Looking for some intelligent company

Added: Yanira Tremper - Date: 01.07.2021 23:22 - Views: 12085 - Clicks: 2846

Cognitive technologies are increasingly being used to solve business problems; indeed, many executives believe that AI will substantially transform their companies within three years. But many of the most ambitious AI projects encounter setbacks or fail. Broadly speaking, AI can support three important business needs: automating business processes typically back-office administrative and financial activitiesgaining insight through data analysis, and engaging with customers and employees. To get the most out of AI, firms must understand which technologies perform what types of tasks, create a prioritized portfolio of projects based on business needs, and develop plans to scale up across the company.

Cognitive technologies are increasingly being used to solve business problems, but many of the most ambitious AI projects encounter setbacks or fail. Companies should take an incremental rather than a transformative approach and focus on augmenting rather than replacing human capabilities.

Despite the setback on the moon shot, MD Anderson remains committed to using cognitive technology—that is, next-generation artificial intelligence—to enhance cancer treatment, and is currently developing a variety of new projects at its center of competency for cognitive computing. The contrast between the two approaches Looking for some intelligent company relevant to anyone planning AI initiatives. But the hype surrounding artificial intelligence has been especially powerful, and some organizations have been seduced by it.

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 and employees. We studied cognitive technology projects and found that they fell into three. Of the projects we studied, the most common type was the automation of digital and physical tasks—typically back-office administrative and financial activities—using robotic process automation technologies.

Tasks include:. It is particularly well suited to working across multiple back-end systems. At NASA, cost pressures led the agency to launch four RPA pilots in s payable and receivable, IT spending, and human resources—all managed by a shared services center. One might imagine that robotic process automation would quickly put people out of work.

Only a few projects led to reductions in head count, and in most cases, the tasks in question Looking for some intelligent company already been shifted to outsourced workers. As technology improves, robotic automation projects are likely to lead to 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 insights provided by machine learning differ from those available from traditional analytics in three ways: They are usually much more data-intensive and detailed, the models typically are trained on some part of the data set, and the models get better—that is, their ability to use new data to make predictions or put things into improves over time.

Versions of machine learning deep learning, in particular, which attempts to mimic the activity in the human brain in order to recognize patterns can perform feats such as recognizing images and 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.

Similarly, a large bank used this technology to extract data on terms from supplier contracts and match it with invoice s, identifying tens of millions of dollars in products and services not supplied.

Looking for some intelligent company

This category includes:. The companies in our study tended to use cognitive engagement technologies more to interact with employees than with customers. That may change as firms become more comfortable turning customer interactions over to 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 agent directly, rather than with the human customer-service agents.

SEBank, in Sweden, and the medical technology giant Becton, Dickinson, in the United States, are using the lifelike intelligent-agent avatar Amelia to serve as an internal employee help desk for IT support. SEBank has recently made Amelia available to customers on a limited basis in order to test its performance and customer response.

Companies tend to take a conservative approach to customer-facing cognitive engagement technologies largely because of their immaturity. As a result, Facebook and several other firms are restricting bot-based interfaces to certain topic domains or conversation types. Our research suggests that cognitive engagement apps are not currently threatening customer service or sales rep jobs. In most of the projects we studied, the goal was not to reduce head count but to Looking for some intelligent company growing s of employee and customer interactions without adding staff.

Some organizations were planning to hand over routine communications to 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 call in with problems.

As companies become more familiar with cognitive tools, they are experimenting with projects that combine elements from all three to reap the benefits of AI. It uses a smart-routing capability business process automation to forward the most complex problems to human representatives, and it uses natural language processing to support user requests in Italian. Despite their rapidly expanding experience with cognitive tools, however, companies face ificant obstacles in development and implementation. Before embarking on an AI initiative, companies must understand which technologies perform what types of tasks, and the strengths and limitations of each.

Rule-based expert systems and robotic process automation, for example, are transparent in how they do their work, but neither is capable of learning and improving. We encountered several organizations that wasted time and money pursuing the wrong technology for the job at hand.

Acquiring this understanding requires ongoing research and education, usually within IT or an innovation group. In particular, companies will need to leverage the capabilities of key employees, such as data scientists, who have the statistical and big-data skills necessary to learn the nuts and bolts of these technologies. Strive to have a high percentage of the former. If you expect to be implementing longer-term AI projects, you will want to recruit expert in-house talent. Either way, having the right capabilities is essential to progress.

Given the scarcity of cognitive technology talent, most organizations should establish a pool of resources—perhaps in a centralized function such as IT or strategy—and make experts available to high-priority projects throughout the organization. As needs and talent proliferate, it may make sense to dedicate groups to particular business functions or units, but even then a central coordinating function can be useful in managing projects and careers. The next step in launching an AI program is to systematically evaluate needs and 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. The first assessment determines which areas of the business could benefit most from cognitive applications. The Looking for some intelligent company area of assessment evaluates the use cases in which cognitive applications would generate substantial value and contribute to business success. Start by asking key questions such as: How critical to your overall strategy is addressing the targeted problem?

How difficult would it be to implement the proposed AI solution—both technically and organizationally? Would the benefits from launching the application be worth the effort? Next, prioritize the use cases according to which offer the most short- and long-term value, and which might ultimately be integrated into a broader platform or suite of cognitive capabilities to create competitive advantage. The third area to assess examines whether the AI tools being considered for each use case are truly up to the task.

Other technologies, like robotic process automation that can streamline simple processes such as invoicing, may in fact slow down Looking for some intelligent company 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. In time, cognitive technologies will transform how companies do business.

Because the gap between current and desired AI capabilities is not always obvious, companies should create pilot projects for cognitive applications before rolling them out across the entire enterprise. Proof-of-concept pilots are particularly suited to initiatives that have high potential business value or allow the organization to test different technologies at the same time. If your firm plans to launch several pilots, consider creating a cognitive center of excellence or similar structure to manage them.

This approach helps build the needed technology skills and capabilities within the organization, while also helping to move small pilots into broader applications that will have a greater impact. Pfizer has more than 60 projects across the company that employ some form of cognitive technology; many are pilots, and some are now in production.

The global automation group uses end-to-end process maps to guide implementation and identify automation opportunities. The company has successfully implemented intelligent agents in IT support processes, but as yet is not ready to support large-scale enterprise processes, like order-to-cash. The health insurer Anthem has developed a similar centralized AI function that it calls the Cognitive Capability Office. As cognitive technology projects are developed, think through how workflows might be redeed, focusing specifically on the Looking for some intelligent company of labor between humans and the AI.

In the new system, cognitive technology is used to perform many of the traditional tasks of investment advising, including constructing a customized portfolio, rebalancing portfolios over time, tax loss harvesting, and tax-efficient investment selection. Advisers are encouraged to learn about behavioral finance to perform these roles effectively. Vanguard, the investment services firm, uses cognitive technology to provide customers with investment advice at a lower cost.

Its Personal Advisor Services system automates many traditional tasks of investment advising, while human advisers take on higher-value activities. By automating established workflows, companies can quickly implement projects and achieve ROI—but they forgo the opportunity to take full advantage of AI capabilities and substantively improve the process.

Most cognitive projects are also suited to iterative, agile approaches to development. To achieve their goals, companies need detailed plans for scaling up, which requires collaboration between technology experts and owners of the business process being automated. Because cognitive technologies typically support individual tasks rather than entire processes, scale-up almost always requires integration with existing systems and processes. Indeed, in our survey, executives reported that such integration was the greatest challenge they faced in AI initiatives.

Companies should begin the scaling-up process by considering whether the required integration is even possible or feasible. If the application depends on special technology that is difficult to source, for example, that will limit scale-up. Make sure your business process owners discuss scaling considerations with the IT organization before or during the pilot phase: An end run around IT is unlikely to be successful, even for relatively simple technologies like RPA.

The health insurer Anthem, for example, is taking on the development of cognitive technologies as part of a major modernization of its existing systems. Rather than bolting new cognitive apps onto legacy technology, Anthem is using a holistic approach that maximizes the value being generated by the cognitive applications, reduces the overall cost of development and integration, and creates a halo effect on legacy systems.

In scaling up, companies may face substantial change-management challenges. At one U. The executive pointed out that the were positive and warranted expanding the project. At the same time, he acknowledged that the merchandisers needed to be educated about a new way of working.

If scale-up is to achieve the desiredfirms must also focus on improving productivity. Many, for example, plan to grow their way into productivity—adding customers and transactions without adding staff. Companies that cite head count reduction as the primary justification for the AI investment should ideally plan to realize that goal over time through attrition or from the elimination of outsourcing. Our survey and interviews suggest that managers experienced with cognitive technology are bullish on its prospects. Although the early successes are relatively modest, we anticipate that these technologies will eventually transform work.

We believe that companies that are adopting AI in moderation now—and have aggressive implementation plans for the future—will find themselves as well positioned to reap benefits as those that embraced analytics early on. Through the application of AI, information-intensive domains such as marketing, health care, financial services, education, and professional services could become simultaneously more valuable and less expensive to society.

Looking for some intelligent company

Business drudgery in every industry and function—overseeing routine transactions, repeatedly answering the same questions, and extracting data from endless documents—could become the province of machines, freeing up human workers to be more productive and creative. Cognitive technologies are also a catalyst for making other data-intensive technologies succeed, including autonomous vehicles, the Internet of Things, and mobile and multichannel consumer technologies.

The great fear about cognitive technologies is that they will put masses of people out of work. Of course, some job loss is likely as smart machines take over certain tasks traditionally done by humans. However, we believe that most workers have little to fear at this point. Cognitive systems perform tasks, not entire jobs. Most managers with whom we discuss the issue of job loss are committed to an augmentation strategy—that is, integrating human and machine work, rather than replacing humans entirely.

We believe that every large company should be exploring cognitive technologies. There will be some bumps in the road, and there is no room for complacency on issues of workforce displacement and the ethics of smart machines. But with the right planning and development, cognitive technology could usher in a golden age of productivity, work satisfaction, and prosperity.

You have 1 free article s left this month. You are reading your last free article for this month. Subscribe for unlimited access. Artificial Intelligence for the Real World. Davenport and Rajeev Ronanki. James Wheaton and Andrew Nguyen. In Brief The Problem Cognitive technologies are increasingly being used to solve business problems, but many of the most ambitious AI Looking for some intelligent company encounter setbacks or fail. The Approach Companies should take an incremental rather than a transformative approach and focus on augmenting rather than replacing human capabilities.

Looking for some intelligent company

The Process To get the most out of AI, firms must understand which technologies perform what types of tasks, create a prioritized portfolio of projects based on business needs, and develop plans to scale up across the company. Cognitive Insight

Looking for some intelligent company

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