Build Your AI Strategy But Invest In A Currency of Trust
Human Element: Jennifer Tomlinson emphasizes maintaining human involvement in AI strategy to ensure trust and perception.
AI Strategy: AI should be used to remove friction in marketing tasks but not for strategic decision-making.
Audience Understanding: Jennifer underscores the importance of knowing your audience, which AI cannot replace.
Adoption Curves: QorusDocs adopts a measured approach to AI, aligning technology use with customer readiness.
Operational Usefulness: Jennifer sees value in agents within workflows, ensuring AI enhances work without replacing humans.
There's a version of the AI story where the CMO comes in, blows up the old playbook, automates everything in sight, and calls it transformation. Thankfully, Jennifer Tomlinson isn't telling this story.
With nearly 30 years of marketing behind her, spanning Ford Motor Company, and Microsoft, Tomlinson is now EVP of Marketing at QorusDocs, an AI-powered proposal and RFP automation platform carving out space in the enterprise software market.
She's seen enough industry shifts to know which ones actually change the job and which ones just create more noise around it.
And her role at QorusDocs, gives her more reason than most to be an AI maximalist. Her company sells AI. Her engineers are building agents. And, her product team is shipping agentic features to customers right now. And yet, the philosophy she keeps returning to is almost stubbornly old-fashioned.
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Knowing Your Audience
No matter how the conversation wanders, there’s one thing Tomlinson, and fellow marketing leaders keep coming back to.
Everything I learned years and years ago in college — getting the messaging right, understanding your audience, understanding their pains and challenges. You can’t really rely on AI to tell you that.
Amplifying What Exists Now
The most important thing she has learned about AI is less about the technology, and more about what it reveals. She's seen firsthand where AI doesn't fix a weak strategy, it accelerates things moving in the wrong direction. Because if the messaging wasn't right before, it's just going to be wrong again, only faster. "AI amplifies whatever clarity or confusion already exists. Strategy still has to come first."
This insight shapes how she thinks about how AI can support collective goals. Her team at QorusDocs uses it to remove friction from marketing work.
We use AI to help us plan, run, and communicate campaigns, spot patterns in performance, and keep systems like marketing automation current, or support content creation and reuse.
And, internally, they use AI to sense-check messages before shipping. This enables them to "translate strategic decisions into execution faster, and explore 'what if' scenarios without the cost and risk of launching them." But friction removal is different from decision-making. And this line, she believes, is non-negotiable.
What stays human are the decisions that shape perception — like positioning, value, and trust.
This is where her 30 years of experience becomes a real competitive advantage, not just a LinkedIn credential. QorusDocs serves a very specific audience of people who write proposals and RFPs for a living. They're often under crushing time pressure, and work without knowing whether they are successful. So Tomlinson doesn't need AI to tell her what those people are anxious about because she's been in the trenches with them.
We’ve been here 12, 15 plus years just on the problem from a manual lens and kind of in the trenches with our customers,” she says. “The true understanding — you can lose that trust so quickly if you do something and abuse it.
And trust, once broken with an audience, especially one in a specific niche, is often impossible to rebuild.
The Case Being in the Middle of AI Adoption Curve
One of the things that makes Jennifer's approach distinctive is how intentional she is about where QorusDocs sits on the AI adoption curve. Rather than being at the bleeding edge, or being slow to adopt, her team is decidedly deliberate in their position.
"If you think of the AI curve of adoption, we try to sit sort of right in the middle," she says. "We don't want to not try things. We keep everything moving forward, but we don't necessarily want to be on the full leading edge of things because we just don't know yet.
Part of this caution is audience-driven. In a benchmark survey QorusDocs ran of the proposal and RFP world, roughly 60–70% of respondents said they were using AI, but not yet for reasoning. They were using it for speed, instead of strategic transformation.
"We know our audience isn't there yet," she says. "And if we're honest with ourselves, we're not there yet either."
This kind of self-awareness, knowing both where your organization is and where your customers are is rarer than it sounds. Most companies either overestimate their readiness or assume their customers are further along than they are. Jennifer does neither, and retains the professional connection with the people her team serves to validate their decisions. This humanized, and trustworthy approach allows her to keep moving without overcorrecting.
"Some days it's Claude, some days it's ChatGPT, some days it's all different and the different things that everyone's doing, particularly for marketing," she says. "But we do have to keep our audience in mind and where they are on their path of adoption."
Insights Before the First AI Pilot
Jennifer is generous about sharing what hasn't worked, too. Early in their AI experimentation, her team made a mistake that's almost universal among first-time AI pilots where they focused on what the technology could do rather than defining what good was supposed to look like.
AI pilots fail when you don’t define what good output means in advance. We initially focused too much on capability and not enough on guardrails, context, and evaluation criteria. Once we treated AI like a junior team member that needs structure, results improved dramatically.
This reframe, in treating AI as a junior hire, and not a magic solution changes everything about how you approach implementation. A junior team member needs onboarding with a continued stream of influence. They need context about the brand, the audience, the tone, the limits. And, they need to know when to escalate, and when not to improvise. You wouldn't hand them a high-stakes deliverable and walk away. And with AI, the exact same logic applies.
In addition, it also reframes the governance question. The issues that come up with using AI is whether you've given it enough structure to be useful without going off the rails. Tomlinson shares how the realized there's a time and place for AI.
We initially tried using AI to give definitive answers too early in the process. What worked better was using it to explore possibilities and surface questions — not to close decisions.
Shifting from AI as an answer machine to AI as a thinking partner, is both subtle and profound. And, it maps directly to where she draws the hard line on human involvement.
The Line Teams Shouldn’t Cross
There are decisions, Tomlinson says plainly, that AI cannot own. And for her team, they can't afford to lose vital touch points with their audience. She admits that these level up to how the company supports their audience, and how they demonstrate value in their final positioning decisions.
Those choices carry reputational risk and long-term brand consequences that AI can't fully understand. AI can inform the decision, but it shouldn't own it.
Something she keeps returning to throughout the conversation is the fragility of trust, especially in a niche market.
"It feels it takes so long to build that trust," she says. "And in this industry too, we have a lot of AI startups coming just coming into the proposal world and they've come out of nowhere. The true understanding—you can lose that trust so quickly if you do something and abuse it."
This cannot be understated. Trust is vital for marketers, and most leaders recognize the importance of working to demonstrate and uphold trust with their audience.
The solution isn't necessarily to slow down, though there's merit in doing this to gain clarity. You want humans to have influence in the places that matter most. For Tomlinson, this includes anything customer-facing that could feel automated, anything that touches brand voice, and anything that makes a claim about value or differentiation.
AI has not been effective as a shortcut to differentiation. It reinforced that technology can accelerate output, but it cannot create strategic clarity where it does not already exist. The real work still starts with humans making deliberate choices about what matters and why.
A Battle Of Restraint
One of the sharpest observations Tomlinson makes, and one that hasn't shown up in the evolving AI playbooks, is where AI falters. AI can handle reasoning, tone, and accuracy, provided you feed it the right information, but it does not draw the line.
AI struggles with restraint. It's very good at filling space and very bad at knowing when less is more. Knowing what to leave out, when to pause, and how something will land still requires human judgment.
In a world where AI can generate infinite content at zero marginal cost, the strategic advantage increasingly belongs to the marketers who know what not to say. You still need people who can look at a piece of AI output and say whether it's good, or too much. The devil may be in the details, and resonating with your audience often requires a human review, not a longer prompt.
Editorial instinct is something Tomlinson believes can't be outsourced, and not because AI isn't capable, but because brand judgment is inherently contextual, relational, and built over time. It's the accumulation of knowing your audience, knowing your voice, knowing what has worked and what has burned you.
"The human is really important," she says. "We want people to free up their time to do the storytelling, to do the insightful work — and not get mired in where's that piece of content somewhere in the organization, just that kind of thing."
Agents Working in Lockstep with People
To be clear, Tomlinson isn't resistant to where AI is heading. She's genuinely excited about it, particularly the agentic layer her own product is building.
I'm most excited about agent-based AI that operates inside defined workflows with humans firmly in the loop. When AI can take responsibility for discrete tasks while remaining transparent and auditable, it becomes operationally useful instead of just impressive.
This framing, operationally useful instead of just impressive, is a good summary of her entire philosophy. AI earns a seat at the table by making the work more coherent, scalable, and human in the places that matter.
She's watching her own engineering and product teams build agentic features and using that front-row seat to calibrate what's real versus what's hype. "We get to be in an experimental pod with our own company moving to agentics," she says. "Watching that process really from engineering and the product team has been super helpful in understanding what might be real or not real on the products and technology stack that we have purchased."
The Part of the Job AI Can't Touch
There's a question underneath all of this that Tomlinson returns to, even when it isn't explicitly asked, what is a marketer actually for?
Her answer, shaped by nearly three decades across auto, tech, and B2B software, hasn't changed much.
You can’t really rely on AI to tell you your audience’s pains and challenges. And if you do, it may not be correct. Don’t let AI do the thinking for you. But certainly once you’ve gotten the ideas, it can influence and offer some ideas around ways to deliver that.
She reiterates the importance of getting messaging right. Understanding the audience, and relating to their pain. These are the fundamentals of marketing and they were true before AI arrived and will still be true when the next thing shows up. And right now, with LLMs still indexing older content and the rules of discoverability in flux, she sees the fundamentals as more valuable, not less.
AI has not been effective as a shortcut to differentiation. It reinforced that technology can accelerate output, but it cannot create strategic clarity where it does not already exist.
The Steady Framework Underneath It All
Tomlinson offers advice that doesn't get enough airtime in a landscape full of transformation narratives and AI-first everything. One thing that's been consistent over every job I had," she says, "is the organizational piece. You have a huge pile of stuff to do, but what do you prioritize today? Look at that list and think about revenue as you cut through it."
It's advice she shares with her teenage kids, and advice she's applied across automotive, enterprise software, and everything in between. It would make sense that this line of thinking applies to AI strategy, too.
Don't let the tool set the agenda or allow capability become a substitute for clarity. Continue to do the thinking first, and support your teams in doing the same. The hard, human, opinionated thinking about who you're for, and what you're trying to say, and then let AI help you deliver it better, faster, and at scale.
In a marketing landscape full of people promising transformation, steady, structured, human-led thinking will win, and AI should be along for the ride, not at the wheel.
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