Remember SMAC? It wasn’t long ago that those of us working on “digital” solutions were almost entirely engulfed in a future focused on four key technologies – Social, Mobile, Analytic and Cloud (SMAC). Organizations were trying to incorporate social into their customer service operations, deliver responsive experiences over every device and overcome the security concerns that paralyzed their decision to move to the cloud. While all four remain incredibly important, it seems few business conversations these days focus exclusively on one of these domains. Instead, today, Artificial intelligence (AI) is the focus from boardrooms to basements. AI now stands out as the transformational technology of the digital age.
There are many reasons why this shift has happened so quickly. Obviously, storage costs continue to fall, the proliferation of data and data sources continues to sky-rocket and computing continues to become more powerful. Just as important, public cloud providers continue to improve, and add to, the impressive machine learning and deep learning capabilities, available to the masses. When you combine all of the technological improvements with the growing corporate investment in this space, it becomes clear why AI is expected to be the defining technology of our future. The number of AI use cases, from enhancing the client experience in call centers (improved language processing and speech recognition) to predictive maintenance (fixing equipment before failures) is resulting in another powerful wave of business improvement driven by technology. In a recent study by McKinsey, the firm estimates that AI has the potential to create between $3.5 trillion and $5.8 trillion in value, annually, across various business functions and industries.
So, with all of this promise, why aren’t more firms adopting AI at scale and growing the number of AI solutions across their business processes? Yes, there is a lack of skills in the data science discipline. Yes, there are regulatory issues. Yes, there remains a trust issue (transparency in how AI decisions were reached). From my experience, however, the primary reason for the lack of AI scale comes back to the quality of AI “nutrients” that the algorithms require for ingestion. That is, many organizations just do not have their data in a state of readiness to take advantage of this AI-powered world.
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The first step in creating value from any applied intelligence solution is accessing all of the information relevant to a given problem. The concept underpinning all of the machine learning is giving an algorithm a massive number of “experiences” (training data) and a generalized strategy for learning, and then letting the AI identify patterns, associations, and insight from that data. But, if the data is siloed in an organization and inaccessible, or if it is difficult to obtain data sets sufficiently large and comprehensible to be used for training, then the AI value cannot be realized.
To overcome these challenges, many organizations need to get back to the basics before attempting the AI “leap.” There are three areas that must be addressed:
1) Data strategy
To build out the required data collection and data architecture, an organization must understand what the data (and associated analytics) will be used for. In many cases, executives worry about their ability to choose the most effective systems for their needs and they get lost in a state of paralysis. Data is no longer about just measuring and managing. Data is core to a firm’s innovation. Defining the data strategy is core to organizational function.
2) Data generation & aggregation
I have met with numerous firms lately that are sourcing and collecting large amounts of data but that still do not have a plan or a platform to consolidate the information. Organizations struggle with creating the right structure for any meaningful synthesis to take place. This is why cloud platforms, such as Microsoft Azure, are fundamental. The ability to generate and aggregate becomes more important with AI since the quantity of available data is core to the machine learning.
3) Driving insight
Driving insight is all about revealing the invisible and gleaning new information from data that can be acted upon. While insight is obviously the output, understanding that business problem upfront is important. In understanding what insight is required, an organization can balance the requirements for traditional analytics and develop AI-powered solutions.
AI is here and now and advancing quickly. The technology can drive significant value and the opportunity is tremendous. For organizations wishing to deploy AI to realize that value, however, there are some basics that must be in place. Developing the data strategy, collecting and aggregating the information in a thoughtful manner and focusing on the insight required to address specific business problems are table stakes. From there, the value of AI can be mined. All companies have the opportunity in front of them. As Mark Twain wrote, “there’s gold in them thar hills!”