AIMatters Simple Mission and Profound Ambition

We want to exponentially reduce the cost and increase the speed of delivering exponential returns to companies and their investors via a proprietary playbook and AI powered research platform.

Barry Libert, Chairman and CEO

Screen Shot 2019-03-10 at 11.45.00 AM.png

Below is a Question and Answer with the co-founders of AIMatters. 

Q: Barry, tell us a bit about yourself and why you started the firm

I am a co-founder of AIMatters, the world’s first AI strategy platform that automates strategy consulting.

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 better and less costly way for executives to obtain these insights and recommendations without spending millions of dollars a month on a team of young MBA’s with a single partner. Further, the answer to these questions are knowable and can be codified using machines'.


Q: Megan, how is AIMatters unique? 

We started with a simple thesis: some business models are more scalable and valuable than others. We did extensive research looking at all companies with $30M in revenues in the world. Finally, we translated that research into training data so that we could use machines to to analyze companies of all sizes quickly and cost effectively. Then we built an operating playbook that our clients and investors could use to create exponential returns without costly and time consuming consulting studies. Basically, it’s all the stuff Barry and I used to do but at a fraction of the time, cost and effort that our alumni firms do (e.g. Mckinsey for Barry and Bain for me).


Q:   Barry, how did you pick the name AIMatters?  

I picked the name because we began with a simple thesis: machines can automate the analysis and discovery of what makes a great business model great without spending months and millions on consultants?” In short, I wanted automate strategy consulting the way the financial services industry did decades ago. My vision: a step function reduction in cost and time that corporate leaders take to make strategy decisions and generate superior returns with known and knowable outcomes.

Q:   Barry:  When did you first envision AIMatters? 

AIMatters grew organically from my CEO and board 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. The first: what makes a great company great; The second: Were the variables discoverable and lastly: Could machines be used to achieve the same or better results than people.

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:  

  1. Automate the 5 basic consulting processes (aka: Research, Analyze, Educate, Recommend and Execute).

  2. Expand the definition of assets so companies can make better capital allocation decisions.

  3. Reduce the cost of consulting the way John Bogle (Vanguard) was doing for investors.  

Q:  Megan:  When did you join Barry and why

Through the Bain alumni network, I had heard about Barry and his objective: To automate strategy consulting and use machines and a research centered approach to deliver superior corporate performance. Like Barry, I knew a lot of clients who wanted a better, more modern technological way to do strategy.  I also knew that corporate leaders are frustrated with the lack of transparency and results. Barry’s approach seemed to be better, more direct and more to boards and CEO’s liking. Plus it followed investors desires to use machines and reduce costs.

Q:  Megan:  How does AIMatters generate its insights?

We started with a simple thesis - different business models deliver different results based on the assets in which they invest. It is clear that tangible assets (things and money) are less scalable than intangible assets (customer, employee and data). In addition, as today’s intangible business model have grown in power and significance, so has the value of intangibles in the market.

AIMatters Generates Highlights

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.

AIMatters 4 Dimensions of AI

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.

AIMatters Good Data to Feed 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.

AIMatters Corporate Leaders

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.  

AIMatters Founders Barry Libert and Megan Beck
Screen Shot 2019-03-10 at 11.45.00 AM.png