Artificial Intelligence (AI) is transforming businesses across various industries. It is accelerating product development, streamlining processes, enabling data-driven decision-making, and delivering hyper-personalised experiences to customers and stakeholders. AI is also unlocking novel value streams, as it leverages the enterprises proprietary data assets.

Fundamentally, AI is empowering organisations to enhance their efficiency & productivity, to innovate, and to create strategic advantages by harnessing the potential of these ‘intelligent’ technologies

Technology, including AI, holds the potential to revolutionise various aspects of an enterprise. From sales and marketing to product development, customer service, legal and compliance, and software development, AI can drive transformative change. Consequently, adopting an enterprise-wide strategy for AI implementation and governance has become essential.

Business strategy aligned with AI strategy

Organisations must address a fundamental question: “How can AI synergise with the business strategy to supercharge our objectives?” … Consider the following aspects:

  1. Process Optimisation: Identify current processes and pain points that can be streamlined using AI.

  2. Go-to-Market Strategies: Explore how AI can enhance market strategies and contribute to organisational success.

  3. Hyper-Personalisation: Leverage data-driven AI to better serve customers with personalised experiences.

AI strategy transcends technology alone; it encompasses people, processes, data, and technology. By aligning AI capabilities with the business strategy, organisations are better placed to achieve these holistic business goals."

Data strategy

Data is the lifeblood of AI. Machine learning models depend on high-quality data; they are only as good as the data they can work with. Before embarking on an enterprise AI journey, it is essential to have a governed enterprise data strategy in place.

Organisations must ask these fundamental questions:

  • Do we have the necessary data quality controls in place?
  • How do we secure and govern our data assets?
  • Are there any biases in our datasets?
  • What are the regulatory and compliance implications of using certain data for business functions?

All of these issues need to be addressed in the enterprise data governance strategy, which supplements the AI governance framework.

Setting up AI foundations

Most organisations embarked on the journey to embrace cloud technology around a decade ago. As part of this journey, organisations needed to establish some fundamental building blocks, such as: Do we want to enable public cloud or hybrid cloud? Which cloud providers should we partner with? Do we have the right talent to support these new technologies, or do we need to hire new resources to support them?

AI is no different; a solid foundation for infrastructure (hardware and software), people (data science and machine learning engineers, MLOps teams, etc.), and processes (MLOps - machine learning operations) needs to be established within an organisation to build and maintain AI services.

It is crucial to ensure that AI technologies integrate seamlessly with the rest of the organisation's services. The smooth integration of AI into existing products and services is key to success.

There is new innovation in the AI space every day. Organisations need to prepare themselves to embrace change quickly. They should establish a robust governance framework to evaluate new technologies, build prototypes, and secure and deploy new technologies quickly, all built on solid foundations.

Compliance & Ethical considerations

As part of their AI enablement journey, organisations need to consider local regulations such as GDPR or industry-specific regulations like HIPAA. Some of the questions to consider include: Are there any requirements on data sovereignty? How should PII (Personally Identifiable Information) data be handled? These issues need to be addressed as part of the enterprise data strategy and extended to any AI solutions.

AI models also need to be continuously monitored for challenges such as biases, discrimination, and "hallucinations". AI models are only as good as the data they are trained on; hence, the quality of the underlying data and continuous monitoring of the algorithm for any biases must be in place. MLOps frameworks must include continuous monitoring of data drifts and model drifts, triggering alerts for action when necessary.

Organisations must implement controls to ensure that AI models are transparent and can explain their performance to regulators and other stakeholders. This is crucial for maintaining trust and compliance with relevant regulations.

Delivering value

Measuring the return on investment (ROI) and business value of AI initiatives is critical. Organisations must develop clear success metrics to measure the impact of AI through cost savings, sales growth, customer satisfaction, and other relevant key performance indicators (KPIs).

It is important to remember that AI is merely a tool, and the success of any AI initiative hinges on the alignment of AI with the strategic business initiatives driven at the core, by human values. The implementation of AI should be guided by a clear understanding of the business objectives and the values that the organisation seeks to uphold.

To ensure that AI delivers tangible value, organisations must focus on identifying specific use cases where AI can provide significant benefits, such as automating repetitive tasks, improving decision-making, or enhancing customer experiences. By prioritising high-impact applications of AI and continuously monitoring and refining their AI models, organisations can maximise the value derived from their AI investments.