Tech leaders need to put AI and its subcategories into practice—and into common business vocabulary that everyone can understand.
Sage, a purveyor of business and accounting software, conducted several artificial intelligence (AI) surveys where 43% of respondents in a US survey, and 47% of respondents in a UK survey said that they had no idea about what AI was about.
There's a reason for the confusion.
Vendors are rushing myriad AI solutions to market before the ultimate decision-makers and buyers are up to speed on what they need.
For CIOs and technology leaders, the plot further thickens when they are asked by the CEO about the specifics of what AI can accomplish—and how AI differs from machine learning and deep learning.
Very quickly, these technology leaders recognize that they need to put AI and its various subcategories (e.g., machine learning, deep learning) into practice—and into a common business vocabulary that everyone can understand.
Step one: Explaining AI
The first step is communicating what the definitions are for AI, machine learning (ML), and deep learning. There is some argument that AI, ML, and deep learning are each individual technologies. I view AI/ML/deep learning as successive stages of computer automation and analytics that are built on a common platform.
On the first tier of this platform sits AI, which analyzes data and quickly delivers analytical outcomes to users. Machine learning sits on the tier two application of AI that not only analyzes raw data, but it also looks for patterns in the data that can yield further insights. Deep learning is a third-tier application that analyzes data and data patterns, but it goes even further. The computer also uses advanced algorithms developed by data scientists that ask more questions about the data with the ability to yield even more insights.
Step two: Putting AI/ML and deep learning into practice
The best way to demonstrate these different layers of increasingly complex analytics is by finding a business example that can show the benefits to the decision makers in the business.
Let's take the sample of traffic planning.
Tier one: AI
You develop an AI application that tells your traffic engineers and planners where the major traffic congestion points are located in the city. This assists them in planning for road repairs, stop lights, and other infrastructure that, hopefully, can relieve congestion in certain areas.
Tier two: Machine learning
You further develop your AI/analytics so that it also looks for patterns in the data. For instance, it notices the traffic at certain intersections is most congested in the morning between 6 am and 8 am, or that traffic queues up in the evening, ahead of a sporting event.
Knowledge of the situation gives planners and engineers more insight because now they can plan not only for traffic snarls but also for future events like concerts and hockey games.
Tier 3: Deep learning
Deep learning is where data analytics moves beyond raw data and data patterns. Deep learning adds specific algorithms that data scientists develop to further expand the querying and insights derived from the data.
Algorithms that could be added to the traffic analysis might include: What areas of the city will see the greatest population growth over the next ten years? Or, which roads will need major repairs in the next five years? Or, do weather projections say that we will have more or less snow over the next five years? By adding these algorithms on top of pattern and data analyses, users get a more complete picture of the situation they are trying to act on and assess.
SEE: Artificial intelligence: Trends, obstacles, and potential wins (Tech Pro Research)
Putting it all together into an AI roadmap
Being able to break down the differences between AI, machine learning, and deep learning is important because it shows management not only the different tiers and capabilities of AI automation but also the increasing levels of business insights that can be gained from it. By visualizing these different AI tiers into a corporate and IT strategic roadmap, an organization can measure tangible results in both IT and business objectives.
So a city, for instance, can say that next year it will have a comprehensive understanding of its road system, and where the traffic congestion is located. In year two, the city will be able to predict traffic jams from rush hour and special event traffic and be able to proactively inform travelers to use alternate routes. And in year three, the city will be able to develop plans for the future by assessing population (and traffic) growth, infrastructure repair shutdowns and also the impact of factors such as climate change.
The IT roadmap will reflect upon these strategies by listing the types of AI technologies (and investments) that will be needed year-to-year to support the business strategy.
SEE: Research: Companies lack skills to implement and support AI and machine learning (Tech Pro Research)
The ultimate goal is to ensure that both the business and IT maintain a firm understanding of what AI (and its embellishments) are and how it will be used to benefit the organization. This understanding should go beyond buzzwords and definitions. It should be built into strategic plans that are tied to budgets, talent acquisition and development, ROI, and outcomes.
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