Five Ways To Make The Transition To Generative AI A Success For Your Business

Successful AI transformations require the right leaders, updated governance models, an “all-in” mindset and a timeline that starts with quick wins

generative ai, ai disruption, ai and leadership, how ai will change the workplace

This article was originally published in the World Economic Forum on May 7, 2024.

Business leaders are devoting enormous amounts of capital and leadership attention to Generative Artificial Intelligence (AI). Now comes the hard work of making those wagers pay off.

Much is on the line. In a new survey of 400 C-level executives in Europe and the Americas by Oliver Wyman, 70% said they have fully implemented or are planning to implement AI technology. Already, these mid- to mega-sized companies are spending an average of 2.2% of their annual revenue as a fixed upfront investment. Some are spending as much as 3.5%.

All respondents said AI would be a critical lever for their business transformation, impacting everything from product development to customer experience. Initial investments are heavily focused on operational efficiency, workforce productivity, insights/analytics, and risk reduction.

Five strategies for implementing generative AI

Just as the internet created new management opportunities and challenges, so too has Generative AI. To get the most out of the technology, companies must pick the right leaders for the moment, embrace new levels of complexity to organizationally transform, rethink governance, improve training, and score quick wins to encourage broad buy-in.

For companies in the generative AI race, 2023 was mostly a year of testing, learning, and piloting. The big question in 2024 is how to begin creating measurable value. Below are five strategies that leaders must bear in mind as they begin their generative AI journey.

1. Pick the right composition of leaders for the AI transformation.

Given the sums being devoted to generative AI, companies have no time for half measures; if they don’t capture the benefits quickly, their competitors will. But spending money isn’t the same as committing to a full-scale corporate transformation. For the latter, leaders must first have the conviction that their organization and leadership are truly ready to change how the business operates on a day-to-day basis.

This requires answering some big questions. How will AI power our growth? How will it change the way work gets done? Who on my leadership team is capable of transforming the work and do they have the managerial bandwidth? Will they evangelize for truly transformational change across the organization?

No functions exist in a vacuum, but most senior managers don’t have a granular enough view across the enterprise to visualize such changes — so they delegate to IT or individual business units. Successful transformations require truly cross-company efforts.

2. Embrace complexity and novel approaches.

Generative AI is rapidly changing how many of us do our jobs. Across organizations, leaders must learn how to harness AI. This means embracing a level of complexity across organizations in the deployment of the tech. Siloes once were the best way to ensure that centers of excellence inside companies could flourish — but siloes will quickly hamper any serious transformation effort with generative AI.

As the transformation scales, it will require new approaches, new skills and resources and fresh sets of eyes. Leaders must manage the polarity of bottom-up innovation with top-down mandates to find the areas of greatest impact and mobilize around them.

3. Incorporate design principles in human-to-machine interactions.

Management needs to continuously reevaluate how to govern generative AI capabilities. Data privacy (25%) and security concerns (22%) are the top factors hindering companies from AI adoption, according to the Oliver Wyman survey. But machines sometimes hallucinate as well. Putting in confidentiality and legal protections or adding a human to the loop isn’t enough because people won’t monitor an issue the machine has already gotten right 999 times in a row.

This isn’t just a control problem; it’s also a design issue. Effective control over complex and autonomously acting machinery requires careful use of human-centric design principles, especially during control hand-offs. This requires design of faster and more detailed monitoring systems. Experience from these fields suggests this isn’t easy — but it can be done.

4. Bring workers along in the AI transformation with upskilling.

One problem hindering generative AI transformations is the disconnect between workers and leaders over training.

Fully 98% of employees say they will need generative AI reskilling or upskilling within the next five years, and three in four millennials and Gen Z workers say they would leave a job if it did not offer the chance to learn new skills. Yet executives believe only 40% of their workforce will need training on AI.

Training must be ongoing and include work on actual projects. If 2023 was about test-and-learn, 2024 and beyond will be about experiment-and-collaborate.

5. Score quick wins via efficiency gains.

By applying AI and generative AI to low hanging fruit and scoring easy wins early on in a company’s implementation, managers can point to tangible success as they make the case for even bigger transformations to come.

What’s more, the corporate transformations that focus on cost-cutting tend to be more successful, so tying generative AI to those broader efforts is a good place to start. Tackling specific work processes and use cases is often easier for companies and employees to imagine and begin with. These early results often provide encouragement to individuals and functions and offer critical lessons.

Ultimately, companies must move toward a full cross-enterprise transformation to create tangible value. Some companies already are doing just that. The most successful in the Oliver Wyman survey said they focused on transformations across the entire business, especially when costs were at play. While just 4% of IT-led transformations were deemed successful, that was 61% for transformations jointly led by business units and IT.

The bottom line: Businesses that commit to a deep AI transformation have the best results — those that don’t risk being left behind.