Fewer technologies are hotter than artificial intelligence (AI) and machine learning (ML) and several enterprises are harnessing the technology, which mimics the behavior of the human mind, to woo customers and bolster business operations. And the trend will only gain more traction in the years ahead, as AI and ML will be a top five investment priority for more than 30 percent of CIOs by 2020, according to Gartner.
Initial fears that bots are coming for everyone’s jobs are easing somewhat, with a high likelihood that humans and machines will work together. But enterprises have fallen woefully behind in reskilling employees whose jobs are highly likely to be automated, says Accenture CTO and Chief Innovation Officer Paul Daugherty, who recently co-authored a book on the impact of AI on the global workforce, Human + Machine: Reimagining Work in the Age of AI.
Daugherty, in surveying 1,500 companies for his book, found that 65 percent of executives acknowledged that their workforces aren’t ready for AI. However, only 3 percent said they had increased their training to counter the AI shift.
“It’s a huge chasm,” Daugherty said at the Forbes CIO Summit last month. “We all think it’s somebody else’s problem, and we have to embrace the issue of how to prepare our people for that to change.”
For better or worse, automation via AI, ML and bots is coming. CIOs who are experimenting with, building and even patenting new AI and ML technologies, shared their ML use cases with CIO.com.
Digital doomsayer app predicts role irrelevance
Daugherty says that emerging digital technologies are changing the workforce paradigm. And Accenture is far from immune; the consultancy has automated roughly 23,000 roles and redeployed staff. He anticipates there will be more role reallocation on tap for the consultancy’s 450,000 employees.
“We believe we need to reskill,” said Daugherty. “There is very little around that.”
To help its employees in this endeavor, Daugherty says Accenture has created a beta version of an ML-fueled app that can scan a resume and predict how fast an employee’s job will be irrelevant.
The app considers an employee’s job experiences and assigns a risk score for their role’s potential irrelevance. For example, the app will note that an employee’s skills will be dated in 18 months due to AI or some other automation.
More than just a digital doomsayer, the app takes into account an employee’s collective work experience and recommends adjacent skills they may wish to pick up to remain more relevant at the company, Daugherty said.
Key advice: It’s incumbent on CIOs to assume responsibility for corporate AI strategies, and work with key stakeholders in HR and other business lines to ensure consensus and continuity. The CIO must also quickly identify and eliminate biases in their AI algorithms, which can proliferate as the solution scales across the enterprise. “Responsible AI must be baked into the organization,” Daugherty said.
Machine learning facilitates predictive maintenance
ML is a core component of the digitalization strategy for $4 billion Lennox International, which uses Spark software from Databricks to analyze information streaming off of the company’s commercial heating and air-conditioning systems, says Sunil Bondalapati, the company’s director of IT. Monitoring machine performance in real time allows the company to predict when a machine will fail, enabling Lennox to give customers, ranging from homeowners to strip mall managers, four hours notice.
“Databricks enables us to consume the data and predict with 90 percent accuracy when equipment is about to fail,” Bondalapati says, adding that Lennox previously guessed when machines might fail and contacted dealers. Such events often included false alarms, which was frustrating for all parties. “We used to struggle to predict equipment failure.”
The road to Databricks was paved with many analytics tools, each of which was used to address single use cases, such as supply chain or internet of things (IoT). But Bondalapati says Databricks provides a unified platform on which the company can manage hundreds of terabytes of data from hundreds of databases, and it runs on Microsoft Azure so Lennox doesn’t have to maintain the system.
With Databricks, Bondalapati’s team and business units work together to build models for data flows. The Spark-based software transforms the data and provides insights for IT and business staff alike. “The collaboration model that Databricks provides was the key for us,” Bondalapati says.
Key advice: Bondalapti says CIOs must put a new tool through its paces, particularly when the stakes are so large. For example, Bondalapati oversaw a proof-of-concept that included 10 billion data records. “We went into it tentatively, but it was eye-opening,” Bondalapati says.
Corporate travel: There’s algorithm for that
Early in his tenure as CIO and CTO of American Express Global Business Travel, David Thompson implemented robotic process automation (RPA) and ML technologies to improve the efficiency of how the provider of corporate travel services processes transactions.
Thompson who spoke on a panel at the Forbes CIO Summit in April, used RPA to automate the process for canceling an airline ticket and issuing refunds. Thompson also presided over the creation of ML algorithms that help customers find better rates for airfare and hotel accommodations, by scouring corporate industry rates, a task that several employees previously performed.
Those employees were redeployed to provide higher value for clients. And Thompson says the technology is boosting customer satisfaction and generating more revenue.
“It’s a pretty big win for us so far,” says Thompson, who used ML tools to scan for fraud in his prior role at Western Union. “Now folks are freed up to spend more time with customers.”
Key advice: Thompson says discussions regarding automation are difficult as people are afraid for their jobs. But CIOs must be clear, decisive and honest if they wish to establish credibility with the business. “I view my role as enabler of technology for the business — to use technologies and processes to solve a business problem,” Thompson says.
AI as product and business enabler
Adobe Systems is harnessing ML to help analyze tickets in help-desk software for trends in system failures, and then proactively fix issues before they result in more significant downtime, CIO Cynthia Stoddard told CIO.com at the MIT CIO Sloan Symposium earlier this month.
The thinking goes, if the system sees events that suggest an outage could occur, the system can be proactive to eliminate or mitigate those events before they trigger failures.
Called HAAS, for “healing-as-a-service,” the tool is catching and remediating anything from failed integrations with Adobe’s ERP and faulty data feeds intended to funnel into the company’s various analytics systems. Stoddard says HAAS has reduced fix times from 30 minutes performed manually by humans to 1 minute. She estimates it has saved Adobe 330 hours of time remediating issues in the past several months. Using reports detailing the issues, Adobe engineers are then able to create permanent fixes.
“If you know you have to fix something and you know how to fix it, then you can automate it,” Stoddard said. “It’s been a tremendous benefit.” The work builds on the ML-based diagnostic testing framework Stoddard’s team created in 2017,
Adobe’s commercial business has also embraced AI. In November 2016, the company introduced Sensei, a layer of AI technology it is applying to its product for creating and publishing documents, and for analyzing and tracking web and mobile application performance.
Key advice: Using ML to identify patterns is the key to creating self-healing capabilities. “If you know how you fixed it you can put self-healing component in there and take the human element out of the equation,” Stoddard says.
Linking medical device databases with ML
Hearst Business Media, whose assets include drug database software maker First Databank and credit ratings concern Fitch Ratings, is using ML to link medical device databases to make it easier for customers to access information, Mark Uhrmacher, the media conglomerate’s senior vice president of software engineering, told CIO.com at the MIT Sloan CIO Symposium in May.
Urmacher, who presides over a small data science team for the company’s AI center of excellence, says Hearst is creating its own ML algorithms and using Google’s TensorFlow ML software, which it uses to train models against the company’s data sets. “We tend to be opportunistic and use the open source tools until they don’t work, and then we’ll look at other alternatives,” Uhrmacher said.
Keep it generalizable. Uhrmacher says taking an approach that is generalizable has been key to Hearst’s success with ML. “One of the incredibly valuable applications of machine learning from our perspective is a generalizable approach to dealing with aligning disparate databases,” Uhrmacher said. For example, Uhrmacher says Fitch needs to understand corporate entities, while First Databank needs to understand how medications are referred to all over the world.
AI augments securities research
Putnam Investments, a provider of mutual funds, institutional investment strategies and retirement services, views AI and ML as essential for driving improved coverage of stocks by the financial services firm’s research analysts, CIO Sumedh Mehta tells CIO.com.
The analysts work closely with Putnam data scientists to create theses that help glean insights from large amounts of data, Mehta says. Putnam is also working on algorithms that will recommend the most important sales prospects.
“It’s a hugely disruptive and transformational power and the whole business driver for it is efficiency and productivity,” says Mehta of AI and ML.
Mehta, who relies on a combination of software engineers, data scientists, analytics and vendors, has created a data science center of excellence, which is essentially ground zero for AI and ML efforts that support business stakeholders. He says his “enlightened” business partners have embraced these approaches to achieve better automation.
The AI and ML work is part of Putnam’s broader digital transformation, which entails modernizing IT infrastructure with cloud computing and creating a single platform on which to run the business.
Key advice: Organizations should take their time and set expectations appropriately, understanding that the first few ideas will lead to new questions rather than answers. “There is no such thing as a eureka moment when it comes to AI,” Mehta says. “It’s not the case that suddenly your algorithm will yield insight you didn’t already know about.”
AI makes finances less taxing
Intuit is accelerating AI and ML efforts under Ashok Srivastava, the software maker’s chief data officer.
Intuit is using Amazon Web Services to help its QuickBooks Assistant chatbot better understand and process natural language, says Srivastava, who in his prior role built out Verizon’s big data platform. A growing area of focus is shepherding users through the hundreds of categorizations that inform Quickbooks.
“We’re dealing with over 1 billion transactions from QuickBooks and we can optimize the categorizations that occur with high accuracy,” Srivastava adds.
The company’s TurboTax uses AI to help users get their maximum refund by guiding them through the itemized deduction process, potentially saving users up to 40 percent of tax prep time and efforts retrieving documents.
The company is using ML and cloud technology from AWS to scale more rapidly, Srivastava says.
Key advice: Cultivating sound algorithms requires attracting the right engineering talent to solve real business challenges. Srivastava, who also worked for NASA’s Ames Research Center, is currently hiring engineers who can work with ML and AI technologies to achieve the company’s goals.
Historical data predicts future performance
Rich Hillebrecht has unique challenges as the CIO of Riverbed Technology, a provider of software designed to improve the performance of wide-area networks. Hillebrecht says he is testing how to use ML to ingest data from multiple sources across the company’s supply chain to drive better business insights.
“We want to apply machine learning techniques to process way more data than we normally would have,” Hillebrecht tells CIO.com.
For example, Riverbed might combine order management and other ERP data with historical data about weather and other factors to find patterns that could predict future performance. “We want to be more predictive in terms of downstream risk in terms of capacity and our ability to fill orders to customers,” Hillebrecht says.
Other Riverbed use cases could include using ML to automatically tune performance configurations and spot cybersecurity threats. Hillebrecht anticipates creating a single data lake from which business insights can be drawn.
Key advice: Sound strategy for AI and ML requires a cautious approach. Hillebrecht says he is carefully evaluating tools and technologies, including IBM Watson.
Banking on better customer insights
Like many large banks, U.S. Bank has collected a wealth of customer data. And like most banks, U.S. Bank has struggled to derive actionable insights from this data. Bill Hoffman, chief analytics officer of U.S. Bank, is working to change that. For the past several months, he has been using Salesforce.com’s Einstein AI/ML technology to increase personalization across the bank’s small business, wholesale, commercial wealth and commercial banking units.
For example, if a customer searched on U.S. Bank’s website for information about mortgage loans, a customer service agent can follow up with that customer the next time they visit a branch. It also helps U.S. Bank find patterns humans might not see. For example, the software can recommend that agents call a prospective client in a particular industry on Thursday between 10 a.m. and 12 p.m. because they are more likely to pick up the phone. Einstein can also put a calendar invite into the agent’s calendar to remind them to call the candidate the following Thursday.
Such capabilities get to the core of what many financial services organizations are trying to do; cultivate a 360-degree view of customers to recommend relevant services in the moment. “We are moving from a world that was describing what happened or what is happening to a world that is more about what will or should happen,” Hoffman says. “The core value is staying a step ahead, anticipating our customer needs and the channel they want to interact with us.”
Key advice: Take a test-and-learn approach to AI and ML and be patient. But also be ready to scale things that are working. “Always have the customer at the center,” Hoffman says. “Ask: How will this benefit the customer?
ML removes ‘toil,’ making work more productive
Ed McLaughlin, president of operations and technology at Mastercard, says ML “pervades everything that we do.” Mastercard is using ML to automate what he calls “toil,” or repetitive and manual tasks, freeing up humans to perform work that adds productivity and value. “It’s clear we’ve reached a state of the art where there is a clear investment case to automate workplace tasks,” McLaughlin says.
Mastercard is also using ML tools to augment change management throughout its product and service ecosystem. For example, ML tools help determine which changes are the most risk-free and which require additional scrutiny. Finally, Mastercard is using ML to detect anomalies in its system that suggest hackers are trying to gain access. McLaughlin also put a “safety net” in the network; when it finds suspicious behavior it trips circuit breakers that protect the network. “We have fraud-scoring systems constantly looking at transactions to update it and score the next transaction that’s going in,” he says.
Key advice: As far as McLaughlin is concerned, AI/ML are just tools in the payment processor’s broad toolkit. Despite all of the shiny new tools on the market, he says CIOs shouldn’t rely on them to magically fix business problems.