From generating advertisements in minutes to personalising campaigns at scale, artificial intelligence is changing how marketing teams work. Yet recent research points to a growing gap between faster execution and measurable business impact, raising questions about whether higher output is translating into better creativity, customer experiences and returns.
A campaign that once took weeks to develop can now move from brief to execution in days. Marketing teams can generate dozens of advertisements, translate content into multiple languages, analyse customer feedback and prepare campaign reports in a fraction of the time previously required.
Artificial intelligence has made these processes faster. Whether it has made the resulting marketing more effective is becoming a different question.
For brands, the distinction matters. Producing more content does not necessarily mean reaching more relevant audiences. Faster personalisation does not guarantee a better customer experience, while automating campaign optimisation does not automatically translate into stronger revenue growth.
Recent industry research suggests that although AI adoption and productivity gains are becoming widespread, measurable organisational returns remain uneven.
McKinsey’s State of AI 2026 survey, covering 1,719 respondents across 97 countries, found that 80% reported improvements in individual productivity from AI, while half said the technology supported better decision-making. However, only 37% reported a positive contribution to organisational earnings before interest and taxes (EBIT).
Just 6% qualified as AI high performers under McKinsey’s criteria, which included significant organisational impact and an EBIT contribution of at least 5%.
Although the findings cover industries beyond marketing, they highlight a wider challenge for businesses investing in AI: improvements in individual tasks do not necessarily produce equivalent gains at the organisational level.
For marketing leaders, this raises questions about how success should be measured. Is AI delivering stronger campaigns, better customer engagement and incremental sales, or primarily reducing the time required to produce existing marketing activities?
The answer is beginning to emerge across creative production, marketing operations, personalisation and performance measurement.
Faster Campaigns, But the Same Marketing Problems?
One of AI’s most immediate advantages has been its ability to automate repetitive marketing tasks.
Copywriting, campaign reporting, audience segmentation, creative adaptation and research summaries are increasingly supported by generative AI tools. Tasks that previously required multiple rounds of manual work can now be completed more quickly, allowing teams to manage larger volumes of content and campaign activity.
However, accelerating individual processes does not necessarily improve the wider marketing operation.
Boston Consulting Group’s 2026 survey of 300 chief marketing officers found that 96% reported AI-driven transformation across marketing functions. Yet 42% were still primarily using generative AI to assist employees with individual tasks across a limited number of workflows.
Only 8% reported running certain campaigns in which multiple AI agents operated autonomously.
Investment was also increasing. The proportion of CMOs reporting annual marketing AI investments exceeding $15 million rose from 28% to 43%.
The findings suggest that organisations are committing substantial resources to AI even as many continue to operate through largely conventional marketing processes.
For instance, an AI writing tool can generate campaign copy quickly, but the work may still pass through lengthy approval cycles. An analytics platform can prepare performance reports automatically, but the information may remain disconnected from sales, customer service or broader business objectives.
Similarly, AI can create multiple advertising variations without addressing whether the original campaign proposition is relevant to the audience.
BCG identified 42% of surveyed CMOs as being at risk of falling behind because they had achieved productivity improvements without sufficiently transforming their underlying operating models or technology systems.
“Investment must now move beyond individual AI tools, and towards fully connected agentic operating systems built on strong data foundations,” said Mark Abraham, Managing Director and Senior Partner at BCG.
The distinction is between using AI to accelerate existing processes and redesigning how marketing decisions are made.
A campaign that takes three days instead of three weeks represents an operational improvement. But whether it produces stronger engagement, customer acquisition or revenue depends on factors beyond production speed.
More Advertisements, But Are Consumers Seeing Better Creativity?
The growing availability of generative AI is also changing the economics of creative production.
Brands can now produce large numbers of advertising variations, adapt campaigns for different platforms and develop content for multiple audience segments without proportionately increasing production resources.
This has made content creation more accessible and scalable. It has also raised concerns about originality, consistency and creative quality.
Canva’s 2026 State of Marketing & AI research, conducted with The Harris Poll among 1,415 marketing leaders and 3,547 consumers across seven countries, including India, found that 97% of marketing leaders were using AI in their daily creative work.
Nearly all respondents, 99%, planned to increase their AI investment.
However, 41% of marketing leaders identified low-quality AI-generated content as a challenge.
Consumer responses revealed a similar tension. Seventy per cent said they could usually identify AI-generated advertising because they felt something was missing, while 87% believed the best advertising still required human involvement.
At the same time, 68% said they were comfortable with AI being used in advertising when it made content more helpful or relevant.
These findings indicate that consumer concerns are not necessarily directed at the technology itself. The perceived quality and usefulness of the resulting advertising remain important.
Gartner’s June 2026 findings reinforced this concern. In a survey of 307 US consumers conducted in March, 49% said generative AI had made the quality of available content worse. The proportion increased to 57% among Gen Z and millennial respondents.
“AI-generated content is increasing the volume of media that consumers encounter, but not necessarily the value,” said Kate Muhl, VP Analyst at Gartner.
For brands, this creates a challenge around creative differentiation.
Consider a beverage company developing a summer campaign. AI can generate multiple headlines, product visuals and social media captions around the same proposition. However, if the underlying message lacks originality or fails to connect with consumer behaviour, producing additional variations may do little to improve its effectiveness.
The same problem can emerge when brands use similar prompts, templates and visual styles across competing campaigns.
Greater production capacity may help marketers test more ideas, but it also increases the importance of deciding which ideas are worth pursuing.
Creative evaluation, brand consistency and audience understanding therefore remain relevant even when execution becomes substantially faster.
The challenge is no longer limited to whether marketing teams can produce enough content. It increasingly involves identifying which content is distinctive, relevant and capable of influencing consumer behaviour.
Personalisation Is Growing, But Customer Data Remains a Weak Link
The difference between faster execution and better outcomes becomes particularly visible in personalisation.
AI allows marketers to create messages for different customer groups, recommend products, adjust campaign content and respond to behavioural signals at scale.
However, these capabilities depend heavily on the quality of customer information available to the organisation.
Salesforce’s 2026 India findings, based on 250 marketing decision-makers within a wider global survey of 4,450 respondents, found that 81% of Indian marketers had adopted AI.
The research also found that 83% needed more personalised content than they could currently produce, while 81% were turning to AI to address that requirement.
Despite growing adoption, 98% of Indian marketers reported barriers to personalisation, including privacy concerns, poor data quality and limited technical expertise.
Another 71% struggled to respond promptly to customers because they lacked sufficient context.
Access to customer information also remained fragmented. Only 60% reported complete access to service data, 61% to sales data and 58% to commerce data.
These gaps matter because personalisation depends on more than generating different versions of a message.
A retailer, for example, may use AI to recommend products based on a customer’s previous browsing activity. But if its systems do not reflect a recent purchase, the customer could continue receiving advertisements for an item already bought.
Similarly, a bank could use AI to prepare personalised offers without having a complete view of a customer’s recent service complaints or changing requirements.
In both situations, the content may be generated efficiently, but the customer experience may remain disconnected.
“The biggest barrier to personalisation today isn’t AI, it’s the quality and connectedness of the data that powers it,” said Nishant Kalra, Vice President, Sales, Salesforce South Asia.
The findings highlight a limitation of treating AI adoption as an indicator of marketing maturity.
A company may have advanced content-generation capabilities while continuing to operate with disconnected customer records, inconsistent information and limited coordination between departments.
AI can accelerate the delivery of a personalised message. Whether that message is appropriate depends on the data and decisions behind it.
When Does Faster Marketing Become Better Marketing?
As AI becomes more deeply embedded in marketing operations, organisations are beginning to reconsider the indicators used to evaluate its contribution.
Time saved, content generated and campaigns launched are relatively straightforward measures of productivity.
Marketing effectiveness is more complicated.
It requires examining whether campaigns improve customer acquisition, conversion, retention, brand consideration or incremental revenue.
McKinsey’s research found that nearly three-quarters of AI high performers were fundamentally redesigning workflows around the technology, compared with approximately one-quarter of other respondents.
These organisations were also more likely to pursue innovation and growth alongside efficiency improvements.
BCG’s marketing-specific findings suggest that measurable commercial benefits are beginning to emerge, although adoption remains uneven.
Among surveyed CMOs, 31% of B2C leaders and 20% of B2B leaders said their agentic marketing transformation was already delivering significant, measurable revenue impact.
The findings suggest that AI’s commercial contribution may depend partly on how organisations integrate the technology into decision-making and operating processes.
For marketing teams, this changes the questions surrounding return on investment.
Rather than measuring only the number of hours saved in producing advertising assets, organisations can examine whether those assets improve campaign performance.
Instead of evaluating personalisation through the number of customer segments reached, marketers can assess whether the resulting interactions improve engagement, conversion or retention.
Creative teams can also compare AI-supported campaigns with conventional approaches through controlled testing, using measures such as attention, brand recall, purchase intent and incremental sales.
Such comparisons are important because faster production and improved effectiveness are not mutually exclusive. AI may contribute to both, but the benefits need to be measured separately.
There are also differences between industries, organisations and marketing activities. AI-generated product descriptions may deliver substantial operational value in ecommerce, while brand campaigns built around emotional storytelling may require more extensive creative development and human review.
The existing research does not establish that AI-generated marketing is universally less effective than human-produced work. Nor does it demonstrate that productivity improvements consistently translate into higher revenue.
Instead, it points to an uneven transition in which adoption and operational efficiency have advanced faster than the systems used to assess commercial and creative outcomes.
For CMOs, the next stage of AI investment is therefore likely to involve more than introducing additional tools.
It will require stronger customer data, clearer performance measures, more consistent creative evaluation and better integration between marketing activity and business results.
AI has already changed how quickly marketing teams can work. The remaining question is how much of that additional capacity translates into stronger customer relationships and measurable growth.
The distinction is becoming increasingly important: producing marketing faster is now easier to demonstrate. Proving that the marketing itself has become better remains the more demanding test.
Disclaimer: All data points and statistics are attributed to published research studies and verified market research. All quotes are either sourced directly or attributed to public statements.