Marketing teams are spending more on AI and using it for more of their work. The evidence says most of them are getting faster output, not better commercial results, and the difference comes down to whether the process changed.
Part 1 of the AI Marketing Reality Check series.
The short answer
Most marketing teams have added AI to the work they already do. Very few have changed the work itself. That is why AI spending and usage keep climbing while measurable returns stay concentrated in a small group of companies. The companies seeing real value treat AI as a reason to redesign how marketing runs: which decisions it supports, which steps disappear, who owns the result and how value is tracked. If a business cannot name the process AI changed and the number that moved because of it, it has bought productivity, not performance.
Three findings support that view.
1. Spending and usage are rising fast, and readiness is not keeping up
Gartner’s 2026 CMO Spend Survey of 401 marketing leaders in North America, the UK and Europe, fielded between January and March 2026, found CMOs now allocate an average of 15.3% of their marketing budgets to AI initiatives. Seventy per cent said becoming an AI leader is a critical goal for the year. Yet only 30% reported mature AI readiness, and 70% said their internal marketing processes are not mature enough to implement and scale AI. Gartner’s chief of marketing research, Ewan McIntyre, put the risk plainly: CMOs may “invest in AI tools faster than they build the data foundations.”
Usage data from the US tells the same story from a different angle. The CMO Survey, run by Duke University’s Fuqua School of Business with 308 senior US marketing leaders in January 2026, found generative AI is now used in 22.4% of marketing activities, up from 7.0% in 2024. Use is highest in technology, software and platform companies, at 36.1% of activities.
A note on the samples: most of Gartner’s respondents work for companies with more than $1 billion in revenue. Smaller businesses rarely have dedicated AI budgets at all, which makes the readiness gap more acute for them, not less.
2. Usage is concentrated in producing more, not deciding better
The CMO Survey shows where AI is actually being applied. Content creation leads, used by 73.9% of companies (up from 49.2% in late 2023), followed by marketing automation at 48.9%, data analysis and reporting at 46.3% and targeting at 45.2%.
The reported gains are far more modest than the usage. Overhead cost reduction attributed to AI rose to 14.6%, and the improvements in sales productivity and customer satisfaction sit in a similar range. Marketing leaders also rate themselves poorly on proving value: demonstrating ROI from marketing technology scored 4.4 out of 7, and integration and data architecture (19.1%) was the second most cited barrier to technology impact, behind budget.
In other words, the most common use of AI in marketing is making more content, faster. That is useful only if content volume was the constraint on growth. For most B2B companies, it was not.
3. Value goes to the companies that change the workflow
The broadest evidence comes from outside marketing, and it is consistent. McKinsey’s The State of AI in 2026 (1,719 participants in 97 countries, surveyed in May and June 2026) found that 89% of organisations use AI regularly in at least one function, but only 37% attribute any EBIT impact to it, a figure essentially unchanged from the year before. Just 6% qualify as high performers, attributing at least 5% of EBIT to AI. Nearly three-quarters of those high performers have fundamentally redesigned workflows because of AI, compared with about a quarter of everyone else. Marketing and sales is the function where respondents most often report AI-driven revenue gains, which shows the opportunity is real when the work is redesigned.
Boston Consulting Group’s The Widening AI Value Gap (October 2025, more than 1,250 senior executives) found only 5% of companies achieving AI value at scale, while 60% report little or no material value. The leaders follow what BCG calls a 10-20-70 split: roughly 10% of effort on algorithms, 20% on technology and 70% on people and processes. More than 60% of them rigorously track AI value, against 17% of companies that are stagnating.
Why it happens, five levels down
- Why is AI not improving marketing ROI? Because it is mostly applied to producing more of the same output.
- Why is it applied that way? Because the easiest place to add AI is the task in front of the team: writing, editing, designing, scheduling.
- Why does the team start there? Because nobody defined which business result the AI investment was meant to change.
- Why was no result defined? Because AI was bought as a tool decision, owned by whoever uses the tool, rather than as an operating decision owned by leadership.
- Why is that the root cause? Because without an owner and a target number, the process never gets redesigned, so AI speeds up the old process and the commercial outcome stays where it was.
What businesses are getting wrong
- Assuming speed equals performance. A team that publishes three times as much content has not necessarily generated three times as much demand. If the bottleneck was follow-up, qualification or positioning, more content changes nothing.
- Measuring usage instead of outcomes. Seats activated, prompts run and assets produced are activity metrics. The question for leadership is which pipeline, conversion or cost number moved.
- Automating a process nobody has examined. AI applied to a poor lead-handling process makes leads go unanswered faster. Automation magnifies whatever process it sits on.
- Buying tools before fixing data. Gartner’s warning about data foundations matters most for smaller companies, where the CRM is often incomplete and source tracking is optional. AI personalisation and targeting are only as good as the records underneath.
The Dizital Connect view
AI is not the strategy. It is a capability that makes a good marketing system faster and a weak one more expensive. In our experience across start-ups, e-commerce, technology and services businesses, the companies that get value from AI in marketing do three things before they buy anything: they name the single commercial constraint they are trying to remove, they map the process that produces that number, and they decide what should change in that process, not just which task should get faster. The AI tool comes last, and its success is judged on the number it was meant to move.
For a growth-stage SaaS or B2B company, the highest-value AI uses are usually not in content production. They sit in the places where revenue leaks: speed and quality of lead follow-up, qualification and routing, sales preparation, and reporting that tells leadership what to change.
What to do: a 30-day AI marketing reset
Week 1: Diagnose
- List every AI tool and use case in marketing, with its owner and monthly cost.
- For each, write the business number it was meant to improve. Mark any where the answer is “time saved” or “more output” with no commercial number attached.
Week 2: Prioritise
- Identify the single biggest constraint between marketing activity and revenue: not enough qualified leads, slow response, poor conversion to meeting, or weak sales handoff.
- Choose one AI use case that directly attacks that constraint. Pause or deprioritise the rest.
Week 3: Execute
- Map the current process step by step, then redesign it: which steps disappear, which are automated, which stay human, and who owns the outcome.
- Record a baseline and date range before switching anything on.
Week 4: Measure and review
- Compare the target number against the baseline, not against expectations.
- Decide: scale, adjust the process, or stop. Write down what was learned so the next use case starts from evidence.
The numbers worth tracking
| Instead of | Track |
|---|---|
| Assets produced per month | Qualified pipeline created per month |
| Hours saved | What those hours were redeployed to, and its result |
| AI tool adoption rate | Change in the target metric against a recorded baseline |
| Email volume sent | Positive replies converted to meetings |
| Cost per asset | Cost per qualified opportunity |
Free resource: AI Marketing Value Audit
A 20-question scorecard that shows whether your AI investment is improving marketing results or just increasing output, with a score band and the first fix for each weak area. Email us to request your copy.
Frequently asked questions
Why is AI not improving marketing ROI for most companies?
Because most companies apply AI to existing tasks, such as content creation, without redesigning the process around it. McKinsey’s 2026 survey found 89% of organisations use AI regularly, but only 37% attribute any EBIT impact to it.
How much of the marketing budget goes to AI?
Gartner’s 2026 CMO Spend Survey found CMOs allocate an average of 15.3% of marketing budgets to AI initiatives, while only 30% report mature AI readiness.
What do companies that get value from AI do differently?
They redesign workflows rather than only automating tasks, put most of their effort into people and processes rather than technology, and track AI value rigorously against business outcomes.
If your business has invested in AI for marketing and you are not sure what it has changed commercially, Dizital Connect can help you assess where the real constraint sits and which use of AI is worth prioritising first.
Sources
- Gartner, 2026 CMO Spend Survey press release, 11 May 2026.
- The CMO Survey, Duke University Fuqua School of Business, Highlights and Insights Report 2026, fielded January 2026.
- McKinsey & Company, The State of AI in 2026: On the road to ROI, August 2026.
- Boston Consulting Group, The Widening AI Value Gap, October 2025.
