Tom Brendlé

In 2013, the MD Anderson Cancer Center launched a “moon shot” project: diagnose và recommover treatment plans for certain forms of cancer using IBM’s Watson cognitive system. But in 2017, the project was put on hold after costs topped $62 million—& the system had yet lớn be used on patients. At the same time, the cancer center’s IT group was experimenting with using cognitive sầu technologies to lớn do much less ambitious jobs, such as making khách sạn & restaurant recommendations for patients’ families, determining which patients needed help paying bills, & addressing staff IT problems. The results of these projects have been much more promising: The new systems have contributed to lớn increased patient satisfaction, improved financial performance, và a decline in time spent on tedious data entry by the hospital’s care managers. Despite the setbaông xã on the moon shot, MD Anderson remains committed lớn using cognitive sầu technology—that is, next-generation artificial intelligence—lớn enhance cancer treatment, & is currently developing a variety of new projects at its center of competency for cognitive sầu computing.

The contrast between the two approaches is relevant to lớn anyone planning AI initiatives. Our survey of 250 executives who are familiar with their companies’ use of cognitive sầu giải pháp công nghệ shows that three-quarters of them believe sầu that AI will substantially transform their companies within three years. However, our study of 152 projects in almost as many companies also reveals that highly ambitious moon shots are less likely khổng lồ be successful than “low-hanging fruit” projects that enhance business processes. This shouldn’t be surprising—such has been the case with the great majority of new technologies that companies have sầu adopted in the past. But the hype surrounding artificial intelligence has been especially powerful, and some organizations have been seduced by it.

In this article, we’ll look at the various categories of AI being employed & provide a framework for how companies should begin to lớn build up their cognitive sầu capabilities in the next several years khổng lồ achieve sầu their business objectives.

Three Types of AI

It is useful for companies to look at AI through the lens of business capabilities rather than technologies. Broadly speaking, AI can support three important business needs: automating business processes, gaining insight through data analysis, and engaging with customers & employees.

Process automation.

Of the 152 projects we studied, the most common type was the automation of digital and physical tasks—typically back-office administrative và financial activities—using robotic process automation technologies. RPA is more advanced than earlier business-process automation tools, because the “robots” (that is, code on a server) act lượt thích a human inputting & consuming information from multiple IT systems. Tasks include:

RPA is the least expensive sầu và easiest lớn implement of the cognitive sầu technologies we’ll discuss here, and typically brings a quichồng và high return on investment. (It’s also the least “smart” in the sense that these applications aren’t programmed khổng lồ learn and improve, though developers are slowly adding more intelligence and learning capability.) It is particularly well suited lớn working across multiple back-kết thúc systems.

At NASA, cost pressures led the agency khổng lồ launch four RPA pilots in accounts payable và receivable, IT spending, and human resources—all managed by a shared services center. The four projects worked well—in the HR application, for example, 86% of transactions were completed without human intervention—and are being rolled out across the organization. NASA is now implementing more RPA bots, some with higher levels of intelligence. As Jyên ổn Walker, project leader for the shared services organization notes, “So far it’s not rocket science.”

One might imagine that robotic process automation would quickly put people out of work. But across the 71 RPA projects we reviewed (47% of the total), replacing administrative employees was neither the primary objective sầu nor a comtháng outcome. Only a few projects led khổng lồ reductions in head count, và in most cases, the tasks in question had already been shifted to lớn outsourced workers. As công nghệ improves, robotic automation projects are likely lớn lead lớn some job losses in the future, particularly in the offshore business-process outsourcing industry. If you can outsource a task, you can probably automate it.

Cognitive insight.

The second most common type of project in our study (38% of the total) used algorithms to lớn detect patterns in vast volumes of data & interpret their meaning. Think of it as “analytics on steroids.” These machine-learning applications are being used to:

Cognitive insights provided by machine learning differ from those available from traditional analytics in three ways: They are usually much more data-intensive và detailed, the models typically are trained on some part of the data set, & the models get better—that is, their ability khổng lồ use new data khổng lồ make predictions or put things into categories improves over time.

Versions of machine learning (deep learning, in particular, which attempts lớn mimic the activity in the human brain in order to lớn recognize patterns) can perkhung feats such as recognizing images & speech. Machine learning can also make available new data for better analytics. While the activity of data curation has historically been quite labor-intensive, now machine learning can identify probabilistic matches—data that is likely to be associated with the same person or company but that appears in slightly different formats—across databases. GE has used this công nghệ khổng lồ integrate supplier data và has saved $80 million in its first year by eliminating redundancies & negotiating contracts that were previously managed at the business unit cấp độ. Similarly, a large ngân hàng used this công nghệ to lớn extract data on terms from supplier contracts và match it with invoice numbers, identifying tens of millions of dollars in products và services not supplied. Deloitte’s audit practice is using cognitive sầu insight to lớn extract terms from contracts, which enables an audit to address a much higher proportion of documents, often 100%, without human auditors’ having lớn painstakingly read through them.

Cognitive sầu insight applications are typically used khổng lồ improve sầu performance on jobs only machines can do—tasks such as programmatic ad buying that involve sầu such high-tốc độ data crunching & automation that they’ve long been beyond human ability—so they’re not generally a threat khổng lồ human jobs.

Cognitive sầu engagement.

Projects that engage employees & customers using natural language processing chatbots, intelligent agents, và machine learning were the least comtháng type in our study (accounting for 16% of the total). This category includes:

The companies in our study tended lớn use cognitive engagement technologies more khổng lồ interact with employees than with customers. That may change as firms become more comfortable turning customer interactions over lớn machines. Vanguard, for example, is piloting an intelligent agent that helps its customer service staff answer frequently asked questions. The plan is to eventually allow customers to engage with the cognitive sầu agent directly, rather than with the human customer-service agents. SEBank, in Sweden, & the medical công nghệ giant Becton, Dickinson, in the United States, are using the lifelượt thích intelligent-agent avatar Amelia lớn serve sầu as an internal employee help desk for IT tư vấn. SEBank has recently made Amelia available to customers on a limited basis in order khổng lồ thử nghiệm its performance và customer response.

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Companies tend to take a conservative sầu approach to lớn customer-facing cognitive engagement technologies largely because of their immaturity. Facebook, for example, found that its Messenger chatbots couldn’t answer 70% of customer requests without human intervention. As a result, Facebook and several other firms are restricting bot-based interfaces khổng lồ certain topic domains or conversation types.

Our retìm kiếm suggests that cognitive sầu engagement apps are not currently threatening customer service or sales rep jobs. In most of the projects we studied, the goal was not to lớn reduce head count but lớn handle growing numbers of employee và customer interactions without adding staff. Some organizations were planning to hand over routine communications lớn machines, while transitioning customer-support personnel to more-complex activities such as handling customer issues that escalate, conducting extended unstructured dialogues, or reaching out to customers before they điện thoại tư vấn in with problems.

As companies become more familiar with cognitive tools, they are experimenting with projects that combine elements from all three categories lớn reap the benefits of AI. An Italian insurer, for example, developed a “cognitive sầu help desk” within its IT organization. The system engages with employees using deep-learning technology (part of the cognitive sầu insights category) to lớn tìm kiếm frequently asked questions & answers, previously resolved cases, và documentation to lớn come up with solutions khổng lồ employees’ problems. It uses a smart-routing capability (business process automation) lớn forward the most complex problems khổng lồ human representatives, và it uses natural language processing to lớn tư vấn user requests in Italian.

Despite their rapidly expanding experience with cognitive sầu tools, however, companies face significant obstacles in development and implementation. On the basis of our retìm kiếm, we’ve developed a four-step framework for integrating AI technologies that can help companies achieve their objectives, whether the projects are moon shoots or business-process enhancements.

1. Understanding The Technologies

Before embarking on an AI initiative sầu, companies must underst& which technologies perkhung what types of tasks, and the strengths và limitations of each. Rule-based expert systems & robotic process automation, for example, are transparent in how they do their work, but neither is capable of learning & improving. Deep learning, on the other hvà, is great at learning from large volumes of labeled data, but it’s almost impossible to understvà how it creates the models it does. This “black box” issue can be problematic in highly regulated industries such as financial services, in which regulators insist on knowing why decisions are made in a certain way.

We encountered several organizations that wasted time and money pursuing the wrong technology for the job at hvà. But if they’re armed with a good understanding of the different technologies, companies are better positioned to lớn determine which might best address specific needs, which vendors lớn work with, and how quickly a system can be implemented. Acquiring this understanding requires ongoing retìm kiếm và education, usually within IT or an innovation group.

In particular, companies will need lớn leverage the capabilities of key employees, such as data scientists, who have sầu the statistical & big-data skills necessary to lớn learn the nuts & bolts of these technologies. A main success factor is your people’s willingness lớn learn. Some will leap at the opportunity, while others will want khổng lồ stick with tools they’re familiar with. Strive khổng lồ have sầu a high percentage of the former.

If you don’t have data science or analytics capabilities in-house, you’ll probably have sầu khổng lồ build an ecosystem of external service providers in the near term. If you expect lớn be implementing longer-term AI projects, you will want khổng lồ recruit expert in-house talent. Either way, having the right capabilities is essential to lớn progress.

Given the scarcity of cognitive giải pháp công nghệ talent, most organizations should establish a pool of resources—perhaps in a centralized function such as IT or strategy—and make experts available lớn high-priority projects throughout the organization. As needs và talent proliferate, it may make sense lớn dedicate groups to particular business functions or units, but even then a central coordinating function can be useful in managing projects và careers.

2. Creating a Portfolio of Projects

The next step in launching an AI program is to systematically evaluate needs & capabilities and then develop a prioritized portfolio of projects. In the companies we studied, this was usually done in workshops or through small consulting engagements. We recommend that companies conduct assessments in three broad areas.

Identifying the opportunities.

The first assessment determines which areas of the business could benefit most from cognitive sầu applications. Typically, they are parts of the company where “knowledge”—insight derived from data analysis or a collection of texts—is at a premium but for some reason is not available.

Determining the use cases.

The second area of assessment evaluates the use cases in which cognitive sầu applications would generate substantial value & contribute to lớn business success. Start by asking key questions such as: How critical to lớn your overall strategy is addressing the targeted problem? How difficult would it be to lớn implement the proposed AI solution—both technically và organizationally? Would the benefits from launching the application be worth the effort? Next, prioritize the use cases according to lớn which offer the most short- and long-term value, và which might ultimately be integrated into a broader platkhung or suite of cognitive capabilities to lớn create competitive advantage.

Selecting the giải pháp công nghệ.

The third area khổng lồ assess examines whether the AI tools being considered for each use case are truly up khổng lồ the task. Chatbots và intelligent agents, for example, may frustrate some companies because most of them can’t yet match human problem solving beyond simple scripted cases (though they are improving rapidly). Other technologies, lượt thích robotic process automation that can streamline simple processes such as invoicing, may in fact slow down more-complex production systems. And while deep learning visual recognition systems can recognize images in photos and videos, they require lots of labeled data and may be unable to make sense of a complex visual field.