By AI Trends Staff
Companies who have some success with their initial AI projects are seeking ways to accelerate adoption to deliver more value to the business. One researcher has defined an AI Adoption Maturity Model that presents a roadmap for accelerating AI adoption.
Dr. Michael Wu, chief AI strategist, PROS Holdings
The first stage of the six-step AI adoption maturity model is the digitization of work, turning work in the physical world into digital processes that can be tracked and recorded as data, suggests Dr. Michael Wu, chief AI strategist for PROS Holdings, providing AI-based software as a service for pricing optimization, with a focus on the airline industry.
“This stage is all about getting the data, which is the raw material for AI,” stated Wu, in an account from ZDNet. “If you are on the digital transformation bandwagon, good for you. You are already in Stage 1 of this maturity curve.”
Wu felt qualified to create his own AI adoption maturity model from his work in applying data science expertise to analyze consumer behaviors on social media for over 10 years in his previous roles. These included chief scientist at Lithium Technologies, now called Khoros, offering software for customer engagement, online community management, and social media analytics.
The move to contactless digital technologies will speed the adoption of AI because it is required to scale to the global nature of ecommerce, and because AI is a differentiator against online competition. “Every digital business must and will eventually adopt some form of AI,” he states.
The critical mass of data required to train AI is high, so it takes a long time for companies to achieve the necessary data volume. In the meantime, companies can derive value from the data they have captured by performing analytics to help managers make better decisions.
Most companies will begin with descriptive analytics that summarize the data they have collected, in reports and dashboards, perhaps powered by business intelligence tools. As the volume and diversity of the data assets grow, the company may be ready to perform predictive analytics. For example, a manufacturing plant may use mechanical operation data to infer the failure time of certain machinery in order to perform predictive maintenance, Wu suggests.
Eventually, the company has gathered enough data to perform prescriptive analytics, used to prescribe actions that optimize some outcomes. For example, a pricing recommendation for a product is set to optimize revenue. Marketing automation can prescribe the engagement frequency for a prospect, to optimize the opportunity to convert prospects to customers. Now the organization is ready for the next stage of the maturity curve.
Wu makes the point that the steps within the first stage of the maturity model are “vendor-agnostic, technology-agnostic and use-case-agnostic.” Rather, they are derived from “social science principles,” which he describes as “not like the fundamental laws in math, physics, or chemistry that are practically absolute. This means there will be exceptions, albeit rare.”
The successive steps in Wu’s AI Maturity Model he derived from principles in behavioral economics and psychology. He states, “So they are agnostic to vendors, technologies, use cases, industries, business models, etc.”
The second stage in Wu’s AI Maturity Model is exchange data for automation. “Prescriptive analytics provide the natural transition into this stage,” Wu states. Automate the actions that the AI has optimized, is the suggestion. “Once the AI is trained, it would be able to help us automate that aspect of our work by mimicking our decisions and actions. So essentially, you are exchanging your data for automation,” Wu states.
To commit to this stage, he suggests that one needs to have faith in the machine, and let it run. “We must be comfortable with letting machines make the call (at least when it’s sufficiently confident) under human supervision,” he states.
The rest of Wu’s steps provide a roadmap for AI adoption. “Together with AI, I believe we can tackle the biggest challenges facing humanity,” he states.
Accelerating Adoption After Initial Success
Organizations that have started out on their road map to AI and have experienced some success, wonder how the process can be accelerated. During the pandemic in 2020, companies put AI through some paces.
David Tareen, director of AI and analytics, SAS
“The pandemic put AI and chatbots in place to answer a flood of pandemic-related questions,” stated David Tareen, director of AI and analytics at SAS, in an account from The Enterprisers Project. “Computer vision supported social distancing efforts. Machine learning models have become indispensable for modeling the effects of the reopening process.”
With these initial successes, companies begin to see the way to higher potential value to the business from more AI projects. “If there’s one reason IT leaders should accelerate the broader adoption of AI, it’s the ability to uncover opportunities that generate real business value through insights and efficiencies where perhaps there were none,” stated Josh Perkins, field CTO at AHEAD, which is a Gold Cloud partner with Microsoft Azure.
One suggestion is to identify the best use cases and begin with those. “AI and machine learning efforts are best directed at specific use cases, and it may require engaging a broader ecosystem to bring it to life, especially if you have a paucity of AI and ML talent,” suggests Peter A. High, president of Metis Strategy, business and technology consultants.
Another suggestion is to manage to milestones. “One overlooked challenge with AI initiatives is the time commitment required before tangible results can be delivered,” stated Ravi Rajan, head of data science at Cowbell Cyber, a cyber insurance company. “Without clear goals and planned milestones to show progress, AI projects can rapidly turn into discovery.”
Regarding staffing for AI, experts suggest a multi-pronged approach to skills acquisition. Expertise in big data, process automation, cybersecurity, human-machine interaction design, robotics engineers and machine learning experts are in demand. Finding needed expertise has become a creative endeavor calling for innovative approaches.
“In addition to having sophisticated hiring and retention plans, organizations need to work harder to leverage the talent they already have,” states Ben Pring, VP and director of the Cognizant Center for the Future of Work. “A root-and-branch reform of upskilling and internal career progression is an important element of the multi-factor HR strategy necessary to succeed at this foundational task.”
Pring is a coauthor of the book, What To Do When Machines Do Everything (2017), and the founder of Cognizant’s Center in 2011. He had worked at Gartner for over 14 years previously.
Oak Ridge National Lab Worked with Partners to Speed Adoption
In nuclear energy, engineers at the Oak Ridge National Laboratory (ORNL) found a way to accelerate AI adoption by working with partners.
“Industry turns to ORNL for scientific and engineering expertise and world-class facilities that can’t easily be replicated,” stated Kathy McCarthy, Associate Laboratory Director of the Fusion and Fission Energy and Science Directorate, in a press release. “Here our researchers share some of the impacts and success of their current industry partnerships.”
Collaboration led to the production this spring of four fuel assembly brackets produced by 3-D printers that have been successfully installed and are now operating at the Tennessee Valley Authority’s Browns Ferry Nuclear Plant Unit 2 in Athens, Alabama. The components are expected to remain in the reactor for six years, with routine inspections.
Produced at ORNL’s Manufacturing Demonstration Facility, they were developed in collaboration with Framatome, TVA, and the DOE Office of Nuclear Energy–funded Transformational Challenge Reactor (TCR) program based at ORNL.
“It took all three vantage points—industry manufacturer, electric utility, and national laboratory—to make this milestone possible,” stated Ben Betzler, TCR program director. “Through this collaboration, we’ve shown that it is indeed possible to deliver a 3D-printed component qualified to operate in what is one of the nation’s most highly regulated industries.”
Researchers in the TCR program are leading 13 projects selected for DOE’s INFUSE (Innovation Network for Fusion Energy) initiative.