IBM’s research supports this. It found that operating model leaders have transitioned to prioritizing outcomes instead of tasks to complete. As a result, these companies report higher profitability/efficiency, revenue growth/effectiveness, innovation, and employee engagement.
In the 1990s, Stategyn founder Tony Ulwick defined a novel approach called outcome-driven innovation. The goal was focus on understanding the underlying process (or job) the “customer is trying to execute when they are using a product or service.” Clay Christensen would later build on this model, creating his framework known as “jobs to be done.”
The theory is based on understanding customer behavior. What works for customers also works for employees and workflows, too. You are trying to get to the heart of why people are doing what they are doing. What is the fundamental outcome they are trying to achieve?
With every technology revolution, behavior changes. This is why Gates and Nadella shifted to the internet and AI in their leadership.
When you recognize the outcome, you can start reimagining how to get there using AI. When you notice customers piecing together solutions and workarounds to get to the outcome they want, this is a moment for innovation. You could use AI to reverse engineer back to a workflow that would enable customers to get to their outcome faster and easier.
When an employee needs to nail their big pitch meeting, they could scenario play the pitch in their own metaverse with AI predicting and acting out the questions and behaviors of the people that will be at the meeting. Or, a business leader might ask AI for a fully vetted business model by using abstract prompt engineering to analyze your business model from the perspective of, say, Steve Jobs, or any other business leader you might admire.
These are just a few examples of the possible playbook you might develop. The idea is to ground your playbook on outcomes, not assumptions.
Shifting from an automated to augmented enterprise
A recent study by BCG of 2,500 companies found that “Modest investments in specific AI use cases can generate up to 6% more revenue, and with rising investments, the revenue impact from AI triples to 20% or more.”
Not all AI-driven change is equal though.
Another study looking at 2,500 firms found that those that took a safer, business as usual, approach, adopting AI at levels below 25% did not find performance benefits. The firms that saw the most benefit adopted AI at higher levels and complemented that with AI R&D. These enterprises developed AI tech that was tailored to their “unique business environment and needs,” which paid off. In other words, they innovated around this new technology to meet evolving market needs and objectives.
What might a new playbook look like?
To get started, here are some of the best practices we’re seeing in our work with leading companies around the world.
Start: Aim GenAI conversations on real business problems and achievable use cases. Then, dream bigger. Identify possibilities that weren’t available before AI.
Understand: Begin with outcomes in mind. Learn how AI is influencing customer and employee behaviors to imagine how AI can unlock new workflows and experiences.
Focus: Explore business impact. Identify meaningful ways GenAI will drive your business goals. Also ask, what new goals are achievable that weren’t possible before?
Organize: Form an AI Center of Excellence. Establish a decision framework and governance.
Strategize: Identify use cases and value maps. Prioritize adoption by value, feasibility, and potential. Categorize investments by quick wins, differentiating use cases, and transformational initiatives.
Assess: Identify the risks (data, regulatory, repetitional, competency, technology), and also risks associated with being too slow or conservative.
Adapt: Remove barriers to generate value. Identify organizational challenges and actions needed to overcome them.
Evolve: Identify literacy, skills, and technologies needed to execute (automate, optimize and augment). Outline a path to tomorrow’s work and skills needed and incentivize progress. Define what augmented performance and success looks like and how it’s trained and rewarded . The goal is to empower them to embrace, explore, and innovate with AI.
Measure: Identify measures of progress and success, such as…
- Customer success
- Cost efficiency
- Business growth; Increased revenue
- Operational efficiency
- Employee experience
- Net new value creation
Reassess: And finally, reassess how AI impacts your overall transformation strategy, develop an upgraded roadmap, and ensure C-Suite alignment.
Generative AI is already changing behaviors faster than any innovation in history. The good news is that, by using AI as a force multiplier and shifting to an outcome-based approach, we can shape how the future unfolds.
