Wednesday, January 30,
2019 05.07PM / By Daniel
Faggella, founder of Emerj
This article is part 4 of a 7-part series called “AI Zeitgeist,” where we’ll be mapping out the details of AI adoption over the next 10 years and explore the critical changes in the AI ecosystem that business leaders need to understand.
In this installment of the Zeitgeist series, we’ll be talking about how artificial intelligence technologies will become more accessible to businesses and non-technical employees.
Today, if you don’t have a strong data science background, you are very unlikely to leverage powerful AI vendor tools. In five years, many vendor tools will require almost no formal data science training, and that transition is something that very few business leaders consider.
AI accessibility will evolve as we move from the “Emergence” phase, where AI remains a wizard-skill that’s inaccessible to many companies, to the “Dispersion” phase, where AI becomes ubiquitous and simple to use across almost all business sectors.
(Note: If you haven’t read the first article in the AI Zeitgeist series, you should familiarize yourself with it first. In that article, I explain what the 3 phases of “Emergence,” “Adoption,” and “Dispersion” actually mean).
AI tools come in many varieties of complexity, so in each phase of the AI Zeitgeist (“Emergence,” “Adoption,” and “Dispersion”), I’ll be referring to the following three rough categories of AI solutions:
We’ll begin by exploring the accessibility of AI in the current phase of AI adoption: the “Emergence” phase:
Companies that aim to build AI solutions in-house (for their own teams, or for a product which clients will use) require resources and capabilities that are rare indeed, including:
The number of companies with those ingredients is remarkably small, and while many other companies may deceive themselves into believing that they should be innovating and developing their own in-house AI applications, it will remain a small upper echelon that is able to genuinely develop novel applications.
While machine learning algorithms and statistical methods have been around for well over a decade, turning those algorithms into business value is still (mostly) the Wild West. Applying machine learning to business problems is hard; building solutions from scratch is often just as hard.
Our own poll of 30 AI researchers points out the biggest barriers to getting an ROI on AI in business:
Source: Emerj AI Research Where to Apply AI First in Your Business?
Make no mistake about it, in order to build in-house AI solutions, all three of these issues will need to be addressed, and most companies (including public companies) have neither the right data nor the right talent to build solutions in-house. I’ve written much more about this in our enterprise AI adoption article from last year.
Warning – we generally find that 66% of companies who claim to be offering an AI solution are in fact not “doing AI” at all, and many such companies have no in-house data science talent whatsoever. Our article on “avoiding AI hype” will help you get a sense of how to tell the real from the fake.
We’ve never been advocates for buying from companies using AI simply because AI is interesting. Ultimately a B2B purchase should be about achieving company goals, and if that gets done with boring “old school” means, so be it. That being said, we dislike it when companies use the AI buzz to get more attention than they deserve, and we’ve previously shared some of our tips for avoiding companies who are lying about AI use.
While there is a gradient of difficulty for vendor solutions, most of today’s present solutions require the following resources on the part of the buying company:
Does that mean that AI vendor solutions aren’t worth working with?
Of course not, but it’s no surprise that the majority of AI vendors are targeting enterprise clients, who are likely to have the budget and data volumes to at least get started. Those behemoths will be the petri dishes in which AI solutions will be tested and worked out before they ever make it en masse to the mid-market.
If you just read press releases and AI vendor company communication (they’d sure like it if those were the only sources you paid attention to!), you’d believe that most vendor solutions are “push-button” east, and integration is a snap. Not so. Not so at all.
Because many businesses have no idea of the data requirements, talent requirements, and time requirements of using AI vendor solutions, they shop around when in fact they shouldn’t. Often they realize that they’re in over their head before buying. Sometimes they buy because AI is “cool” and they want to feel hip and modern (we call these “toy applications”, and we generally frown on this kind of decision-making).
It will be years before even enterprises realize the demands that working with AI vendors requires, and so the majority of vendors will have to educate enterprise buyers and teams from scratch – not just about their solution – but about what artificial intelligence is in the first place. This ain’t changing any time soon.
Also, it should be understood that many vendor companies, even those who have raised $30MM-$80MM, are still figuring out their product, their ideal customer, and their value proposition. They’ve raised money by convincing investors they’ve figured it out, but in many cases, they’re still “feeling out” what problem they solve and for who.
What this means for buyers is that they are often the guinea pig for a solution that’s only ever been integrated at 2-4 other companies ever, with no real robust evidence of ROI as of yet. Again, this doesn’t mean that vendors of this kind shouldn’t be worked with at all; it’s just important to understand the situation you’re getting involved in, and the risks and rewards involved.
There are an increasingly large number of AI tools which are available for free, or for a relatively low cost – without requiring data science staff (at all!), or existing data assets at all.
Google offers APIs for image recognition, speech recognition, and more via Google Cloud.
There are basic tools and scripts of this kind that simply require a relatively average level of development talent to simply set up the system itself. The
Currently, simple APIs from providers like Google or Amazon (or a whole ecosystem of smaller players) offer relatively braod AI capabilities. These capabilities might not be hyper-tailored to a specific business use-case, but they apply widely across a variety of businesses and so might be useful.
For example, today’s simple computer vision APIs can help with the following broad tasks:
Today’s natural language processing APIs can help with the following broad tasks:
In the future, we might expect more custom capabilities to develop, allowing businesses to tailor specific AI applications just to their own particular needs.
In the “Adoption” phase, “doing” artificial intelligence (building a robust AI solution in-house) will be significantly less burdensome than it is today.
We can look at the various required factors of “Doing AI” in the “Emergence” phase, and see how those factors might change in the “Adoption” phase:
Experimentation: I believe that the most important factor in making AI easier to “do” is the number of actual applications that are developed, and the evolutionary process of trial and error.
As more companies build AI solutions, they’ll be forced to think seriously about cleaning their data – and maybe even about storing it in more effective ways to future AI applications. They’ll learn how many team members of what kinds need to be involved in various kinds of AI applications.
They’ll have a sense of how long these projects take – and once they see some tangible “wins.” They’ll be willing to endure the time and investment in iteratively improving an AI application.
In addition, companies who develop AI solutions will be providing more and more of their staff (programmers, data scientists, subject-matter experts) to the process of building AI solutions, and as these employees leave to join or start other companies, they’ll be able to pollinate those firms with a better sense of “best practices.”
This experimental process takes time, but even in just two years we’ll be drastically farther ahead than we are today, and building solutions will be easier.
Good news: In the “Adoption” phase, vendor solutions will not only be easier to integrate and easier to use, but also more accessible by mid-market clients with less data, less data science talent and less budget to work with.
Because capitalism, that’s why.
Vendor companies are fighting vigorously in the market in order to win market share and win deals. “Winning” in the market implies (among other things):
As more buyer companies understand data and AI, and as more vendors understand their value proposition and their exact offering, more “wins” will come about from vendor solutions. These “wins” (positive case studies) will help to solve the chicken and egg problem:
Without case studies, companies don’t feel good about adopting a product. Without selling a product, no good case studies can be developed.
We’ve seen this dynamic most clearly in the intersection of machine learning in healthcare, but it exists in essentially all sectors. Some fields – like eCommerce – will have an easier time with this case study traction than other stodgy industry – like healthcare – but they’ll all be chipped away over the years ahead.
In the “Adoption” phase, a huge bulk of the AI solutions in any given sector (from banking to mining and beyond) will have established case studies and a clear onboarding process – and buyers will have a more intuitive sense of AI and data, and a better sense of expectations about what a vendor solution can do.
Tools and scripts will expand to more and more domains of AI capability and functionality.
While intricately customized needs (i.e. A lead scoring system customized for one specific business, or a recommendation engine for a very novel online jewelry store catering to women in India) will not have a “plug and play” solution to their problems, there will be an increasingly broad set of services available at low prices.
In addition, we can expect a wider and wider pool of API providers for AI capabilites (like NLP or computer vision) and wider and wider use of simple scripts (like logistical regression) in various coding languages for simple applications.
Any and all established enterprises in the “Dispersion” phase (that aren’t on the verge of dying) will have a strong understanding of:
Think about “the internet” today.
Not every company has a masterful internet strategy – but any global company that’s alive today at least has enough capability to get by. AI will be much the same in the “Dispersion” phase. “Doing AI” will be synonymous with “doing business” for most large and even mid-market firms, and certainly any capable venture-backed company.
While the “Dispersion” phase may not bring about the ability for provincial small businesses to innovate in AI – these smaller and less technical players will be able to use vendor solutions.
Think about it this way.
At some point in the past, say 1996, the idea of “marketing automation” was extremely complicated and foreign. Sending email messages based on actions and segments of an email list, and automatically tagging, organizing, and communicating with contacts based on their actions – that would have sounded like magic.
In 1996, it probably was magic, it was “wizard skills” to be able to do something of that kind. Only enterprises could purchase and use such complicated, novel solutions.
Now, any Joe or Mary who decides to open a business can open a MailChimp account for free and set up email automation and email segments in minutes, in a simple, intuitive interface.
A modern marketing automation software interface boils down to a set of templates, lists, and drag-and-drop items that allow non-technical users to set up complex and customized sequences of rules and events. AI solutions will follow suit over time, making much of the algorithm training somewhat “invisible” to most users. Source: Screenshot from MailChimp.
What happened? Time and capitalism.
Companies fight to find easier interfaces, simpler functionality, lower prices, lower barriers of use – and eventually “wizard skills” become accessible even to small businesses.
By the “Dispersion” phase, AI will be synonymous with almost all enterprise software offerings, and many small and mid-size business software offerings, too. It’ll just be software. Users will think: “All software is smart – what’s the big deal?”
Just as online contractors can set up WordPress Plugins or other simple HTML scripts from the internet, eventually all commoditized AI capabilities (text, speech, vision, and more) will be reasonably accessible to anyone with basic technical skills, no formal data science training needed.
This has been week 4 of 7 from the “AI Zeitgeist” article series.
In the coming weeks, we’ll be exploring the following Zeitgeist topics, in order:
2. January 14th – How “AI” Will be Discussed in the Future (AI Zeitgeist 2)
3. January 21st – The Evolution of AI Talent and Training (AI Zeitgeist 3)
4. January 28th – The Increased Accessibility of AI in Business (AI Zeitgeist 4) <— You are here.
5. February 4th – Buying and Adoption Readiness for AI (AI Zeitgeist 5)
6. February 11th – The Changing Landscape of AI Priorities of Business Leaders (AI Zeitgeist 6)
7. February 18th – The Competitive Dynamics of AI – Now and in the Future (AI Zeitgeist 7)
Next week we’ll explore how the purchasing and selling of AI solutions will change in the years ahead (Preview: The way AI is sold and marketed today is quite different from how it will be in the next five years – and in general this will make AI more accessible to business leaders, and easier to understand).
About The Author
Daniel Faggella is the founder and CEO at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and many global enterprises, Daniel is a sought-after expert on the competitive strategy implications of AI for business and government leaders.