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Overview

Identifying the right use-cases is one of the most critical challenges when adopting AI-driven analytics. This guide presents a structured, impact-oriented framework to help you uncover high-value opportunities that align with your organisation’s goals and are feasible with your current data. The approach is based on our experience consulting for Fortune 500 companies and has been designed to support cross-functional teams across operations, marketing, analytics, product, and executive leadership. By the end of this guide, you’ll be able to:
  • Align analytics initiatives to your strategic objectives
  • Map your core processes and uncover bottlenecks
  • Match AI capabilities to real-world decision workflows
  • Score and prioritise opportunities based on impact and feasibility
The framework is based on the following steps:
  1. Define business objectives
  2. Identify key processes
  3. Review AI capabilities
  4. Match capabilities to subprocesses
  5. Score opportunities
1

Define business objectives

Effective analytics starts with a clear strategic goal. Every use-case should tie directly back to an organisational objective—this is how you justify investment and measure impact.Start by identifying 1–2 primary objectives. These may include increasing shareholder value, improving customer retention, reducing operational costs, or enhancing product experience.Each opportunity will then be judged by a simple question derived from your objective.
Example 1Objective: Increase shareholder value
→ “How much will this improve shareholder value?”
Example 2Objective: Improve marketing efficiency
→ “How will this improve the ROAS of digital campaigns?”
2

Identify key processes

Next, identify the key processes that support your objectives. These are typically end-to-end workflows—such as marketing funnels, sales pipelines, customer support flows, or internal production chains.Focus your effort based on the scope of the objective:
  • Organisation-level objectives: Map the entire value chain, from customer acquisition to delivery and support.
  • Product or service innovation: Focus on how users interact with your offering—via Jobs-To-Be-Done or service delivery steps.
Example 1
A manufacturing company aiming to improve shareholder value might map:
  • Marketing → Sales → Delivery → Support
  • Internal flow: Production → Quality Control → Supply Chain
Example 2
A software company aiming to improve its product might map:
  • User onboarding → Core workflows → Feedback loops
Once identified, translate the key processes into process maps ensuring you include:
  • Inputs and outputs
  • Stakeholders
  • Process steps and decisions
  • Tools and data sources
Conduct this collaboratively with domain experts, data professionals, and managers. Ask each group to prepare a process map from their perspective, then synthesize them into a single view. Here’s a high-level example of key processes mapped out for a fictional insurance brokerage:
Example: Insurance Brokerage
  1. Marketing
    • Segment customers:
      • Inputs: Customer data
      • Outputs: Target segments
      • Stakeholders: Marketing team
      • Tools: CRM, marketing automation
      • Process:
        1. Load customer data from CRM
        2. Clean up data to generate a 360 degree view of the customer
        3. Segment customers by demographics, purchase history, etc.
        4. Provide high-level summary of segments to stakeholders and save for downstream use.
    • Select messages/channels
      • Inputs: Customer segments
      • Outputs: Messages/channels
      • Stakeholders: Marketing team
      • Tools: Marketing automation
      • Process:
        1. Load customer segments from CRM
        2. Anticipate suitability of messages/channels for each customer given their characteristics (e.g. age, location, purchase history, segments, etc.)
        3. Select the most appropriate messages/channels for each segment.
  2. Sales
    • Score leads:
      • Inputs: Lead data, past performance data
      • Outputs: Lead scores
      • Stakeholders: Sales team
      • Tools: CRM, sales automation
      • Process:
        1. Load lead data from CRM
        2. Clean up data to generate a 360 degree view of the lead, including key characteristics, eventual purchase behaviour, and proxies for likelihood to convert.
        3. Anticipate lead score based on the lead’s characteristics and past performance data.
    • Engage and convert:
      • Inputs: Lead data, past performance data
      • Outputs: Engagement and conversion data
      • Stakeholders: Sales team
      • Tools: CRM, sales automation
      • Process:
        1. Load lead scores from CRM
        2. Anticipate engagement and conversion likelihood based on the lead’s characteristics and past performance data.
        3. Select the most appropriate messages/channels for each lead given their characteristics (e.g. age, location, purchase history, segments, etc.)
        4. Engage and convert the lead.
  3. Renewals
    • Identify customers due for renewal
      • Inputs: Customer data, renewal data
      • Outputs: Renewal data
      • Stakeholders: Renewals team
      • Tools: CRM, renewals automation
      • Process:
        1. Load data from CRM, including customer data, renewal data, and past performance data.
        2. Clean up data to generate a 360 degree view of the customer, including key characteristics, renewal history, and proxies for likelihood to renew.
        3. Anticipate renewal likelihood based on the customer’s characteristics and past performance data.
    • Proactively engage:
      • Inputs: Customer data, renewal data
      • Outputs: Engagement data
      • Stakeholders: Renewals team
      • Tools: CRM, renewals automation
      • Process:
        1. Load renewal data from CRM
        2. Anticipate engagement likelihood based on the customer’s characteristics and past performance data.
        3. Proactively engage the customer.
3

Review AI capabilities

Before identifying solutions, ensure everyone is aware of the current capabilities of AI-driven analytics.Capabilities often fall into the following categories:
  • Event Prediction
  • Classification
  • Granular Forecasting
  • Segmentation
  • Search
  • Writing
Refer to the Capabilities page for a full overview.
4

Match capabilities to subprocesses

For each process, choose a few key decisions within the process and simulate how a human would solve them.Ask: What information do they use? What steps do they take? How do they assess quality? This helps uncover the logic and data dependencies behind decision-making—and prepares you to match AI capabilities.Once you’ve identified the key decisions, match them to the appropriate AI capabilities.For our insurance examples, we might to map the processes to the following capabilities:
Example: Insurance Brokerage
  1. Marketing
    • Segment customers = Data cleaning + segmentation
    • Select messages/channels = Data cleaning + classification
  2. Sales
    • Score leads = Data cleaning + event prediction
    • Engage and convert = Data cleaning + event prediction + writing
  3. Renewals
    • Identify customers due for renewal = Data cleaning + event prediction
    • Proactively engage = Data cleaning + event prediction + writing
5

Score opportunities

Now assess the use-cases you’ve identified for both impact and feasibility.Impact
  • How strongly does the opportunity support the original business objective?
Feasibility
  • Is it achievable with current AI capabilities?
  • Is the required data available?
  • Can it be delivered within timeline and budget constraints?
  • Will stakeholders support the initiative?
Scoring isn’t about precision—it’s about consistent comparison. Use this process to structure conversations and prioritise action, not to perfect a mathematical model.

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Next Steps

  • Run a pilot workshop with one team or department
  • Select one high-feasibility, high-impact use-case for implementation
  • Use outcomes to build buy-in and refine the framework for broader rollout
For templates, guides, and capability demos, visit the TrueState Essentials Hub.