Lifecycles, not Use Cases. A 5-Step Guide to Maximise Your GenAI Investment

Jul 10, 2024 | min read

Mark Rodseth

Use Case selection and prioritisation are the default starting points for organisations deciding where to invest in GenAI initiatives.

The problem with this approach is that it can lead to disconnected point solutions scattered across the organisation with no measure of impact and value.
To illustrate this, let's say an organisation decides to trial several different GenAI solutions in the following Use Cases.

Writing Code. GitHub CoPilot for Engineering team
Office Productivity. Microsoft CoPilot for the BackOffice team
Customer Insight Analysis. Salesforce Einstein for Sales Team
Content Creation. CMS GenAI Content Creation AddOn for the Marketing Team

The trial runs for a few months, and the feedback is mostly positive.
The engineers thought CoPilot was pretty cool
Office Workers had some success with Microsoft CoPilot
The Sales team gathered some new insights from their customer data
The Content team sped up their content writing output

But, so what?

Where does this organisation go next?

Continue with these experiments, ramp them down where costs are high, or trial GenAI in different parts of the organisation?

It's hard to make the next call without the measure of impact. But how do you achieve this?

Focus on LifeCycles, Not Use Cases.

Here is what, why and how.

What is a Lifecycle?

Organisations can be thought of as a collection of different value-generating, interconnected lifecycles. Some lifecycles are served by other lifecycles. Others are connected to and dependent on one another.

Lifecycles can be circular; the end of one cycle leading naturally to the beginning of the next. Some run in tight loops, while others turn slowly.

An example is the Software Development Lifecycle in an Agile Scrum context.

Backlog Refinement > Sprint Planning > Sprint Execution > Daily Scrums > Sprint Demo > Sprint Retrospective > Back to Refinement.

Other lifecycles are linear, running once to an end.
(Semantically, this is more of a journey with a sequence of stages, but in practice, these terms are interchangeable.)

The Human Resources Lifecycle demonstrates this.

Recruitment > Onboarding > Training > Retention > Offboarding.

The interconnectivity of lifecycles is a complex web that varies across organisations. How well each one runs and how well they feed value to their peers and parents will influence the journey of the whole through the stages of inception, evolution, growth, renewal or demise.

Mapping Lifecycles

Mapping your organisational lifecycles is the first step to understanding how value flows through your organisation and where there are inefficiencies, redundancies, and duplications.
Once you have identified these and the connections between each, you have a model that you can use to focus your optimisation efforts.
Here is an illustration of this idea using the example of a digital product development company.

This diagram crudely tells the story of different lifecycles focused on delivering value in key areas and how that value impacts other lifecycles.

The Product Lifecycle delivers a roadmap into the Software Development Lifecycle.
The Software Development Lifecycle delivers working software into the Product(s) lifecycle.
The Product Lifecycle generates customers for the Customer Relationship Lifecycle. And it generated customer insights that influence the product lifecycle.
The Human Resource Lifecycle delivers people and skills into all lifecycles.
Many more Lifecycles relating to supply chain management, IT service management, financial management, and sub lifecycles within the larger lifecycles can be mapped out.

Mapping out at this level, however, is a good starting point to paint a picture at how your organisation works or doesn’t.

Optimising Lifecycles with GenAI

With mapped lifecycles, you can now focus your GenAI experiments on your organisation’s most important value-generating mechanisms. Focusing at this level brings several benefits over selecting Use Cases.

Better Value From Experimentation

Running a GenAI experiment across a lifecycle assesses broader impacts, ensuring changes at one stage benefit subsequent stages, leading to more comprehensive and systemic improvements. While use case experiments can provide valuable insights into specific areas, lifecycle experiments offer a more holistic view, addressing broader issues and potentially delivering greater overall effectiveness and value.

Measurement

Measuring lifecycles offers a more comprehensive and strategic approach to performance management than focusing solely on use cases. While use case measurements are crucial for tactical insights, lifecycle measurements align with strategic frameworks like the Balanced Scorecard, providing a broader perspective on organisational performance.

Efficiency Measurement

A lifecycle consists of steps executed in sequence, allowing efficiency to be measured by tracking time-based metrics such as wait times, cycle times, lead times, and transition times. This approach aligns with Lean and Six Sigma principles, which focus on reducing waste and improving process efficiency. Tracking these metrics provides a clear understanding of optimization opportunities across the whole process and helps identify areas causing the biggest problems.

Throughput Measurement

A lifecycle typically produces measurable units of output. For example, in the Software Development Lifecycle, these can be features or story points; in the Marketing Lifecycle, these can be articles published. Measuring the impact on output produced by a lifecycle offers a meaningful perspective on how GenAI affects production throughput. While lifecycle throughput provides a comprehensive view, use case metrics can still offer detailed insights into specific process improvements.

Outcome Measurement

Outcomes ultimately matter, and efficiency and throughput metrics should drive better outcome metrics. Outcomes are typically measured with OKRs and linked to KPIs. Accurately understanding the cause and effect between KPIs and OKRs is essential but challenging. However, it is often easier to correlate the impact of efficiency and throughput lifecycle KPIs to Objectives and Key Results than with scattered use case metrics relating to tasks or interactions.

Lifecycle Reinvention

We are in the midst of a major transformation in how people deliver work and how customers interact with digital products. The former is most apparent, and the latter is evolving fast.

Both productivity and experience are underpinned by their own lifecycles, so it stands to reason that organisations that lead in lifecycle re-invention will be better positioned to achieve transformative outcomes and gain a competitive edge.

Your 5 Step Guide

Investing in GenAI can be transformative for organisations, but focusing solely on use cases can lead to fragmented solutions with unclear value. Instead, adopting a lifecycle approach ensures holistic improvements and strategic value.
Here’s a five-step guide to maximise your GenAI investment through lifecycle optimisation:

1. Map Your organisational Lifecycles

Identify Key Lifecycles: Understand your organisation’s value-generating processes, both circular (e.g., Software Development Lifecycle) and linear (e.g., Human Resources Lifecycle).

Visualise Interconnections: Map out how these lifecycles interact and depend on each other, highlighting inefficiencies and redundancies.

Create a Comprehensive Model: Develop a model that illustrates the flow of value and interdependencies among lifecycles to guide optimisation efforts.

2. Focus GenAI Experiments on Lifecycles

Select Critical Lifecycles: Choose lifecycles that are crucial to your organisation’s success for your GenAI experiments.

Assess Broader Impacts: Implement GenAI solutions across entire lifecycles to evaluate systemic improvements rather than isolated use case benefits.

Ensure Strategic Alignment: Align GenAI initiatives with strategic goals to enhance overall organisational performance.

3. Measure Lifecycle Performance

Efficiency Metrics: Track time-based metrics such as wait times, cycle times, lead times, and transition times to identify optimisation opportunities.

Throughput Metrics: Measure the output produced by each lifecycle (e.g., features in software development, articles in marketing) to assess GenAI’s impact on productivity.

Outcome Metrics: Use OKRs and KPIs to measure the ultimate outcomes, ensuring that lifecycle improvements translate into achieving strategic objectives.

4. Optimize Based on Data Insights

Identify Bottlenecks: Use the collected data to pinpoint stages causing delays or inefficiencies.

Implement Improvements: Apply Lean and Six Sigma principles to streamline processes, reduce waste, and enhance lifecycle performance.

Monitor and Adjust: Continuously track performance metrics to ensure sustained improvements and make adjustments as necessary.

5. Drive Lifecycle Reinvention

Embrace Transformation: Leverage GenAI to fundamentally redesign how work is delivered and how customers interact with digital products.

Enhance Productivity and Experience: Focus on reinventing lifecycles to improve both internal productivity and customer experiences.

Gain Competitive Edge: Position your organisation to achieve transformative outcomes and maintain a competitive advantage in a rapidly evolving market.

By adopting a lifecycle-focused approach, organisations can ensure their GenAI investments lead to meaningful, strategic improvements that enhance overall performance and drive long-term success.