Fueled by the exponential proliferation of big data, advances in AI have grown with unprecedented speed over the past decade. Programming AI is no longer an arduous process of building it line by line. Rather, AI is able to learn by itself, from itself. And the market has taken notice. In 2016, the enterprise market demand for AI-related products reached more than $8 billion.
AI promises to transform business. Except … it hasn’t. From chatbots to BI and digital assistants, the business applications of AI are not living up to the potential catalyzed by big data.
The problem, however, does not lie in actual AI technologies. The greatest hindrances to effectively deploying AI today are the philosophical, cultural, and pragmatic frameworks we have put in place to understand its capabilities.
The misunderstood life of AI
A fundamental crack in the system is that while many businesses think they’re in the artificial intelligence game, there is still a gap in understanding — both for consumers and the enterprise — of what AI actually is, what it does, what it can do, and most importantly, the work required to make AI live up to all the hype. To a vast majority of companies who want to deploy it, AI means both everything and nothing at all. Most see it as a magic wand for their business without understanding the full depth of what goes into the process, or the why behind it.
AI, at its core, is actually defined as 1) a branch of computer science dealing with the simulation of intelligent behavior in computers, and 2) the capability of a machine to imitate intelligent human behavior. Based on this loose definition, AI continues to be defined in a way that allows for technology to overpromise and undersell the actual work required to make the technology actually work. It’s also not necessarily an easy definition to translate to an improvement in customer satisfaction surveys or better insight into customer queries.
Don’t get me wrong, the adoption of AI-related technologies has already had a significant impact on enterprise and customer interactions. In 2011, Gartner predicted that by 2020, customers will manage 85 percent of their relationships with enterprises without ever talking to a human. In 2017, we’re well on the way to reaching that number.
Despite these promising figures, we are still experiencing a fundamental gap in our understanding of what AI is, what it does, and how it affects us. In a survey of 1,400 consumers, 37 percent of respondents said they have used an AI tool. Of the respondents who said they have not used AI, 63 percent were actually using some form of AI. They just were not aware that they were.
According to Angel.co, there are 2,200+ artificial intelligence startups in existence today. The same report states that well over 50 percent of these startups emerged within the past two years. On top of this surge in the creation of AI startups, funding within the space has increased significantly. In a Cowen and Company study, 81 percent of IT leaders are either currently investing in or planning to invest in AI — and that number is rising steadily. The demand for AI-related technologies is high and getting higher.
As with any bullish market, that kind of demand leads to a lot of waste, confusion, and misleading over promises. Of those 2,200+ AI startups, only a small handful are actually delivering value to customers. After all, selling the idea of an AI solution isn’t really hard — the promise of the magic wand can be alluring to any brand and company. People are still looking to capitalize on their data and the emergence of net-new channels to reach their customers. The opportunities are endless, but so are the shenanigans.
From 2014 to 2016, inquiries about AI to Gartner increased from 14 to 290. There are several implications hidden in this number. Partially, it has to do with the increase in availability of AI and the widespread adoption in the enterprise. Quantitatively, it bodes well for the industry.
However, six years after big data and AI became the boardroom words of the year, the types of questions changed dramatically. People began to finally ask more and more, “What is AI, and how can it help my business?”
Which brings us to the other side of the coin. Buying AI is not buying a one-size-solves-all solution for your business. Business leaders must treat AI like any other technology they’re investing in: It should have a specific purpose to solve a specific goal. Executives must track KPIs and look for solutions that bring them closer to adding specific value to their businesses.
AI isn’t magic, it’s math. The magic is only in the problem you’re solving and building a strategic solution to a specific problem. If you go in knowing you want AI, but not knowing why you want it, you will fail AI, and AI will fail you. This is a situation that I have experienced with companies on numerous occasions.
One sales conversation, in particular, comes to mind. We were presenting to a major brand in a bid to win their business. The first question the company asked me before I could even finish setting up was, “We have had 14 companies come in here and said they have solved AI. We are trying to make sure we are comparing everyone equally. Have you solved AI?” I stopped setting up, packed up the laptop, and started to walk out of the room. The team asked what I was doing and I simply said, “We haven’t solved AI. I am not even sure what that sentence fully means since AI isn’t a puzzle. What I am sure of is that 14 other companies just told you what you wanted to hear.” The key stakeholder then said, “You can stay.”
Why did this dialog happen? Because any company that would ask me that kind of question needs to do a little more research before integrating an AI technology in its operations. AI is not a one-size-fits-all solution. You can’t turn on the “fix it” switch. It takes an honest conversation about goals, values, and business considerations to build any tech solution for a company. AI is no different. Too many “AI companies” are just pushing a technology agenda and not accurately helping brands understand the impact AI can have and the massive amount of work required in order to fully realize this impact.
It’s not about magic AI solutions. Rather, it’s about the work it takes to solve a particular problem. Part of this is asking the right questions. Business leaders should be asking things like “How do I apply technology to solve my business problems?” It’s about evaluating, choosing, and strategically implementing a set of technologies in a way that makes sense for the problem you’re solving. Once we understand that AI isn’t magic, but math, work, and strategic training, we’ll set better expectations for the technology.
A change in tune
In order to realize the full potential of artificial intelligence, we need to shift our philosophical, cultural, and pragmatic frameworks at the top of an organization. It is as much of an expectations management problem as it is a technology problem.
While we’ve thrown around the phrase “artificial intelligence” fairly liberally, the reality is that a lot of what we’re calling AI is just predictive data analytics. And data analytics is truly just business as usual. It takes a combination of a committed organization-wide strategy, set of technologies, training data, and ongoing training, as well as refinement to fully glean meaning from data insights.
I tell companies considering any AI solutions that they need to have three clear questions in mind before truly exploring potential AI integrations.
- What are the actual business goals or challenges that you’re looking to address with AI solutions?
- Do you have the internal expertise to maintain AI integration and a team to commit to training and improving the technology across your organization?
- How do you measure the success of an AI deployment?
These are not magic questions any more than AI is a magic solution. This is business 101. These are straightforward business questions a business must answer before integrating any new technology, regardless of whether it’s a sexy AI buzzword. There are no “easy” buttons in business, and AI is certainly not going to become one. It takes dedication, commitment, and careful consideration to make it work. Business hasn’t changed. We’ve just improved the tools we use to execute advanced business strategies.
Ben Lamm is a serial technology entrepreneur. He is currently the chief executive officer and cofounder of Conversable, the leading enterprise conversational intelligence platform for creating intuitive, on-demand, automated experiences on any messaging or voice channel.
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