We’re drowning in data, tools and tech—but how do you cut through the noise to uncover real insight and opportunity?
Satish Thiagarajan, Founder and CEO of UK digital transformation consultancy Brysa, shares his advice.
Ever feel that you’re flooded with tech, but you’re just not using it properly? You’ve got platforms for everything, from social to CRM, creative to analytics, but none of them talk to each other, so you end up missing insights and doing things manually.
If that’s the case, it might be time for your agency to refocus on insight.
Data isn’t insight
The majority of marketing agencies are inundated with data. It comes from every channel and is scattered across platforms, spreadsheets, and tools.
But it doesn’t tell you anything because you don’t have access to any form of cohesive overview, and by the time you’ve stitched it all together, even if the picture isn’t sketchy, the insights have often become irrelevant.
This means a significant loss of opportunity, impacting your reaction times and revenue. And it’s frustrating, because with the right tech stack, it doesn’t have to be this way.
How the right tech tools and AI can help
AI is everywhere right now, but it’s not being used to its full advantage.
Rather than using AI to create copy-and-paste content, with carefully chosen AI tools, agencies can predict, plan, test, and evolve campaigns before they even launch, helping them to work more proactively.
Once you’ve built your hypothesis, tested it in the market, and measured the results, you can use AI to guide you through tweaking, repeating, scaling, and tweaking again, creating a virtuous campaign management cycle of continuous improvement.
How to use AI in this way
You need clean, connected, and consistent data for AI to work. The problem is, few agencies have that.
In most cases, data is strewn across various CRMs and Google Drives, which can make it incredibly difficult to use for insight management. AI can resolve that.
If you use an AI system that has access to all of your data, across platforms, with clean inputs, including a zero-copy data lake with metadata lookups, and structured campaign metadata, it can help you to create a relatively simple system that will deliver long-term value for your agency.
The trick lies in choosing the right tools to help you achieve this.
How to choose your AI and tech stack
There are so many AI tools available now that it can be difficult to know where to start. And every business will need a different combination of tech and AI tools.
But there are things that I use everyday, which can help to support a whole host of core business processes.
I use ChatGPT for everyday brainstorming and ideation.
For smarter search and research, Perplexity is my go-to.
For deeper document analysis, it’s Claude.
While Gemini is great for creative content generation.
There’s also Copilot Studio, CrewAI, Salesforce Agentforce, and Stack AI, if you want to build AI agents.
The key is to choose intuitive tools that support experimentation, are scalable, and quickly add value.
Making AI tools work for you
There are a number of different options that can help agencies to get the best from AI.
To automatically adjust bids and allocate spend based on machine learning models, there’s Amazon DSP’s Predictive Audiences and Performance+ tools, which use supervised learning models trained on cohort behaviour and historical campaign outcomes to forecast ROAS across audience segments and inventory types.
Then there are predictive LTV models, which agencies can use to prioritise high-value customer segments for targeting and ensuring media spend is focused on users with the greatest projected revenue impact.
While closed-loop optimisation frameworks enabled by Einstein Attribution and Salesforce Marketing Cloud Personalisation allow the continuous updating of model parameters, based on observed performance data.
So, predictive lead and opportunity scores can be refined using live conversion data, while dynamic content delivery models are retrained on real-time behavioural data.
This enables the dynamic adjustment of targeting and budgeting in correlation with performance insights.
It doesn’t take much to make this shift work for your agency.
All you need is to encourage a culture of continuous learning, rather than reporting; to invest in flexible, connected tools, free from silos, so you have an infrastructure that grows with you; and to adopt a test-learn-repeat model, so that every result from every campaign can be used to improve your next.
Data can carry so much value – but it needs to be used properly.
It’s time for agencies to forget about past performance and shift their focus to building real-time AI learning systems that can actually make use of the data they have.
Because without the right technology, and a willingness to adapt to the changing demands of the industry, swathes of agencies are going to be left behind.