Closing the AI Readiness Gap in Modern Marketing
New global data shows 72% of marketers plan to use more AI in the next year, but only 45% feel ready to do it well. This AI readiness in marketing gap is large, yet very fixable when teams invest in skills, data, and clear goals instead of chasing every new tool.
Understanding the AI readiness gap in marketing
AI readiness in marketing is your team’s ability to use AI tools in a safe, repeatable, and measurable way to reach real business goals. It is not only about having access to tools. It is about having the skills, data, and workflows to turn those tools into results.
Programmatic media partner MiQ surveyed 3,169 marketers across 16 countries and found a 27 point gap between AI usage and confidence. While 72% of marketers plan to apply AI in more ways over the next 12 months, only 45% feel confident in their ability to use it successfully, according to the company’s AI Confidence Curve report.¹
PPC Land’s coverage of the same study highlights that two thirds of respondents already use AI on most or all projects, yet many say their organization does not understand AI or large language models well enough.² This is the core of the readiness gap: fast adoption, slow enablement.
- Teams are eager to use AI and are already testing it in daily work.
- Leadership often has no clear roadmap, guardrails, or training plan.
- Data, measurement, and skills lag behind tool access.
| Factor | AI curious | AI experimenting | AI ready |
|---|---|---|---|
| Skills | Only a few team members test AI in their spare time. | Some staff trained on key tools, knowledge is scattered. | Clear roles, repeatable playbooks, and regular training cycles. |
| Data | Most work uses public tools and generic prompts. | Some systems share data, but access is limited and manual. | First party and ad platform data feed into AI safely and at scale. |
| Measurement | Focus on vanity metrics like clicks and time saved. | Some tests tie AI work to campaigns and simple KPIs. | Every AI use case connects to revenue, cost, or customer value. |
| Governance | No clear policy, people make their own rules. | Basic do and do not lists shared by email or chat. | Written guidelines, approvals, and audits built into workflows. |
Where marketers are already using AI today
The MiQ report and PPC Land coverage show that marketers are not waiting to experiment. Today, AI is most used in areas that feel safe and familiar, like copy support and campaign reporting.¹²
According to the survey:
- 40% use AI for social media management.
- 39% use it for marketing automation tasks.
- 38% use it for customer engagement and support.
- 37% use it for visual design tasks.
- 35% use it for campaign management.
- Around one third use AI for content creation and creative optimization.
- 33% use AI for SEO and content optimization.
These are all good starting points. They are repeatable tasks with lots of data and clear outcomes, which makes them ideal for structured tests. The challenge is moving from ad hoc use to a stable system that your whole team can trust.
If you want ideas on when AI adds value and when it can hurt your brand, see this guide on when and when not to use AI in marketing.
Why so many marketers still feel unprepared
In the same MiQ study, 40% of respondents said their organization does not understand AI or large language models well enough. A further 38% cited lack of training, 42% said they cannot safely share data with AI tools, and 44% said they struggle to track results against the right goals.¹
Other research tells a similar story:
- An Adobe and Econsultancy study found that advanced AI skills for key team members and a basic AI understanding across the company are top priorities for senior marketers, yet only a quarter say they have already run AI skill programs.³
- Kantar’s “Fear or FOMO” work with global marketing leaders shows that many rate generative AI’s future impact at 9 out of 10, but score their current readiness under 5 out of 10, held back by training, cost, and data quality concerns.⁴
The main blockers inside most marketing teams
- Skills and training gaps. People are told to “use AI more” but are not taught prompt basics, brand safeguards, or how to review outputs.
- Data access and quality. Legal or tech limits prevent tools from seeing the data they need, so teams fall back to generic prompts and generic answers.
- Weak measurement. Teams track clicks or time saved instead of business results, so AI experiments never move past “interesting tests.”
- Fragmented tools. Each platform adds its own AI helpers. Without a plan, this creates chaos instead of clarity.
- Cultural fear. Some staff worry that AI will replace their role, which makes them less likely to experiment and share what works.
The good news is that each blocker can be addressed with simple, focused habits. You do not need a huge budget to raise your AI readiness level. You do need a clear framework.
Simple AI readiness framework for marketing teams
To move from scattered tests to confident use, it helps to think about AI readiness in four layers: goals, people, data, and tools. Start small in each layer and build over time.
1. Start with goals, not tools
Before you roll out any new feature, decide what success looks like. Pick one or two business outcomes per use case, such as more qualified leads, lower cost per acquisition, higher repeat purchase rate, or faster creative testing.
- Write simple goal statements like “Use AI to cut ad creative production time by 30% while holding click through rate steady.”
- Agree in advance which metric will tell you if the test worked.
- Set limits, such as which channels, audiences, or budgets AI can touch during a pilot.
2. Map your current AI use
You may already have more AI in your stack than you realize. Many ad and CRM platforms now ship with AI features turned on by default.
- List where AI already appears across your ads, email, social, and analytics tools.
- Note who owns each use case, which data feeds it, and how you measure success.
- Flag “shadow AI” such as personal ChatGPT accounts used for work content.
3. Close the skills gap with simple training
Training does not have to mean a long course. Many teams see gains by giving people a clear baseline and a safe space to practice.
- Teach three or four prompt patterns that match your main tasks, like briefs, outlines, or analysis.
- Share examples of good and bad outputs so people know what to look for.
- Pair AI with review checklists, not with blind trust.
For a deeper look at how small companies can fold AI into everyday work without losing their voice, explore this overview of how small businesses can use artificial intelligence in their everyday operations.
4. Fix your data and measurement basics
AI works best when it can see clean, connected data and when you judge it on meaningful results. That might mean improving tracking before you expand AI use.
- Audit what first party data you have and where it lives.
- Make sure conversion tracking and key events are set up correctly.
- Use clear, privacy safe rules for what data you share with third party tools.
5. Choose fewer, better tools
You do not need every AI feature on the market. You need a small set of tools that plug into your channels and data with as little friction as possible.
- Start with the tools already built into your ad and CRM platforms.
- Only add new tools if they solve a clear problem that current tools cannot.
- Review tools at least twice a year and remove low value overlap.
12 month roadmap to close your AI readiness gap
If you want to turn the next year into a confidence building cycle, here is a simple roadmap you can adapt to your team size and budget.
Quarter 1: Listen, audit, and set guardrails
- Survey your team to learn how they already use AI, at work and at home.
- Run a short audit of tools, data, and current AI features across your stack.
- Draft a one page AI policy that covers data, approvals, and brand safety.
- Pick two or three priority use cases that match clear outcomes.
Quarter 2: Run focused pilots
- Design small tests around your chosen use cases, with clear start and end dates.
- Document prompts, workflows, and review steps as you go.
- Compare results to a recent baseline, not to a perfect world.
- Share both wins and failures openly so people can learn from each other.
Quarter 3: Scale what works
- Turn successful pilots into playbooks with step by step checklists.
- Train the rest of the team on those playbooks, including how to say no to bad outputs.
- Integrate AI steps into your briefs, templates, and campaign checklists.
- Adjust goals and budgets where AI clearly improves results.
Quarter 4: Review, refine, and plan again
- Review which AI use cases created real business value and which did not.
- Update your AI policy and training based on what you learned.
- Set next year’s AI goals, tied to your broader digital strategy.
- Decide which emerging tools you will test and which you will ignore for now.
For help tying AI plans to a full funnel strategy, you can also review this step by step guide to creating a digital marketing strategy for 2025.
Bridging the AI readiness gap starts with small, focused steps
The gap between AI ambition and AI readiness is real. Studies from MiQ, Adobe and Econsultancy, and Kantar all point to the same pattern: marketers see AI as a game changer, but many teams lack skills, data access, and measurement to use it with confidence.
You do not have to fix everything at once. Start with a few high impact use cases, teach your team simple habits, and connect AI work to real business goals. As you build better systems and guardrails, AI becomes less of a risky experiment and more of a reliable part of your marketing engine.
If you are ready to turn “we should use more AI” into a clear plan, consider partnering with TrueFuture Media for a practical, evidence based approach that respects your brand and your budget.
FAQs about AI readiness in marketing
What is AI readiness in marketing?
AI readiness in marketing is your ability to use AI tools in a way that is safe, consistent, and tied to real outcomes. It includes trained people, clean data, clear goals, and simple processes that turn AI features into better campaigns, not just faster content.
Why are marketers not confident using AI yet?
How can a small marketing team build AI skills without a big budget?
Start with free or low cost training from platform partners and trusted industry sources. Create short internal practice sessions, share prompt templates, and encourage peer reviews of AI work. One or two hours per week of guided practice can make a big difference over a few months.
Which marketing tasks are best to test AI on first?
Begin with tasks that are repeatable and low risk, such as draft copy ideas, subject line tests, ad variations, basic reports, or audience insights from existing data. These areas are already popular in industry surveys and give you fast feedback on what works.¹
How often should we review our AI readiness plan?
At a minimum, review your AI use cases, tools, and policies once a year. Many teams benefit from a lighter quarterly review focused on new features, risks, and results. Regular check ins keep your AI program aligned with changing platforms, privacy rules, and business goals.
Last updated: November 16, 2025