Identifying High-Value AI Use Cases

According to RSM US, reality is setting in for artificial intelligence after months of hype. Companies developing AI tools face critical questions: What does your AI actually do? What return can I expect on my investment? How does your gadget differentiate you from the other guys?

Understanding the Current Landscape

The latest McKinsey Global Survey on AI says that 65% of organizations now regularly use AI, nearly double from ten months ago. While most of the respondents predict AI will lead to significant industry changes, the path to value isn't obvious. These companies report both cost decreases and revenue increases, but McKinsey notes that only a small subset of of them attribute more than 10% of their earnings to AI projects.

Business Reporter emphasizes that despite significant progress in capabilities, AI is not a magic bullet. Many AI projects fail to move beyond the tinkering stage, and those that do often require substantial resources to implement effectively. The current IT landscape is already crowded with apps, and many businesses are struggling with data quality and old-school systems.

This tension between potential and reality reflects what Matthew Ball, CEO of Epyllion, describes in RSM US: pinpointing the onset of a new technological era can be tricky. Tech integrates into daily life in small ways that aren't immediately noticeable. Changes build momentum gradually until it becomes obvious only in hindsight that a new era has emerged and you're living in it. We don't want to get caught with our pants down.

Ok, you've got the big picture. Now how do you find the right opportunities?

Framework for Identifying AI Opportunities

The success of AI adoption depends on identifying the right opportunities. While Wavestone emphasizes starting with strategic alignment, our approach combines principles from Lean methodology and Jobs-to-be-done to ensure projects address real user needs and business challenges.

1. Start with Jobs to be Done

Begin by identifying the core jobs your customers and employees need to accomplish:

Map current process steps and workflows
Document pain points and inefficiencies
Measure time spent and error rates
Define desired outcomes clearly

2. Apply Lean Problem Analysis

Use the "5 Whys" technique to understand each identified problem deeply:

Why is this a problem worth solving?
Why does it occur repeatedly?
Why haven't current solutions worked?
Why would AI be more effective?
Why is now the right time to address it?

3. Evaluate AI Potential

For each identified job or problem, assess three key areas:

Data Readiness

Availability of relevant historical data
Quality and structure of existing data
Capability to collect needed data going forward

Problem Characteristics

Presence of clear pattern recognition elements
Frequency of repetitive decision-making
Potential value from automation

Implementation Feasibility

Required technical capabilities
Integration with existing systems
Risk factors and mitigation strategies

4. Prioritize High-Impact Opportunities

Review potential projects through multiple lenses:

Strategic alignment with business goals
Potential for measurable impact
Resource requirements and availability
Implementation complexity
Time to value

Focus on selecting 2-3 opportunities that offer the best combination of impact and feasibility for initial projects.

High-Impact AI Use Cases

You came for the use cases. Here are the goods:

1. Customer Experience and Service

The Financial Times reports how Octopus Energy transformed their customer service using AI:

Automatic transcription and response generation for customer queries
50% of customer service emails now AI-assisted
Improved response times and account health metrics

Gartner highlights two additional successful implementations:

Healthcare: Mayo Clinic

AI-enabled medical chatbot conducts dynamic patient interviews
Efficiently gathers symptom information and medical history
Provides personalized care recommendations
Reduces clinician burnout through streamlined intake

Banking: Ally

Contact center assistant
Automates post-call note-taking and summarization
Reduces manual documentation work
Enables associates to focus on customer interactions
Improves speed and accuracy of call documentation

2. Business Operations and Strategy

The Times highlights how Grind, a medium-sized coffee retailer, integrates AI across their operations:

Marketing content generation
Customer query management
Performance report creation
Company-wide AI training program for staff

3. Process Automation and Efficiency

The Guardian highlights how Employment Hero uses AI to transform HR processes:

Automate new hire onboarding and contract organization
Complete payroll system setup in 20 seconds
Handle CV screening for both volume and complexity
Enable HR staff to focus on team integration

Survey results show:

52% report increased efficiency 42% see faster operations 65% reduce paper usage

4. Documentation and Knowledge Management

Deloitte identifies several high-impact applications for medium-sized businesses:

Legal document automation
Technical documentation generation
Policy and procedure updates
Knowledge base maintenance and updates

Each of these applications shares common success factors:

Clear alignment with business objectives
Measurable impact on efficiency or revenue
Focus on augmenting rather than replacing human capabilities
Structured approach to implementation and training

Common Challenges and Solutions

McKinsey's research identifies several key challenges:

Accuracy Concerns
- 44% of organizations have experienced negative consequences from AI use, with inaccuracy being the most common issue
Risk Management
- Only 18% of organizations have an enterprise-wide council for AI governance decisions
- Just one-third require AI risk awareness for technical talent
Implementation Time
- Most organizations need one to four months to put AI into production
- Time varies by business function and level of customization

Next Steps for Medium-Sized Businesses

The experiences of companies like Grind and Octopus Energy show that successful AI implementation requires four key elements:

Start with real problems, not technology solutions
Use data you already have rather than creating new data streams
Begin with 2-3 focused projects where success is clearly measurable
Give teams time to learn and adapt to new ways of working

By focusing on specific business problems and measuring results carefully, we can move past the hype to find practical applications that deliver real value.

The most successful organizations treat AI as a tool to enhance human capabilities, not replace them. For deeper insights into AI implementation strategies and updates, follow the referenced sources throughout this article.