Artificial Intelligence Readiness Assessment

Stages of the journey: Learning > Scaling > Differentiating > Leading



I had the opportunity to speak with Meeta Dash, V.P. of Product, and Sid Mistry, V.P. of Marketing at Appen about their new artificial intelligence (AI) readiness assessment designed to help companies determine where they are in their AI journey and to get guidance on how to proceed to the next stage. Meeta and Sid are seeing more interest and engagement on the business side, including business owners and executives. This is particularly true where AI significantly differentiates the business.


In order to move up the AI maturity curve, Meeta suggests companies need to determine how much AI is critical to the value they provide. They also need to get executive buy-in to AI, including visibility and participation. Invest in tools, processes, and frameworks to continuously improve their models. And, continuously improve sophistication around tooling and structure.


A clear data strategy is a key to improving success. Another is whether or not a company has a center of excellence for AI. Ultimately these are driven by whether or not AI is a small feature or critical for the value the business is providing.


Appen has identified four stages in the journey: 1) learning; 2) scaling - this is where most companies are; 3) differentiating - in the marketplace, serious about the process, framework, and scalability; and, 4) leading - there are very few companies here.


Nearly three-quarters (73%) of respondents are at the scaling stage. Companies in the scaling stage have had early success working on AI initiatives, but AI is not how they differentiate themselves in the market.


Teams at this stage have had some success with pilots and are working to scale their initiatives throughout the organization. However, they start running into security, compliance, bias, and infrastructure challenges that are typical for scaling technology projects. More teams start working on AI programs, and budgets are scaling with ROI.


Leadership at the scaling stage has more visibility into AI programs and is starting to actively participate. Teams expand both in size and across the organization - typically ranging from 6-10 people in this stage. These teams may move from the innovation or research part of the business to product and engineering or IT teams.


Budgets are usually between $25,000 and $500,000, with a hybrid focus on experiments and continuous programs. At this stage, organizations start to consider tech and data partners, as they get access to vendor budgets between $20,000 and $100,000 per year.


Teams stuck at the scaling stage might consider getting more confident in the learning stage if they are not seeing consistent results from their AI programs, rather than focusing on scaling their efforts. Without the confidence the team is delivering value, it's hard to think about turning it into a competitive advantage. Organizations considering moving back to the learning stage might roll back the scope of their AI and rethink the business value and the business problem they are trying to solve, restart experiments, and gather more data so they can come back to this stage with more confidence.


Generally, organizations that move forward from this stage to the next are confident that their AI is becoming a competitive advantage in their markets. Before advancing to the differentiation stage, teams should feel confident they can:

  • Consistently gather and increase value as data, infrastructure, and processes are scaled

  • Understand the competitive landscape and the differentiation AI brings to the table

  • Elevate executive participation to direct involvement and ownership

  • Secure more resources to turn AI into a business differentiator

Click here if you’d like to take the AI readiness assessment.

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© 2020 by Tom Smith | ctsmithiii@gmail.com | @ctsmithiii