Using AI and Machine Learning to beat the competition
Artificial intelligence and machine learning promise to permanently change the enterprise. Several Fortune 500 companies are already deeply invested in reinventing large internal processes — recruiting, sales, marketing, finance — using machine intelligence software.
However, as with any other form of technology that’s being put into active use, big question marks still hover around the topic. “How does one separate hype from actual business opportunity?” “Where does a company with no previous AI experience start moving into the future?” “How can enterprises and startups use AI and ML technology to their competitive advantage NOW — not five years from now?”
I sat down for a conversation with Jensen Harris (Co-Founder and CTO, Textio), Derik Pridmore (CEO, Osaro), Matt Zeiler (CEO, Clarifai), and Puneet Mehta, (CEO, msg.ai) to answer these questions. Although each of these CEOs uses AI and ML to innovate in different ways and in different sectors, several common threads emerged during our discussion. What follows are my top five takeaways.
To hear the conversation in its entirety, watch the video or listen to the podcast.
1. The value of AI is not an algorithm
The algorithms behind AI and ML technology are becoming more commoditized. Now that AI and ML technology is being built into cloud platforms such as Salesforce, everyone has access to great algorithms. And, in some cases, new algorithms aren’t even necessary — existing algorithms are being stitched together in unique ways that deliver innovative results.
That’s not to say that algorithms can’t be innovated. Over the past few years, algorithm development has allowed AI to make giant steps into the future. But as the speed of innovations picks up, commoditization of these algorithms has accelerated as well.
“Saying that you have ML in your product is like saying ‘we also have internet.’ All software is going to have machine learning, all software is going to have aspects of machine intelligence.” — Jensen Harris, Textio
2. The value of AI is founded on data
AI’s real value to enterprises depends on data — quantifying proprietary information to create competitive advantage. Panelists imagined a not-too-distant future in which data scientists (those who can interpret data) would become more valuable than software engineers (those who create algorithms). In order to capitalize on the benefits of AI, companies should adopt a data-centric approach. From business plan to product to engineering, every aspect of a company should focus on data.
“I think people continue to overrate the algorithms. [Another company] will invent the algorithm and publish it, and then the question is how do you apply it to your data in order to have an advantage?” — Derik Pridmore, Osaro
3. Lower barriers to entry
As algorithms become more commoditized, enterprises and startups are not being forced to make the same large investments that were required to enter the AI and ML playing field a few years ago. AI-as-a-service lets enterprises reap the benefits of artificial intelligence without having to start from scratch or hire internal researchers.
“[AI and ML service providers] have the experts, they have the data, and they also accumulate data from their customers that make the service better … You wouldn’t write your own database — you would use one that’s hosted in the cloud for you. So why would you think about writing your own artificial intelligence in your company?” — Matthew Zeiler, Clarifai
4. AI is changing the enterprise, but not in the way that people fear
A huge promise of all AI and ML rests in its quantitative promise — now you can actually automate the collection of data, measurement of performance, and tracking of improvement. This means that data is available for day-to-day coaching and feedback. The panelists agreed that AI changes the game, but not simply as an employee replacement. Rather, it equips employees to create more impact and perform better in the real world.
“The salesperson’s job is to get information out of the head of the other person so he understands their problem. He’s acquiring data for the company — and a piece of software can track it and correlate it with other people’s data. So, [with AI] his job becomes more important actually. He spends less of his time in spreadsheets and email; more of his time talking to people.” — Derik Pridmore, Osaro
5. AI is already here
The panelists roundly refuted the notion that artificial intelligence and machine learning are still five to 10 years out or reserved solely for the giants of the tech world. Panelists pointed to a myriad of existing use cases where smaller AI startups were having a profound impact on enterprises’ bottom lines.
“In our case, our company builds conversational AIs — bots that could live within messaging apps, etc. What we are seeing is three types of KPIs being affected for large companies: the first one, obviously, is conversion lift. Secondly, we’re looking at service costs. We’re saying, instead of hiring an army of people to do that, how can you configure it to be an AI and a human team together? The third one we’re seeing is increasing customer sat scores. So it’s not just the ability to sell more products; it’s the ability to be available to establish that connection where the customer satisfaction starts to go up.” — Puneet Mehta, Msg.ai
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