AI may transform media planning, but media planner remains indispensable

In today’s increasingly competitive and fragmented business environment, media planning has become far more than deciding where and when to place advertisements. It is a critical business function that connects marketing investment with business growth by identifying the right audiences, selecting the most relevant channels, managing budgets effectively and balancing immediate performance with long-term brand building. The quality of media planning can directly influence whether a business builds awareness, creates demand, increases penetration, protects customer loyalty or wastes resources by pursuing the wrong objective. This makes the discussion around Artificial Intelligence and the future of media planning especially important. 

Artificial Intelligence (AI) is rapidly changing the way media planning is performed. Tasks that once required several days of data collection, calculation and manual analysis can now be completed within minutes or less. AI can process large volumes of audience data to identify patterns, forecast performance, optimise budgets and even recommend an effective combination of media channels.

This poses an important question: Will AI eventually replace the media planner?

The answer is no or at least not in the forcible future.

AI may replace many of the tasks traditionally performed by media planners, but it cannot completely replace the multi-faceted role that media planners play. This is because media planning is not simply solving a mathematical problem. It is a strategic decision-making process that often requires human judgement, contextual understanding, creativity and the ability to connect business problems with consumer behaviour.

So, this leads us to ask the question: What can AI actually replace?

A significant portion of traditional media planning involves repetitive and data-intensive work. These are areas where AI can perform faster and, in many cases, more accurately than humans. For example, AI can analyse audience reach, frequency, duplication and cost efficiency across multiple channels. It can evaluate thousands of possible budget combinations and recommend an allocation that is likely to produce the highest return. It can also forecast media inflation, identify underperforming placements and adjust campaigns based on real-time results.

In programmatic and digital advertising, AI already plays a central role in audience targeting, bidding, placement selection and campaign optimisation. Machine-Learning (ML) models can identify which audiences are more likely to respond, which messages perform better and which platforms are producing stronger outcomes.

AI can therefore replace or significantly reduce the time required for:

nData collection and organisation

nAudience profiling and segmentation

nReach and frequency calculations

nMedia cost comparisons

nBudget allocation and optimisation

nCampaign performance monitoring

nPredictive modelling and forecasting

nStandard reporting and dashboard preparation

These capabilities will make media planning faster, more efficient and increasingly evidence based. 

However, efficiency is not the same as strategy.

AI works with data — a media planner works with meanings

AI is excellent at identifying patterns in available data. But it does not automatically understand and explain why those patterns exist or whether they are strategically important.

For example, an AI model may identify that a particular television program provides the lowest cost per rating point. A good media planner may still decide not to prioritise it because the program environment does not match some important attributes such as brand’s personality, creative idea and emotional context in which the message should be received.

Similarly, an algorithm may recommend concentrating the budget on platforms that provide immediate conversions. But a media planner may recognise that the brand also needs long-term salience, cultural relevance and future demand creation.

AI can tell us what is happening. A planner must interpret what and how it means for the brand.

Media strategy begins with the business problem

Before selecting channels, audiences or media weights, someone must define the real problem the brand is trying to solve. That is: Is the objective to increase penetration? Build awareness? Encourage product usage? Protect loyalty? Enter a new market? Attract younger consumers? Change a deeply established perception?

The above questions cannot be answered by media data alone.

The same set of media numbers can lead to very different strategies depending on the business context. A category leader seeking to defend its market share requires a different approach from a new entrant searching for better visibility. A low-involvement FMCG brand cannot be planned in the same way as a bank, automobile or telecommunications brand.

AI can optimise against an objective, but the media planner must decide whether it is the right objective.

Optimising the wrong objective simply allows a brand to make the wrong decision more efficiently.

Human judgement is essential

Media decisions are often made with incomplete, imperfect or conflicting information. Research data may be outdated. Digital platforms may use different metrics. Competitive activity may change unexpectedly. Consumer behaviour may shift because of economic, social or cultural developments.

In such situations, the planner must use experience and judgement.

A strong planner knows when to trust the data, when to challenge it and when to search for additional evidence. The planner also understands that not everything valuable can be measured immediately.

Brand associations, cultural impact, word of mouth, program context, consumer trust and emotional relevance are difficult to capture through a single metric. Yet these factors can strongly influence the long-term success of a campaign.

AI can calculate probabilities. Human planners must take responsibility for decisions.

Creativity cannot be reduced to an optimisation solution

Media planning is also a creative discipline.

The most powerful media ideas do not always emerge from selecting the cheapest channel or the most efficient placement. They come from finding a meaningful connection between the brand, the consumer, the message and the moment.

A media planner may identify a television program that naturally reflects the brand’s purpose. The planner may create a partnership that turns a normal sponsorship into a cultural conversation. The planner may recognise an overlooked occasion, location or behaviour that allows the brand to enter consumers’ lives in a more relevant way. AI can generate options and identify patterns. But original media thinking requires curiosity, imagination and an understanding of human emotions. A machine can recommend where an advertisement should appear. A planner decides how the brand should participate.

Media planner must understand culture

Media consumption does not happen in isolation. It is shaped by language, family structures, social values, economic realities, local traditions and cultural tensions.

A platform that is growing globally may not have the same role in every market. A message that succeeds among urban youth may be inappropriate for rural families. A program with high ratings may have very different meanings across social groups.

These cultural nuances are often difficult to capture in structured datasets.

Media planners bring local knowledge and sensitivity to the process. They understand not only where consumers can be reached, but also how they may interpret the message.

This becomes even more important in diverse markets where audiences consume content across different languages, regions and social contexts.

Future is not about a contest between AI and the media planner

The real future of media planning is not a competition between humans and machines. It is a partnership between the two.

AI should take over repetitive calculations, large-scale data processing, scenario modelling and continuous optimisation. This will allow media planners to spend more time on areas where human contribution creates the greatest value:

nUnderstanding the business challenge

nIdentifying meaningful consumer insights

nDeveloping differentiated media strategies

nEvaluating cultural and brand context

nCreating media ideas and partnerships

nChallenging assumptions and interpreting results

nAligning clients, creative teams, media owners and technology partners

The role of the planner will therefore evolve. Tomorrow’s media planner may spend less time preparing spreadsheets and more time framing problems, questioning models, interpreting evidence and designing strategic solutions.

AI will not replace media planners — but planners using AI may replace those who do not

Instead of resistance, media planners should embrace AI by learning to use it effectively. The strongest planners will understand both the possibilities and limitations of AI. They will know how to provide the right inputs, question the outputs and combine machine intelligence with human insight.

A planner who simply repeats what an algorithm recommends will add limited value. But a planner who can use AI to explore more possibilities, improve decision-making and create stronger strategies will become even more valuable.

AI can replace tasks—but not human judgement. It can automate calculations, repetitive analysis, and manual processes, but it cannot replace curiosity, empathy, cultural understanding, strategic judgement, creativity, or accountability.AI can help create a media plan, but still a media planner can develop a winning media strategy.

((The author is an experienced media planning professional currently serving at MTM Group, where he has played a key strategic role in shaping the direction of one of Sri Lanka’s largest media investment management groups.He holds a BSc (Hons) from the University of Peradeniya and a Master of Business Analytics from the University of Moratuwa. Dr. Indra Mahakalanda is a Senior Lecturer at the Department of Decision Sciences, University of Moratuwa. He holds a PhD from University of Canterbury, an MSc and BSc (Hons) in Engineering from the University of Moratuwa. His research interests are business analytics and electricity markets)