Cloud-based AI is crucial in today’s complicated marketing landscape

It might seem more challenging than ever to operate. But, AI presents a means to rise to the occasion – for those willing to invest in a platform with the right data and analytical firepower. AI offers marketers the promise of developing responsive brands that can adapt in real-time to changes in consumer sentiment, market conditions, and business inputs, such as supply chain data, margin, real-time changes in consumer sentiment, market conditions, and promotional signals.

But this can seem like a distant vision in a world in which advertisers operate disjointed technology and data ecosystems. Not to mention working with minimal access to data insights used by colleagues elsewhere in the business. They should look to the cloud for answers.

Think big

The native AI capabilities of individual marketing platforms can offer effective solutions for narrow challenges, such as optimizing a campaign within a specific walled garden. However, in a world of platform fragmentation, this inevitably means fragmented models working on fragmented data to execute fragmented use cases.

While this might improve the cost per acquisition of a single channel within a wider campaign, it won’t deliver transformational value for the chief marketing (CMO). Instead of looking for improvements in media metrics, our most advanced clients are designing broader solutions to far bigger challenges – and delivering shareholder value.

They might be a global consumer packaged goods (CPG) company with more than 50 brands, trying to operationalize improved budget allocation for every campaign, channel, and placement. Some are looking to industrialize testing and identify where the learnings can be applied at scale to drive growth across categories. Others are developing systems to dynamically adjust marketing activities in response to promotions triggers, consumer demand sensing, and stock availability data.

Look within

These are diverse challenges. A marketing organization looking to solve them requires a platform with three critical characteristics.

The first is access to sensitive and timely data from across the organization, plus embedded data governance and AI guardrails. The second is the ability to build and train best-in-class models that combine generative AI with more established machine learning approaches, computer vision, and so on. The third and final characteristic is the seamless integration of an extensive range of marketing platforms: programmatic and social, customer data platforms, digital asset management solutions, and identity resolution partners to name a few.

Large enterprises have typically spent the last few years pursuing data centralization and governance on a platform hosted with their preferred cloud service provider (CSP). Happily, today these providers also lead the market in providing access to native AI components that can support scalable, cost-effective, and flexible use cases. Recently, CSPs have also started rolling out API layers that provide turnkey integrations with commonly used marketing platforms, substantially reducing the overhead of integrating cloud with marketing technology providers.

These innovations have collapsed the cost and complexity of rolling out your own AI-enabled cloud marketing platform, while the growing power and flexibility of their models have dramatically increased the potential returns. For many enterprise advertisers, the benefits of simple use cases, such as centralizing measurement, will justify the cost of the foundational platform setup, delivering a straightforward business case to start work.

Place your bets

Building a cloud solution that integrates individual channels, marketing technologies, and data sources enables enterprises to orchestrate their marketing ecosystem more effectively, mitigating fragmentation issues.

Developing the models and underlying data infrastructure that underpin these solutions can unlock immediate value and create a flexible foundation for future development. Crucially, by doing this within your own cloud environment, you avoid training models owned by external vendors and the associated risk of locking yourself into a legacy partner ecosystem.

The AI landscape is evolving quickly. Foundational models along with the applications and use cases they support may change beyond recognition in the coming years. In the face of this unpredictability, partnering with your choice of enterprise cloud provider, building the infrastructure to integrate diverse data sets, and learning how to train and derive value from your models is the safest strategic bet that you can make.

Technology may change fast. But, adapting your organizational culture to embed these tools takes time, and this is a good reason to place a bet.

The sooner you start, the quicker you’ll see rewards.