Technology: Strategic AI planning – from vision to implementation

AI is transforming industries by driving innovation and efficiency. But to harness its fill potential, organisations need a strategic approach that aligns AI initiatives with broader business goals.

Here are some key considerations for strategic AI planning, from vision to implementation.

Link AI strategy to group strategy

First, establish a vision for how AI will contribute to enterprise goals. Set clear goals, identify possible benefits, and decide on the metrics you will use to measure success.

Once your goals are set, detail how AI can help achieve them and identify some use cases to pursue. Involve key stakeholders from various areas of the business to understand diverse needs and gain buy-in right from the beginning.

Stating AI goals clearly is key to encouraging and enabling organisation-wide understanding and adoption of AI. It will also help you find appropriate use cases – ones that will deliver ROI and achieve your goals.

Establish guard rails and controls

Implementing AI responsibly requires robust guardrails and controls to mitigate risks and ensure ethical use.
Develop a framework for ethical AI use to address biases, ensure transparency, and protect privacy. A framework will also ensure you stay compliant with local and international regulations, including data protection laws.
At this stage, identify potential risks associated with the AI projects and detail possible ways to mitigate them.

Establish an AI implementation plan

Successful AI implementation depends on identifying the right use cases to achieve the goals set in the first instance and equipping the workforce with necessary skills to work towards them.

Steps to achieve this include:

Identify use cases

Conduct a thorough analysis to identify high-impact AI use cases that align with the goals established at the start of the process. This involves evaluating mthe feasibility, potential benefits, and resource requirements of each use case.

For example, if you have a goal of increasing topline revenue, some potential use cases for AI could be:

SALES FORECASTING
AI algorithms can predict future sales trends based on historical data, providing information for organisations to make informed decisions about inventory, staffing, and marketing strategies.

DYNAMIC PRICING
Using AI can adjust prices in real-time based on demand, competition, and other factors, maximising revenue.

CHURN PREDICTION
AI can identify customers who are at risk of leaving and suggest proactive measures to retain them, such as personalised offers or improved customer service.

LEAD SCORING
AI can rank leads based on their likelihood to convert, enabling sales teams to focus their efforts on the most promising prospects.

Team Skills

Investing in training programs to upskill existing employees ensures they are equipped to work with AI technologies. This includes technical training for key roles like data scientists and engineers, as well as awareness programs for non-technical staff.

By following these steps, organisations can develop a strategic AI plan that not only drives innovation but also aligns with their broader business goals, ensuring sustainable and ethical AI implementation.

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