Generating AI Use Cases

AI is in full swing and most companies are in thinking mode, rummaging mode or creation mode. Today’s post is how to companies can uncover use cases, link them to a strategic objective and apply a SMART approach.

Here is the approach:

Understand Your Business Strategic Objectives and Goals:
These may be clearly written out in the business strategy or it might involve studying the mission statement, annual reports, and recent communications from your CEO.

Identify Key Business Challenges:
Pinpoint specific challenges or areas for improvement needed within your organisation. These could be related to efficiency, customer satisfaction, cost reduction, innovation, etc.

Generate Use Cases:
Brainstorm potential use cases that address the identified challenges. Consider how AI can be applied to enhance processes, make predictions, automate tasks, or provide valuable insights.

Connect Use Cases to Strategic Objectives:
Clearly articulate how each use case contributes to the achievement of strategic objectives. Highlight the specific benefits and outcomes that align with the organization’s goals.

Explore AI Technologies:
Investigate AI technologies that align with your identified challenges. This could include machine learning, LLMs, computer vision, etc.

Prioritise Use Cases:
Prioritise the generated use cases based on their potential impact on the strategic objectives. Consider factors such as feasibility, cost, and alignment with organisational priorities.

Include Metrics and Success Criteria:
Define key performance indicators (KPIs) and success criteria for each use case. This adds a quantitative dimension to your narrative, showcasing the measurable impact of AI on strategic goals.

Communicate:
Develop a compelling narrative that communicates the strategic importance of AI. Use language that resonates with both technical and non-technical audiences.

Here is an example Use Case using the SMART principle:

Automated Customer Support with LLM

👉Specific: Implement an AI-driven automated customer support system using an LLM to handle 30% of routine customer queries within the next three months.

👉Measurable: Track the reduction in the volume of routine customer queries handled by human agents through analytics.

👉Achievable: Leverage the capabilities of the LLM to understand and respond effectively to common customer inquiries, freeing up human agents to focus on more complex issues.

👉Relevant: Enhance customer support efficiency and responsiveness to align with the strategic objective of improving customer satisfaction and loyalty.

👉Time-bound: Achieve the specified percentage of automation within three months.

Organisations are inundated with AI hype, and being use case focused is THE best approach for creating data and AI strategies, to turn their ambitions into transformative realities.

It’s not about playing the AI game; that won’t get you far!

It’s about winning it with precision, impact, and undeniable success.

What’s your approach?