3 Ways Companies Are Building a Business Around AI
There is no argument about whether artificial intelligence (AI) is coming. It is here, in automobiles, smartphones, aircraft, and much else. Not least in the online search abilities, speech and translation features, and image recognition technology of my employer, Alphabet.
The question now moves to how broadly AI will be employed in industry and society, and by what means. Many other companies, including Microsoft and Amazon, also already offer AI tools which, like Google Cloud, where I work, will be sold online as cloud computing services. There are numerous other AI products available to business, like IBM’s Watson, or software from emerging vendors. Whatever hype businesspeople read around AI — and there is a great deal — the intentions and actions of so many players should alert them to the fundamental importance of this new technology.
This is no simple matter, as AI is both familiar and strange. At heart, the algorithms and computation are dedicated to unearthing novel patterns, which is what science, technology, markets, and the humanistic arts have done throughout the story of humankind. The strange part is how today’s AI works, building subroutines of patterns, and loops of patterns about other patterns, training itself through multiple layers that are only possible with very large amounts of computation. For perhaps the first time, we have invented a machine that cannot readily explain itself.
In the face of such technical progress, paralysis is rarely a good strategy. The question then becomes: How should a company that isn’t involved in building AI think about using it? Even in these early days, practices of successful early adopters offer several useful lessons:
- Find and own valuable data no one else has.
- Take a systemic view of your business, and find data adjacencies.
- Package AI for the customer experience.
Capture the Scarce Data
CAMP3 is a 26-person company, headquartered in Alpharetta, Georgia, that deploys and manages wireless sensor networks for agriculture. The company also sells Google’s G Suite email and collaboration products on a commission basis.
Founder and chief executive Craig Ganssle was an early user of Google Glass. Glass failed as a consumer product, but the experience of wearing a camera and collecting images in the field inspired Ganssle to think about ways farmers could use AI to spot plant diseases and pests early on.
AI typically works by crunching very large amounts of data to figure out telltale patterns, then testing provisional patterns against similar data it hasn’t yet processed. Once validated, the pattern-finding methodology is strengthened by feeding it more data.
CAMP3’s initial challenge was securing enough visual data to train its AI product. Not only were there relatively few pictures of diseased crops and crop pests, but they were scattered across numerous institutions, often without proper identification.
“Finding enough images of northern corn leaf blight [NCLB] took 10 months,” said Ganssle. “There were lots of pictures in big agricultural universities, but no one had the information well-tagged. Seed companies had pictures too, but no one had pictures of healthy corn, corn with early NCLB, corn with advanced NCLB.”
That visual training data is a scarce commodity, and a defensible business asset. Initial training for things like NCLB, cucumber downy mildew, or sweet corn worm initially required “tens of thousands” of images, he said. With a system trained, he added, it now requires far fewer images to train for a disease.
CAMP3 trains the images on TensorFlow, an AI software framework first developed by Google and then open sourced. For computing, he relied on Amazon Web Services and Google Compute Engine. “Now we can take the machine from kindergarten to PhD-style analysis in a few hours,” Ganssle said.
The painful process of acquiring and correctly tagging the data, including time and location information for new pictures the company and customers take, gave CAMP3 what Ganssle considers a key strategic asset. “Capture something other people don’t have, and organize it with a plan for other uses down the road,” he said.
“With AI, you never know what problem you will need to tackle next. This could be used for thinking about soils, or changing water needs. When we look at new stuff, or start to do predictive modeling, this will be data that falls off the truck, that we pick up and use.”
Explore Your Data Adjacencies
TalkIQ is a company that monitors sales and customer service phone calls, turns the talk into text, and then scans the words in real time for keywords and patterns that predict whether a company is headed for a good outcome — a new sale, a happy customer.
The company got its start after Jack Abraham, a former eBay executive and entrepreneur, founded ZenReach, a Phoenix company that connects online and offline commerce, in part through extensive call centers.
“I kept thinking that if I could listen to everything our customers were asking for, I would capture the giant brain of the company,” said Abraham. “Why does one rep close 50% of his calls, while the other gets 25%?”
The data from those calls could improve performance at ZenReach, he realized, but could also be the training set for a new business that served other companies. TalkIQ, based in San Francisco, took two years to build. Data scientists examined half a million conversations preserved in the company’s computer-based ZenReach phone system.
As with CAMP3, part of the challenge was correctly mapping information — in this case, conversations in crowded rooms, sometimes over bad phone connections — and tagging things like product names, features, and competitors. TalkIQ uses automated voice recognition and algorithms that understand natural language, among other tools.
Since products and human interactions change even faster than biology, the training corpus for TalkIQ needs to train almost continuously to predict well, said Dan O’Connell, the company’s chief executive. “Every prediction depends on accurate information,” he said. “At the same time, you have to be careful of ‘overfitting,’ or building a model so complex that the noise is contributing to results as much as good data.
Built as an adjacency to ZenReach, TalkIQ must also tweak for individual customer and vertical industry needs. The product went into commercial release in January, and according to Abraham now has 27 companies paying for the service. “If we’re right, this is how every company will run in the future.”
Focus on Customer Experience
Last March the Denver-based company Blinker launched a mobile app for buying and selling cars in the state of Colorado. Customers are asked to photograph the back of their vehicle, and within moments of uploading the image the car’s year, make and model, and resale value are identified. From there it is a relatively simple matter to offer the car, or seek refinancing and insurance.
The AI that identifies the car so readily seems like magic. In fact, the process is done using TensorFlow, along with the Google Vision API, to identify the vehicle. Blinker has agreements with third-party providers of motor vehicle data, and once it identifies the plate number, it can get the other information from the files (where possible, the machine also checks available image data.)
Blinker has filed for patents on a number of the things it does, but the company’s founder and chief executive thinks his real edge is his 44 years in the business of car dealerships.
“Whatever you do, you are still selling cars,” said Rod Buscher. “People forget that the way it feels, and the pain points of buying a car, are still there.”
He noted that Beepi, an earlier peer-to-peer attempt to sell cars online, “raised $150 million, with a great concept and smart guys. They still lost it all. The key to our success is domain knowledge: I have a team of experts from the auto selling business.”
That means taking out the intrusive ads and multi-click processes usually associated with selling cars online and giving customers a sense of fast, responsive action. If the car is on sale, the license number is covered with a Blinker logo, offering the seller a sense of privacy (and Blinker some free advertising.)
Blinker, which hopes to go national over the next few years, does have AI specialists, who have trained a system with over 70,000 images of cars. Even these had the human touch — the results were verified on Amazon’s Mechanical Turk, a service where humans perform inexpensive tasks online.
While the AI work goes on, Buscher spent over a year bringing in focus groups to see what worked, and then watched how buyers and sellers interacted (frequently, they did their sales away from Blinker, something else the company had to fix).
“I’ve never been in tech, but I’m learning that on the go,” he said. “You still have to know what a good and bad customer experience is like.”
No single tool, even one as powerful as AI, determines the fate of a business. As much as the world changes, deep truths — around unearthing customer knowledge, capturing scarce goods, and finding profitable adjacencies — will matter greatly. As ever, the technology works to the extent that its owners know what it can do, and know their market.