28 October 2026 | Marketing Technology
AI marketing productivity has become a major selling point for software companies. Marketing teams can now generate emails, social media posts and campaign images in seconds. Yet producing content faster does not necessarily mean campaigns reach customers any sooner.
Nick Millward, founder and CEO of UK customer engagement platform Lyberate, believes the marketing industry is concentrating on the wrong problem.
His argument is that the biggest productivity losses often happen after the creative work is finished, when teams must turn campaign ideas into messages that reach customers across several channels.
It raises a question for businesses investing in artificial intelligence: are they improving marketing performance, or simply producing more material for their teams to manage?
The Hidden Productivity Problem in Marketing
Creating a campaign is only one part of a marketer’s job.
Once an idea receives approval, teams must prepare content for different platforms, check customer data, coordinate delivery schedules and measure results.
What starts as a single campaign can quickly become several separate projects.
An email needs a subject line and responsive layout. A WhatsApp message needs different formatting and a suitable call to action. Social media content requires platform-specific assets, while RCS messaging introduces another set of technical requirements.
The same campaign may also need approval from brand, legal, compliance and senior management teams.
Each stage creates opportunities for delays.
Millward argues that these operational demands deserve more attention than the speed of AI-generated content.
“The AI conversation in marketing has been dominated by content generation. Can it write the email? Can it create the image? Can it produce a social post? Those things matter, but they are not where most marketing teams lose their time.”
His comments come as marketers face growing pressure to produce more campaigns without comparable increases in staffing or budgets.
Research Shows Where Marketing Teams Lose Time
Independent research supports the argument that campaign production remains a major drain on marketing resources.
Knak’s 2026 Marketing Production in the Age of AI report surveyed 333 enterprise marketing decision-makers across the US, UK and Canada.
It found that 82% of enterprise marketing teams spend at least half their time on production activities, including building, reviewing, approving and sending campaigns, rather than strategy or planning.
The report also found that 70% had deployed AI in their production workflows, while 64% used it to generate first-draft email or landing page copy.
This suggests that AI adoption alone has not removed the operational workload.
The wider picture is similar.
The March 2026 CMO Survey found that AI use had grown from 13.1% of marketing activities in 2024 to 24.2% in 2026.
However, the survey also identified technology integration, limited time and insufficient resources as barriers to getting more value from marketing technology.
These findings point to a gap between acquiring new tools and changing how campaigns actually get delivered.
Where AI Could Make the Biggest Difference
| Marketing activity | Common bottleneck | Potential role for AI |
|---|---|---|
| Campaign preparation | Repeating briefs across teams | Generate channel-specific briefs from one approved campaign |
| Content adaptation | Manually rewriting material | Prepare drafts suited to different channels |
| Brand approvals | Repeated checks and revisions | Flag potential inconsistencies before human review |
| Customer segmentation | Data spread across platforms | Help identify relevant audiences using approved data |
| Campaign scheduling | Coordinating separate systems | Assist with delivery planning and workflow management |
| Performance reporting | Exporting and combining results | Prepare consolidated reports and identify patterns |
Not every task should be automated. However, reducing repetitive work could give marketers more time to develop strategy, test campaigns and improve customer experiences.
Why More AI Content Can Create More Work
Generative AI reduces the time needed to produce an initial draft. But it can also increase the amount of material requiring review.
A marketer who previously wrote three campaign variations may now generate 20.
Someone must still decide which versions are suitable, accurate and relevant.
Without clear approval processes, faster content production risks moving the bottleneck rather than removing it.
This is particularly important for businesses operating across several markets or regulated industries.
A campaign may require different legal wording, product information or customer permissions depending on where it appears.
AI can help identify potential problems, but it should not become the final authority on compliance.
The distinction is between using AI to accelerate individual tasks and using AI to improve the complete campaign process.
Boston Consulting Group’s June 2026 CMO Survey illustrates how far the industry still has to go. Although 96% of surveyed CMOs said AI was driving end-to-end transformation, BCG found that only about a third had carried out that level of change.
Many organisations were still using generative AI to assist with isolated tasks rather than redesigning their marketing operations.
Campaign Execution Is Becoming a Marketing Technology Priority
Lyberate is positioning itself around this problem.
The company launched its AI Operating System for Customer Engagement in September 2026, describing a platform designed to automate repetitive work involved in preparing and delivering customer communications.
Its focus includes email, social media, WhatsApp and Rich Communication Services (RCS).
Rather than treating each channel as a separate project, the company aims to help marketers adapt content and coordinate activity across multiple systems.
Millward sees that as a more useful application of AI than producing additional copy.
“The real drag is what happens after the idea exists. Someone has to adapt it for each channel, brief different people, chase approvals, copy information between systems, check consistency and make sure the right message reaches the right audience at the right time.”
The commercial opportunity is clear. Businesses already pay for customer relationship management systems, email platforms, analytics software and campaign management tools.
Yet those products do not always work together smoothly.
Adding another AI application will not automatically solve that problem. The value depends on whether it removes duplicated work and connects existing processes.
Lyberate is one company pursuing this approach, but the wider challenge applies across the marketing technology industry.
Claims about faster execution should also be tested against real campaign results, rather than demonstrations or estimates of time saved.
WhatsApp, RCS and the Challenge of Managing More Channels
Marketing teams increasingly have more ways to communicate with customers.
Email remains important, but businesses can also use social media, SMS, WhatsApp and RCS to support customer engagement.
Each channel brings different expectations.
A promotional email might contain detailed information and several calls to action. A mobile message is often better suited to a shorter, more immediate interaction.
A customer may encounter both messages during the same purchasing journey.
The challenge is making those communications work together without becoming repetitive or intrusive.
Millward argues that businesses must stop treating individual channels as disconnected activities.
“Customers do not experience a brand in neat departmental silos. They may see a social post, receive an email, open a WhatsApp message and visit a website, all within a few hours.”
However, a connected customer experience requires more than sending consistent messages.
Marketers must understand which channels customers prefer, what they have already received and whether another message serves a useful purpose.
AI could help coordinate those decisions. It also introduces the risk of automating unwanted communications at a greater scale.
Customer Consent Must Remain a Priority
The Information Commissioner’s Office makes clear that UK electronic direct marketing is subject to the Privacy and Electronic Communications Regulations (PECR).
Businesses generally need consent before sending unsolicited electronic marketing to individual subscribers, unless an applicable exception applies. Different rules can apply to corporate subscribers.
Consent must also be sufficiently specific to the communication method when consent is required.
This matters when businesses expand from email into mobile messaging.
Permission to receive one type of communication should not automatically be treated as permission for every other channel.
For Millward, the customer relationship must remain more important than the technology.
“The opportunity with channels such as WhatsApp and RCS is huge, but it should never become an excuse to be more intrusive.”
Automated communications should therefore include appropriate consent checks, suppression rules and straightforward ways to opt out.
How Should Marketers Measure AI Productivity?
One weakness in the AI marketing debate is the tendency to measure output rather than outcomes.
Generating 50 social posts instead of ten is easy to quantify. Determining whether those extra posts improved business performance is more difficult.
Marketing leaders need to measure whether AI improves campaign delivery without reducing quality.
Useful measures include:
- Time from approved brief to launch: Is the complete campaign taking less time?
- Approval cycles: Are campaigns passing through fewer rounds of amendments?
- Manual production hours: Has repetitive work decreased?
- Campaign error rates: Are teams making fewer mistakes?
- Customer engagement: Are messages generating more meaningful responses?
- Conversion performance: Are campaigns producing stronger commercial results?
These measures can also reveal whether an AI system has simply transferred work from one department to another.
For example, faster copy generation offers limited savings if compliance teams spend longer reviewing the output.
A useful test would compare similar campaigns before and after automation, accounting for audience size, channels and campaign complexity.
The aim should be better delivery and stronger results, not simply more activity.
What This Means for Marketing Leaders
AI is already changing marketing production. The next challenge is making those gains useful across the entire customer journey.
For marketing leaders, that means examining how teams work before investing in additional software.
A campaign workflow audit can expose repeated approvals, unnecessary data transfers and tasks that employees perform manually across several systems.
Some improvements may require AI. Others may simply need better processes, clearer responsibilities or stronger integration between existing platforms.
The important distinction is that technology should solve an identified problem.
Millward believes the most valuable applications may be those customers never notice.
“The best use of AI will be the least visible to the customer. It will sit behind the scenes, removing repetitive tasks and helping teams respond faster, while marketers stay responsible for the strategy, the brand and the relationship.”
That offers a useful counterpoint to the growing volume of AI-generated marketing content.
The future of AI marketing productivity may depend less on how quickly companies can create campaigns and more on how reliably they can deliver relevant communications to the right customers.
For businesses, the measure of success is not how much content AI produces. It is whether marketing teams achieve better results with less unnecessary work.
Frequently Asked Questions
What is AI marketing productivity?
AI marketing productivity refers to using artificial intelligence to improve the efficiency and effectiveness of marketing work. This includes content creation, campaign coordination, customer segmentation, reporting and workflow automation.
How can AI improve campaign execution?
AI can help prepare channel-specific material, organise campaign workflows, flag potential errors and combine performance data. Its value depends on how well it works with a company’s existing systems.
What is the difference between AI content generation and marketing automation?
AI content generation produces text, images or other creative material. Marketing automation manages activities such as customer journeys, campaign scheduling and message delivery. The two can work together, but they address different tasks.
Can AI replace marketing teams?
AI can reduce repetitive tasks, but marketing teams still need human judgement for strategy, brand decisions, customer relationships and compliance. Its impact on staffing will depend on how organisations use the technology.
Sources and Further Reading
- Lyberate — company background and customer engagement platform.
- Knak: Marketing Production in the Age of AI — 2026 survey of 333 enterprise marketing decision-makers.
- The CMO Survey, March 2026 — marketing technology and AI adoption.
- Boston Consulting Group: Agentic Marketing Transformation — June 2026 CMO research.
- ICO: Electronic and Telephone Marketing — UK direct marketing guidance.
Nick Millward’s comments are drawn from a Lyberate statement issued on 28 October 2026.