How do most organizations begin their Artificial Intelligence (AI) journey?
Let’s look at how leaders of some large enterprises planned their foray into AI. Here are a couple of recent examples from McKinsey:
- The leader of a large organization spent two years and hundreds of millions of dollars on a company-wide data-cleansing initiative. The intent was to have one data meta-model before starting any AI initiative.
- The CEO of a large financial services firm hired 1,000 data scientists, each at an average cost of $250K, to unleash AI’s power.
And here’s an example that I witnessed first-hand.
- The CEO of a large manufacturer lined up a series of ambitious projects that used unstructured data, since AI techniques are very effective with text, image, and video data.
What do all of these initiatives have in common? They all failed.
In addition to the massive sunk costs suffered by these projects, they led to the organization’s disillusionment with advanced analytics.
This is not uncommon. McKinsey’s State of AI survey found that only 22 percent of companies using AI reported a sizable bottom-line impact. Why do so many projects fail, and how can leaders avoid this?
[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]
Most leaders pursuing AI miss out on three areas of ownership. These responsibilities start well before you plan your AI projects, and they extend long after your projects go live.
Here are the three ways to fail your AI initiative:
Mistake 1: Starting AI projects that don't align with the corporate vision
McKinsey found that only 30 percent of organizations aligned their AI strategy with the corporate strategy. Isn’t it shocking that a majority of leaders are burning their cash in the name of AI? Organizations often pursue AI initiatives that appear interesting or those that are just urgent.
True, your projects must address a business pain point. But, what’s more important is that these outcomes must align with your corporate strategy. Start with your business vision and identify how data will enable it. Clarify who your target stakeholders are and define what success will look like for them.
Then, identify the strategic initiatives that will empower the stakeholders and get them closer to their business goals. Now, you’re ready to brainstorm to come up with the long list of AI projects that are worth evaluating.
In a report by MIT Sloan Management Review, Steve Guise, the CIO of Roche Pharmaceuticals, explains how AI helps transform the company’s business model. Roche is working toward making personalized health care a reality. Guise points out that the current model of drug delivery will not help them achieve this vision. They see a need to accelerate the pace of drug discovery from three drugs per year to 30. Guise says that AI can help them get this exponential improvement.
Roche is making AI mainstream within the organization by building capabilities across screening, diagnosis, and treatment. It augments this by partnering with startups pursuing AI-driven drug discovery,. Thanks to these efforts, Roche has made significant breakthroughs in the treatment of diseases such as Hepatitis B and Parkinsons. By starting with their corporate vision and aligning all their AI initiatives with this overarching objective, Roche’s efforts are bearing fruits.
Mistake 2: Waiting to plan for ROI after the project goes live
When should you think about Return on Investment (ROI) from your AI project? Most organizations make the mistake of tracking ROI when the project goes live. Leaders settle for fuzzy outcomes such as “efficiency improvement,” “brand value,” or “happier customers,” to make matters worse.
True, it’s not easy to quantify the dollar value of outcomes. But it’s not impossible. You must demand quantification of business benefits even before greenlighting a project. AI can deliver value by either growing revenue or lowering expenses. Both are valuable. Define which of these outcomes your project will enable.
Identify a mix of leading and lagging metrics that will help measure these outcomes. Collect the data needed to compute the metrics by updating your processes or creating new ones. Finally, track your investments by going beyond the hardware, software, and technical team costs. Include your spending on adoption and change management programs. This ROI metric should be a critical factor in your project approval decision.
Deutsche Bank rolled out its AI-driven consumer credit product in Germany. The solution made a real-time decision on the loan even as the customer filled out the loan application. Consumers were worried about loan denials impacting their credit ratings. This product removed that risk by telling them whether their loan would be approved, even before they hit “apply.”
Deutsche Bank found that loan issuance shot up by 10 to 15-fold in eight months after the AI-powered service was launched. The gains were achieved by bringing in customers who wouldn’t have applied in the first place. This was a clear case of AI helping grow revenue.
Mistake 3: Expecting AI-driven transformation without fixing the organizational culture
In its 2019 annual survey, Gartner asked Chief Data Officers about their biggest inhibitor to gaining value from analytics. The topmost challenge had nothing to do with data or technology. It was culture.
As Peter Drucker famously said, "Organizational culture eats strategy for breakfast.” Even the best-laid AI strategy will amount to nothing if you don’t carefully shape the organizational culture. A culture change must start at the top. Leaders must use storytelling to inspire and demonstrate how AI can help the organization achieve its vision.
Leaders must address the fear around AI and improve the data literacy of all employees. They must lead by example and sustain change by onboarding data champions across all levels. The cultural shift takes years, and leaders must influence it long after the projects have gone live.
Wonder what the main ingredient in a Domino’s Pizza is? It’s data! Dominos Pizza is the poster child of technology transformation. The organization lives the data-driven decision-making culture and uses AI across sales, customer experience, and delivery. This wasn’t the case 10 years ago.
Patrick Doyle took over as CEO of the 50-year old pizza maker in 2010 when it was panned by customers and investors alike. Doyle took the bold step of going public with the harvest reviews. He then did a full reboot inside-out and set the organization on the path of digital transformation. He placed some bold bets on technology by taking on risky projects, empowering people, and building several AI innovations in-house.
When Doyle retired in 2018, Dominos’ sales had increased for 28 quarters straight, and it delivered stock returns that outpaced Google’s. The outgoing CEO summed it up best, “We are a technology company that happens to sell pizza.” By leading a cultural transformation within Dominos, Doyle ensured a shift to data-driven decisions that has sustained even after he transitioned to a new CEO.
How will you get AI past the innovation chasm in your organization?
Adoption of technology innovation is never easy. Whether it’s the launch of new technology such as AI in the marketplace or its adoption within an organization, the challenges are similar.
Innovators seed this journey within an organization. The innovation is then embraced by early adopters, thanks to their initial enthusiasm and openness to change. But then, the pace slows down and enters a chasm. There often is a lack of visibility, uncertainty in outcomes, and broader resistance to change.
This is where most initiatives fail.
For an innovation like AI to cross this chasm and go mainstream, it needs leadership intervention. Leaders must make AI successful by aligning the initiative with their corporate vision. They must demonstrate economic value by institutionalizing conversations on ROI from AI. Finally, they must shape the organizational culture to facilitate change and enable the viral adoption of AI-driven decision making.
[ Get the eBook: Top considerations for building a production-ready AI/ML environment. ]
