AI maturity in marketing: What the research reveals

by Hubspot Staff 02 November 2026 15 min read 54x views

AI maturity in marketing: What the research reveals

AI maturity explains why 98% of promotion teams now use AI in several way, yet lone 10% study transformational results, according to our 2026 State of AI in Marketing research. AI maturity measures how consistently a customer-facing squad turns AI into measurable endeavor outcomes, and it matters additional than the figure of tools or pilots you’ve launched.

So if your squad has tools, pilots, and plentifulness of enthusiasm but can't yet item to the income or client cognition gains, you're not alone. Adoption is mostly solved, but the gap is effect — and decision it starts alongside knowing anywhere you stand.

This guide defines the four AI maturity stages (Early, Emerging, Established, and Transformational), shows how to run an AI maturity assessment, and maps all phase to a next stage alongside Loop Marketing. If you desire the benchmarks first, download the 2026 State of AI in Marketing Report.

Table of Contents

What is AI maturity for go-to-market teams?

AI maturity measures how consistently a go-to-market squad turns AI into measurable endeavor outcomes. For marketing, that method AI runs in documented workflows, draws on spotless client data, operates under individual oversight, and ties to metrics akin MQLs, client acquisition disbursal (CAC), and revenue. Sales and assistance use the identical definition alongside distinct workflows and metrics.

Maturity strikes counting pilots since pilots measure action and maturity measures repeatability. A squad can run ten experiments and motionless sit at the base of the curve if none nexus to the CRM, have an owner, or display a before-and-after result.

Marketing leads to another GTM functions, alongside 42% of marketers reporting advanced AI use (Established or Transformational), compared alongside 27% in revenue and 23% in service. For those functions, see the sales maturity model and the customer achievement maturity model.

The Research Behind This Guide

HubSpot’s 2026 State of AI in Marketing research: 1,754 marketers throughout North America, Europe, Asia, Australia, and Latin America. Stages are respondent-rated, so they display how teams depict their own maturity, not an audit of results.
HubSpot’s cross-functional study: 6,000+ professionals established that 90% say their business uses AI and 6% charge it a “transformative” adopter. This is a distinct community than the marketing-only 98% and 10%, so don’t difference them directly.
McKinsey’s 2026 State of AI survey: 1,719 participants in 97 countries, fielded May 4 to June 8, 2026, for an autonomous perspective of scaling and financial impact.

The four stages arrive from HubSpot’s research. The six-dimension scoring afterward in this guide is an editorial framework, not an authoritative HubSpot metric. Treat all benchmarks as directional.

What are the AI maturity levels in marketing?

An AI maturity example includes stages that run from experimentation to scaled activation.

AI maturity stages mapped to Loop Marketing

The four AI maturity levels differentiate Early, Emerging, Established, and Transformational acceptance patterns. A squad can sit at distinct levels in distinct workflows, so mark the workflow first.

Early: AI use is idiosyncratic and ad hoc. No shared prompts, documented strategy, or CRM connection. Governance is informal. Results are anecdotal (“it saves me time”).
Emerging: A few team-level use cases exist, all alongside an owner. Data admission is partial, workflows run beside the center process, and measure stops at period saved or output volume.
Established: AI runs inner repeatable workflows throughout channels and uses connected client data. A documented scheme and essential governance exist, agents grip defined multi-step tasks, and results are tracked against chimney metrics.
Transformational: Workflows were redesigned about AI. Agents activity under individual oversight, governance runs daily, and results are measured against holdouts or phased tests. Saved period goes toward strategy.

The payoff at the top is measurable: 96% of Transformational adopters met or exceeded their MQL targets, and they were approximately 3.5x additional apt to outperform CAC targets. Among all marketers surveyed, 72% stated AI makes it easier to hit income and MQL targets, and concerning 65% stated the identical for CAC goals. These results are self-reported, so treat them as directional benchmarks.

How These AI Maturity Levels Compare to Analyst Models

Most AI maturity models concur that capability builds in stages. They differ in scope: the two best-known references shield entire enterprises, during HubSpot’s stages depict promotion adoption.

MIT CISR’s Enterprise AI Maturity Model has four stages, from experimenting and preparing to becoming AI future-ready. MIT Sloan reports that organizations in the archetypal two stages performed below their industry’s financial average, and those in the final two performed complete it.
The Gartner AI maturity model has five stages: Foundational, Emerging, Operational, Scaled, and Transformational. Gartner’s assessment scores seven pillars (strategy, data, governance, engineering, functioning model, culture, and AI merchandise or value) and can be applied to a sole function akin marketing.

Roughly, Early matches Gartner’s Foundational, Emerging matches Emerging, Established spans Operational and Scaled, and Transformational matches the top phase in the two models. The criteria differ, so use the mapping for orientation, not mark conversion.

How to Run an AI Maturity Assessment and Benchmark

An AI maturity appraisal combines evidence concerning use cases, data access, workflow integration, governance, and outcomes. Proof method the workflow, the data source, the owner, and the result.

List all AI use case, including unofficial ones, grouped by chimney phase or channel.
Score all one on the six dimensions below, from 1 (Early) to 4 (Transformational).
Attach evidence to all mark of 3 or 4: a documented workflow, dashboard, policy, or test result.
Find the binding constraint, your lowest-scoring dimension.
Benchmark alongside caveats. The HubSpot, McKinsey, and Gartner figures are mostly self-reported, so your own trend complete period is the additional dependable comparison.

Don’t equate self-reported maturity alongside measured performance. A squad that rates itself Transformational without measure in location hasn’t shown item yet.

Who should own the AI maturity assessment?

A promotion operations or income operations guide normally has the correct mix of procedure cognition and data access, alongside a elder promotion chief as sponsor. Add IT or safety for governance and a revenue or assistance equal for shared data. MIT CISR additionally recommends assembling elder specialized and data leaders to set the current phase and target.

How frequently should teams reassess?

Reassess quarterly in twelvemonth one, afterward twice a twelvemonth formerly scores stabilize. The site moves fast: McKinsey established 44% of organizations now study scaling AI throughout the enterprise, up from 38% a twelvemonth earlier. Reassess following any big change, akin a new agent, a CRM migration, or a guideline update.

The AI Maturity Index For GTM Teams

This AI maturity indicator is the article’s applicable appraisal framework, not an authoritative HubSpot metric. Score all size from 1 to 4:

Use cases: How many are in regular manufacturing and tied to a chimney or customer-experience goal?
Data and CRM context: Can AI admission accurate, permissioned client data?
Workflow integration: Does AI run inner center processes, including agent-driven steps?
Governance and oversight: Are approved tools, assessment steps, and escalation paths documented and followed?
Measurement: Can you display AI’s consequence on pipeline, conversion, CAC, or client satisfaction?
Strategy and ownership: Is there a documented strategy, a named owner, and a prosperity line?

Add the scores for a quick read: 6 to 10 is Early, 11 to 15 is Emerging, 16 to 20 is Established, and 21 to 24 is Transformational. Then use the cap rule: your productive phase can’t sit additional than one flat complete your lowest dimension. A squad that scores 4 on use cases and 1 on governance has a hazard problem, not a Transformational program. The lowest size is your binding constraint, so allocate there next.

What evidence proves all size is advancing?

Use cases: An inventory alongside an owner and mark metric for each.
Data and CRM context: A data norm study showing small duplicates and gaps, affirmative field-level admission rules. This guide to master data and this overview of data integrity create a fine checklist.
Workflow integration: Process maps showing anywhere AI acts and anywhere a individual reviews, affirmative run logs.
Governance and oversight: A written AI use policy, an approved-tools list, and an audit trail.
Measurement: A recurring study comparing AI-assisted activity against a baseline or holdout.
Strategy and ownership: A one-page scheme tied to GTM goals, alongside an owner and assessment cadence.

If you can’t create the artifact, mark the size one flat lower.

How to Move Up the AI Maturity Curve alongside Loop Marketing

The AI maturity curve rises fastest whenever teams halt adding tools and build a repeatable cycle. Loop Marketing helps teams move from AI pilots to measured, repeatable workflows through four stages: Express, Tailor, Amplify, and Evolve. AI supports the group in the loop; it doesn’t substitute their judgment.

Early → Express. Define your item of perspective and value proposition before scaling AI output. HubSpot’s Loop research established nearly 6 in 10 promotion teams don’t have theirs documented, and without it AI output drifts generic.
Emerging → Tailor. Connect one aviator to client data. Companies using AI for personalization are 3x additional apt to have considerably risen revenue complete the former year, and this guide to AI personalization for growth shows how to start.
Established → Amplify. Extend proven workflows throughout channels and create certain your brand shows up in AI answers. HubSpot’s AI Search Grader is a free, one-time inspect of how ChatGPT, Perplexity, and Gemini depict your brand. It measures AI visibility only, not general maturity.
Transformational → Evolve. Run all run as a test and nourish results into the next cycle. As HubSpot’s Kieran Flanagan says, “The teams reaching transformational maturity are the ones using that saved period on scheme and differentiation, not fair producing additional of the same.”

What is the archetypal finest stage from all maturity level?

From Early: Write downward your brand item of perspective and choice one use case alongside an owner and a metric.
From Emerging: Connect that use case to CRM data. HubSpot’s Smart CRM includes built-in enrichment, copy detection, and permissions, which location the data obstacle at this stage.
From Established: Add measurement. Set a baseline and holdout for your two highest-volume workflows.
From Transformational: Redesign one workflow end to end about AI and individual review, afterward document it for another teams.

McKinsey’s findings item the identical way. Nearly three-quarters of elevated performers study basically redesigning workflows since of AI, versus concerning one-quarter of another respondents, and they’re twice as apt to have defined processes for measuring impact.

How do we evade AI sprawl and shade AI

Shadow AI grows whenever group deficiency an approved choice that works. Inventory all tool in use, including liberated ones. Publish a abbreviated approved-tools list, allocate an owner to all use case, and create the approved way the easiest one. Review quarterly and quit overlapping tools. Sprawl additionally hides cost: about one in five organizations say AI functioning expenses constrain their AI use.

Data Readiness and Responsible AI Governance

Data norm sets the ceiling for AI results. HubSpot’s cross-functional study established 74% of companies that use AI and have outstanding data additionally attack income targets. That shows correlation, not cause, but it matches the form that AI amplifies any data it gets.

Data preparedness comes downward to five practices: accurate, deduplicated records; apparent site definitions; documented consent and permissions; role-based access; and a named data norm owner. Smart CRM unifies client data in one location and includes a centralized audit log of actions taken by people, automations, integrations, and AI.

Trust is a genuine involvement as 29% of marketers citation trust, privacy, or safety as their biggest individual challenge alongside AI. Privacy rules change by region. If you market to group in the EU or UK, assessment what the GDPR requires alongside your lawful team. This part isn’t lawful advice.

How do we operationalize liable AI day to day?

Set assessment rules by risk. Spot-check low-risk work. Review customer-facing content, claims, and personalization before they go out.
Document all workflow: the data it uses, the example or agent, the reviewer, and the nonaccomplishment plan.
Define escalation. Name who handles a biased, inaccurate, or privacy-sensitive output, and how fast.
Sample outputs monthly for bias and accuracy, and log what you find.
Name a individual owner for all workflow who can intermission it.

McKinsey established that high performers activity to mitigate additional AI-related risks than another organizations do. Governance and results lean to increase together.

Activate AI throughout the chimney and measure impact.

Smart CRM provides connected client environment for applicable activation and measurement, so the document that informs a personalized email additionally feeds the study that shows whether it worked. In McKinsey’s survey, revenue gains from AI are most frequently reported in promotion and sales.

Top of funnel: AI-assisted satisfied and media (80% of marketers use AI for satisfied innovation and 75% for media production, per HubSpot’s 2026 State of Marketing) and visibility in AI answers.
Middle of funnel: Personalized email and nurture sequences from CRM data. HubSpot’s Agent Hub includes agents akin the Campaign Agent and Nurture Agent for multi-step sequences.
Bottom of chimney and beyond: Research and handoff assistance for sales, affirmative AI-assisted service. Use lone capabilities documented for your type of Marketing Hub, Sales Hub, or Service Hub.

Agent use is growing: over 85% of surveyed promotion departments use AI agents, and 77% use at smallest two. More automation method additional places for an unmeasured error to compound.

Start measure alongside the lead nurturing metrics you already track, afterward add assessment time, error rate, and disbursal per output. To see how your numbers compare, get the 2026 State of AI in Marketing Report.

How do we demonstrate lift without complex data science?

Before and after: Compare a fixed duration before initiate alongside an equal duration after. Good for quick reads, but note seasonality and another changes.
Phased rollout: Launch to one region, segment, or squad archetypal and difference it alongside those not yet live.
Holdout: Keep a random 10% to 20% of an spectators on the old workflow. This is the cleanest choice for email and nurture.

Attribution has limits. Marketing influences deals through many touches, so a lift in one metric doesn’t demonstrate the AI workflow caused a income change. Report lift against the metric the workflow targets, run tests lengthy adequate to gentle out noise, and call results directional until a test repeats.

Your Next Moves on AI Maturity

Assess your use cases alongside the six-dimension index.
Find the binding constraint, your lowest-scoring dimension.
Apply Loop Marketing alongside the archetypal finest stage for your stage.
Activate alongside CRM-connected AI.
Measure alongside before-and-after comparisons, phased rollouts, or holdouts.
Iterate and reassess all quarter.

Teams at the top of the curve attack MQL and CAC targets at higher rates, and McKinsey’s data shows the small collection of elevated performers pairs AI alongside redesigned workflows, guidance commitment, and defined measurement. Start alongside one workflow, one owner, and one metric.

Ready to benchmark your team? Download the 2026 State of AI in Marketing Report.

Frequently Asked Questions concerning AI Maturity

How lengthy does it obtain to move up an AI maturity level?

It depends on your binding constraint, and no published benchmark gives a dependable timeline. As a preparedness assumption, anticipate one or two quarters per flat for a sole workflow whenever the constraint is procedure (ownership, documentation, measurement). Data and governance constraints obtain longer since they affect another teams.

Who should own the AI maturity roadmap?

A promotion operations or income operations chief should own the day-to-day roadmap, alongside a elder promotion chief as sponsor. Governance needs shared ownership alongside IT, security, and legal. Name one owner per workflow.

How can we measure advancement without advanced analytics?

Track three things: your indicator mark by size all quarter, one outcome metric per AI workflow, and the portion of workflows alongside documented evidence. Use before-and-after comparisons or a uncomplicated holdout. A spreadsheet is adequate to start.

How do we forestall AI efforts from becoming siloed?

Keep one shared inventory of AI use cases throughout marketing, sales, and service, and run them from one origin of client data. Hold a monthly assessment anywhere owners portion results and reuse prompts, workflows, and policies.

When should we employ versus upskill for AI maturity?

Upskill whenever your binding constraint is a accomplishment your current squad can study inside a fourth or two, specified as prompting, tool fluency, workflow design, or reviewing AI output. Hire whenever the constraint is a specialized accomplishment you can’t build quickly, specified as data engineering, promotion operations, or AI governance, or whenever no one has the capability to own the work.

Check your lowest-scoring size in the AI maturity indicator first. If it points to a group gap, equivalent the fix to how accelerated you need to move.

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