How to find a bridge to cross the AI chasm

03.22.2022 | By Mark Speyers

Around 30 years ago, companies began launching websites. They didn’t know exactly why they needed websites. But farsighted leaders believed their companies ought to have one. As the years since then have demonstrated, they were right.

Today a similar awareness is dawning regarding enterprise AI. Farsighted leaders know AI will disrupt industries. They want to take advantage of it rather than lose to competitors who beat them to it. They might not yet be able to articulate why they need to cross the AI chasm — a version of the phrase Geoffrey Moore coined to describe how businesses innovate. They just know they better leap fast or else fall behind.

Those hunches are justified. Whether in manufacturing, healthcare, information technology, the media, finance, or retail, companies are sitting on data containing important lessons for operations, knowledge about customers, and other information that could help generate new insights about the business

Furthermore, savvy leaders know they need AI to leverage those insights, because they see other companies doing it.

Take Seagate Technology, the $10 billion data storage company. As Barry Johnson, president of digital manufacturing at Symphony IndustrialAI, wrote in SME, Seagate was using only automated machine vision to detect anomalies in their wafers. Their tech worked well, picking out half of their faulty products. But when Seagate deployed AI manufacturing software to the process in 2017, they boosted the anomaly detection rate to 90 percent.

The data Seagate routinely generated contained information that AI analyzed to produce actionable intelligence to improve their high-precision tools. But that was only the first-order benefit. Secondly, AI machine learning gleaned more lessons to improve anomaly detection and other factory control systems over time. The more data the AI software analyzed, the more efficiencies it recognized. 

Seagate was soon using AI/ML to monitor equipment performance, predict when equipment might be preparing to fail, and identify the necessary maintenance to extend the life of their assets. This second-order benefit resulted in savings on inspections and other labor, capital outlays on new equipment, scrapping, and additional costs. Like humans, AI improves as it accumulates data. When applied to enterprises, AI software improves by analyzing business workflows between people, technologies, and other resources.

History buffs might appreciate an analogy here with British historian Alfred Thayer Mahan’s 1890 classic, The Influence of Sea Power upon History: 1660–1783 — a book on the cutting edge of technology when it was published in 1890. Mahan emphasized how the logistical, commercial, political, and other mundane concerns were vital to naval supremacy. Mahan reasoned that a robust shipbuilding industry, coaling stations, and blockades were arguably more important than guns and torpedoes in projecting power because the former allowed navies to bring the latter to bear on less-fortified enemies more quickly and at scale. Intelligently integrating the two — closing the loop between the strategic and physical — resulted in exponential efficiencies and productivity.

Today, the “strategic” goal in this analogy is leveraging data for business value. AI closes the loop between the digital and material worlds, harnessing AI/ML insights to improve enterprise domain expertise, labor, assets, and resources in the quickest, most efficient, and most productive ways possible.

Again, the accumulation of data and how it’s marshaled matters most in AI adoption. Writing in Crunchbase, SymphonyAI’s Mark Tice shared how researchers and physicians at Hungary’s Semmelweis University turned to AI to analyze 20 years of historical patient data to estimate the risks of patients suffering from heart failure. Researchers used AI to comb through that data to learn to identify how and why patients died after receiving a special pacemaker. These “personalized risk assessments” were automated, giving doctors and patients invaluable, in-depth information quickly about whether to proceed with implanting a pacemaker and whether other care after the procedure might be necessary. 

Leaders interested in crossing the AI chasm should know best practices. Tim Lawes at SymphonyAI Summit laid them out in his piece for Information Age. Lawes specifically addresses the adoption of AI-powered informational technology service management or IT asset management platforms. But three of his suggestions apply to enterprise AI anywhere.

  1. Only customizable AI can understand and adapt to specific enterprises: AI needs to learn from the deep domain expertise of the people who work in companies. To do that, AI software must be flexible enough to be tailored to wherever it is deployed. If an AI application can’t adapt to a specific business, it can’t learn from that business, and if it can’t learn, it can’t deliver insights. Flexibility is the first watchword that leaders need to consider when investigating AI.
  2. AI platforms must work throughout an enterprise: AI must be integrated throughout an organization. Otherwise, it won’t understand the full scope of the organization’s workflows. It must be able to communicate and collaborate with different systems and technologies throughout a company to obtain data for analysis. When siloed systems don’t “talk” to one another, AI should be able to standardize the data from disparate systems for its purposes.
  3. AI interfaces must be smooth and intuitive: If AI is hard to use or requires expensive teams of data scientists to leverage, people won’t engage with it. If people don’t engage it, the AI won’t obtain the data necessary to learn. Easy-to-use AI that plant managers, heart surgeons, and IT desks can assimilate into their workflows, in contrast, will deliver incremental benefits over time that will add up to significant improvements and savings. Those improvements will spark a virtuous cycle of more engagement, more insights, and more solutions.

SymphonyAI’s cloud-based AI SaaS offerings comport to these best practices. Under our new CEO Sanjay Dhawan, we’re incredibly excited to partner with enterprises seeking to unlock the value latent in their talented professionals, resources, and technological assets. Disruptive power is waiting to be unleashed on the other side of the AI chasm. Companies that build bridges with SymphonyAI won’t be left behind.

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