From administrative and analytics tasks present in nearly every industry, to niche processes that serve only a handful of businesses, the diversity of how we apply Artificial Intelligence grows, seemingly by the moment. And with it, the size of the market is exploding.
When discussing the growth of AI, many have a hard time separating it from the advancement of AI. While the latter will naturally follow the former, the vast majority of companies that are integrating, and expanding the size of this market, will be in a constant state of catching up to some of the world’s foremost leaders in AI advancement.
And I’m here to tell you, that’s okay.
Because, as the growth of the AI market continues, the diversity in humans that will use it, and human demand that will be met by it, will grow too. And your responsibility as an executive or leader is not necessarily to push the envelope of AI, or discover the next game changing function at which the world can marvel; it is to meet the needs of the humans, be they your employees or customers, who will benefit from your integration.
Of course, this is a very exciting time in the history of advanced digital technologies. Despite how far we have come, when we consider the potentially unending future of this transformative technology, we realize that AI is still in its infancy. As such, we often approach from a performance advancement mindset.
However, if you confine your definition of performance to objective technical metrics, you may find yourself perpetually pursuing faster load times, smaller bundle sizes, and more impressive features. This may work out for your team if you have an unending line of investors, or get incredibly lucky. But even some of the biggest companies that neglected to first think of their user experience above all else have found themselves victims of Icarian pitfalls that have shelved their projects, depleted their financial resources, and denied users of products that they need.
I do want to note, however, that the focus of this article isn’t to suggest that we shouldn’t strive to advance our understanding of the “nuts and bolts” of AI and other transformative digital technologies.
But it’s time to make tough decisions about your company’s place in the AI marketplace. Do you have the budget, the demand, or a responsibility that requires you to implement the most cutting edge technologies available to the market? The answer for most companies is, no.
For many, their future with AI is a continual, and incremental process of meeting the minimal needs of their customers, employees, or processes. And I am here to say that not only is this okay, but it’s a good thing. In this article, I implore you to look at AI as a tool for promoting human excellence and experience, and rethink what it means for AI technology to be “performant”. Ultimately, I want you to feel inspired and empowered to begin your digital transformation voyage by placing people and purpose at the helm.
According to leading educational non-profit Autism Speaks, 1 in every 59 children born today will fall somewhere on the autism spectrum. Although symptoms are diverse, many individuals with this diagnosis struggle with normative social conventions in a manner that impacts their daily lives.
Due to advancements in our understanding of autism, it’s now typically diagnosed in early childhood, tasking many parents with the responsibility of helping their children navigate a world that better accommodates neurotypical behaviors.
Laura Krieger is one of these parents.
Matthew, her eight year old son, has autism, and struggles to read others’ emotions. The pair were featured in a PBS Newshour segment on Brain Power, a company founded by award winning neuroscientist and entrepreneur, Ned Sahin.
Brain Power’s product is a Google Glass aided software that utilizes both AI and augmented reality technologies to help kids with learning differences build skills such as identifying emotions, and maintaining eye contact, through play.
When Krieger plays one of these skill building games with her son, she can’t help but break out in tears, saying she feels like he can truly see her for the first time.
Krieger is not responding to just how low the latency of the codebase powering the software is, or whether its deploying the latest architectural concepts. She likely doesn’t care whether the software was created using Amazon Web Services, TensorFlow, or a proprietary system.
What she does care about is getting the chance to connect with her son in a way she never thought possible.
And when we look at Brain Power through the eyes of children who use it, we see its strength in its mode of education. Rather than running users through drills or lesson plans, it rewards them through collaboration, natural interaction, and a gamified points system.
When you see the children using the system, they aren’t marveling at the natural feel of the interface that betrays its complexity. They’re simply having fun.
This is the type of performance for which we should strive.
THE CASE OF WATSON FOR ONCOLOGY
In 2013, IBM launched a partner project with The University of Texas MD Anderson Cancer Center to create a “Watson for Oncology” software that would help doctors identify and prescribe courses of action for cancer treatment.
After pumping $62 million dollars into the project, MD Anderson officially shelved it in 2017, halting their pursuit of a cure for cancer.
But the problem wasn’t a technical one. That’s to say, it was not found within the codebase. It was with the data being processed by it.
The reason that MD Anderson pulled the plug on the project, which would eventually be revealed by StatNews after reviewing internal slide decks from MD Anderson, was that the program was prescribing “unsafe and incorrect” treatment plans to real patients after the product had been sold to hospitals around the world.
Anderson sourced the problem back to IBM engineers and New York City-based Memorial Sloan Kettering Cancer Center, who were responsible for training Watson for Oncology. It was later discovered that their ML training process relied on a relatively small collection of hypothetical oncological cases rather than using actual patient data.
Of course, we cannot presuppose intent. I am of the opinion that IBM and Kettering truly wanted to provide a product that would revolutionize oncology treatment, and save lives. But it is hard to imagine a reason why a company creating a product meant to help doctors treat their patients in the field, would not have trained their AI software with data produced by doctors working with actual cancer patients.
So after indefinitely stopping this project, MD Anderson had exchanged $62 million, and four years of its time, to create a product that may have reflected some of the most advanced technical concepts available at that time, but is completely useless, in its current form, for the purpose it was meant to serve.
Imagine where we could be, how many lives could have been saved or prolonged, and how much money would have been saved, in the nearly three years since this product was pulled, if developers had placed equal focus on their users and the purpose of their product as they did the technical elements.
“As researchers, we make decisions about what our AI systems can do. It may not necessarily be optimal. It may not be necessarily efficient if you look at all of the metrics… but it may be optimal with respect to the human… which means that the system works.”
- Ayanna Howard, Ph.D Chair, School of Interactive Computing Georgia Institute of Technology
Kaden Bowen of Lincoln, Nebraska shares his father’s passion for cars. Though he is non-verbal due to cerebral palsy, he asks his father, James, to “go for a ride”, multiple times a day with the help of a digital talkboard.
James dreamed of taking his son on a roadtrip in a vehicle that was more stylish and fun than their wheelchair-accessible van. He searched high and low for a sporty car that he could modify to at least allow him to store a foldable wheelchair, until he realized that the hatchback of a standard Corvette might just be big enough for one.
Two weeks later, the duo took a 728 mile round trip to the Corvette museum in Bowling Green, Kentucky in their brand-new (to them) Corvette, which came standard with a trunk big enough for Kaden’s chair.
The Bowens don’t seem to over-complicate the accommodations that they have put in place for their son. To help increase his level of independence, they outfitted their home with Amazon Echo devices that are sensitive enough to understand the commands that are programmed into Kaden’s talkboard.
Using a combination of the two technologies, Kaden can do a lot on his own. He can call his parents on the phone, stream videos on Netflix, and operate lights. It seems so obvious, but it really is quite inventive.
Sure, one day we will have widely accessible neural link technology that will allow Kaden to circumvent the talkboard, and do so much more than what is permitted by an Echo. But just like his dad’s Corvette, that comes standard with enough storage space, sometimes, the best fixes are the best fixes because they are available, and they work. The technologies that Kaden uses may not boast the best metrics, feature the most jaw dropping functions, or offer the most direct route to helping him achieve increased independence. I’m sure that the communication between the talkboard and the Echo device is not always perfect. But the combination of these two relatively affordable, and accessible technologies have given Kaden more autonomy with reliable, usable services, and that, in and of itself, is high performance.
AI INITIATIVES ARE FAILING
These past few years have proven to be extremely exciting for AI technologies, and businesses are responsive, with a 2019 Gartner report showing that 37% of the nation’s leading enterprises are or are shortly planning to integrate some form of AI into their products or processes. This percentage reflects a 270% growth in that statistic when compared to research conducted in 2015. And this proportion bumps up to 62% when we look at Supply Chain and Logistics, and reaches nearly 80% when discussing the Healthcare industry.
But these awe-inspiring stats come with troubling predictions that, through 2020, roughly 80% of enterprise AI programs will remain in a limbotic state of development due to their inability to properly scale with their organizations. In essence, we may be creating fabulous algorithms, with wildly impressive features and functions that may become little more than multi-million dollar proof-of-concepts. And this might be okay for some businesses who have the resources to pursue multiple avenues and digital transformation. But I am of the belief that most companies that have yet to enter AI space by now will depend on a significant ROI from the programs that they start over the next few years.
For these companies, it is imperative not only to balance resources, and create realistic time-frames for integrating and/or shipping their products, but to stay diligent against the propensity for AI programs to become isolated within an organization.
With all of the buzz around AI, and the seemingly endless supply of jaw dropping products and services coming out of the world’s foremost information technology companies, it’s natural that smaller programs want to keep up with the Joneses. It is, therefore, the responsibility of executives, and others in business development roles, to remain involved with their AI development programs to ensure that products and services do not outpace the demands and capacities of their customers or businesses.
“The current wave of Artificial Intelligence is going to hit a peak inside of the enterprise. But when it does, it’s not going to be a monumental revolution of technology, but rather a monumental revolution of people.”
- Traci Gusher Partner, US Leader- Artificial Intelligence Analytics and Engineering, KPMG
There is a lot of chatter about the need for enterprises to integrate AI powered technologies into their workflows, and products. Anxiety about the need to introduce this transformative technology is warranted.
Products like Alexa, Siri, and Echo Dot are changing consumer expectations, while Forrestor predicts that, in 2020, 25% of Fortune 500 companies will include AI building blocks in their automated processes.
From product to process, AI is infiltrating nearly every sect of business.
That being said, it is a mistake for companies to rush an AI program without first internalizing and developing concepts of how the resulting products will better the lives or capacities of those who will use it.
I get it. It’s so tempting to want to patent the next game changing system or algorithm, but if your customers, users, or employees, could be equally, if not better served by a simpler system, or another company’s proprietary tools, what is the point? Is it 100% necessary that your business be on the cutting edge of Artificial Intelligence? Will it truly better your products, services, workflows, or customers?
AI has such an amazing capacity to enrich the human experience, be that personal, or professional. We overemphasize minute technical details without giving that same attention to the aspects of user needs, and experience at our own peril.
Don’t feel the need to push the boundaries of our understanding of transformative technologies simply to implement your own products and services. All you need is something that sees your user, empowers them, strengthens their skills, and most importantly, works.
Ready to begin your digital transformation journey, but don’t know how to start? Don’t hesitate to reach out to the team at This Dot Labs by emailing firstname.lastname@example.org.