In an era where automation is transforming nearly every facet of digital marketing, many agencies are now asking a crucial question: Can AI tools truly support and optimise the complex process of budgeting and forecasting for paid media over a 12-month period?

While AI brings powerful advantages to the forecasting table, its effectiveness hinges on how well it is trained, the quality of historical data it analyses, and how sensitively it interprets the nuances of month-by-month campaign performance. Let’s take a closer look.

The Value of Forecasting in Paid Media

Budgeting and forecasting are at the core of any successful paid media strategy. Agencies must anticipate fluctuations in demand, shifts in user behaviour, and seasonal trends, all while ensuring spend delivers optimal returns (whether that be site visits, ROAS, CAC, CPA or otherwise). Traditionally, this process has been time-consuming and reliant on human experience and interpretation.

Enter AI: with its ability to analyse vast datasets, detect patterns, and make real-time predictions, AI has the potential to dramatically streamline this process, freeing up marketers to focus on strategy and creativity. However, is that potential currently realised, and can AI genuinely save time?

How AI Tools Work in Media Forecasting

Modern AI tools for marketing use a combination of machine learning algorithms, predictive analytics, and natural language processing to interpret historical performance data and recommend future spend. These tools typically:

• Analyse historic performance data across platforms (e.g. Google Ads, Meta, LinkedIn)
  
  
• Identify seasonal patterns, such as spikes during Q4 or lulls in summer
  
  
• Incorporate external variables, like holidays, major events, or economic shifts
  
  
• Optimise spend allocation based on performance forecasts and audience behaviour
  
  
• Generate visual budget models to guide media planners and clients
  
  

Platforms like Google’s Performance Max and Meta’s Advantage+ are already embedding AI recommendations into campaign setup and budget recommendations. However, these tools can be biased towards a platform's own objectives of wanting businesses to spend more on their platform. Furthermore, these tools fall well short of having a cross-channel month by month budget plan in a Google sheet… a necessary requirement of any business.

The blending of data from multiple sources is key here because this is the most time-consuming element of forecasting.

Can AI Tools Handle Month-by-Month Nuance?

One of the most common concerns about relying on AI for long-term planning is whether these tools can genuinely capture the nuance between different months.

AI excels at identifying macro patterns - for example, that CPCs tend to rise in November due to Black Friday, or that January often sees lower conversion rates in some industries. However, the nuance comes in the form of business-specific patterns:

• Is there an annual product launch in March that consistently drives better ROI?
  
  
• Does a client’s audience behaviour change during school holidays?
  
  
• Are there new channels or creative formats being introduced that will shift performance?

In this regard, AI needs contextual input from humans. While it can highlight trends, marketers must guide the system with strategic insights. The best tools are those that allow for collaborative forecasting - combining AI’s data crunching with planner intuition.

Using Historical Data to Inform Optimised Budgets

AI thrives on data - the richer the historical dataset, the more accurate its predictions. For paid media, that includes:

• Previous year’s monthly spend, conversions, and ROI
  
  
• Audience segmentation and engagement metrics
  
  
• Changes in bid strategy and creative formats
  
  
• Platform algorithm changes (e.g. Meta’s targeting shifts)
  
  

By training models on this data, AI can produce budget allocations optimised for performance, highlighting high-potential months and areas for cautious investment. For instance, it might recommend a heavier spend in September due to historically high ROAS, or suggest reducing investment in February based on past underperformance.

What Agencies Should Consider

While AI can be a powerful co-pilot, agencies need to approach these tools with a critical mindset:

1. Data hygiene is everything: Poor or incomplete data will lead to flawed predictions.
   
   
2. Blend AI with human strategy: The best results come from pairing automated forecasts with seasoned human judgement.
   
   
3. Monitor and adjust frequently: A 12-month forecast should never be static. AI tools can help update projections dynamically based on live data.
   
   

Choose tools that fit your workflow: Not all AI forecasting tools are created equal. Prioritise those that offer transparency and flexibility.

Final Thoughts

AI is not here to replace the media planner - it’s here to enhance decision-making, reduce manual workload, and provide sharper insights. When used correctly, AI tools for budget planning such as Kenshoo SKAI can offer significant value in budgeting and forecasting over a 12-month period.

However, given the need to account for seasonal trends and historic performance combined with upcoming offers, product launches and other industry changes nothing can replace human intelligence for this area of marketing.

Platform tools, and other third party tools can be used to provide certain stats such as conversion rates and average cost per click, but these should simply be copied and pasted manually into worksheets that combine multiple data sets. The process of budgeting is too complex to rely on robots.

At SearchUp, we believe the future of media planning is a blend of intelligent automation and human expertise. With the right tools and mindset, agencies can gain a competitive edge - making forecasting not just faster, but smarter.