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.