Below is a couple of useful areas:

1. Approach to identify & prioritise AI use case
2. Suggested AI use cases
3. Potential growth of AI and improvements over the next 3 years

Approach to Identify and Prioritise AI Use Cases

Identifying and prioritising AI use cases is crucial for maximising business value and ensuring successful AI implementation. Here’s a structured approach inspired by Gartner’s methodologies (Gartner Toolkit).

1. Ideation and Identification

• Crowdsource Ideas: Gather input from various stakeholders, including employees, customers, and partners. Use workshops, brainstorming sessions, and surveys to collect a wide range of potential AI use cases.
• Strategic Alignment: Ensure that the identified use cases align with the organization’s strategic goals and objectives. Focus on areas that can drive significant business value and support long-term strategies.
• Pain Point Analysis: Identify critical business problems and pain points that AI can address. Prioritize use cases that solve high-impact issues and offer substantial improvements in efficiency or customer satisfaction.

2. Assessment and Feasibility

• Technical Feasibility: Evaluate the technical requirements and feasibility of each use case. Consider data availability, quality, and the complexity of AI models needed.
• Business Impact: Assess the potential business impact of each use case. Estimate the expected ROI, cost savings, revenue generation, and other tangible benefits.
• Resource Availability: Determine the resources required, including budget, talent, and technology. Ensure that the organization has or can acquire the necessary resources to implement the use case (or choose a different first use case).

3. Prioritisation Framework

• Scoring System: Develop a scoring system to rank use cases based on criteria such as strategic alignment, business impact, technical feasibility, and resource availability. Assign weights to each criterion based on its importance to the organisation.
• Quick Wins vs. Big Bets: Categorize use cases into “quick wins” (low effort, high impact) and “big bets” (high effort, high impact). Prioritize quick wins to demonstrate early success and build momentum but also outline big bets to build on early success.
• Risk-Reward Analysis: Conduct a risk-reward analysis to evaluate the potential risks and rewards associated with each use case. Focus initially on the low risk options to minimise hurdles.

4. Implementation and Monitoring

• Pilot Projects: Start with pilot projects to test the feasibility and impact of prioritised use cases. Use the results to refine the approach and scale successful pilots (and take lessons from unsuccessful pilots to enhance future success).
• Continuous Monitoring: Implement a continuous monitoring system to track the performance and impact of AI use cases. Use metrics and KPIs to measure success and identify areas for improvement through an objective lense.
• Iterative Improvement: Regularly reassess and reprioritize AI use cases based on new data, changing business needs, and technological advancements. Maintain flexibility to adapt to evolving circumstances.

By following this structured approach, organizations can effectively identify and prioritise AI use cases that align with their strategic goals, maximize business value, and ensure successful AI implementation.

If you’re stuck for ideas, below is an outline of a few ideas to get you started.

Potential Business Use Cases for AI

Artificial Intelligence (AI) is revolutionising various industries by enhancing efficiency, reducing costs, and driving innovation. Here, we explore some of the key business use cases for AI and the potential for growth in the next three years.

1. Customer Service and Support

AI-powered chatbots and virtual assistants are transforming customer service by providing 24/7 support, handling routine inquiries, and improving response times. Companies like Amazon and Netflix use AI to personalize customer interactions, leading to increased satisfaction and loyalty.

2. Cybersecurity and Fraud Detection

AI is crucial in identifying and mitigating cyber threats. Machine learning algorithms analyse patterns to detect anomalies and potential security breaches. Businesses are increasingly adopting AI for cybersecurity, with over half of businesses using AI in these areas.

3. Personalised Marketing

AI enables businesses to tailor marketing campaigns to individual customer preferences. By analysing data from various touchpoints, AI can predict customer behaviour and recommend products. This approach has significantly boosted sales for companies like Amazon and Sephora.

4. Supply Chain Optimization

AI helps in optimising supply chain operations by predicting demand, managing inventory, and identifying inefficiencies. This leads to cost savings and improved operational efficiency. AI-driven supply chain management is expected to grow significantly as businesses seek to enhance their logistics.

5. Human Resources and Recruitment

AI streamlines the recruitment process by automating resume screening, scheduling interviews, and even assessing candidate fit through predictive analytics. This not only saves time but also improves the quality of hires. AI in HR is projected to grow as companies look to enhance their talent acquisition strategies.

6. Healthcare and Diagnostics

AI is making significant strides in healthcare by assisting in diagnostics, personalized treatment plans, and patient monitoring. AI algorithms can analyse medical images, predict disease outbreaks, and even suggest treatment options. The healthcare AI market is expected to see substantial growth as technology continues to advance.

7. Financial Services

In the financial sector, AI is used for fraud detection, risk management, and personalized banking services. AI-driven algorithms can analyse transaction patterns to detect fraudulent activities and assess credit risks. The adoption of AI in finance is anticipated to increase as institutions seek to enhance security and customer experience.

Potential for AI Growth in the Next 3 Years

The AI market is poised for significant growth in the coming years. According to various industry reports, the global AI market is expected to grow at a compound annual growth rate (CAGR) of over 35% from 2023 to 2026. This growth is driven by advancements in AI technology, increased adoption across industries, and the continuous need for automation and efficiency.

Key Drivers of Growth:

1. Technological Advancements: Continuous improvements in AI algorithms, computing power, and data availability are driving the adoption of AI.
2. Increased Investment: Businesses are investing heavily in AI to gain a competitive edge, leading to rapid innovation and deployment.
3. Regulatory Support: Governments worldwide are recognising the potential of AI and are implementing policies to support its growth.
4. Consumer Demand: As consumers become more accustomed to AI-driven services, their demand for personalized and efficient solutions is increasing.

In conclusion, AI is set to play a pivotal role in transforming business operations across various sectors. The next three years will likely see accelerated adoption and innovation, making AI a key tool for businesses aiming to remain competitive.