How companies can accelerate AI adoption

In today’s rapidly evolving digital landscape, the adoption of AI has emerged as a critical component for companies looking to improve their marketing strategies and operational efficiency.

However, recent research from Acxiom shows that 54% of companies in the UK and US have not yet integrated AI into their marketing technology stack, with only 21% citing AI implementation as a top priority. This significant gap highlights the challenges many companies face in navigating the complexities of AI adoption.

As external factors such as changing regulations continue to shape the marketing environment, companies must act decisively to overcome internal obstacles and accelerate their journey to leveraging AI.

External factors affecting AI adoption

At the forefront of this change are regulatory changes. As governments introduce stricter AI regulations in addition to existing data protection regulations, companies are increasingly being forced to rethink their data practices.

The introduction of laws such as GDPR in Europe has already created increased awareness of transparency, customer consent and data security. Then there is the AI law recently introduced in the EU, which is set to be followed by an equivalent omnibus law in the UK. As companies navigate this complex compliance landscape, many are forced to overhaul their systems to meet legal requirements, diverting resources and attention from actually implementing AI solutions.

Additionally, the recent delay in phasing out third-party cookies adds even more complexity to AI adoption. While companies can still use cookies for targeted advertising, there is also an opportunity for companies to rethink their data strategies and test and implement alternative solutions. As companies focus on transforming their data strategies to meet new regulatory requirements, AI adoption often takes a back seat.

Changing consumer expectations also play a crucial role in this dynamic. Consumers today increasingly expect personalized experiences and are pushing brands to find innovative ways to understand and engage their audiences.

However, the demand for personalization requires companies to first invest in tools and infrastructure to effectively analyze data, further delaying their focus on AI technologies. As companies strive to meet these changing expectations, they often face resistance in their efforts to implement AI technologies.

Challenges in AI implementation

While these external forces create a sense of urgency for adopting AI in organizations, they also present challenges that can slow the implementation process. In addition to dealing with the regulatory landscape and ever-evolving consumer demands, companies must also ensure that their internal systems are equipped to effectively use AI. However, in addition to having robust internal systems, companies also need to ensure that they have internal AI expertise, which represents a particular challenge.

Companies may struggle to find and attract the talent needed to effectively implement and use AI, which can significantly hinder progress. Without this in-house expertise, companies will struggle to take full advantage of AI’s capabilities, highlighting the importance of having skilled team members.

Budget constraints pose another major challenge in AI implementation. Many companies find it difficult to allocate sufficient financial resources to invest in AI technologies and the necessary infrastructure.

This limitation often leads companies to prioritize immediate operational needs over long-term strategic initiatives such as AI adoption. As a result, companies may miss out on the competitive advantages that AI can provide and be left behind in an increasingly data-driven market.

Another important challenge is data preparation. Many companies lack the solid data foundation needed to support AI initiatives. AI relies heavily on high-quality, context-rich data that can be used to train models and generate actionable insights. However, many companies face the challenge of accessing and organizing their data in a way that enables AI capabilities.

Without a solid foundation of data, companies may find it difficult to implement AI solutions tailored to their specific needs, delaying progress and limiting the impact of AI on their operations. Therefore, building a strong data infrastructure should be a top priority for companies that want to realize the full potential of AI.

Accelerate the use of AI: Great AI needs great data

To effectively implement a robust AI strategy, companies must ensure they dedicate internal resources to their efforts. These can be cross-functional teams, such as IT experts and data scientists, who can work together to develop and implement AI solutions based on the specific needs of their organization. It is also critical to engage AI compliance staff from the outset to avoid wasting time and effort on the transition to legality.

By investing in training and upskilling existing staff, companies can foster a workforce that has the expertise needed to effectively use AI technologies. Without a targeted focus on internal resources, companies could struggle to realize the full potential of AI, resulting in missed opportunities for growth and efficiency.

Another key element for the effective use of AI is prioritizing the quality of company data, especially through the use of first-party data. As we know, great AI is based on great data. High-quality, well-organized data serves as the foundation for every AI initiative, enabling companies to gain accurate insights and make informed decisions. The best place to start is with first-party data collected directly from customers that reflects their actual behavior and preferences.

By investing in robust data management practices and ensuring data cleanliness and consistency, companies can maximize the potential of their AI systems to drive personalized experiences, improve customer loyalty, and ultimately achieve better business results.

An effective approach to introducing AI

To ensure successful AI adoption, companies should take a structured approach that focuses on key strategic steps. First, they should build and curate their organizational data assets. A solid data foundation is critical to effective AI initiatives, enabling companies to generate meaningful insights that lead to accurate AI outcomes and consumer interactions.

Next, it is important to identify applicable use cases tailored to specific business needs. This can include generative, visual or conversational AI applications to ensure alignment with business goals.

When investing in AI capabilities, it is advisable to choose off-the-shelf solutions unless there is a compelling business justification for custom development. This allows companies to quickly implement new technologies without accumulating technical debt.

Finally, maintaining an active data feedback loop is critical to the effectiveness of AI. Regularly refreshing data ensures AI models produce accurate results and avoids issues related to “stale” data that can impact performance and limit insights.

Unleash the full potential of AI

Although the path to AI adoption is not without challenges, there are clear benefits for companies to adopt. As external pressures such as regulatory changes and changing consumer expectations create a sense of urgency and complexity, it is critical that companies proactively address internal obstacles.

A critical element in fully leveraging AI is a holistic data strategy that unifies data across the organization. Companies need to ensure that their data infrastructure is well structured, integrated and accessible. By breaking down silos and creating a unified data ecosystem, companies can empower their AI systems to deliver accurate insights, drive automation, and realize the full value of their data.

By dedicating resources to building internal expertise, ensuring the highest data quality, and doubling down on first-party data strategies, companies can position themselves to fully realize the transformative potential of AI.

Those who act quickly will not only stay one step ahead of the competition, but also open up new revenue opportunities, strengthen customer loyalty and drive long-term growth in an increasingly digital market.

About the author

Dimitrios Koromilas is Director of Platform Services EMEA at Acxiom. Acxiom®, an IPG company, is a global leader in customer intelligence and is at the forefront of AI-powered, data-driven marketing. As part of the Interpublic Group of Companies, Inc. (IPG), we specialize in powerful solutions that increase customer acquisition and retention while driving growth for the world’s largest brands and agencies. We transform omnichannel marketing strategies and execution using our AI-powered data and identity foundation, cloud-based data management, and martech and analytics services. For over 55 years, our teams in the US, UK, Germany, China, Poland and Mexico have helped companies optimize their marketing and advertising investments while prioritizing customer privacy. Discover more at Acxiom.com, where marketing is made better.

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In today’s rapidly evolving digital landscape, the adoption of AI has emerged as a critical component for companies looking to improve their marketing strategies and operational efficiency.

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