Takeaway:

From chatbots to predictive analytics, IT leaders will share how they are using ARTIFICIAL intelligence and machine learning to generate business insights and new application scenarios.

No technology is hotter than AI and machine learning. Now, some companies are using these technologies to mimic the way humans think in order to attract customers and support business operations. This trend will gain even more attention in the coming years, as more than 30% of CIOs will make AI and machine learning one of their top five investment priorities by 2020, according to Gartner.

At first people were worried that robots would replace everyone’s job, but now the trend has eased somewhat. Because humans and machines are more likely to work together. Paul, Accenture’s CTO and chief innovation officer, says companies have lost interest in rehiring people in highly automated jobs. He also recently co-authored a book on ai’s impact on the global workforce, Humans + Machines: Reinventing Work in the Age of AI.

In a survey of 1,500 companies, 65 percent of ceos admitted that their employees, ciOs, were not ready for AI. Only 3% of executives said they had increased their training efforts to cope with the shift brought about by AI. So it’s a huge gap, and ceos have to quickly embrace change and tell their employees what to do to prepare for it.

But whatever the outcome, the age of automation through AI, machine learning and robot work has arrived. Cios are learning, experimenting, and building new business scenarios in ARTIFICIAL intelligence and machine learning.

Scenario 1: AI becomes the employee’s doomsday prophet

Emerging digital technologies are changing the way workers work, even at Accenture. They have automated about 23,000 positions and redeployed employees. Even more surprising, Accenture’s 450,000 employees will be reassigned to new roles at any time and will need to learn new skills.

To help employees make the transition, Accenture has developed its own machine learning app that scans resumes and predicts when an employee’s work will become less relevant. The APP will combine employees’ work experience to rate the potential undesirability of their positions. For example, an employee’s skills will become obsolete in 18 months because of AI and automation.

Paul sees the app as more than just a doomsday prognosticator for employees. It also takes into account employees’ overall qualities and ideas, and recommends skills they may need in the future to keep their jobs.

The take-home message: The CIO needs to take ownership of the enterprise’s AI strategy and actively work with hr and other business key interests to ensure strategic consensus. Cios must also quickly identify and eliminate biases in their PERCEPTIONS of AI, as responsible organizations quickly integrate AI into the enterprise.

Scenario 2: Spark allows enterprises to discover secrets in 10 billion pieces of data

Machine learning is a central part of many global companies’ digital strategies, says Sunil, CIO of Lennox, a $4 billion company that uses Spark in its database to analyze the flow of information from its commercial heating and air-conditioning systems and monitor machine performance in real time to predict when a machine will fail. And make plans quickly.

“Databricks [a technology company in the Spark space] allows us to use this data and predict with 90 percent accuracy what the device will do. In the past, we often made our own predictions and contacted air-conditioner distributors. But it’s often a false alarm and it’s very upsetting to us.” The CIO said.

Lennox investigated a number of analytical tools before choosing Databricks, but each was used to deal with a single case. Databricks provides a unified platform on which they can manage hundreds of megabytes of data across hundreds of databases, and it runs on Microsoft Azure, so Lennox doesn’t have to maintain the system. With Databricks, CIO Sunil’s team works with business people to build data flow models. Based on sparks software, they can easily extract value from data analysis.

The key point: When manual forecasting is too risky for the business, ciOs must decisively introduce new tools. Sunil, for example, has 10 billion pieces of data at his disposal. But when he actively analyzed the software to find secrets in the data, he got surprising results.

Scenario 3: Corporate travel companies to improve customer satisfaction

David is CIO of American Express Global Business Travel. Early in his tenure, he implemented robotic process automation (RPA) and machine learning technologies to improve business efficiency for corporate travel service providers. He has now used RPA to automate the cancellation and refund process. David also created a new machine learning algorithm that helps customers find better airfare and hotel accommodations by searching for prices in the industry. This had previously been done manually by employees. It can be argued that this technology improves customer satisfaction and generates more business revenue.

David says he used machine learning to scan for financial fraud in his last job, and is using the same technology to boost his business.

Key point: About automated processing, machine learning, CIO is usually cautious, because no one has enough grasp of new things. But CIOs must be clear and decisive in establishing their presence in the business, using technology and automated processes to solve the business’s problems, because they are the true enablers of the technology business.

Scenario 4: Adobe’s self-healing capabilities

Cynthia, CIO of Adobe, is using machine learning to help analyze failure trends in Adobe systems, and then automatically fix problems before they lead to more serious consequences. The idea is that if there is a tool that can autonomously detect a potential outage, it can proactively eliminate or mitigate the damage caused by the problem.

The tool is called HaaS, Health-as-Service. The tool can catch and correct errors, such as a failed integration with Adobe ERP, or an error source trying to access a company’s various analytics systems. Cynthia says HaaS has saved Adobe 330 hours to fix the problem by reducing the manual time from 30 minutes to one minute. With detailed problem reporting, Adobe engineers can create permanent fixes. Automated monitoring and remediation is a huge benefit for Adobe. This work is based on a machine learning diagnostic testing framework created in 2017.

Adobe is known to use ARTIFICIAL intelligence for its business. Back in late 2016, the company launched Sensei, the creator of the world’s best digital experiences. With a unified framework for AI and machine learning, developers can create and publish documents in the cloud, analyze and track the performance of Web and mobile applications, and drive innovation.

The take-home message: Using machine learning to recognize patterns is key to creating self-healing capabilities. The CIO needs to let the system heal itself without human intervention.

Scenario 5: Machine learning to connect to a global database of medical devices

Hearst Business Media, whose core business assets include First Databank, a drug database software, and Fitch Ratings. And they’re using machine learning to connect to medical device databases to make it easier for customers to access information.

Mark, an enterprise SVP and CIO, says they are developing their own machine learning algorithm and using Google’s TensorFlow machine learning software for model training on the company’s data set. He thinks companies tend to use open source tools as a way to deal with business challenges.

The take-home message: Adopting a scalable approach is key to Hearst’s successful use of machine learning. One of the reasons machine learning is so useful from an enterprise perspective is that it can work with different databases in a common way. Fitch Ratings products, for example, require knowledge of many corporate entities, while First Databank requires knowledge of how drugs are used around the world.

Scenario 6: AI enhances the success rate of stock investing

Putnam Investments, a fund and institutional investment strategy firm, sees AI and machine learning as key to helping analysts at financial services firms improve their stock investing success rates.

Its CIO, Mehta, says analysts work closely with company data scientists to write papers that help them glean information from vast amounts of data. The company is also working on algorithms to improve the accuracy of sales recommendations. This is a huge change for them to come to book and drives efficiency and productivity across the business drivers.

Starting with the AI and machine learning strategy, the CIO assembled a portfolio of software engineers, data scientists, analysts, and vendors and created a data science center of excellence. And these business partners are already embracing change as a way to make better automated recommendations. He sees AI and machine learning as part of the digital transformation of the enterprise, which requires cloud computing to take the lead in IT infrastructure and create a single platform to run the business.

The take-home message: When it comes to AI adoption, companies should take steps, set expectations appropriately, consider new questions that arise, and actively seek answers. In ARTIFICIAL intelligence, it’s not like all of a sudden algorithms have insight.

Scenario 7: AI reduces tax burden

Led by software maker CDO Ashok, Intuit is accelerating ai and machine learning. They are using Amazon Web Services to help their QuickBooks chat assistant bot better understand and process natural language, and have established a role in Verizon’s big data platform. More and more processing will guide users through the hundreds of Quickbooks categories.

Cios say they are processing more than a billion QuickBooks transactions and can optimize categorization with precision as they scale up using AWS machine learning and cloud technologies. The company’s TurboTax system uses ARTIFICIAL intelligence to help users get the most out of their tax refunds, which can save users up to 40 percent of their tax preparation time and the effort of retrieving documents.

Key point: Ashok, who previously worked at NASA’s Ames Research Center, believes that fostering sound algorithms requires attracting the right engineering talent to solve real business challenges. They are currently hiring engineers to ensure they can achieve their business goals through AI.

Scenario 8: Historical data predicts future performance

As CIO of Riverbed Technologies, Rich faces unique challenges. Riverbed is known as a software provider that aims to improve the performance of wide area networks. They are testing how machine learning can be used to extract data from the company’s multiple supply chains for better business insights. “We want to use machine learning to process more data than ever before.” He said.

For example, Riverbed can combine order management and ERP data with historical data on other factors, such as the weather, to find patterns that predict future performance. They want to be able to more accurately predict risks down the supply chain, including capacity and the ability to meet customer orders. Among other things, they use machine learning to automatically tune performance configurations and spot network security risks. Rich wants to create a single data lake and gain more business insights from it.

The take-home message: For AI and machine learning, good strategies also require a cautious approach. Rich is carefully evaluating tools and technologies, including IBM Watson, to address business risks.

Scenario 9: Provide customers with better personalized financial services

Bank of America, like many big banks, collects vast amounts of customer data. But they also struggle to make actionable recommendations from the data. Bill, bank of America’s chief analyst, is trying to change that. Over the past few months, he has been leveraging Salesforce.com’s Einstein AI/ machine learning technology to enhance personalized services across the bank’s small business, wholesale, commercial wealth and commercial banking divisions.

For example, if a customer searches for information about a mortgage on Bank of America’s web site, a customer service agent can track that customer the next time they visit. For example, the software suggests that agents call potential customers on Thursdays between 10am and 2pm, since they are more likely to answer the phone. Einstein can also put an invitation calendar on an agent’s calendar to remind them to call potential clients the following Thursday.

These capabilities will be at the heart of what many financial services organisations are striving for. Develop a 360-degree view of the customer so that they can accurately recommend relevant services. Bill argues that we are moving from a world that can be described to a world that cannot be described. The core value of this world is to stay one step ahead and expect that customer demand and our channels will be interconnected.

The take-home message: Be patient with the way companies and CIOs test and learn about AI and machine learning. But be prepared to extend the running algorithm as well. Always putting the customer at the center is the only law of technology implementation.

Scenario 10: Machine learning eliminates “hard work” and makes work more productive

Ed, president of operations and technology at Mastercard, said artificial intelligence and machine learning pervade every aspect of Mastercard. They are using these algorithms to eliminate repetitive manual tasks and free up employees to do more creative and valuable work.

At the same time, mastercard uses machine learning tools to enhance change management in its ecosystem of products and services. For example, machine learning tools help determine which changes are risk-free and which require additional review. In addition, mastercard is using machine learning to detect anomalies in its systems, suggesting hackers are trying to gain access. Ed also created a “safety net” on the network. When it detects suspicious behavior, it cuts the circuit breakers that protect the network. “We have a fraud scoring system,” Ed says. Update it by constantly looking at deals and scoring the next deal that’s coming up.”

Key point: AI/ML is just one tool in a broad toolkit for the payments processor segment. While there are so many shiny new tools on the market, Ed says CIOs shouldn’t expect them to magically solve business problems.




Giiso Technology, founded in 2013, is the first domestic high-tech enterprise focusing on the research and development of intelligent information processing technology and the development and operation of core software for writing robots. At the beginning of its establishment, the company received angel round investment, and in August 2015, GSR Venture Capital received $5 million pre-A round of investment.

Above, Chen Ruchu’s humble opinion! I’m sorry if I offended you.

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