Discover why customer empathy, human insight, and brand trust matter more than automation for effective AI-driven marketing in 2026.
Between January and March of this year, Gartner surveyed 401 chief marketing officers across North America, Europe, and the U.K. about how they plan to spend. The results, published in May, describe a profession in the middle of an expensive change of mind.
Marketing technology, the software stack most teams have spent a decade building, has fallen to 19.4% of the average marketing budget. That is the lowest share in five years, down from 26.6% in 2021. Yet 62% of those same CMOs said they plan to invest more in marketing technology. AI alone now takes 15.3% of marketing budgets, but only 30% of CMOs say their organizations are ready to scale AI. Meanwhile, the share of budget going to labor, meaning the people who run the work, rose from 21.9% to 24.5%.
Read those numbers together and a pattern emerges. Companies are still buying tools, but they are discovering that tools do not run themselves, and that the expensive part is having people who know what to do with them. Gartner's separate 2025 Marketing Technology Survey found that marketers use just 49% of the capabilities in the stacks they already own.
On the other side of the screen, customers are sending a related message. In Gartner's September 2026 consumer survey, 65% said brands produce too much AI-generated content, and 43% said they now rely more on real people for recommendations.
That is the paradox this post is about. The tools have never been more capable. The advantage, increasingly, belongs to the companies that understand their customers well enough to use those tools in ways people actually want. For a leadership team, the short version is this: budget for insight before you budget for scale.
Empathy is a business capability, not a soft skill
In practical terms, empathy means understanding what a customer needs and feels, then acting on it.
The word empathy can sound out of place in a budget meeting, so it helps to define it plainly. In marketing, empathy means understanding what a customer is trying to do, what worries them, and what would make their decision easier, and then designing your messages, products, and service around that understanding. It is a capability a team either has or does not, much like analytics or engineering.
The current research suggests it is also one of the clearest drivers of results. Edelman's 2026 Trust Barometer Special Report on brands, published in June and based on 17,688 respondents in 15 countries, found that 88% of consumers call trust in a brand an important or critical reason to buy, nearly level with product quality at 89%. When asked what shapes that trust, 41% pointed to what unpaid people say about a brand, compared with 20% who pointed to what the brand says about itself. Trust is earned by how a company behaves toward people, not by how much it publishes.
The Content Marketing Institute's most recent B2B research points the same way from inside marketing teams. Among the 1,015 marketers surveyed, those who rated their marketing effective most often credited content relevance and quality (65%) and team skills and capabilities (53%), ahead of technology and tools (43%). Yet when asked where they plan to increase investment in 2026, only 9% named people, meaning salaries, training, and development, the lowest of any category. Teams say people drive results, then fund almost everything else first.
Empathy is the new algorithm
Insight first. Then automation.
AI can scale what you understand about your customers. It can’t supply the understanding.
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Listen before you scale.
Before any new automated program, someone talks directly with customers about the problem it solves.
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Automate the routine, not the relationship.
Let tools handle scheduling and common questions. Give complaints, big purchases and customers about to leave to a person.
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Measure trust alongside efficiency.
Put satisfaction, repeat purchase and review sentiment on the same dashboard as cost per acquisition.
The mismatch
Source: CMI and MarketingProfs, B2B Content and Marketing Trends: Insights for 2026, October 2025, survey of 1,015 B2B marketers.
Where automation without insight goes wrong
The failures are rarely technical. They come from tools acting on data without understanding.
Most leaders have seen some version of this. A customer who just bought a product keeps getting ads for it. A personalized email uses a first name and then recommends something irrelevant. A chatbot answers quickly but cannot handle the one question the customer actually has. Each of these is a system working exactly as designed, on data that does not capture what the person needs.
The gap is not between having data and lacking it. It is between knowing what customers did and understanding why. Behavioral data tells you someone visited a pricing page three times. It does not tell you whether they are excited, confused, or waiting for a manager's approval. Those are different situations that call for different responses, and only human insight, from interviews, support conversations, and sales calls, can tell them apart.
This is also why AI makes empathy more important rather than less. AI can produce more messages, faster, for more segments. If the understanding behind those messages is thin, AI scales the misunderstanding.
The numbers behind the shift
Martech and AI budgets
Martech’s share of the marketing budget is at a five-year low. 62% of CMOs plan to spend more on it anyway.
AI now takes 15.3% of the marketing budget, but only 30% of CMOs say they’re ready to scale it. Marketers report using about half of the martech they already own.
| Finding | Figure | Source |
|---|---|---|
| Martech and AI budgets · Gartner survey of 401 CMOs | ||
| Martech share of the marketing budget, 2026 versus 2021 | 19.4% vs 26.6% | Gartner CMO Spend Survey, May 11, 2026 |
| CMOs planning to invest more in martech | 62% | Gartner CMO Spend Survey, May 11, 2026 |
| Share of the marketing budget going to AI initiatives | 15.3% | Gartner CMO Spend Survey, May 11, 2026 |
| CMOs who say their organization is ready to scale AI | 30% | Gartner CMO Spend Survey, May 11, 2026 |
| Using what you already own | ||
| Share of their martech stack’s capabilities marketers say they use | 49% | Gartner Marketing Technology Survey, 2025 |
| Content teams · CMI survey of B2B marketers | ||
| Effective B2B teams crediting content relevance and quality | 65% | CMI, October 2025 |
| Teams planning more investment in people | 9% | CMI, October 2025 |
| Buyers and consumers | ||
| US consumers who now rely more on real people for shopping recommendations | 43% | Gartner, September 22, 2026 |
| Consumers who call brand trust an important or critical purchase criterion, just behind quality at 89% | 88% | Edelman, June 2026 |
Gartner’s CMO Spend Survey was fielded from January to March 2026 and draws heavily on large companies. Martech use is self-reported. The last two figures describe consumers, not marketing teams; Edelman surveyed 15 countries.
These are surveys of marketers and consumers. They describe stated priorities and attitudes, which point in a consistent direction but do not by themselves prove cause and effect.
What approachability looks like as a strategy
Two familiar brands show that understanding people can be the core of the business, not a layer on top.
Mailchimp is the clearest business example. It competed in email marketing, a category full of powerful but complicated software, and chose to be the approachable option, with plain language, friendly design, and tools a small business owner could use without training. That positioning helped it grow into a company Intuit acquired in 2021 for roughly $12 billion. Mailchimp did not win by having the most features. It won by understanding what its customers found intimidating and removing it.
Nike's "Just Do It" campaign, launched in 1988, is a brand-level example. It did not sell shoe specifications. It connected the brand to something people already felt about effort, ambition, and identity, and that connection has outlasted decades of product changes. Its lesson for 2026 is that the message that lasts is the one rooted in what people care about, not the one optimized for this quarter's channel.
AI can scale what you understand about your customers. It cannot supply the understanding.
A simple framework: insight first, then automation
Three rules for putting tools in service of understanding.
I group the work into three rules, because they map to the decisions leadership actually makes about budget and process.
Listen before you scale. Before launching a new automated program, make sure someone on the team has talked directly with customers about the problem it addresses. Customer interviews, support transcripts, and sales call notes are the raw material. A campaign built on real customer language will usually outperform one built on assumed personas, and it will be cheaper to fix when it misses.
Automate the routine, not the relationship. AI and automation are excellent at scheduling, sorting, testing variations, and answering common questions. They are weaker at moments that carry emotional weight, such as a complaint, a high-stakes purchase, or a customer who is about to leave. Decide in advance which moments get a person, and protect them.
Measure trust alongside efficiency. Cost per acquisition and click-through rate measure activity. Add measures that reflect the relationship: customer satisfaction, repeat purchase rate, review sentiment, and how often customers recommend you. Report them in the same meeting, so efficiency gains that erode trust are visible before they show up in revenue.
Questions leadership should ask
These three questions surface whether a company is buying tools or building understanding.
First, what share of our marketing budget goes to understanding customers, through research, interviews, and the people who do that work, compared with tools? The CMI data suggests most teams underinvest here. Second, are we using the technology we already own before buying more? Gartner's 49% utilization figure suggests many teams could get more value from their current stack. Third, where in our customer journey are we using automation in a moment that calls for a person? Those moments are usually where trust is won or lost.
Challenger brands have a structural advantage
Smaller teams can stay closer to customers, if they choose to.
Emerging companies often have an advantage here that larger ones struggle to match. With fewer layers between leadership and customers, founders can still hear objections firsthand, change messaging in days, and avoid the complexity that comes with large stacks. The risk is that growing companies give that advantage away by adding tools faster than they add understanding. Keeping a habit of direct customer contact as the company scales is one of the simplest ways to protect it.
What this likely means over the next 6 to 18 months
Expect AI budgets to keep rising, and the gap between spending and readiness to matter more.
If current trends hold, AI's share of marketing budgets will most likely keep growing, while the share of organizations ready to use it well grows more slowly. Gartner's data already shows money moving toward the people who run the tools, with labor's share of budget rising. That suggests the next competitive edge will come less from which tools a company buys and more from how well its people understand customers and direct those tools accordingly. Consumer skepticism toward AI-generated content, as Gartner's September survey shows, is likely to keep raising the value of communication that feels human and specific.
If you want help mapping where your customer journey needs a person rather than a process, Camino5's Consumer Journey Strategy work starts there.
How to start today
Add up what your team spends on marketing technology and what it spends on customer research, including the time of the people who do it. If the ratio surprises you, that is useful information for your next budget cycle.
Ask each tool owner on your team what share of their tool's features they actually use. Retire or consolidate anything well below half before adding new tools.
Have one person on your marketing team listen to five recorded sales or support calls this month and write down the three phrases customers use most often. Compare them with the language in your top campaigns.
Map your customer journey and mark the three moments where a customer is most likely to be anxious or frustrated. Confirm that a person, not an automated message, handles each of them.
Add one trust measure, such as customer satisfaction, review sentiment, or repeat purchase rate, to the same dashboard as your efficiency metrics, and review them together monthly.
The companies that win with AI will not be the ones that know the most about their customers; they will be the ones that understand them best, and use technology to act on that understanding at scale.