- 3rd July 2018
- Posted by: Manolis
- Category: Blockchain
When you spend some time working in the corporate world, you quickly learn how difficult it is to operate a publicly traded company. Senior leadership in most corporations are often criticized for being overly compensated, but such criticisms are rarely offered up by people who truly understand how difficult the job can be. For example, try ranking the below stakeholders in order of importance for a publicly traded company:
Unfortunately, the reality is that the order of importance seen above holds true for most CEOs of publicly traded companies. Shareholders above all else, clients because they pay the bills, and employees because you need them to give you a decent rating on Glassdoor. Of course, the messaging needs to be that all three parties are in fact equal, each being a number one priority. It’s hard to do. Just ask any CEO who needs to follow these priorities while at the same time, proselytize in town halls about how “employees are our greatest asset”.
To make matters worse, today’s CEO isn’t even afforded due process but is expected to resign whenever the court of public opinion decides the CEO looked at somebody wrong. At the same time, you have CEOs like Marissa Mayer who make out like thieves after destroying companies. Business leaders could use some help these days, and that’s where some artificial intelligence (AI) might come in handy.
AI for Enterprise Applications
Human Intelligence in Jobs
When company executives at Facebook say things like “there is no such thing as a meritocracy“, they embarrass themselves because of something called science. Complex jobs require people with above average intelligence. While 80% of people believe they are above average intelligence, science tells us that intelligence is not evenly distributed among populations. That’s why intelligence is what helps determine who does what in a society:
As AI becomes “smarter”, it only makes sense for AI to start helping humans with more complex tasks that require a higher level of intelligence. That’s why a company called Noodle.ai has developed “the world’s most sophisticated Enterprise AI system to manage complex business operations” which they affectionately refer to as “the beast”.
Founded in 2016, Noodle Analytics has taken in $51 million in funding so far to develop an AI platform that “offers pioneering business solutions in Enterprise Artificial Intelligence.” The majority of that funding came in the form of a $35 million funding round that just closed days ago. Participants in that round include Dell and TPG, one of the largest private equity investment firms in the world with over $80 billion in assets under management.
Since we started out this article talking about CEOs, we’re going to continue talking about CEOs and take a closer look at who is leading the charge at Noodle.ai – a man named Stephen Pratt – who was previously “responsible for all Watson implementations worldwide for IBM Global Business Services.” (He’s also used to play rugby, one of the last sports played by real men and women.) This short story behind the company’s inception is largely taken from an excellent interview that was conducted with Mr. Pratt by ZDnet in November of 2017.
Once Upon a Time…
The story starts in 2004 when Mr. Pratt was working for Deloitte and decided to start a firm called “Infosys Consulting” which is largely responsible for giving us “John in Mumbai“. A decade later when he left Infosys in 2014, he had grown the company from a two-person startup to a $2.3 billion consulting and systems integration group with 32,000 employees. (For all you armchair CEOs out there who think you know how airlines should handle unruly passengers, these are the sort of accomplishments you should have on your resume before you’re allowed to get on Twitter and tell everyone how wrong they are.)
During his time at Infosys, Mr. Pratt had used some primitive AI algorithms to look for drug smuggling ships. This provoked his interest in further exploring AI, so he reached out to private equity firm TPG. They then scoured the landscape of AI startups to find “a company that combines deep expertise in business operations with learning algorithms”. He didn’t like what he found, so when his friend from IBM called, Mr. Pratt decided to take on the role of commercializing IBM Watson – something that lasted less than a year. Here’s what he had to say about that experience:
It wasn’t really my thing. I wanted to design an operation that was custom-built for this new age of technology, and it was much easier to do that as an independent entity than fitting in with something else.
Sounds like some of IBM’s bureaucracy got in the way of progress. That’s when Mr. Pratt decided to co-found Noodle.ai alongside Raj Joshi, a man with whom he co-founded Infosys consulting with – a company that “went from zero to $800 million in annual book-of-business in seven years.” Mr. Joshi serves as the President and COO alongside another gentleman, Matt Denesuk, who was previously the Chief Data Science Officer at GE. Continuing with the Infosys theme, the senior leadership team at Noodle.ai also includes the previous “Global Human Resources Leader for Infosys Consulting” who is now Chief Talent Officer at Noodle. Lastly, we see CTO Dr. Ted Gaubert, also from Infosys, who was responsible for creating “the intellectual property and operational backbone of the organization” which helped them scale so quickly. (The next time you hear someone complain about “how broken venture capital is”, ask them if their leadership team bears any resemblance to this talented group of individuals.)
As we’ve talked about before, AI software is free for anyone to use. It’s the data that will determine the best algorithms. But even with all the right data, you still need to sell your solution in a credible manner to C-suite executives, most of who can smell cow manure a mile away. One thing we can be sure of is that this team of seasoned consultants has a track record of being able to sell enterprise solutions at scale.
What Enterprise AI Is
The crux of the problem that Noodle.ai wants to solve lies in something called supply chain planning. Going back to the ZDnet interview, Mr. Pratt addresses this with a great example:
If you look at companies with complex supply chains, the way they do planning every month — the ‘sales and operations planning’ process — is fundamentally broken. It’s basically a group of people coming together each month with bad information, having arguments and then settling on a suboptimal plan, because it’s what they could agree to.
(Doesn’t that just perfectly describe how most business meetings take place?) The interview goes on to talk about how the vast majority of useful data that companies like Amazon use to dominate retail is structured data – as opposed to the unstructured data exhaust that companies scrape to use for new credit scores as an example. When it comes to enterprise planning, structured data like “transaction history, economic data, weather data, competitor behavior, events, demographics, psychographics, and customer data” provides strong signals. Unstructured data provides “weak signals”. In other words, ignore the hype – like chatbots and speakers that eavesdrop on your conversations.
What Enterprise AI Isn’t
We’ve talked before about the “Myth of the Clever AI Chatbot“, and Noodle.ai published a blog post that largely dispels commodity AI functions like chatbots or natural language processing as the most unlikely places that enterprises should be looking when adopting AI. In the words of Mr. Pratt:
Natural language processing (NLP), chatbots, and humanoid robots are the most expensive, least value-added, most technically sketchy places any company can start to apply AI to their core operations
In this blog post, Mr. Pratt goes on to talk about enterprise AI needs to be all about demand forecasting – “to predict accurately what products your customers want — and then how many, where, when, at what price, and bundled with what other products.” Chatbots that answer HR questions aren’t going to move the needle. Using NLP to screen-scrape some weak signals isn’t AI either. When it comes to enterprises, the data is already there. It just needs to be analyzed by AI algorithms – housed in a supercomputer called “the beast” – which is made up of the following ten applications:
Don’t have all the data you need? Not a problem. Noodle provides “data cartridges” that be used interchangeably through their applications, things like weather data, demographic data, etc. For each of the above ten applications, an Nvidia supercomputer is used to first find hidden patterns in the data. Then, the prediction engine analyzes billions of possible outcomes, the best of which comes out the other end as a “recommendation”. Once implemented, the results are fed back into “the beast” so it can learn from its mistakes – unlike most human consultants.
The leadership structure of a corporation isn’t likely to change just because we have ultra-intelligent AI algorithms now. In the same way that we expect AI to look over a doctor’s shoulder, we can expect AI to be peering over the shoulders of company executives everywhere offering them up tidbits of advice that help in decision making. Based on the acumen of Noodle.ai’s leadership team, there seems to be an above average likelihood that they will succeed in creating and selling such a tool. Now that consulting companies are desperately trying to establish “AI centers of excellence” (whatever that means), there are plenty of possibilities for an exit here. Maybe IBM’s overpaid CEO can take some notes and finally get Watson off the ground.
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