SAP Emarsys research found that 92 percent of marketing professionals used AI in everyday work. The study covered more than 10,000 consumers and 1,250 marketers, revealing a widening divide between operational benefits and customer confidence.
Seventy-one percent of marketers said AI accelerated campaign launches by more than two hours on average, while 72 percent gained more time for creative and strategic work. Sixty percent reported higher engagement and 58 percent stronger loyalty.
Consumers were less positive. Forty percent felt brands did not understand them, up from 25 percent the previous year, and 60 percent described most marketing emails as irrelevant. Global distrust of AI handling personal data rose from 44 to 63 percent.
Useful experiences still attracted support: 55 percent said AI made shopping easier and 53 percent said it made it faster. The challenge was to connect data use to clear value, transparency and control rather than assuming more personalisation would automatically create relevance.
The development should be read in its original publication context. It described the evidence, forecasts and vendor claims available at that time; later outcomes should not be projected backwards into promises that had not yet been tested.
A useful assessment separates demonstrated functions from proposed scale. It compares the new approach with a realistic baseline, including manual work, waiting time, existing infrastructure and the cost of correcting errors.
Long-term value requires more than launch performance. Data, models, organisations and external rules change, so version records, periodic checks and a named owner are needed to detect drift and decide whether the original case still holds.
Distribution also matters. An average gain can conceal predictable weakness for a language, site, workload or user group. Breaking results down and inviting feedback from people closest to the process makes expansion decisions more defensible.
Before scaling, leaders should map the complete delivery chain. Data sources, interfaces, cloud services, suppliers and human review can each become the limiting factor, even when the most visible model performs well. That map also exposes concentration, security and recovery requirements that a product demonstration rarely shows.
Financial analysis should follow outcomes rather than activity. More generated content, processed alerts or automated steps is not automatically more value. Completed tasks, avoided incidents, lower correction effort and improved service quality give decision-makers a stronger basis for continuing investment.
Reversibility is equally important in a fast-moving market. Portable records, documented interfaces and clear stop criteria let an organisation change provider, narrow a deployment or return a decision to people when performance, pricing or policy changes. That flexibility is practical resilience, not a lack of commitment.
Efficiency Improved Faster Than Relevance
Campaign teams saved time and reported commercial gains, but customers experienced more irrelevant communication. The contrast suggested that production speed could increase the volume of weak targeting as easily as it improved quality.
The practical importance was that success measures needed to include opt-outs, complaints and long-term satisfaction rather than only send frequency, clicks or short-term conversion. A credible evaluation would connect the claim to representative outcomes, record exceptions and human intervention, and identify who could act when conditions changed. It should compare different workloads or user groups and document the resources required to sustain the result, preventing one headline metric from substituting for operational evidence. Teams would also need a baseline, a review schedule and a defined threshold for pausing or changing the system. Those controls would make progress measurable while preserving room to respond when costs, risks or user needs diverged from the original assumptions.
Data Distrust Became the Central Constraint
Nearly two-thirds of consumers globally did not trust AI with their data, with unease even higher in the UK. That sentiment threatened the information flows used to train and operate personalisation systems.
The practical importance was that brands needed plain explanations, meaningful consent choices, minimisation and retention limits that customers could verify through their experience. A credible evaluation would connect the claim to representative outcomes, record exceptions and human intervention, and identify who could act when conditions changed. It should compare different workloads or user groups and document the resources required to sustain the result, preventing one headline metric from substituting for operational evidence. Teams would also need a baseline, a review schedule and a defined threshold for pausing or changing the system. Those controls would make progress measurable while preserving room to respond when costs, risks or user needs diverged from the original assumptions.
Regulation Changed Marketing Practice
More than a third of UK marketers had revised their AI approach after the EU AI Act, and 44 percent said their use had become more ethical. Some also worried that rigid requirements could reduce experimentation.
The practical importance was that practical guidance, risk-tiered controls and reusable approval processes could protect customers without making responsible campaigns prohibitively slow. A credible evaluation would connect the claim to representative outcomes, record exceptions and human intervention, and identify who could act when conditions changed. It should compare different workloads or user groups and document the resources required to sustain the result, preventing one headline metric from substituting for operational evidence. Teams would also need a baseline, a review schedule and a defined threshold for pausing or changing the system. Those controls would make progress measurable while preserving room to respond when costs, risks or user needs diverged from the original assumptions.
Helpful Utility Offered a Route to Trust
Consumers welcomed assistance with discovery, comparison and gifts when it saved time. City Beach used lifecycle predictions to win back 48 percent of a targeted group within three months, illustrating a defined customer problem.
The practical importance was that the strongest deployments would show why a recommendation appeared and allow people to correct preferences rather than inferring endlessly from hidden behavioural data. A credible evaluation would connect the claim to representative outcomes, record exceptions and human intervention, and identify who could act when conditions changed. It should compare different workloads or user groups and document the resources required to sustain the result, preventing one headline metric from substituting for operational evidence. Teams would also need a baseline, a review schedule and a defined threshold for pausing or changing the system. Those controls would make progress measurable while preserving room to respond when costs, risks or user needs diverged from the original assumptions.
Across the source report, technical capability was only one part of the system. Skills, access, integration, governance, cost and affected people could each determine whether the promised value survived outside a demonstration.
The durable lesson is to preserve traceability and choice. Organisations should document assumptions, keep consequential outputs reviewable, monitor downstream effects and retain a realistic exit route. Those controls allow ambitious AI work to improve without making early momentum an irreversible commitment.
