Companies looking to apply AI are looking for a competitive advantage in their industry, something that will give them an edge in the market and help them grow. However, not every AI application can give a company a competitive advantage. Many AI applications are simply going to become the new normal.
For example, AI-enabled payments fraud detection is likely to become the new normal in eCommerce. Most of the fraud detection companies out there will almost necessarily use machine learning, and the data infrastructures at eCommerce companies will naturally evolve to allow for applying anomaly detection algorithms easier. As such, an AI-enabled fraud detection application will not be a firm differentiator; everyone else will be doing it, too.
But there are some ways in which companies can tangibly pull ahead, and in this article, we detail three different methods for gaining a competitive advantage with AI: establishing an AI transformation vision, data dominance, and cultivating Critical Capabilities.
AI Transformation Vision
An AI transformation vision, which we discuss further in our Generating AI ROI – Best Practices and Frameworks report, is a vision for how a company’s strengths and market position can be leveraged alongside AI’s new capabilities in order to unlock new ways of serving its customers or new ways to open up access to new customers.
It’s important to have an AI transformation vision for three reasons:
- Prioritizing AI projects
- Aligning functional leaders to a common goal
- Preparing a company for long-term investment in AI
We’ll begin our discussion of all three with how an AI transformation vision can steer a company toward the right initial AI projects.
AI Transformation Visions Prioritize Projects
If a company wants to determine which application it wants to invest in, it should think about what will help move the company in the direction of its differentiator. This investment might be a certain area of a company’s data it wants to get familiar with. It might be a certain kind of AI capability the company wants to unlock. It might be a certain kind of cross-functional AI team the company wants to build out in one specific department. No matter what it is, prioritizing projects is one of the clear benefits of having a transformation vision.
AI Transformation Visions Align Functional Leaders
AI transformation visions align company leadership. When there’s a shared vision, it makes it a little bit easier to educate leaders on where the company wants to go with AI. Company leaders won’t all be looking for AI opportunities in different directions, and they won’t all be explaining what AI could do for their business in a different way to their different departments. Instead, conversations about AI can be more targeted and more about forwarding that shared vision.
AI Transformation Visions Prepare Companies for Long-Term Investment
When a company knows where it’s ultimately going with AI, it can invest in improving its data infrastructure for its payments or its online behavior tracking, for example. Even if it doesn’t generate a strong ROI right away, it will build the Critical Capabilities necessary to reach the company’s AI transformation vision. We’ll discuss these Critical Capabilities further in the article.
A lot of the time, company leadership wants to know when an AI project is going to generate an ROI. Ideally, companies will want their initial AI projects to have an ROI, but some projects are going to fail. Some projects are mostly going to exist so that a company can build its Critical Capabilities, and this is okay.
In the next section, we give an example of how a company might determine its AI transformation vision based on the data its currently collecting and what it sees happening in its industry.
AI Transformation Vision: HVAC Company Example
An HVAC company may understand that its products are not differentiated enough in the market. It has the advantage of many years in business and strong business relationships with hundreds of clients. The company has an advantage in the way it has worked with large office buildings, assessing their energy and equipment needs and estimating costs. The company has seen a consistent trend toward office buildings with automatic controls and more energy-efficient heating and cooling systems.
The company is using sensor data to improve energy efficiency in its clients’ office buildings, but it isn’t using that data to automatically adjust their temperatures. The company could decide that if it could master the internet of things and sensor data, along with AI, it could use its existing customer base to build a massively differentiated HVAC product that could immediately reduce costs for clients. The company may believe this value proposition would allow it to dominate the national market.
The company may come up with an AI transformation vision in which predictive sensors will be the most important enabler of growth in the next ten years. The company could then pick first AI projects that reflect this vision.
Data Dominance
Venture funding for AI in 2019 reached $26.6 billion, the highest it ever has been, according to Venturebeat’s coverage of CB Insight’s AI in Numbers report. We’ve spoken to dozens of venture capitalists who have invested in AI startups, and the biggest reason they were excited about AI was the possibility of data dominance. Data dominance is the concept that as companies acquire more data for AI applications, they can acquire a greater and greater advantage over their competitors. In essence, it is a snowball effect.
Venture capitalists want to invest in companies that can acquire a data dominance dynamic because the end result of that dynamic is a company that is extremely difficult to compete with: think Amazon in the eCommerce space, Google in the search engine space, and Facebook in the social media space.
The following is a more detailed breakdown of how data dominance works:
How Data Dominance Works
- Acquire more users, customers or installs
- This leads to more data
- More data leads to more learning and more AI applications
- More learning and more AI applications lead to a better product
- A better product that is widely known leads to acquiring more users
- This leads to more data
In our interview with Gary Swart of Polaris Partners, Gary gave an example of a company in his firm’s portfolio that secured data dominance called Inside Sales, now Xant.ai:
They take the learnings from all of their customers, and those learnings benefit all of their other customers. They have more than 100,000 users on their platform, and they’re passively observing the behavior of sales results…It’s a dialer that enables you to more effectively and efficiently contact your customers…Every time you contact a customer, the platform learns from that behavior and those best practices, and then that in turn benefits everybody else when making a call.
So when you can take the dialer information, you can marry it with what happens in the CRM system—Salesforce, etc.—and you know the outcome…[You can] Take all the situational data and then…marry it with all the other data that you can pull into the system, and now you can start to get prescriptive and predictive about what a rep should do…with a platform like this you can prioritize those calls, so they (sales reps) call them in the right priority order.
Xant.ai collects data from their customers, which then feeds a machine learning algorithm for prioritizing sales calls. The algorithm allows sales representatives to better determine who to call, which makes it more likely they will get a sale. This leads to more customers and therefore more data on which to train the algorithm.
A data dominance strategy should be in-line with the company’s AI transformation vision. Sometimes a company will discover a potential strategy for data dominance and create its AI transformation vision off of that. Sometimes, it goes the other way around.
But the fact of the matter is that if a business has a data dominance dynamic that it’s going to try to exploit within its industry, then the business will want to make sure that it’s very in line with its AI transformation vision. This is because the business will never be willing to invest in that data dominance dynamic unless it has that transformation vision.
Critical Capabilities
Most companies probably will not be in a position to take advantage of a data dominance dynamic within their business. A lot of companies won’t even have an AI transformation vision that’s particularly creative; it may not be something that’s too different than most other companies. That said, most companies will not have an AI transformation vision. As such, just having one will be an advantage unto itself.
But a company can still have a tangible edge over the competition by building its Critical Capabilities. Critical Capabilities are the building blocks that enable a company to use AI. Critical Capabilities are the system that allows companies to actually use AI. A company can’t make progress unless it has these fundamentals in place, and the companies that have these fundamentals in place will be able to act more quickly.
Below are the three Critical Capabilities for adopting AI in the enterprise:
- Skills: This includes procuring and fostering data science talent, building cross-functional AI teams, and educating functional leaders on basic AI concepts.
- Resources: Access to quality data and in-house guides on how to apply AI to specific use-cases.
- Culture: This involves establishing a culture that encourages experimentation, values data, and values cross-functional collaboration.
Companies that want to leverage AI should ask themselves where they are in terms of establishing these Critical Capabilities. An eCommerce company, for example, might realize it doesn’t have the proper data resources to build the AI software it wants.
It may invest a lot into collecting data on user behavior, including purchase activity, and into figuring out how that data ties to a user’s email activity. A company whose leaders don’t have a good grasp of what its AI transformation vision could be and are not very familiar with AI as a concept may invest in education for these leaders.
Companies that master their Critical Capabilities will be able to act, learn, and enable AI, unlocking AI’s value in different parts of their business more swiftly. That ability to move more quickly with these critical tools that are going to define the business landscape in the coming decade is an advantage in and of itself.
As such, companies that have a transformation vision and that use initial AI projects to help them build their Critical Capabilities are likely the ones that will win into the future.
Concluding Thoughts on Building an AI Advantage
In conclusion, if a company wants to gain a competitive advantage over others in its industry, it will want to follow three core tenants, three methods of success:
AI Transformation Vision: A company wants to align its long-term strategy with an AI transformation vision. This will require a certain level of executive education. It may also involve some outside experts to help create this vision.
Data Dominance: A company can ask itself if there is a place where it can acquire and/or keep data dominance so that it can pull away from its competition. Again, most companies will not be able to take advantage of a data dominance dynamic, but if they can, they should.
Critical Capabilities: Perhaps most importantly, a company wants to establish the Critical Capabilities that are going to support this longterm vision. Whether the company has a data dominance dynamic or not, the question it can ask itself is, “Which of the Critical Capabilities is actually going to help the company reach its longterm goal?”
Answering this question will allow a company to make longer-term investments and feel safe about them because it is building towards something tangibly beneficial. The company is not building towards using AI; it’s building towards what’s more important than any single AI application: the company’s own nimbleness and ability to leverage AI in any direction it wants in the future because it has a strong foundation.
