In a recent IDC survey, only about 30 percent of companies reported a 90 percent success rate for AI projects
Most reported failure rates of 10 to 49 percent, while 3 percent said that more than half of their AI projects failed. That’s a pretty daunting success rate. But the benefits outweigh the risks.
We see this time and again in our customer base. For a world-renowned department store chain, investing in AI-powered technology means a boost in conversions from 4% to 7.6%; for RedHat’s customer support team it meant a 50K reduction in support tickets. The opportunity to boost revenue and cut costs is what makes the investment worthwhile.
Investing in AI is table stakes for business survival. According to data from the 2020 Gartner CIO Survey, 92% of technology decision makers plan to or have already deployed AI. The question therefore is not whether to invest in AI – it is how to overcome the significant challenges associated with AI adoption and ensure the project’s success.
According to Gartner, “organizations in the study foresee substantial acceleration in their adoption of AI-powered applications. More than 30% of organizations in the survey believe that, in 24 months from the survey date, they will be deploying 10 or more products (in addition to products deployed in the previous two years). In fact, if we trace the organizations in the survey with AI or ML projects deployed today, we see that these organizations expect substantial project acceleration over the next three years” (Gartner “Survey Analysis: AI and ML Development Strategies, Motivators and Adoption Challenges,” Whit Andrews, Jim Hare, 19 June 2019). Unfortunately, the landscape is littered with AI projects that never graduated beyond science experiments, showed limited business value, or which failed outright.
Here are the three areas to focus on to overcome the major challenges to AI adoption:
1. Prioritize Organizational Alignment
2. Establish a Realistic Scope
3. Take a Problem-Focused Approach
Don’t be a part of the 70% of AI projects that fail — read on to learn how to invest in an AI strategy that has a long-term positive impact on your bottom line.
Prioritize Organizational Alignment
The skills of staff is one of the top challenges or barriers to the adoption of AI and ML according to data from Gartner. However, the reality is much more complex. Most organizations encounter multiple barriers to cross-team collaboration, including obstacles in processes, hierarchies, personalities, and culture in general. Organizational alignment stifles two thirds of all AI projects, as companies have trouble defining an AI strategy, deciding on the priority use cases, or finding funding for implementation.
This is one of the main learning curves for organizations— not only deciding what to work on, but when you decide on what to work on, how you organize to design and execute. That can be learned by starting small and targeted and then scaling to the rest of the organization and use cases. Plan ahead so you’re not taking by surprise when you hit organizational road bumps.
Establish a Realistic Scope
Organizations also grapple with the issue of scope. Ambitious CEOs and enthusiastic IT leaders rightfully avoid doing too little and instead bite off more than their business can chew. According to Gartner there are two phases in AI adoption. “During Phase 1, organizations should implement only use cases that have well-understood data and KPIs for business value. Just as importantly, the business must be ready to act on the AI deliverables for each use case. Ambitious, ground-breaking AI projects (“moonshots”) should only be undertaken during the later adoption of Phase 2, when organizations already understand the above principles and have experience in delivering AI solutions.” (Gartner “AI Governance Spotlight: Early Lessons and Next Practices,” Svetlana Sicular, Van Baker, 8 April 2019).
These larger initiatives often require new personnel and processes, and introduce more complexity than a problem-focused initiative. It’s better to conquer a smaller project in the short term than set yourself up for a large longer-term project that may not succeed.
Take a Problem-Focused Approach
Businesses adopting AI usually begin with little experience of the technology and organizational requirements for success. Resourceful companies might have hired experts like data scientists who understand AI principles, but yet the organizational maturity to make that expertise felt by tens or hundreds of other people in the business.
One of the best ways to create a realistic strategy and gain buy-in from leadership, is by adopting a problem-focused approach that has a clear problem and easily-measured KPIs. Leaders are right to be skeptical of big transformational projects that don’t have a clear picture of success. Starting small with a targeted problem increases your chances of success. Smaller organizational scope gives you more control and agility, which allows for better planning and iteration. Better planning means faster execution, which leads to faster business outcomes and an organization that’s reaping the benefits of AI.
The AI-Powered Enterprise
An AI-powered enterprise can leverage knowledge across the organization and get the maximum value out of their data. As the well-oiled machine gains momentum, and the enterprise is confident in picking up additional AI projects, the business scales interdisciplinary innovation and achieves digital transformation.
Start small to establish your proof of concept with AI-powered solutions. Prioritize organizational alignment over technical hurdles and get leadership buy-in on a realistic scope by defining a problem and staying focused. Don’t let the potential challenges hold you back from starting now. The sooner you get going, the sooner you can take on larger projects and reap the biggest rewards.
About the Author
Radu Miclaus is Director of Product, AI & Cloud at Lucidworks. Lucidworks builds AI-powered search solutions for many of the world’s largest brands. Companies across all industries, from consumer retail and healthcare to insurance and financial services, rely on Lucidworks everyday to power their consumer-facing and enterprise search apps.
Featured image: ©Metamorworks
