Business leaders see much in Artificial Intelligence, including new ways to save money, serve customers, and figure out what to build. What’s often tougher is deciding how to engage. That’s understandable, since getting into AI often raises questions about costs, data integrity, project length, and similar issues of planning and execution.
For CIOs concerned with the deployment and use of IT at their companies, these are critical issues. Here is one way to think about this complexity, by breaking it down into three different areas; Early Automation, Learning and working, and System Views.
First, a bit of good news and myth dispelling for readers who think AI is some far off promise, still in the labs and not safe for work. The reality is that AI is very much here, and almost everyone in your workplace is using it every day. People work with AI when they use Google Search, Photos, the autocomplete feature in Docs, live translation in Pixel phones, and many other areas. It is in the products of many other companies too.
“IDC reports that the market for AI software, hardware and services is expected to break the $500 billion mark in 2023.”
Additionally, it’s increasingly clear that AI can be adapted as a virtuous process, not just a one-off purchase (though there is much to recommend that.) During a recent Alphabet earnings call, CEO Sundar Pichai noted that “investments in AI will be key” to its near-term strategy, with new techniques that make it faster and easier to train and build AI for a number of uses. Additionally, the company is offering AI-driven “insights, new tools, and automation” to its advertising clients. The striking thing in this was the way that developing AI in one area could lead to growth in many others.
So, how does an IT leader foster a growth process like this for their stakeholders? By leading people through the well-established stages of Awareness, Learning, and Extension. Here’s what I mean.
Awareness: Early Automation
Consumer-facing AI is particularly strong in communications functions like voice recognition, translation, and writing tips. It’s similar in business uses: One of the most effective early instances of AI in the workplace has been Contact Center AI (CCAI), which manages basic customer communications, automatically answering common questions and prioritizing calls that require human assistance. It is doing what automation has always done best, automating the rote stuff and leaving the higher-value imaginative activity to people. It has been used by governments, retailers, telecommunications companies, and others, in a wide variety of use cases.
These and similar language-centric products, like DocAI for extracting information from things like invoices, receipts, or AI that extracts information from business contracts, have a number of benefits. For one, the investment is relatively easy to control, unlike with a research project leading to a formal launch. The payoff is also clearer. In the case of contact centers, in particular, the automation relieves stress, wins loyalty and slows disaffection in a high-turnover area. In both cases, successful results build allies in the business, who can testify to the earlier benefits when it’s time to take on something more complex.
Perhaps best of all, creating interest in basic AI services for business, right now, means people become engaged in learning more, since they see the early benefits and wonder what else might be done.
Learning: The Human Factor
The Natural Language Processing (NLP) that goes into these ready made, “out of the box” AI products can, not surprisingly, be used on much more sophisticated levels. Twitter, for example, processes 400 billion different events in real time, and its staff queries this trove using advanced NLP, answering questions and improving customer experiences.
There is clearly an enormous gap between Call Center AI and processing Twitter’s 400 billion events per day, but it’s not noticed enough how quickly that gap is closing. Look at how many products, partners, and training resources have emerged in the past few years. The gap makes sense, insofar as both the means of AI (like large data sets, good algorithms, and sufficient computing) and the value of AI, are new.
Increasingly, as AI is incorporated into standard enterprise tools like spreadsheets and analytic tools, easier to use AI becomes a skill within reach for many (even as the advanced end becomes more complex, meaning this ease of use process will continue for some time.)
It’s so new that AI skills demand isn’t met by conventional education means, creating lots of good opportunities for both nonstandard skills training, and in-house learning in the workforce. Companies offering AI skills training could well gain a competitive advantage and retain staff better.
Extension: Building System Views
When a new technology lands and gains in popularity, people seek to find new uses for it, or build connections among its different uses. Networked computing is one example, but think also of the way cars were soon followed by trucks and fire engines, or the way the data services on wireless phones soon morphed into the App Economy. If something is useful, people look for ways to grow it.
How will AI grow? My colleague Dominik Wee recently wrote about ways that AI will soon change supply chains, change product design, and improve sustainability. Most interestingly, he talked about how customers in manufacturing were realizing savings and gaining insights when once separate quality control data was combined with system wide views of the quality process.
There are several reasons to think AI will promote many such system views. For one thing, successful AI promotes the collection of data from more places, at greater frequency, since that leads to insight (and the cost of data collection is dropping.) Additionally, AI is good at spotting patterns and interactions that are not currently known. As well, AI is used in prediction and scenario planning, which leads to better understanding of how large-scale systems interact.
This comes at a time when we have more ways of seeing the world, from satellites, sensors, social media, and much more. We have more awareness of interactions, and a demand to understand them, in everything from the supply chain crisis, to human rights and sourcing regulations, or in the business realities of partnering, and serving customers in all sorts of ways, online and in the physical world.
Whether by coincidence or design, the Age of AI is also an age when organizations see themselves more accurately with a rich web of connections, with their choices and actions having more resonance than ever. That awareness is both a competitive tool, and a call to greater responsibility, potentially affording more customer loyalty and a more satisfied workforce for those who get it right.
That transformation won’t happen everywhere overnight, but it seems to be happening at all sorts of companies. And the trend for AI to assist, to be studied and grown, and to provide a richer understanding of the world, is happening every time someone touches this technology, at whatever level they need.
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