While the world debates the future of AI, the team at Austin-based CognitiveScale is busy building practical applications for the here and now. CognitiveScale has created AI software that emulates specific human cognitive abilities – such as memory, sequencing and perception, and then cuts through masses of data to help businesses make sense of the information more quickly and easily.
CognitiveScale makes “augmented intelligence” software targeted at the financial services, healthcare and commerce markets. Its two primary product lines are Engage, which applies machine learning to customer interactions by learning from preferences and transforming customer experiences to a “Profile-of-One”, and Amplify, which augments employee performance and boosts employee productivity by suggesting intelligent insights at the right time.
As Akshay Sabhikhi, CEO of CognitiveScale, says, “Our value proposition is not replacing humans but rather making them smarter. By augmenting humans with insights and intelligence derived from diverse sets of data, many of which are external to their organization, you’re helping them make better decisions and scale to support greater workloads.”
By augmenting humans with insights and intelligence derived from diverse sets of data, many of which are external to their organization, you’re helping them make better decisions and scale to support greater workloads.
Akshay suggests that we think about augmented intelligence as Jarvis of the Ironman series, the highly advanced AI assistant developed by Tony Stark to help him manage his overwhelming array of technology and live a life beyond normal human means. Akshay says unlike AI that pits human versus machine, augmented intelligence systems pair human with machine and provides the potential to reimagine every business process, user experience, and business model.
We recently sat down with Akshay to catch up on his company’s milestones, how he sees the AI market developing in the future, and lessons he’s learned that are useful for other AI entrepreneurs.
What were the considerations to first tackle healthcare, commerce and financial markets?
We choose markets based on the following questions and criteria: Are they ripe for disruption? Are there hard ROI problems we can solve with AI? Do we have a distribution channel to be successful? What we discovered is financial services, healthcare and commerce are ripe for disruption due to the high volume of user-centric data and fluctuating external market conditions.
These data-intensive industries need machine intelligence and learning to identify patterns, surface critical information and make evidence-based recommendations. According to a recent McKinsey study, half the time spent by workers in finance and insurance is allocated to processing and collecting data. Other industries ripe for AI transformation are telecom, media and travel.
How do you see the AI market evolving and how do you see businesses adopting solutions like CognitiveScale over the next few years?
AI is still in its infancy and its maturity now is akin to the adolescent internet in 1997, long before the web, app servers and Facebook came along. Large and small enterprises are just beginning to learn what it takes to build and deploy AI powered applications, let alone building the right control and assurance into their AI to prevent rogue AI. This is where CognitiveScale fits in. Our software delivers enterprise grade AI that scales to solve real problems with measurable business outcomes. According to this year’s Fortune 500, 81% cited “artificial intelligence and machine learning” as either “very important” or “extremely important” to their company’s future, which is up from just 54% in 2016. Research analyst firm Gartner said 45% of the fastest-growing companies in the world will ‘employ’ more smart machines and virtual assistants than people by 2018. And analyst firm CB Insights reported that more than 550 startups using AI as a core part of their products raised $5 billion in funding in 2016.
There’s been a boom of AI businesses and applications designed to focus on a variety of industry sectors, such as healthcare, drug discovery, business intelligence, gaming, manufacturing and much more. But as in any young and fast-growing market, there’s a lot to learn.
What are important lessons you’ve learned from your work in AI that would help other entrepreneurs navigate this hot space?
Well, I’ll give you a list, if I may.
1. Start with the business outcome.
2. Go after specific high-ROI problems.
3. Choose the right markets.
4. Remember that it’s about efficiency and cost improvement.
5. Make sure you have a distribution channel to take you into your target industry.
6. Control your own destiny. This new AI marketplace makes us aware that we have the controls and need to be out in front of the customer.
CognitiveScale is now making significant inroads in its own target markets by helping businesses make sense of messy, disparate, unstructured data from 1st and 3rd parties. The company is expanding and evolving quickly, more quickly than they imagined, but it’s a sign of promise and potential of AI. Akshay says he expected to encounter more resistance emanating from that quarter where the implications of AI for workers and society are still hotly debated. Instead, he says, he’s found that companies are readily embracing CognitiveScale’s augmented intelligence offerings and eager to implement them to solve their everyday business problems.
Also published on Medium.