Dr Paul Pallath, VP of Applied Science, Searce, says by focusing on AI adoption strategies for competitive advantage, companies can bridge the gap between ambition and execution.
AI, particularly Generative AI (GenAI), is transforming businesses, driving growth and enhancing efficiency. As more organisations adopt AI, the AI maturity gap in business transformation has become a focal point. Closing this gap is essential for businesses to remain competitive in a rapidly changing landscape.
AI is now seen as a key driver for growth. It’s predicted to add trillions to the global economy, prompting significant investment.
Searce’s 2024 State of AI study polled 300 C-Suite and senior technology executives from organisations across the US and UK with at least $500 million in revenue, revealing that nearly 10% of organisations are investing over $25 million annually in AI projects.
However, only 51% of respondents say their AI initiatives have been “very successful”.
Success with AI isn’t just about pouring money into technology – it’s about overcoming the obstacles to full-scale implementation.
Bridging the AI maturity divide
Despite the excitement around AI, many organisations struggle to move beyond pilot projects. The challenges that companies face highlight the AI maturity gap – where ambitions often exceed execution.
Firstly, while C-suite executives across various functions generally agree that data privacy and security are the primary barriers to AI adoption, Chief Data Officers tend to be more confident in their current data management practices.
Digging deeper into this, this difference likely stems from the specific challenges and perspectives that different leadership roles encounter.
Ultimately, a compliant AI system is only as strong as its data. This healthy tension helps strengthen the organisation’s data and AI strategy, ensuring sensitive information is protected, regulations are followed and C-suite leaders share responsibility addressing these challenges collectively.
Technology infrastructure is another area where companies stumble. Many organisations lack the necessary upgrades to integrate AI fully into their existing systems.
For Chief Digital Officers, modernizing the tech stack is critical, but it must be balanced with operational needs and budgets. AI adoption strategies for competitive advantage depend on these crucial updates.
Thirdly, skepticism around AI’s reliability and accuracy emerges as a key adoption barrier, particularly from those deeply involved in its development, like Chief AI and Data Officers. Concerns about data quality, biases and the experimental nature of some AI technologies can slow down adoption.
For Chief Transformation Officers, the priority is often speed and organizational buy-in, which can create tension. To close the AI maturity gap, businesses need to bridge this divide by fostering collaboration between technical and strategic teams.
Successfully bridging the AI maturity gap requires tailored strategies that address the specific needs and concerns of each decision-maker in your organisation’s value chain. By understanding these nuances, businesses can effectively navigate the complexities of AI implementation and unlock its transformative potential.
Which brings us onto a key question for leaders:
AI: Build it or buy it?
Organisations starting AI projects must carefully evaluate whether to build AI capabilities internally or acquire solutions from external vendors. This decision significantly impacts factors such as cost, speed of deployment, control over intellectual property and access to specialised expertise.
Building AI internally can be an excellent choice for businesses where AI is core to their strategy. It offers the flexibility to fully customize solutions and maintain complete control over data security and intellectual property. However, this approach requires a dedicated team of experts and significant financial resources. Organisations must invest in skilled data scientists and engineers, robust infrastructure and the time needed to develop these capabilities from scratch.
While this path can offer long-term competitive advantages and deep integration into business operations, the upfront investment and longer development timelines are important considerations.
On the other hand, partnering with external vendors for AI solutions can significantly speed up deployment and reduce upfront costs. Mature, pre-built solutions allow businesses to implement AI quickly without the need for a large in-house team. This is particularly advantageous for companies seeking rapid scalability or quick results.
However, working with external providers can sometimes introduce risks, such as vendor lock-in, which may limit flexibility down the road. This is where specialised tech consultancies with multi-OEM engineering expertise can help. They can help businesses avoid the pitfalls of generic AI solutions by offering tailored, industry/function-specific AI solutions that balance speed with customization to address unique organizational needs and objectives.
Businesses should carefully consider these factors and weigh their relative importance. This decision matrix can be a valuable tool for evaluating the pros and cons of each option. By adopting a strategic evaluative approach, enterprises can maximise the benefits of AI and position themselves for long-term success.
AI success formula
To enhance AI capabilities and achieve greater returns on investment, organisations should focus on three critical areas: upskilling talent, improving data quality and establishing robust evaluation metrics.
First, upskilling is crucial. AI evolves quickly, and businesses must ensure their teams stay up to date with the latest advancements. This includes continuous professional development and fostering a culture of innovation. Cross-functional collaboration and mentorship are essential to align AI initiatives with broader business objectives.
Second, data quality is a cornerstone of effective AI deployment. High-quality data is essential for generating accurate insights. Regular data cleansing, integration, and governance must be prioritized to avoid errors that could undermine AI’s effectiveness.
Finally, by establishing clear performance metrics and conducting rigorous monitoring and benchmarking, organisations can refine their AI strategies and optimise outcomes. Through these strategic actions, enterprises can build a solid foundation for leveraging AI technologies to make a real business impact.
Closing the gap and moving forward
The path to AI maturity is not without its challenges, but the rewards are significant. By focusing on AI adoption strategies for competitive advantage, companies can bridge the gap between ambition and execution. AI holds the potential to revolutionise industries, streamline operations and provide businesses with a competitive edge.
