Why Faster Marketing Decisions Still Depend on Better Measurement
As AI accelerates marketing decisions, Sarah Maina of AppsFlyer explains why trusted measurement, reliable data and human oversight will determine whether that speed translates into sustainable business growth.
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Marketing has spent years trying to become faster.
Campaigns launch in minutes. Dashboards update continuously. Budgets can be shifted almost instantly. AI can spot patterns, recommend actions and increasingly make decisions without waiting for a marketer to intervene.
But speed introduces an uncomfortable question: what happens when the decision is made faster than the evidence can support it?
That tension is becoming more important as marketing teams operate across fragmented digital ecosystems, tighter privacy expectations and increasingly autonomous technology. The problem is no longer simply having enough data. It is knowing whether the signals being used to make decisions are trustworthy.
For Sarah Maina, Regional Manager, Middle East & France at AppsFlyer, that distinction sits at the centre of the industry’s next phase. The opportunity, she argues, is to build the measurement foundation that allows speed, automation and AI to work without turning uncertainty into action.
When Faster Decisions Still Need Better Evidence
Marketing has never had a shortage of metrics. The harder problem has been deciding which ones deserve to influence the next move.
Maina sees this tension playing out across organisations navigating privacy changes, AI adoption and fragmented customer journeys. “AI has undoubtedly transformed the speed at which marketers can operate,” she says.
Campaigns can launch faster, dashboards update in real time, and budget optimisation happens almost instantly. Yet the acceleration comes with a warning.
“However, speed alone should not be confused with accuracy,” she says.
The difference is easy to overlook when technology is capable of acting almost immediately. A weak signal that once took a marketer hours to interpret can now be converted into an automated decision in seconds.
The danger is that organisations may not notice why the decision was wrong until the consequences have already spread.
For Maina, the answer begins with measurement rather than automation.
- Better signals before faster decisions: AI can only optimise what the underlying data can reliably explain.
- Measurement before scale: Organisations need confidence in the foundation before allowing automation to act more broadly.
- Actionable insight over reporting: Data becomes valuable when it can change a decision, not simply populate another dashboard.
The objective, then, is not simply to make marketing faster. It is to make faster marketing more defensible.
The New Marketing Advantage is Knowing What to Trust
Marketing, product and data teams have historically operated with different priorities and different views of the customer. But the growing complexity of digital behaviour is making those boundaries harder to maintain.
Maina argues that the transformation is less about moving boxes around an organisational chart and more about establishing a common basis for decision-making.
“The biggest transformation we’re seeing isn’t necessarily organisational, it’s cultural,” she says. Rather than restructuring departments, companies are bringing marketing, product, and data teams closer together around one fundamental question: can they trust the data driving their decisions?
That question is becoming a kind of organisational test.
When teams trust the measurement framework, data can become the common language between functions.
- Marketing can bring customer behaviour into product conversations.
- Product teams can understand engagement beyond individual features.
- Data teams can connect signals to commercial decisions rather than simply maintaining infrastructure.
This is particularly significant in mobile-first markets such as the Middle East, where Maina sees high smartphone penetration, app-first behaviour and ambitious digital transformation creating an opportunity to build modern measurement practices without being constrained by legacy systems.
But opportunity does not remove the need for discipline. “Reliable data remains the starting point for every successful digital strategy,” she says.
That principle may sound straightforward. Its implications are not. As marketing becomes increasingly automated, trust in the measurement layer becomes a competitive advantage in its own right.
AI Agents Are Moving From Reporting to Making the Call
There is a point at which AI stops explaining what happened and starts deciding what happens next.
Marketing is approaching that point quickly.
Maina believes AI agents are already moving beyond reporting and campaign execution into increasingly autonomous decision-making. The change is significant because it shifts the role of the marketer from manually managing every optimisation to defining the conditions under which machines are allowed to act.
“I believe that shift is already happening,” she says.
A system making thousands of decisions every minute cannot depend on a marketer checking each one individually. Instead, humans need to establish the objectives, boundaries and measures that govern those decisions.
That creates a new hierarchy of responsibility:
- Business objectives: Humans determine what the organisation is ultimately trying to achieve.
- Strategic guardrails: Marketers define where an AI agent can act and where intervention is required.
- Measurement and auditability: Organisations need to understand the data behind decisions and evaluate whether those decisions produced the intended outcome.
- Autonomous optimisation: Agents can increasingly manage decisions once the underlying framework is trusted.
The last point is where measurement becomes more than a reporting function.
“If an AI agent is making thousands of decisions every minute, organisations must be able to understand the data behind those decisions and trust the measurement that supports them,” Maina explains.
That requirement changes the architecture of marketing itself. AI makes reliable measurement more consequential, because an unreliable signal can now travel through an automated system at extraordinary speed.
The smarter the agent becomes, the more important it is to know what it is learning from.
The Future of Marketing Will Be Measured in Business Value
The next stage of marketing performance will be defined by whether those decisions create value that the business can actually recognise.
That is already changing how marketing is expected to operate.
Maina sees marketers moving beyond traditional performance metrics toward a broader understanding of sustainable growth and long-term customer value. The implication is that marketing measurement can no longer sit at the end of the process, quietly explaining performance after the fact.
It has to move closer to the decision itself. This also explains why the relationship between marketing and the rest of the organisation is changing. When measurement is trusted, marketing can contribute to conversations about product, CX and business strategy rather than simply defend campaign performance.
“Marketing is evolving beyond simply reporting on campaign performance to playing a much broader role in shaping product decisions, customer experience, and overall business strategy.” That evolution will become even more important as AI takes over more operational decisions.
The future marketer is therefore unlikely to be the person who makes every decision. Instead, the role will increasingly involve deciding which decisions should be automated, which signals can be trusted, and what outcomes should matter.
Maina’s larger point is ultimately about foundations. Innovation can accelerate growth, but only when the infrastructure beneath it is reliable.
“Ultimately, this reinforces why measurement should no longer be viewed simply as a reporting function. It is the foundation upon which AI, automation, and future marketing performance will increasingly depend.”
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