In response to the productivity gains AI promises to deliver, UK business leaders have made the decision to adopt and shift their focus to incorporating AI into operations. PWC’s latest Global CEO Survey shows that in the past year, 48% of UK chief executives have already changed their company’s technology strategy because of gen AI. And, according to Mckinsey’s State of AI 2023, 40% of global respondents say their organisations will increase their investment in AI overall because of advances in gen AI.

Despite Gartner’s AI Hype Cycle projecting a decline in interest and investment in AI, the current reality is more promising. AI is not just a futuristic promise; it's actively contributing to the enhancement of existing business processes, learning and adapting dynamically. Amidst this transitional phase, success in AI implementation lies in aligning expectations with the technology's current capabilities, recognising its strengths in specific domains, such as content generation, automation and sophisticated data analysis.

To move beyond the buzz and embrace a pragmatic approach to AI, business leaders need to set tangible, strategic goals for 2024. Let's delve into three key resolutions that can steer organisations towards reaping obtainable benefits from AI implementation.

Goal 1: Establish a Strategic Centre of Excellence for AI Innovation

As the initial hype of AI passes, business leaders are focusing on demonstrating value-added use cases. This necessitates a more centralised approach to deploying AI, one that strategically aligns with business objectives. Enter the Centre of Excellence (CoE) for AI, a multidisciplinary hub that ensures deployment is not just about hype but is strategically tied to organisational goals.

A CoE brings a range of benefits, from proving and measuring impact to continuous improvement of AI projects. It accelerates training and learning, ensuring that AI initiatives are not just innovative but also practical and impactful. Creating a competitive advantage with AI demands a test-and-adapt approach, and a CoE is instrumental in ensuring a balanced and measured deployment that generates real value.

CoEs are highly tailored to an organisation's unique needs and level of AI maturity. But a solid starting point is the formation of a multidisciplinary team comprising both technical and executive leaders. This collaborative approach draws on diverse skills for a successful AI deployment.

Once the strategic goals for the AI CoE are established, it becomes imperative to conduct a comprehensive mapping of the organisation's processes. This meticulous examination aims to identify existing inefficiencies, overcome obstacles, and pinpoint potential opportunities for AI use cases. 

For instance, if data collection processes are largely manual, they may be prone to human errors. For AI projects to thrive, they necessitate accurate real-time information. Consequently, it becomes essential to automate processes related to data collection before launching an AI project. This strategic move ensures a solid foundation for successful AI initiatives by leveraging precise and timely data inputs.

Goal 2: Streamline process analytics with AI

A realistic yet impactful goal business leaders should set for AI is process analytics. Applying AI to process analytics serves as an ideal starting point, given that nearly every process involves a reporting element to ensure optimal efficiency. AI can revolutionise data collection, sanitation, and real-time error identification. As the algorithm evolves, it becomes adept at suggesting process optimisations by modelling predictions and simulations for continuous improvement.

AI doesn't just stop at optimisation; it enables teams to close the loop on continuous improvement. By linking to an organisation's tech stack, AI can bring about process automation changes based on the improved ways of working.

Goal 3: Experiment with AI and empower teams to do the same

Business leaders should also take it upon themselves to promote careful experimentation of AI through AI democratisation. It is important for experimentation to take place on two levels. 

The role of business leaders in AI democratisation is to work with IT teams to identify AI-powered tools that have the potential to boost team or organisation-wide productivity. Business users will best understand the bottlenecks in their processes and using this insight they can identify where AI should be implemented to promote cross-functional productivity gains. For example, leaders can begin experimenting with AI-powered process mapping and optimisation tools to streamline collaboration between teams. 

At the same time, non-technical users should be encouraged to use AI to take control of their personal productivity. Enabling employees to decide how and for what they utilise AI-powered tools will help them see firsthand how their roles can be enhanced with AI. The Office for National Statistics found that one in three workers fear AI could take their job. AI democratisation can address the concerns of employees and lays the groundwork for the successful scaling of AI projects in the future. 

As employees become more comfortable using AI tools on an individual level, it will be easier for business leaders to implement these tools across the organisation.

AI in 2024 demands a shift from hype to pragmatism. Business leaders must set specific, achievable goals and ensure their organisations align with the current capabilities of AI. By streamlining process analytics, empowering employees to experiment with AI, and establishing a strategic CoE, organisations can navigate the AI landscape successfully and drive tangible results in the year ahead.