In today’s rapidly evolving digital landscape, the adoption of AI has emerged as a crucial component for businesses aiming to enhance their marketing strategies and operational efficiency.
However, recent research by Acxiom reveals that 54% of businesses in the UK and US have yet to integrate AI into their marketing technology stack, with only 21% identifying AI implementation as a top priority. This significant gap highlights the challenges that many organisations face as they navigate the complexities of AI adoption.
As external factors, such as changing regulations, continue to shape the marketing environment, businesses must act decisively to overcome internal obstacles and accelerate their journey towards leveraging AI.
External factors affecting AI adoption
Regulatory changes are at the forefront of this shift. As governments implement stricter regulations regarding AI on top of those already in place for data privacy, businesses are increasingly compelled to reassess their data practices.
The introduction of laws like GDPR in Europe has already created a heightened awareness around transparency, customer consent and data security. Then there is the recently introduced AI Act in the EU, with an equivalent omnibus law to follow 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 away from actually implementing AI solutions.
In addition, the recent delay in phasing out third-party cookies adds another layer of complexity to AI adoption. While businesses can continue to use cookies in targeted advertising, there is also an opportunity here for businesses to rethink their data strategies, and test and adopt alternative solutions. As companies focus on reshaping their data strategies to meet new regulatory demands, AI adoption often takes a backseat..
Shifting consumer expectations also play a crucial role in this dynamic. Consumers today expect increasingly personalised experiences, pushing brands to seek innovative ways to understand and engage their audiences.
However, the demand for personalisation requires businesses to first invest in tools and infrastructure to analyse data effectively, further delaying their focus on AI technologies. As companies strive to meet these evolving expectations, they can often encounter resistance in their efforts to implement AI technologies.
Challenges to AI implementation
While these external forces create a sense of urgency for businesses to adopt AI, they also introduce challenges that can slow down the implementation process. Organisations must not only deal with the regulatory landscape and ever-evolving consumer demands, but also ensure that their internal systems are equipped to leverage AI effectively. However, alongside robust internal systems, businesses must also ensure that they have internal expertise on AI, which is a particular challenge.
Businesses can struggle to find and attract the talent needed to effectively implement and leverage AI, which can significantly hinder progress. Without this internal expertise, organisations will face difficulties in fully leveraging AI’s capabilities, highlighting the importance of having skilled team members in place.
Budget constraints represent another significant challenge in AI implementation. Many organisations struggle to allocate sufficient financial resources to invest in AI technologies and the necessary infrastructure.
This limitation often leads businesses to prioritise immediate operational needs over long-term strategic initiatives like AI adoption. Consequently, organisations may miss out on the competitive advantages that AI can offer, leaving them behind in an increasingly data-driven marketplace.
Another key challenge is data preparedness. Many organisations lack a robust data foundation that is necessary 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 businesses face challenges in accessing and organising their data in a way that enables AI capabilities.
Without a solid data foundation, companies may struggle to implement AI solutions that are tailored to their specific needs, which delays progress and limits the impact of AI on their operations. As such, building a strong data infrastructure should be a top priority for businesses looking to unlock the full potential of AI.
Speeding up the use of AI: Great AI needs great data
To effectively implement a robust AI strategy, businesses must ensure they dedicate internal resources to their efforts. This may involve cross-functional teams that include figures such as IT professionals and data scientists, who can work together to develop and implement AI solutions based on the specific needs of their organisation. It’s also crucial to involve AI compliance folk from the outset, to avoid having to waste time and effort reengineering to lawfulness.
By investing in training and upskilling existing staff, companies can promote a workforce equipped with the necessary expertise to harness AI technologies effectively. Without a dedicated focus on internal resources, businesses may struggle to unlock the full potential of AI, leading to missed opportunities for growth and efficiency.
Another central element of effective use of AI is prioritising the quality of businesses’ data, particularly by leveraging first-party data. As we know, great AI is based on great data; high-quality, well-organised data serves as the foundation for any AI initiative, enabling organisations to gain accurate insights and make informed decisions. first-party data collected directly from customers reflecting their actual behaviours and preferences is the best place to start.
By investing in robust data management practices and ensuring data cleanliness and consistency, organisations can maximise the potential of their AI systems, allowing them to drive personalised experiences, enhance customer engagement, and ultimately achieve better business outcomes.
An effective approach to AI adoption
To ensure successful AI adoption, businesses should follow a structured approach that focuses on key strategic steps. First, they should build and curate their organisational data assets. A solid data foundation is crucial for effective AI initiatives, enabling companies to draw meaningful insights that drive accurate AI results and consumer interactions.
Next, identifying applicable use cases tailored to specific business needs is essential. This may include generative, visual, or conversational AI applications, ensuring alignment with organisational goals.
When investing in AI capabilities, choosing off-the-shelf solutions is advisable, 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 vital for AI effectiveness. Regularly updating data ensures AI models produce accurate results and helps prevent issues associated with “stale” data, which can hinder performance and limit insights.
Unlocking the full potential of AI
While the road to AI adoption is not without its challenges, there are clear benefits of adoption for businesses. As external pressures such as regulatory changes and shifting consumer expectations create a sense of urgency and complexity, it’s critical that organisations are proactive in overcoming internal obstacles.
A critical element in fully leveraging AI is having a holistic data strategy that unifies data across the organisation. Businesses must 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 unlock the full value of their data.
By dedicating resources to build internal expertise, ensuring their data is of the highest quality, and doubling down on first-party data strategies, businesses can position themselves to fully leverage AI’s transformative potential.
Those who act swiftly will not only stay ahead of the competition but will also unlock new revenue opportunities, enhance customer loyalty, and drive long-term growth in an increasingly digital marketplace.
About the Author
Dimitrios Koromilas is Director for Platform Services, EMEA at Acxiom. Acxiom®, an IPG company, is the global leader in customer intelligence and stands at the forefront of AI-enabled, data-driven marketing. As part of the Interpublic Group of Companies, Inc. (IPG), we specialize in high-performance solutions that boost customer acquisition and retention while fueling growth for the world’s biggest 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 across the US, UK, Germany, China, Poland, and Mexico have helped businesses optimize their marketing and advertising investments while prioritizing customer privacy. Discover more at Acxiom.com, where marketing is made better.
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