by Kimberly Nevala, SAS
10 January 2020
Artificial Intelligence (AI): automating, augmenting and ambient. The more ubiquitous AI, the less visible AI will become. This statement may appear ominous, particularly to fans of popular science fiction depictions of AI.
The reality, however, is that analytics – including AI – will be increasingly integrated into the fabric of your business. As opposed to the dashboards of old, AI lives inside.
Far from threatening, this evolution reflects a healthy understanding of AI as an enabler, rather than an end in-and-of itself. In other words: AI is not the goal. Optimized business processes and innovative, engaging products and services are the goal. Leading companies apply AI broadly across their business portfolio: applications run the gamut from deceptively simple automation to more ambitious augmentation of both systems and humans.
At the most basic level, AI can be applied to refactor current practices: delivering the same outcomes better and faster. Very often, such applications apply AI algorithms to supercharge existing analytics applications or exploit previously unutilized data sources. Examples include applying deep learning to enhance network analysis or augment traditional fraud detection algorithms. Another common starting point is the use of natural language processing (NLP) to interrogate and make sense of written text and the spoken word. Applications range from automating the onerous job of medical coding and verifying bills of lading to analyzing social media posts, customer calls and service logs to identify emerging product issues and consumer trends in near real-time.
At the other end of the spectrum, AI allows the business to be reimagined: transforming both what the product/service is and how it is experienced. AI is integral to deploying an adaptive supply chain, providing an AR-assisted preventative maintenance service, operating a cashier-less store or navigating a self-driving car. Without AI, the product or service could not exist. Like the best symphonic composers, practitioners at this level mindfully instrument AI to work harmoniously with other systems and humans.
Of course, AI applications are not limited to core business operations and services. AI is also fundamentally changing historical data and analytics practices. From augmented data management (automating data curation and integration at scale) to augmented analytics (utilizing machine learning to surface emerging patterns of interest or assist in algorithmic tuning) AI-enhanced tools are transforming the data business.
AI toolsets are increasingly accessible and easy to use: potential applications emerge daily. Yet, AI solutions remain notoriously hard to deploy. From identifying impactful use cases to managing change, challenges abound. To cut through the hype and complexity, it is useful to remember that AI functions inside: as an enabling component, not an end-state. The ‘AI Inside’ mindset enforces the discipline to identify the problem being solved and the desired outcome before determining if and how AI might be employed. This outside-in approach also promotes design-thinking: ensuring the delivered experience will meet customer’s expectations and enlisting collaboration from diverse parties early in the process.
AI applications are proliferating: the most successful work tirelessly behind the scenes to deliver new services and engaging experiences to consumers and employees alike. Where will you put AI to work inside your business?
Kimberly Nevala is a Strategic Advisor at SAS where she advises customers on how to balance forward-thinking strategies with real-world perspectives on business analytics, data governance, analytic culture and change management. Kimberly’s current focus is helping customers understand both the business potential and practical limitations of AI and machine learning.