Leveraging artificial intelligence (AI) provides companies with a unique and enduring competitive advantage, witnessed by the fact that AI-first companies are the world's only trillion-dollar companies. That's the view of Ash Fontana, a leading global start-up investor with a specialist interest in artificial intelligence and author of The AI First, How to Compete and Win with Artificial Intelligence.
“AI is the one that compounds most quickly and is the hardest to catch up to. Once you build it, it becomes a loop and it builds itself which is why it is so powerful,” he tells The Irish Times.
In the book, Fontana frames AI as the third wave of economic development.
The first wave, the physical, dates back to the Stone Age. Think rope traps and spears, tools that allowed us to go beyond our immediate physical reach to gather more food than we could with our bare hands. However, this physical leverage was limited by scale and our intellectual capacity.
The second wave brought intellectual leverage. It started with the printing press which allowed us to distribute information, with computers following, extending our intellectual reach. Insights were limited, however. Artificial intelligence, the third wave, provides decision-making leverage, he says.
Making fabrics
Fontana uses the example of producing fabrics to illustrate the point. The physical age brought the loom which made it possible to stitch fabrics faster than by hand. The information age allowed computers to turn drawings into patterns for the loom to weave.
The third wave changes the game: computers scan photos on social media, figure out consumer trends, draw up new styles and turn drawings into patterns for the loom. New styles hit the stores just as they become fashionable.
In the world of AI, the collection of data is merely the starting point. Deploy the right network of interlocking datasets, filters and tools and you can develop a flywheel effect.
Consider the vast amount of money Google invests in its Google Maps for instance or Amazon on its Alexa-based models, both of which hoover up huge amounts of useful information. As Fontana notes, these are not stand-alone products for either company but part of a suite of products that helps fulfil their data strategy.
Put simply, the power of AI lies in its capacity to turn data into really useful information to aid decision-making. Big tech does it at scale with sometimes alarming efficiency, but Fontana says small businesses can use it too. A sandwich shop can use simple AI tools and techniques to monitor its inventory on the shelves during the day and recalibrate its prices.
In his book he also explains how a lean version of AI can be employed by almost any business.
Many businesses have AI working in the background, whether they are conscious of it or not. Payment service providers such as banks and credit card companies employ it to detect fraud, for example.
Consider the example of point-of-sale solutions group Square. It has the capacity to access real-time information from the tills of its retail customers with their permission which can be used to inform decisions made by its lending subsidiary, Square Capital. By comparing the data from one retail customer to a range of till information from other similar customers it has already lent to, it can predict whether a customer is good for a loan.
“You don’t get a loan if you don’t add data and you qualify for a loan because your data can be compared with other data to make a prediction.”
Success in becoming an AI-first company, he says, is about how effectively you can collect the right information and use it to create good predictive models. Master this and you create powerful network effects to outpace your competitors.
“First organise the data you have. Spend the time and money you need to get everyone storing data in the right place, with all the tags needed to show context around any particular dimension of the data. Then try some of the more basic machine learning methods available in free easy-to use software packages,” he advises.
Fontana distinguishes between entry level and what he calls “next level” network effects. A simple entry level effect, for example, might tell you that half of your customers are women over 45.
The “next level” is when the machine is automatically learning over lots of data points throughout a network and is then also creating predictive information – “We think the next person to buy your product will have the following attributes ...”
The most common mistake when trying to become an AI-first company is not having everyone aligned with an AI strategy, he says, in other words, not thinking about where to get data, how to process it into information and build models that generate data network effects.
The next most common mistake is not investing enough in security, infrastructure and governance.
AI is already being deployed as a management tool in traditional offices and work-from-home settings as a way of measuring productivity and possibly engagement, with obvious concerns on the part of some employees.
It is also being using used as an active listening tool. For example, calls to a customer service centre can be monitored to determine customer pain points. Insights from this information can then be incorporated into the design of new products and services.
Fontana says the sci-fi nightmare of rogue robots and supercomputers remains very much in the realm of fiction. “We are not at the point where machines are doing things that humans are not ultimately controlling.
“AI can be weaponised, but I think ultimately that’s about how humans behave and not AI doing something by itself.”
Ethical concerns about surveillance capitalism by big tech abound but Fontana argues that AI can be a force for wider good. Consider its role in combatting the Covid 19-pandemic, he says. The development of vaccines and the choice of the most efficacious drugs to treat Covid-infected patients was accelerated through AI while the complex logistical challenges of the mass rollout of vaccines was also helped by the use of AI.
Panel: Principles of Lean AI
Distinguish Lean AI from Lean start-ups: In a start-up, you are looking to develop a minimum viable product. In Lean AI, you are attempting to make an existing model more accurate. The output is a prediction, not a calculation.
Make better predictions: The aim is to get to a point where the prediction starts getting better than a human’s. A prediction often takes the form of a classification, for example classifying the information available in a photograph.
Be selective: Collecting data and performing calculations can be expensive. Spend time figuring out exactly what you want to predict in order to settle on the prediction usability threshold, the point at which a prediction becomes useful.
Play with statistics: Get one answer with one statistical method then use that to discover the next answer using another statistical method.
Be clear on your aim: Lean AI is about solving a specific problem with AI and building a small but complete AI-first product that can either grow into other domains or remain focused on one.
The AI-First Company, how to compete and win with artificial intelligence, by Ash Fontana is published by Penguin/Portfolio
