For more than a decade, big-tech companies have invested heavily in artificial intelligence (AI). Now, pre-digital companies are starting to do the same. The International Data Corporation (IDC) predicts that the compound annual growth rate for global spending on AI will be 50.1%, reaching US$57.6 billion by 2021. This is thanks to investments in retail, banking, healthcare and manufacturing, which will make up over half of the worldwide spending on AI.
Artificial intelligence will eventually permeate all industries. However, it does not provide a point of difference on its own. To realise the growth benefits of AI, it must be considered in a systems context, or what I call a system of intelligence.
A system of intelligence consists of an algorithm (a process or set of rules to be followed in calculations or other problem-solving operations) that provides the ‘smarts’, the necessary computer processing power, and a fantastic user experience. All of this can combine to produce tangible business benefits.
How systems of intelligence can reduce costs and increase efficiency
Systems of intelligence can be used to replace or supplement expert judgement and manual decision-making processes.
Predictive models are faster because automated decision making can be applied to millions of data sets simultaneously. For humans to do this work would be extremely expensive and time-consuming.
These models are often more accurate than their human counterpart and don’t suffer from being inconsistent. Once the model has been trained, it is cheaper to run than a human employee, especially at scale (think digital distribution).
Artificial intelligence harnesses the power of these predictive models, but, as stated earlier, the algorithm alone is not enough to act as your digital moat. (As I explain in my book, your moat refers to your company’s competitive advantage. The wider and more defensible the moat, the more sustainable your competitive advantage is.) Your moat comes from creating a system of intelligence that drives customer interactions, and creates lock-in as the experience becomes more personalised and effective. So, what constitutes a real system of intelligence that creates a highly defensible moat? Here are five different ideas to consider.
- Vertical expertise
In many industries, there are specific domains where human capital, business-specific knowledge and intellectual property can act as a moat. Through harnessing this expertise, you can create a system of intelligence that will provide a competitive advantage relative to a system of intelligence created by a team without this expertise.
Note that artificial intelligence does not tell you where and how it should be applied – that is a human job. This relates back to having both the technical and business capabilities in one team, and having them work together. So, where and with whom does your domain expertise lie? Can it be combined with data to create a system of intelligence?
Just a quick note: some people don’t categorise focusing on one vertical as a ‘system’, but we certainly do. Even in one vertical, there are numerous data sets from varied sources to collect, and these can be used to unlock huge amounts of value when coupled with machine learning.
- Personalisation at scale
A system of intelligence must be scalable, in that the owner shouldn’t have to make large changes whenever there is a new user. The point is to build a system that can easily bring in new users, while providing them with a personalised experience from the first interaction. Having to create this bespoke experience manually isn’t a system of intelligence. This attribute is probably more critical with customer-facing solutions, but is important for internal systems as well.
- Man and machine
In many circumstances, the best outcome will be achieved through a mix of human and machine intelligence. This is similar to the vertical expertise moat, but different in that an expert and a trained intelligent algorithm will work together to provide optimal outcomes. Your system is then half man, half machine.
This has proven to work well where there is a need for human emotion, judgement or creativity.
For instance, AI can help a customer service representative troubleshoot problems faced by customers and find solutions extremely quickly, while the customer service representative can convey the solution in a manner and tone that is agreeable to the customer. This combination creates highly defensible moats, as both the intelligent algorithm and the human expert evolve over time.
- Network intelligence
The network effects of data partners with an algorithm to provide incremental value to players across the value chain. The more parties are involved, the more data it can use to provide increased value. More value attracts more parties and more interactions.
Network intelligence on a platform can improve the experience on both the demand side (recommendation engines recommend products to customers based on purchasing behaviours of similar customers, as well as other data sources like weather or seasons), and the supply side (pricing optimisation engines or forecasting engines help sellers set the price of their goods and forecast demand based on data sources like time of the year, the purchasing habits of similar customers, weather, and remaining and future stock).
- Integration intelligence
When smart algorithms meet physical hardware, an almost infinite range of opportunities present themselves. Integrating hardware and software, so they are built to run optimally together, creates tangible products that provide new experiences and open up new business opportunities. Incredibly strong moats can be created through building products that live in both the digital and physical worlds.
A prime example of this is the iPhone 8, which was built with an Apple-designed GPU (graphics processing unit), allowing Apple to optimise the machine learning workloads running on the iPhones, which is especially handy for Apple’s intelligent personal assistant, Siri.
Ask yourself the tough questions to get ahead
The barriers you raise will not come from intelligence alone, but from the system you create.
These ideas offer five ways to think about how to create a system of intelligence. There are varying degrees of overlap between them, but they suggest a number of different ways to approach the creation of a highly defensible digital moat. Before proceeding, always ask yourself: will this improve the bottom line in a sustainable way?
Whether through cost saving, creating a better customer experience that encourages more users, or helping you make faster and better decisions, all of these factors affect the bottom line. Technology for the sake of technology does not. The power of these intelligent systems is the continual, progressive, automated learning from data. The question that then needs to be asked is: is my company’s organisational design and culture ready to respond proactively to this evolving intelligence?
Encourage inquisitiveness and adaptability to best utilise AI
An intelligent system continues to improve over time, and your teams need to move with it. Otherwise, the investment in machine learning is pointless. In fact, it could cost your company valuable time and resources.If you’re going to implement machine learning and create a system of intelligence, make sure your teams can respond and move with agility.
Windows are small, opportunities are big, and missing out can be devastating. In the end, to utilise artificial intelligence, you need a culture that is willing to learn continuously. Therefore, the implementation of your system of intelligence should certainly be a key priority of your Engine B, which isn’t shackled to your old ways of doing business.
Rather, your new agile team can operate in an agile way – continuously learning, adapting and improving with the technology, which in turn is driven by the people and hardware that interact with it.
Have you read?
Anthony Stevens – co-author of Chasing Digital: A Playbook for the New Economy (Wiley).
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