Before the popularization of DALL-E, Stable Diffusion, and Chat GPT, very few business executives were tasking technology leaders with accelerating AI strategies. That’s all changed. Now, Gartner estimates that by next year, 35% of large organizations will have named a Chief AI Officer reporting to a CEO or COO. And by 2033, the same study predicts that AI solutions will result in more than half a billion net new human jobs.
AI creating, rather than eliminating, jobs is one of the many surprising ideas that have emerged. We don’t fully understand AI’s long-term impact, but it’s clear that it will augment people and fundamentally transform how we work. And that is why now is the time to challenge assumptions, redefine roles, and reimagine how a business should operate in this AI-first world. Instead of improving what you already do, how will you empower your business to do things you never thought possible?
Do it right—now
In 1995, Microsoft co-founder Bill Gates sent an internal company memo calling the internet a “tidal wave” that would be crucial to every part of the company’s business. Gates described it as “the most important single development since the IBM PC was introduced in 1981.” Almost 30 years later, Microsoft’s current leader, Satya Nadella, told Bloomberg that artificial intelligence will be just as impactful.
The mistake so many executives made at the dawn of the internet, and continue to make, is that they didn’t take the time to evaluate how digital-first innovations could reimagine essential elements of a business for the new world. Instead, many digitized analog-first processes and bolted them onto aging operating models.
Hall of Fame basketball player and coach John Wooden once wisely observed, “If you don’t have time to do it right, when will you have time to do it over?”
Without a doubt, this is the time to do business transformation right.
AI as a force multiplier for business (r)evolution
In a time when everyone is speeding up, why would we slow down?
Companies will miss out if they make assumptions about business and operational models without reflecting on the possibilities afforded by AI. Your enterprise must ask the larger questions:
- How does AI change a fundamental assumption about my business or operational model?
- How can AI create new value for the customer or our employees?
- What are my customers’ and employees’ fundamental outcomes?
- How can we rethink the playbook to achieve these outcomes?
Your answers to these questions will help you forge the path forward for innovation. Embracing this type of deep dive and seeking transformative change can be daunting, but it can have major payoffs.
IBM research found that 83% of executives say generative AI will reinvent the way their organization works. And organizations that revamp their operating models before putting new technologies at the core of their business notably outperform their peers. In short, modern operating models will be the enabler of business transformation.
AI calls for a revolution in how we operate and do business. It’s a unique moment in time that invites us to reimagine how we work, how we serve customers, and how we empower employees. When leaders place transformational outcomes at the center of their strategies, AI becomes a force multiplier for growth.
Like the internet, AI was born in a digital-first world, using data as its food for artificial thought. AI is not derived from analog processes and models that shaped organizations over the last century. Every evolution of AI reshapes markets in its own image and potential. And as AI becomes more proficient, its potential to reshape markets, and in turn, organizations, becomes exponentially faster and more effective.
AI is only as intelligent as the platform it runs on. You can’t run the enterprise of tomorrow with yesterday’s operating model. For AI to thrive in any organization, it must be powered by integrated data, processes, resources, and governance.
The businesses of 2040 or 2050 will have more in common with the operating models built for 2030 than they will with those in 2020. Organizations will have to keep evolving, but this time, from a different core. And to do that, new operating models must be fashioned to become AI-native and optimized.
In other words, a new AI-first playbook is needed to rewrite the future of what an organization looks like.
Shift from pursuing tasks to achieving outcomes
One approach to a new playbook could start by focusing on the fundamental outcomes that define a successful enterprise. As Steven Covey famously recommended, “begin with the end in mind.”
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.
Artificial Intelligence, Enterprise
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Source: News