Enterprise AI Technology: Moving from Application-Aware to Customer-Aware
This is the fourth article in the Radius series exploring issues related to enterprise AI technology.
It’s been another exciting year for artificial intelligence (AI). Its potential for digital transformation is poised to hit a new wave of maturity. Enterprise AI technology, such as machine learning (ML) and deep learning, already impact businesses.
Speaking at VMworld Europe, VMware CEO Pat Gelsinger dubbed AI and ML among the technology superpowers. In their own right, these tech innovations are significant. Together, they could drive development exponentially.
As we close 2018, it’s a great moment for enterprise IT to think strategically about AI. The question driving AI use is shifting from how—how and where best to use it—to why. What questions do we want AI asking of applications, network and other enterprise components to better serve our customers?
Over the past few years, enterprise AI helped infrastructure become more application-aware—even as the number and types of applications in the ecosystem soared. The next wave of enterprise AI technology will focus on behavior and organizational goals. This marks a significant shift beyond application-aware AI to customer-aware AI.
The Early Wave
Many technologies take hold first in research institutions and government (notably, DARPA’s development of the internet) and then expand into businesses before becoming consumer facing. AI adoption trends in the reverse.
In the consumer space, AI focused on the buyer journey and on providing insight into buyer behavior. The next wave of enterprise AI technology is not that different. It’s about behavior and moving toward helping leaders better understand their businesses. For IT, that means new ways to deliver architectures and services that help businesses reach its goals.
Looking deeper into the case studies, AI at work helps sales teams get closer to customers by communicating through questions in key enterprise systems, such as CRMs. Another example are AI-powered digital voice assistants that can help simplify virtual meetings—saving time and boosting engagement.
The Next Wave
IT teams and employees are seeking to better understand why a business works the way it does and when events occur. All are increasingly savvy about how infrastructure resource utilization can become predictive of certain business events, for example, spikes in compute during high-transaction seasons like the holidays.
Enterprises can finally exploit big data to their benefit using AI to boost employee knowledge. This helps companies deliver more consistent customer experiences, such as H&R Block’s success during busy tax seasons.
AI also helps businesses use data predictively to drive business decisions around things like:
- Cloud adoption.
- Vendor management.
- Security decisions.
- Customer service.
- Much more.
All these use cases enable leaps forward in operational efficiency compared to what we had yesterday.
Driven by the masses of data collected by doing business online, AI could automate and remediate IT functions. This AI-enabled automation helps humans better understand behavior—of apps, networks, components and users. What more can enterprises IT do with AI?
Enterprise AI Technology Introduces New Questions
We are on the precipice of AI discovery radically impacting enterprises. Bracing for disruption is wise if anything was learned from how intellectual property changed whole worlds of law, ethics and social norms.
What needs to be answered by and for enterprises is this: Who owns the data, who owns the aggregated data and decisions based on workloads that run across many clouds? Who owns the insights? These tough questions will have to be part of the routine protocol given data volumes continue to soar, and the pace is accelerating.
As AI goes mainstream, cost and feasibility will become imperative to adoption. Organizations will look for cheaper ways to embed AI into their systems and to maintain those systems efficiently. For example, facial recognition technologies may ease the costs of biometrics for healthcare organizations or government agencies.
Hold AI Accountable
Tech leaders caution enterprises to stay close to the rules that govern AI technology because without intentional and transparent rules, it risks being a case of garbage in, garbage out. VMware Chief Research Officer David Tennenhouse said in a previous Radius blog that developers need to understand why and how their AI is “thinking.” And if they don’t understand now, they will understand it no better as algorithms grow more complex. Tennenhouse advocates strongly for “explainable AI” that can document its own reasoning for making “correct” decisions.
Enterprise AI technology is promising. Disruptions will follow as it grows from emerging tech to full adoption. But AI should have one goal in mind: smarter, more customer-focused businesses. With that foundation, IT communities can then focus far less on administration and far more on innovation. Here’s to more robust AI in 2019.