Sixty-one percent. That’s the share of marketers now using AI tools every single day, according to a new survey from Basis Technologies. Now here’s the number that should keep CMOs up at night: only 6.5 percent of those same marketers measure AI’s return on investment against any formal benchmark. Adoption has sprinted ahead. Accountability crawled behind it. For brands pouring budget into AI powered influencer discovery, content generation, and campaign optimization, that gap is a liability waiting to surface at the next budget review.
The Basis Survey, By the Numbers
Basis polled marketing professionals across agency and brand side roles, and the headline finding is stark: daily AI usage has become the norm, not the exception. Three years ago, that figure would have seemed implausible. Today it’s table stakes. Tools for copywriting, creative briefs, audience segmentation, and influencer matching have moved from experimental sidebars into the daily workflow of account managers and strategists alike.
But adoption without measurement is just spending with extra steps. The survey found that while most marketers believe AI improves their output, fewer than 1 in 15 can point to a documented benchmark proving it. That’s not a rounding error. It’s a structural blind spot.
Sixty one percent of marketers touch AI tools daily, but fewer than 7 percent can show a benchmarked ROI figure to back up the investment.
Why Adoption Outpaced Measurement
This isn’t unique to AI. Every major martech wave, from marketing automation to programmatic display, followed the same arc: tools get adopted fast because they solve an immediate pain point, and measurement infrastructure gets bolted on later, if at all. AI is just moving faster than previous cycles because the barrier to entry is so low. Anyone with a ChatGPT login can “use AI.” Far fewer teams have built the reporting pipelines to prove what that usage is actually worth.
There’s also an incentive problem. Marketers under pressure to show innovation have every reason to report high AI usage. There’s far less career upside in admitting you haven’t built a benchmark yet. So usage numbers climb, ROI numbers lag, and nobody wants to be the one asking the uncomfortable question in the quarterly review.
Compare this to the creator economy’s own measurement struggles. Influencer budgets have ballooned in recent years, yet measurement frameworks haven’t kept pace with that growth. The pattern repeats: spend first, instrument later. AI adoption inside influencer marketing workflows is simply the latest chapter in the same story.
What Happens When Nobody Benchmarks ROI
Unmeasured AI spend doesn’t just sit quietly on a budget line. It creates three specific risks for brand and agency teams.
- Budget exposure. Finance teams eventually ask what the AI line item delivered. Without benchmarks, marketing has no defense when that budget gets cut in favor of something more quantifiable.
- Vendor lock-in without leverage. If you can’t prove which AI tool outperforms another, you lose negotiating power at renewal time. Procurement teams increasingly want proof, not vibes, as influencer spend itself gets reclassified as media and routed through formal procurement.
- Compliance drift. AI generated creative and disclosures move fast. Without a review benchmark, brands risk inconsistent FTC disclosure language slipping through, especially when creators or AI tools auto generate captions at scale.
Agentic AI tools make this worse, not better. When an AI agent is executing campaign decisions live, rather than just drafting copy for a human to approve, the absence of a benchmark means nobody catches a bad decision until spend has already gone out the door. Agentic martech is already replacing dashboards with autonomous execution, which raises the stakes on measurement considerably.
Agentic AI Pilots Are Already Stalling, And This Is Part of Why
It’s not a coincidence that agentic AI pilots have struggled to scale past early testing. Recent reporting found that agentic AI pilots stall out around 11 percent adoption, well below the enthusiasm the category generates in vendor pitches. A big reason is trust. Leadership won’t greenlight full scale rollout of autonomous AI agents if the team running the pilot can’t produce a clean benchmark showing the pilot actually beat the manual process it replaced.
Enterprise AI budgets have grown enormously, reportedly consuming close to two thirds of total marketing technology spend at some organizations. That’s money being pulled directly from creator and campaign budgets. If that reallocation isn’t backed by measurable performance gains, it’s going to be very hard to defend at the next budget cycle, and creator funding will be the first thing finance points to when asking for cuts.
What a Real AI Benchmark Looks Like
So what should the 6.5 percent be doing that the other 93.5 percent aren’t? A workable AI ROI benchmark doesn’t need to be complicated, but it does need to be consistent and tied to business outcomes, not vanity metrics like “time saved drafting captions.”
- Baseline first. Measure the output quality, speed, and cost of the manual process before introducing AI. No baseline means no comparison, ever.
- Pick one metric that matters to finance. Cost per asset, conversion lift, or time to campaign launch. Avoid stacking five vague metrics that dilute accountability.
- Run a controlled comparison. A/B the AI assisted workflow against the traditional one for at least one full campaign cycle before declaring victory.
- Review quarterly, not annually. AI tools change fast. A benchmark set a year ago may already be measuring the wrong model version.
This mirrors what’s already happening in performance driven influencer campaigns, where creator content is increasingly judged on cost per acquisition rather than follower count. The same discipline applied to follower vanity metrics needs to apply to AI vanity metrics like “daily active usage.”
Brands already blending influencer and performance data have a head start here. Teams running conversion blended creator plans that close the ROI gap (if that exact strategy resonates, it’s worth studying how those teams built attribution before adding AI into the mix. The infrastructure for tracking AI contribution isn’t dramatically different from the infrastructure needed to track creator contribution: both require tagging, controlled testing, and a willingness to say “this didn’t work” when the data says so.
Where This Intersects With Creator Sourcing and Contracts
AI tools are increasingly the first stop for creator discovery and vetting, speeding up a process that used to take weeks. But speed without a benchmark creates its own problem. Teams report that AI finds creators fast while contracts still run on spreadsheets, which means the AI layer and the operational layer are moving at two different speeds. If you can’t benchmark the AI sourcing tool’s accuracy against a human vetted list, you have no way of knowing whether faster is actually better, or just faster.
The same logic applies to persona based casting tools that have replaced simple demographic filters. Persona matching tools promise better creator fit, but “better fit” is meaningless without a benchmark for what counts as a successful match: engagement rate, conversion rate, or brand safety score. Pick the metric before you adopt the tool, not after.
A Practical Starting Point for Teams Behind the Curve
If your team is part of the 93.5 percent without a formal AI benchmark, don’t try to boil the ocean. Start with the single highest spend AI use case, whether that’s creative generation, media buying optimization, or influencer discovery, and build one benchmark around it this quarter. Document the baseline, run the comparison, report the number up the chain. One clean benchmark beats five vague dashboards.
Industry research from eMarketer and Statista consistently shows marketing AI spend climbing year over year, while measurement maturity studies from HubSpot show most teams still rate their own measurement capability as “developing” at best. That gap won’t close itself. It closes when someone on the team owns the benchmark and reports it with the same rigor applied to media spend.
FAQs
What did the Basis survey actually find?
Basis Technologies surveyed marketing professionals and found that 61 percent now use AI tools on a daily basis, but only 6.5 percent measure that AI usage against a formal ROI benchmark, revealing a wide gap between adoption and accountability.
Why do so few marketers benchmark AI ROI?
Benchmarking requires baseline data, controlled comparisons, and consistent reporting, all of which take more time to build than simply adopting a new tool. Most teams prioritize speed of adoption over measurement infrastructure, especially under pressure to show innovation quickly.
How does this affect influencer marketing budgets specifically?
Many brands now use AI for creator discovery, content generation, and campaign optimization. Without benchmarks, it’s difficult to prove these tools improve outcomes, which puts influencer and AI line items at risk when finance teams review spend.
What’s the simplest way to start measuring AI ROI?
Pick one high spend AI use case, document the manual process performance as a baseline, run an A/B comparison against the AI assisted version for one full campaign cycle, and report a single metric, such as cost per asset or conversion lift, to leadership.
Does low ROI measurement mean AI tools aren’t working?
Not necessarily. It means most marketers can’t yet prove whether they’re working. The tools may be delivering real value, but without a benchmark, that value is anecdotal rather than demonstrated, which makes it harder to defend budgets or justify scaling up.
FAQs
The teams that win the next budget cycle won’t be the ones using AI most. They’ll be the ones who can put a number next to it. Pick your highest spend AI use case this week and build one benchmark before your next quarterly review.
Top Influencer Marketing Agencies
The leading agencies shaping influencer marketing in 2026
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Moburst
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The Shelf
Boutique Beauty & Lifestyle Influencer AgencyA data-driven boutique agency specializing exclusively in beauty, wellness, and lifestyle influencer campaigns on Instagram and TikTok. Best for brands already focused on the beauty/personal care space that need curated, aesthetic-driven content.Clients: Pepsi, The Honest Company, Hims, Elf Cosmetics, Pure LeafVisit The Shelf → -
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Viral Nation
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The Influencer Marketing Factory
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NeoReach
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Ubiquitous
Creator-First Marketing PlatformA tech-driven platform combining self-service tools with managed campaign options, emphasizing speed and scalability for brands managing multiple influencer relationships.Clients: Lyft, Disney, Target, American Eagle, NetflixVisit Ubiquitous → -
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Obviously
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