Best practices for AI adoption: Driving modernisation and transformation

In the past year, artificial intelligence (AI), particularly generative AI, has become a focal point for business leaders seeking to modernise and transform their operations. While there is pressure to begin adopting AI quickly due to the clear commercial and competitive advantage it offers, many enterprises struggle to identify and prioritize the right GenAI use cases that offer a competitive edge and justify the costs.

To be implemented successfully, AI strategies must be fully comprehensive, encompassing not just technology but also data management, customer-centricity, and strategic partners.

Data preparedness

Well organised, structured data is the foundation of any successful AI deployment, with the management of unstructured, scattered data proving a challenge that will take time and expertise to address. Building a strong and scalable foundation involves consolidating disparate data sources and ensuring data quality and accessibility.

To begin with, a thorough assessment of the current data landscape is crucial. This step involves mapping out existing data sources, understanding their formats, and identifying gaps. This foundational assessment is critical for recognising the scope of data integration required for effective AI implementation.

Following the assessment, the next step is data consolidation. Using automated tools to collect, consolidate, and clean data ensures compatibility and coherence across datasets. By consolidating data, organisations can eliminate silos and create a unified data environment that supports advanced analytics by different teams and levels throughout the business.

Designing and updating data infrastructure to support advanced analytics and AI capabilities is also essential. This includes adopting scalable cloud solutions, ensuring robust data storage, and establishing efficient data processing capabilities. With a modernised infrastructure, organisations can handle the increasing data volumes and growing complexities associated with AI adoption.

Customer-centric approach

When it comes to using AI - and specifically generative AI - for specific IT functions, one standout benefit is the improved end product it allows you to deliver to your customers. To realise the full potential of generative AI, businesses should adopt a customer-centric approach that focuses on delivering tangible business value. By leveraging generative AI tools correctly, businesses can boost return on investment for their customers, leading to improved customer satisfaction and retention.

Rather than just ‘create more’, generative AI helps businesses create new services that are more effective, targeted, and secure and meet regulatory requirements. One such practical application of generative AI is accelerated code development. Employing AI-driven tools can significantly speed up code development and improve productivity. This helps not only transform developers' skill sets, but also prepares them for broader AI integration within the organisation.

Improving IT operations and services is also crucial for organisations seeking to take a customer-centric approach. Deploying intelligent knowledge engines can provide quick, accurate responses to IT operations queries. This results in faster ticket resolution and better user experiences, ultimately enhancing customer satisfaction.

Generative AI also offers a whole host of industry specific use cases which enable customers to extract information from their data and serve up powerful tailored insights to their users, such as automated HR chatbots. Leaders looking to implement AI should consider which of these use cases will best support their organisational goals and deliver the best ROI.

Strategic partnerships

Forming strategic partnerships with expert IT consultancies will also help with the heavy lifting. These partnerships help organisations accurately assess their current operations and data environments, develop tailored adoption strategies, and ensure a smooth, low-risk transition to AI.

The first step involves taking stock of current operations and data environments. Undertaking a comprehensive review of existing processes and systems will help businesses understand where AI can provide the most value. By collaborating with partners, organisations can then develop a clear roadmap for AI adoption. This also includes defining clear objectives and metrics to measure the success of AI initiatives.

Partners can also help businesses seamlessly migrate applications to cloud platforms. They can support the migration process, ensuring a smooth transition and effortless integration of AI tools. Migrating systems requires detailed planning and execution to minimise disruptions and ensure continuity. Experienced partners will work alongside your data teams to develop a personalised roadmap and help the organisation navigate the complexities of AI deployment, allowing you to achieve your modernisation and transformation goals more efficiently.

Preparing for Future AI Advancements

The landscape of AI is ever-evolving, and to keep ahead of the game organisations must take a holistic approach to AI deployment. This includes ensuring ethical and regulatory compliance, staying abreast of technological advancements, and implementing end-to-end solutions that integrate AI seamlessly into business operations. By following these best practices, business leaders can adopt a strategic approach to AI that enables them to realise the full potential of AI, driving modernisation and transformation in a sustainable and impactful manner.