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Key Takeaways
AI for content generation is now table stakes; today’s marketers need to use AI for better decision-making.
Trust in marketing depends on transparency, governance, guardrails and set data processes.
The smartest AI strategy starts with what brands choose not to automate.
DALLAS — Marketing’s trust problem is not a new one. AI didn’t create it, but it’s making it a lot harder to ignore.
For years, marketers have optimized, targeted, retargeted, scored, segmented, personalized and occasionally annoyed people across every available channel. Then genAI arrived, and suddenly the industry had a new excuse to do more of it, faster.
More ads. More automated journeys. More Hi [First Name] emails aiming for that “they really get me” intimacy.
But is speed still the right goal?
Like it or not, marketing has entered a new phase, one where AI-generated content is expected and AI-assisted decision systems are the new objective.
Table of Contents
- Content Generation Is Yesterday’s Strategy
- What AI-First Marketing Looks Like in Practice
- Marketers Ask the Wrong Question
- Trust Requires More Than a Privacy Policy
- AI Personalization Tests the Value Exchange
- AI Decisioning Runs Into the Data Problem
- The Human Role Moves Upstream
Content Generation Is Yesterday’s Strategy
Inside SAS’s marketing division, AI is not new. According to Jenn Chase, the company’s chief marketing officer, they’ve already been using models for decisions such as lead scoring and who to invite to events — long before genAI became the center of every conversation.
Now, said Chase, her team is moving from individual, personal productivity to building AI into department-wide processes.
“My vision for the organization is that we are an AI-first marketing organization that is operating at scale and with a customer first mentality,” she explained.
Using AI to draft an email or summarize a call is useful. It can save time and boost productivity. But it’s not true transformation. That only happens when AI starts changing how marketing decisions are made before the campaign ever reaches the customer.
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What AI-First Marketing Looks Like in Practice
One example of AI as part of departmental processes is the campaign brief, according to Chase. With AI, her team can ensure the brief meets the brand’s objectives and is differentiated on the market. As she noted, “If the brief isn't good, then none of the outcome is going to be.”
Then, with SAS Customer Intelligence 360, they can import that brief into their audience mapping, creating the audience journey in a generative way.
“And what's really exciting about that is it's not a human in the loop where the human’s checking in. It is like human-centered design the entire time when the audience journey is being created, the marketer is actually watching it happen as they're putting the prompt in. So it's almost like a thought partner in that way.”
Marketers Ask the Wrong Question
That upstream role of the marketer is important. Automation has a habit of sounding impressive out of the gate — until the customer has to deal with it.
Jonathan Moran, senior manager of horizontal solutions marketing at SAS, said many brands are approaching automation backward. Instead of asking where to insert a human, he said, they ask how much they can automate.
“They want to automate everything. And as a result, it sucks trust. When the experience sucks, then trust goes down.”
Automation might move a customer through the funnel faster, but it doesn’t automatically improve the experience. It just makes that bad experience more efficient.
Ray Wang, principal analyst and founder of Constellation Research, framed the design challenge differently: “The question you have to ask is, when and where do you insert a human, not where you automate.”
That’s a much harder question. But there is an answer. “In the experience design session we're doing now,” Wang added, “it's really about where to collapse decision trees.”
Trust Requires More Than a Privacy Policy
Moran did not hesitate when asked whether he thinks trust in AI or marketing is declining.
“Yes, I do, absolutely,” he said.
Consumers are increasingly wary of the messages they receive, he said, including “who actually sent the message” and whether it came from the brand or from someone trying to scam them.
With generative and agentic AI, he said, companies will need “governance, guardrails, data processes put in place to foster that trust and transparency.”
And trust is no longer just a matter of privacy policy language or a “we care about your data” email no one reads. It has to be designed into the system:
- When is the customer interacting with AI?
- How is the decision being made?
- What data is being used?
- Why is this offer or message being shown?
- Can the customer understand the value exchange?
Transparency, Moran said, is one way to build trust. Experience matters too.
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If the chatbot lies, the journey breaks. If the offer is wrong or the brand pretends an agent is a person, customers won’t care that the company has a responsible AI slide somewhere. They’ll just leave.
AI Personalization Tests the Value Exchange
Wang put the trust-loyalty connection in sharper terms. “We believe people are trading loyalty for privacy. Trading loyalty for value. Trading loyalty for convenience.”
That is the bargain behind AI-powered personalization. Customers may accept more data use if the exchange feels worth it. Better service. Better recommendations. Less friction. More relevance.
But the line between helpful and creepy is thin, and AI is making it thinner.
“Is that a good thing or a bad thing?” Wang asked. “Is that creepy, or is that like, ‘Oh, that’s awesome’?”
Marketers love to talk about personalization as if it is inherently customer-centric. But it’s only customer-centric when the customer gets something valuable enough to justify what the brand knows, infers or predicts. Otherwise, it’s surveillance with better UX.
AI Decisioning Runs Into the Data Problem
Here is where the AI dream slams into a wall. Everyone wants agentic AI and hyper-individualized customer experiences. But before you can get there, you need to inspect the data plumbing underneath.
“It all comes back to data,” said Chase. “It all comes back to data because you’re not going to have great AI without great data.”
Moran made the same point from the customer experience side.
“If you don’t have good data, you don’t have good decisions, or the ability to make good decisions,” he said. “Then your AI is going to be terrible. Simply put.”
The blockers are not mysterious. Companies don’t have the right data, the quality is poor, the sources are not integrated or teams don’t have access to what they need.
Sure, it’s not a fun topic, but it’s a necessary one:
AI agents need decisions → Decisions need context → Context needs data
If the data is siloed, the agents will be too. One agent will offer what another just denied (like Air Canada’s chatbot, which offered a customer an imaginary discount). Or one system personalizes using demographic data while another uses behavioral context.
Simply put: garbage in, garbage out.
Related Article: In the Age of AI, Marketing Is Product
The Human Role Moves Upstream
So what was the AI marketing mandate at SAS Innovate? It wasn’t “make more stuff” or “automate everything.” It was something much harder.
Marketing leaders now have to build systems that can make better decisions without burning down trust. That means better data, better governance, better content architecture, better journey design and more honesty about the value exchange with customers.
It also means asking: “What shouldn’t we automate?”
Chase said SAS tries to work backward from the problem, not forward from the tool. That might sound like common sense, but in the current AI market, it’s pretty progressive.