We want to deliver to corporate leaders the same advantage that the best investors receive from machines: superior returns.
Barry Libert, Chairman and CEO
Below is the AIMatters story and a Question and Answer session with the co-founders of the firm.
Q: Barry, tell us a bit about yourself and your journey?
I am the founder of AIMatters which is the world’s first AI platform for corporate strategy and decision making. I split my time between Santa Fe, NM and Boston, MA.
I began my career at McKinsey & Company more than 30 years ago. For each assignment, I was surprised by how similar the questions asked by executives were: Can you…. 1. analyze our business ; 2. tell me what my competitors are doing and how I compare; 3. recommend the best strategies to drive growth and profits; and 4. help me execute your recommendations?. I thought, “There must be a more better and less costly way for executives to obtain these insights and recommendations and then execute rather than spend millions of dollars a month on a team of young MBA’s and a partner that creates lengthy presentations that don’t execute.”
Since then, my work has been focused on how companies can get the same benefit as the best best investors — using machines, rather than people, to make analyze, decide and execute their ‘corporate trades’.
Q: Megan, what is AIMatters and what’s unique about it?
Our AI platform today makes machines available to companies of all sizes so they can avoid the high cost and long time delays that Mckinsey, Bain and BCG (aka MBB) take and charge. With the AIMatters’ platform, she (that’s right, our computer is a woman) : 1) Benchmarks your business (e.g. we call it our Corporate GPS); 2) Provides leaders with education to better understand their choices; 3). Delivers machine generated recommendations; and 4) Connects our clients to our partners to help them execute the machines recommendations.
Basically, it’s all the stuff Barry used to do at McKinsey & Company, and I did at Bain & Company, but at a fraction of the cost and in a fraction of the time while using the same technologies that the best investors use to generate superior returns. .
Q: Barry, how did you pick the name AIMatters?
I picked the name because it begins with “AI.” Every one of my boards and executives ask me the same question: “How do I transform my business and become an AI-powered platform with network effect that delivers superior growth and value without spending months and millions on consultants?”
I wanted to ensure we provided more than just AI strategy and analytics. I also wanted to build a platform that provides education and helps company execute their ‘trades - e.g. what companies to buy or sell, what products to make and which investment to make in what assets’. In short, I wanted automate consulting the way the financial services industry did decades ago. The result would be a significant step function reduction in cost and time that corporate leaders take to make their investments be it in things, people, knowledge, relationships or technology.
Q: Barry: When did you first envision AIMatters?
AIMatters grew organically from my CEO and board advisory business that I started in 1993 after I left the Hancock Insurance Company. I already had the idea that I could automate the strategy industry, but I was missing a few ingredients. What would the technology I wanted to build systematize and automate: 1. The consulting process or 2. the answers? In the end, I decided to automate both, given Mckinsey had taught me to start with ‘the end in mind - e.g. answers’.
However, to automate ‘answers’ I needed a framework - aka an investment thesis for business value. At the time, I was co-managing director of a $2B institutional real estate investment portfolio, and my wife was the Dean of Students at Harvard Dental School. She used to ask me when all I talked about were things and money: “Don’t leaders realize that people, their knowledge and networks are also assets? I began to think – ‘she could be on to something’. In addition, I began to wonder, if all the ‘assets’ in which companies invest weren’t properly captured or categorized and could ‘assets’ also become liabilities?
In addition, I kept hearing John Bogle, Chairman and CEO of Vanguard, preach that — ‘fees are the devil of returns’. If he was right for investing, wasn’t his premise also true for companies? And weren’t the fees that Mckinsey, Bain and BCG (MBB) charging degrading corporate returns?. Together, these 3 insights formed the foundation for my thinking and future business strategy:
Automate the 4 basic consulting processes (aka: Analyze, Educate, Recommend and Execute).
Expand the definition of assets so companies can make better capital allocation decisions.
Reduce the cost of consulting the way John Bogle (Vanguard) was doing for investors.
Q: Megan: When did you join Barry and why?
I joined Barry in 2013. I was a Bain Alumna, and mother to my first child,, Sylvie. I had received my master’s in computer science and wanted to do something that combined my strategy consulting background and my technology education.
Through the Bain alumni network, I had heard about Barry and his objective: To automate strategy consulting and incorporate intangibles – people, knowledge, and relationships – into a more comprehensive capital allocation system that could be delivered easily and cost effectively to businesses like it was for investors.
Like Barry, I knew a lot of clients who wanted a better, more modern technological way to help them manage and reposition their company. I wanted to work at home while building my family. I envisioned logging on every day to a system that could tell me where my clients were, what they were doing, and how they should change their position without traditional consultant approaches to clients - e.g. billable hours.
Q: Megan: How does AIMatters generate its insights?
We started with a simple thesis - intangible assets are the basis for all future value. It is clear that tangible assets (things and money) have given way to intangible assets (customer, employee and intellectual) and as intangible assets grow, so will the data that is derived from those assets.
Based on the reality, starting working with data to create our own training data set to form a new picture of companies as capital allocators - e.g. active investors in their own companies.
We then built our AI platform using the Google Cloud Platform (GCP). We also leverage open source code. Our intention: To track every company’s ‘trades’ - e.g. investments in tangible and intangible assets - to determine how they create value (e.g. what the make in sell).
The good news: we have a clear road map about where we are going and a process to ensure we achieve our vision - to build the first corporate strategy platform that delivers the same benefits to companies that institutional investors receive from machines - a platform that analyzes, recommends and helps them execute via partners.
Q: Barry: What’s your vision for the future of AIMatters?
To build the first corporate strategy platform - where members of the corporate C Suite can analyze every move they make and every investment decision they take - be it in things, people, money, knowledge or networks - to insure that they create superior returns (Alpha). It is a big vision, but it is clear that AI and platform business models are the way forward for all companies that want to generate exponential growth and profits. This vision will require our machines to observe a corporate current position, orient the leaders about where they are (relative to their peers and the best performers, regardless of industry), decide what to do next via machine learning recommendations and faster, cheaper and better execution without the high cost of consultants.
Q: Megan: What has been the biggest challenge so far?
I’m always trying to improve our algorithms, data, and modeling- either improving our platform or adding new data features based on our Corporate agenda. It is hard work because the data I am using was not intended to be used for this agenda. That is why we spent so much time analyzing companies manually at first in order to build a proprietary training data set. Now that we have taught the machines all that we have learned, we have begun to scale the model and are working on automated feature selection so we can take in an ever increasing amount of data. I am confident that as more ‘alternative data’ sets become available and our team grows - including our data engineering team - we will be able to accelerate our progress.
Q: Barry: What are the key lessons learned?
My answer should come as no surprise – it’s the data in the machine age. As Megan has said, getting good data to feed our machines is no easy task. Its clear to me that machine learning and all of its derivatives including deep learning - will get commoditized as the big players - Google, Microsoft and Amazon - square off to get everyone’s business. So the answer lies in creating a unique business model with a unique data set and point of view (investment thesis) about where value is being created in the world and where it is being destroyed. In essence, in the machine - transitioning from good to great is not just about getting the right people on the bus, but getting the right data into the machines.
Q: Barry: What are the biggest challenges to scaling your business?
The biggest challenge is helping corporate leaders understand that machines (robots) can be used to help them make better decisions just like the do for the best investors. Further, it is hard for them to imagine sitting in front of a ‘corporate trading’ system which helps them understand every trade - be it BUY another business, HOLD the people the have, or SELL different products - is the same that investors do - but just with different assets - intangibles and tangibles. And that some ‘trades’ are more valuable than others. However I am convinced that with every new article about AI and how ‘software is eating the world’ - that we can eat the corporate strategy world with our unique data sets, man eating machines. I strongly believe that the companies that start using our platform will achieve the same superior returns that the best investors do from the machines and the platforms they use.
Q: Barry: What are your three key focus areas to achieve success?
McKinsey left me with some important skills. The three most important being: 1. Start with the end in mind (e.g. results, not process); 2). Think in three’s – ABC, Do Re Me and Tic Tack Toe and 3). Its easier to change the people than to change the people (which means, you can’t change anyone).. Said differently, we want to provide answers, not presentations, in simple formats without all the change management that it will take to change your consultants business models - e.g. billable hours. To do that, our unique value proposition (UVP) is:
FASTER: Decrease the time it takes by 100X to get an answer.
BETTER: Focus on results and returns, not power point presentations.
CHEAPER: Reduce cost by 100 fold by leverage the power of AI.
About Barry Libert and Megan Beck
Barry Libert is the CEO and co-founder of AIMatters. Everyone says the same thing about him – there has never been a board, leadership team, company, or industry he didn’t want to disrupt. Now, he is finally achieving his lifelong goal – disrupting the strategy and leadership consulting industry.
Megan Beck is the president and co-founder of AIMatters. Megan, as much as she hates to admit, is also a real disrupter. She also wants to bring woman leadership and values to the world. This is her chance to make it happen — 30 years earlier than Barry.