B2B Marketing Teams Use AI but Haven’t Adapted Their Workflows

AI tools are now used across most B2B marketing organisations, but the structures responsible for planning, approving and measuring campaigns have changed much less. New research from Forrester and Gartner suggests that companies have invested in technology before deciding how marketing departments need to operate.

Eighty-eight percent of B2B marketing organisations surveyed by Forrester had adopted AI tools or developed their own by mid-2026. The figure comes from research involving more than 1’000 B2B marketing decision-makers, published in August as part of Forrester’s examination of marketing organisations and their capabilities.

The researchers found that many companies were still struggling with unclear AI strategies, incomplete data infrastructure and difficulties measuring the commercial contribution of the technology.

A separate Gartner report published on 1 October found that 57 percent of B2B chief marketing officers considered AI integration an urgent priority. Gartner identified a tendency to approach integration through technology adoption rather than changes to organisational structures and responsibilities.

Marketing departments can now generate content, prepare campaign materials and analyse data with tools that were unavailable only a few years ago. The underlying work, however, continues to depend on established divisions between marketing, sales, IT and external service providers. Those divisions become harder to maintain when an AI system performs activities previously assigned to several teams.

More Output, Without a Clearer Operating Model

The immediate attraction of generative AI has been its ability to shorten production work. Marketers can prepare an initial article, translate campaign copy or develop presentation materials without commissioning each task separately.

Forrester’s September 2026 report on content creation and optimisation found that AI had become a mainstream capability in marketing departments. Organisations were continuing to examine specialist content systems alongside general-purpose enterprise AI assistants.

The availability of these tools creates overlapping capabilities. A CRM platform can generate customer communications, a content-management system can prepare text and images, and a separate AI assistant can produce similar material without direct access to the organisation’s campaign records.

Departments then have to decide which system owns the approved version, where the content is stored and how changes move through the publication process.

A B2B company producing technical marketing materials has additional requirements. Product claims may need confirmation from engineers, legal teams or product managers. Customer references require permission, while pricing, availability and service conditions can change before the material reaches the intended audience.

AI can accelerate the initial production of these materials without reducing the work involved in verifying their accuracy. If a company produces three times as many drafts but retains the same approval procedures, the review stage can become a more substantial constraint. Marketing leaders need to assess the entire production process rather than the time saved during a single task.

Sales and Marketing Still Work with Different Information

The organisational problem becomes more pronounced in B2B businesses with long purchasing cycles.

Marketing teams commonly measure website traffic, campaign responses, leads and engagement. Sales departments concentrate on qualified opportunities, commercial negotiations and revenue. An AI system working with marketing data may have limited knowledge of what happened after a prospect entered the sales process.

An automated lead-scoring tool can identify a company whose employees have downloaded several reports. That activity does not establish whether the organisation has an approved budget, an active project or the authority to purchase the supplier’s services.

Sales teams hold part of that information in conversations and account records. Connecting it with campaign activity requires consistent data structures, shared definitions and disciplined use of the CRM system.

The technology can make existing inconsistencies more visible. Two departments using different definitions of a qualified lead will continue to report different results even when they employ the same AI platform.

Forrester’s 2026 research identified difficulties with marketing measurement and data infrastructure alongside the rapid adoption of AI. Those findings suggest that purchasing additional software does not automatically solve the underlying reporting problems.

Companies introducing AI into customer acquisition need to establish which interactions indicate a genuine commercial opportunity and how those signals will be confirmed by sales.

The Skills Requirement Has Changed

Marketing teams have traditionally combined specialists in content, advertising, design, analytics, customer research and campaign operations. AI tools now perform parts of several functions, although their output still depends on the information provided and the instructions used.

A content specialist working with AI needs to verify claims, assess sources and preserve the company’s editorial standards. Campaign managers increasingly need to understand data connections, automated decision rules and the behaviour of software agents. Analysts must examine whether the results generated by AI tools reflect the underlying business.

Gartner’s October research calls for organisational changes to accompany AI integration, identifying the limitations of adopting the technology without adjusting the way marketing teams work. That requires managers to examine responsibilities rather than merely add AI proficiency to existing job descriptions.

A department using AI to produce research-based articles needs someone accountable for the accuracy of the final publication. A team automating campaigns needs clear authority over customer selection, budget limits and approval conditions. When a system produces an error, the company must know who can stop the process and correct it.

These responsibilities often cross departmental boundaries. Marketing may own the campaign, IT may administer the platform, legal staff may approve claims and a specialist agency may configure the automation.

The organisation needs a working process through which those participants can make decisions without repeatedly referring routine questions to senior management.

What Happens to Agency Relationships?

The growth of AI has also changed the work companies expect from external marketing providers. Businesses that previously commissioned large volumes of basic content can now generate initial material internally. Agencies are responding by incorporating AI into research, production, translation, analytics and campaign management.

Clients still need work that depends on understanding their markets, products and commercial objectives. A financial institution preparing a product launch requires accurate documentation, regulatory review and communication tailored to its intended audience. An industrial supplier entering a new country needs research into customers, competitors and purchasing arrangements.

AI can support these activities, but much of the information is held within the business or must be established through research. The financial arrangements between agencies and clients also come under scrutiny. A fee based largely on production hours becomes harder to justify when software shortens the production stage. Projects can instead be evaluated against their scope, complexity, quality requirements and commercial results.

A faster production process can also change what clients expect. When an agency delivers an initial draft sooner, the client may assume that the entire project can be completed sooner, even though technical approvals and customer decisions still take time.

Both parties need to distinguish between activities accelerated by software and those dependent on information, judgement or approval from people.

Measuring the Work Rather Than the Tools

An AI adoption report can show how many employees use a particular application, how often they access it and how much content they generate. These figures establish the extent of use, but companies need other information to assess whether their marketing performance has improved.

A B2B organisation can measure the time required to launch a campaign, the cost of producing approved material, the proportion of enquiries accepted by sales and the revenue associated with resulting opportunities.

It must also account for corrections, unsuccessful campaigns and the cost of maintaining the technology. The comparison becomes more informative when departments examine processes that existed before AI adoption. An improvement in campaign preparation time is easier to establish when the organisation has reliable records of its earlier performance.

Gartner’s finding that a majority of B2B CMOs now treat AI integration as urgent coincides with evidence that adoption has already become widespread. The next phase of investment will take place inside organisations where many employees are already using the tools.

Marketing departments now have to determine how that work is commissioned, reviewed, measured and connected to sales. The technology has changed the amount of material they can produce; their financial results will depend on what they do with it.