystanders to automation. Their sensitivity to voice, ethics, and ambiguity was part of building it well.
This was category creation with dirt under its nails. Next IT opened its AI tools and conversation platform to other businesses. Its library had been built across industries and years of actual exchanges. Snell explained the shift while the market around it accelerated from a small field of specialists to competition with Microsoft, Google, and Amazon.
A startup education, with an acquisition in the final exam
By 2014, Next IT had named Snell its Innovator of the Year. A year later, she received a silver Trailblazer of the Year award from PM360. The recognition is revealing because she was not the engineer in the story. Her innovation was organizational and linguistic. She built the bridge between what a technical team had made and what a customer might be ready to believe.
Then came the test that scrambles many carefully built narratives: acquisition. Verint bought Next IT in December 2017. The scrappy company and its conversational AI became part of a much larger customer-engagement portfolio. Snell stayed, moving through product marketing, product strategy, and go-to-market leadership. Instead of explaining one pioneer to a new market, she now had to locate that technology inside a broad enterprise system.
A former colleague remembered that she gave people ownership of their work while staying close enough to offer useful insight. Another remembered her willingness to travel and speak for the company, even if she might resist the description “talented public speaker.” The picture is of an operator who delegates without disappearing. It is also a clue to her staying power. Categories change. Teams remember whether a leader made them larger.
Asked once what drove her, Snell answered with one word: “winning.” Taken alone, it sounds like the standard executive bumper sticker. Paired with another of her rules, it becomes more interesting. The moment she takes herself too seriously, she has said, the fun leaves the work. Competitiveness supplies motion. Humor keeps it from becoming pomp.
From helping machines converse to helping people decide
Snell joined Babel Street in June 2022 as senior vice president of marketing. The new subject was not customer service. Babel Street works with publicly and commercially available information, multilingual search, identity resolution, and risk analysis. Its users may be evaluating an identity, a supplier, a threat, or a fast-changing event. The vocabulary grew sharper because the consequences did too.
Yet her old questions survived the move. What does the system understand? Where did the answer come from? How should a person use it? A conversational assistant needs to interpret intent. A risk-intelligence platform needs to distinguish one person or organization from another, connect scattered signals, and preserve a route back to evidence. Both are translation problems with different stakes.
As CMO, Snell has written about the “risk-confidence gap,” the distance between the quantity of available data and an analyst’s ability to turn it into warranted confidence. The phrase is good marketing because it names an emotional fact hiding inside a technical one. More information can produce less certainty. A million additional hay strands are not helpful unless someone also improves the magnet.
“A faster answer isn’t always a better one.”
That principle anchors her current case for agentic AI. The latest systems can perform a chain of tasks rather than wait for one prompt at a time. For an investigator, that could mean gathering, validating, correlating, questioning, synthesizing, and reporting. The appealing promise is speed. Snell emphasizes the less glamorous requirements: provenance, transparency, governance, and human authority.
In 2026 she drew a clean line between a general productivity copilot and intelligence work. The latter is adversarial, evidentiary, and capable of driving serious decisions. Its users cannot settle for an answer that merely sounds complete. They need to examine the path, challenge the inference, and remain responsible for what happens next.
The long view is the advantage
AI marketing suffers from a short memory. Every interface arrives dressed as a revolution, and every revolution brings a fresh glossary. Snell has seen bots, virtual assistants, intelligent self-service, copilots, and agents pass across the stage. She has also seen what stays seated in the audience: people who need help making sense of complexity.
Her early argument for the humanities looks less quaint with time. The models are more capable now. That increases the premium on understanding why a response feels persuasive, where a system may flatten ambiguity, how values enter a decision, and when speed outruns judgment. Technical power does not retire those questions. It puts them on a tighter deadline.
The farm-to-AI arc is tempting to polish into folklore, but its useful meaning is plainer. Snell built a career by noticing what the work required before the role or market had fully caught up. At Next IT, that meant creating a marketing focus. At Verint, it meant carrying a young category into enterprise scale. At Babel Street, it means explaining why better intelligence depends on both a wider field of signals and a disciplined human hand.
The machines have learned to talk back. They can now search, summarize, plan, and act with unnerving fluency. Snell’s contribution has been to keep asking the question beneath the demonstration: useful to whom, for what decision, and with what evidence? It is less glamorous than prophecy. It is also how technology survives contact with the world.