Q&A: Fractal Analytics' Co-Founder on Decision-Making and AI - InformationWeek

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Q&A: Fractal Analytics’ Co-Founder on Decision-Making and AI

AI, design, and engineering can help consumer-facing enterprises create new recipes for advance decision-making.

No one wants to let artificial intelligence completely replace human decisions, but there are ways it can augment the process, according to Fractal Analytics. The provider of AI and analytics services to enterprises has been working to enhance how organizations process business information. The company also incubates other startups internally to build upon the offerings it makes available.

Data is increasingly made available to enterprises, but figuring out what it means and what to do with it can remain elusive.

Srikanth Velamakanni, co-founder and group chief executive for Fractal Analytics, spoke with InformationWeek during last week’s ai.nyc conference. He shared a few thoughts on where AI can be put to use within the enterprise, especially in consumer goods.

What are some pain points that top enterprises have where AI can be of use, and where does Fractal Analytics fit into the conversation?

"If you look at the Fortune 100 companies, they are all worried about digital transformation. IT players get pretty excited about talking digital and usually that means they make websites for companies. If you just dig a little deeper, when Fortune 100 companies are thinking of digital transformation they are essentially saying, ‘How can I drive better executive decision-making inside the organization?’ They are also thinking, ‘How do I connect and engage with my consumers in a much deeper way at those places where consumers are interested in engaging with me?’ They are asking, ‘How do I improve productivity?’

"It’s a C-level topic. Revenue is growing but costs may be growing faster. Especially marketing productivity, they are asking how they can make marketing dollars more effective. Can it be done with fewer people or can the same number of people drive more growth? The idea is eliminating inefficiencies through automation.

Where else can enterprises make the most out of AI?  

"They think about improving the cycle, from the time when something goes into R&D to when a product gets launched. How do you reduce that cycle? How do you increase the success rate of these product launches? Among consumer goods companies, there is a 95% failure rate of product launches.

"Most of the companies we serve, the Fortune 100, are very large and always being challenged by some startup somewhere trying to bring in a new angle to the business. Most of them want to be very proactive and want to secure their future. They are also thinking about business model transformation. For example, Dollar Shave Club came along and disrupted the shaving business.

"Companies are worried about AI in general, because it has such transformational potential. That’s where we come in, usually. We think solving these problems is not just IT, technology, it is not only consulting. Just analytics or algorithms won’t work because it’s about driving organizational change.

Srikanth Velamakanni, Fractal AnalyticsImage: Joao-Pierre S. Ruth
Srikanth Velamakanni, Fractal Analytics

Image: Joao-Pierre S. Ruth

How do the startups built within Fractal Analytics play into the conversations you have with enterprises?

"Most of the ideas incubated within Fractal have been inspired by client conversations. It’s not that clients necessarily ask for something, but we know what they might ask two or three years from now. The idea is to build it before they ask.

"Cuddle.ai is our way to think of how AI can be applied to business information. We thought, what if enterprise information was like a Facebook news feed? That’s how it came about. We imagined what was possible if you had a Facebook-like interface and a Google search-like interface for enterprise information. We realized that clients were struggling. [Business information] was not scaling; people were not using it. It wasn’t personalized.

"Ideas have been incubated based on client conversations, what we have seen, and where we think the world is headed. We think it’s about empowering human decisions using analytics and AI.

Are there distinct needs that consumer-facing enterprises have when it comes to what AI can help solve?

"Consumer goods companies have somewhat unique needs. Most of them spend tremendous amounts of money on marketing. About 25% of their spends are marketing related. Trade and marketing. Making marketing more effective is one the most important questions. It’s important for everybody, but for them it has been very important for a long time. The change in the marketing mix from offline to digital -- some of their models are no longer very useful. Look at Gillette as a brand, I think three-quarters of their spends are digital. They have to make sure they use AI to solve those marketing effectiveness problems.

"Product failures are very huge. They launch a product and have to support with enough kind of advertising and trade so they meet their targets for success.

"The other problem with consumer goods companies is they are very reliant on external data sources. If I am a bank, most of my data is internal because every time a credit proposal is filed or an account is used anywhere, I am getting the data. When consumers buy a product, that data does not normally go to a CPG [consumer packaged goods] company. Usually, they don’t see it because it’s Target or Walmart who sees that. Even if it does, it flows many months or weeks later. One of the things we work with them on is that data and decision-making journey.

With the specialized expertise needed for this niche, how do you go about cultivating or hiring talent for this kind of work?

"Everyone wants AI and very few people actually get it and can really solve problems. Sometimes it’s not about whether you can run TensorFlow. You have to solve the challenge of connecting the pipelines and neural designs with human machines in mind.

That talent is very rare. This talent has to be created; it can’t be bought. We get raw talent from the industry and what we train is experience. When you have great talent, with a great attitude, they learn very quickly."

Joao-Pierre S. Ruth has spent his career immersed in business and technology journalism first covering local industries in New Jersey, later as the New York editor for Xconomy delving into the city's tech startup community, and then as a freelancer for such outlets as ... View Full Bio
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