Technology and Business Strategy: Getting the Most Out of Technological Assets

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So, if you need large amounts of data and a sizeable investment to implement, where does that leave the small business community? I believe entrepreneurs and small businesses can leverage AI as an essential tool to unlocking a competitive edge in today's accelerating small business landscape. Whether it's automating basic tasks or leveraging data to unearth insights that were previously inaccessible, AI can make a small business more productive, efficient and informed.

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But the advantages run far deeper than mere operational efficiencies. Advanced technology like AI has leveled the playing field, allowing you to scale your small business and go head-to-head with large corporations. In turn, those unique advantages that small businesses have always had - close customer engagement, niche products and services and strong company identity - will be even more valued in the eyes of the customer. The good news - today, AI is more accessible to the small business than you might think. Here are five tangible ways you can use AI to power your small business and stay ahead of the competition:.

In a recent Bank of America study , small business owners said that the biggest roadblock to work-life balance was the administrative tasks involved in keeping their business afloat. AI can take care of those repetitive, time-consuming and mundane administrative tasks, so your time can be spent on more strategic, entrepreneurial work. This can range from automating simple customer service inquiries to putting AI to work on accounting needs. In fact, you might already be using AI at your business in this fashion: Intuit has 30 AI and machine learning-based products and services in the market already - including the expense management system built into QuickBooks Self-Employed.

Entrepreneurs need to make a ton of difficult decisions. 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 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.

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.

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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 redesigned, focusing specifically on the division 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 results 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 desired results, firms 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.

Many organizations rely on distributed problem solving, tapping the brain power of customers and experts from within and outside the company for breakthrough thinking. Pharmaceutical player Boehringer Ingelheim sponsored a competition on Kaggle a platform for data-analysis contests to predict the likelihood that a new drug molecule would cause genetic mutations. The winning team, from among nearly 9, competitors, combined experience in insurance, physics, and neuroscience, and its analysis beat existing predictive methods by more than 25 percent.

Companies also are becoming more porous, able to reach across units speedily and to assemble teams with specialized knowledge. Kraft Foods, for example, has invested in a more powerful social-technology platform that supports microblogging, content tagging, and the creation and maintenance of communities of practice such as pricing experts.

Benefits include accelerated knowledge sharing, shorter product-development cycles, and faster competitive response times. Companies still have ample running room, though: just 10 percent of the executives we surveyed last year said their organizations were realizing substantial value from the use of social technologies to connect all stakeholders: customers, employees, and business partners.

Social features, meanwhile, can become part of any digital communication or transaction—embedded in products, markets, and business systems. A steady stream of reactions from avid fans allows RTL to fine-tune episode plots. Indeed, our research suggests that when social perceptions and user experiences both individual and collective matter in product selection and satisfaction, the potential impact of social technologies on revenue streams can be pronounced.

See the full McKinsey Global Institute report, The social economy: Unlocking value and productivity through social technologies , July We are starting to see these effects in sectors ranging from automobiles to retailing as innovative companies mine social experiences to shape their products and services.

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Three years ago, we described new opportunities to experiment with and segment consumer markets using big data. As with the social matrix, we now see data and analytics as part of a new foundation for competitiveness.


Global data volumes—surging from social Web sites, sensors, smartphones, and more—are doubling faster than every two years. The power of analytics is rising while costs are falling. Data visualization, wireless communications, and cloud infrastructure are extending the power and reach of information. With abundant data from multiple touch points and new analytic tools, companies are getting better and better at customizing products and services through the creation of ever-finer consumer microsegments.

US-based Acxiom offers clients, from banks to auto companies, profiles of million customers—each profile enriched by more than 1, data points gleaned from the analysis of up to 50 trillion transactions. Companies are learning to test and experiment using this type of data.

They are borrowing from the pioneering efforts of companies such as Amazon. Many advanced marketing organizations are assembling data from real-time monitoring of blogs, news reports, and Tweets to detect subtle shifts in sentiment that can affect product and pricing strategy.

Advanced analytic software allows machines to identify patterns hidden in massive data flows or documents. And as companies collect more data from operations, they may gain additional new revenue streams by selling sanitized information on spending patterns or physical activities to third parties ranging from economic forecasters to health-care companies. Gaps between leaders and laggards are opening up as the former find new ways to test, learn, organize, and compete.

For companies trying to keep pace, developing a big-data plan is becoming a critical new priority—one whose importance our colleagues likened, in a recent article, to the birth of strategic planning 40 years ago.

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Planning must extend beyond data strategy to encompass needed changes in organization and culture, the design of analytic and visualization tools frontline managers can use effectively, and the recruitment of scarce data scientists which may require creative approaches, such as partnering with universities. Decisions about where corporate capabilities should reside, how external data will be merged with propriety information, and how to instill a culture of data-driven experimentation are becoming major leadership issues.

Tiny sensors and actuators, proliferating at astounding rates, are expected to explode in number over the next decade, potentially linking over 50 billion physical entities as costs plummet and networks become more pervasive. What we described as nascent three years ago is fast becoming ubiquitous, which gives managers unimagined possibilities to fine-tune processes and manage operations. The device includes a global positioning system, as well as sensors to monitor temperature, light, humidity, barometric pressure, and more—critical to some biological products and sensitive electronics.

The customer knows continuously not only where a product is but also whether ambient conditions have changed. These new data-rich renditions of radio-frequency-identification RFID tags have major implications for companies managing complex supply chains. Companies are starting to use such technologies to run—not just monitor—complex operations, so that systems make autonomous decisions based on data the sensors report.

Smart networks now use sensors to monitor vehicle flows and reprogram traffic signals accordingly or to confirm whether repairs have been made effectively in electric-power grids. Leading-edge ingestible sensors take this approach further, relaying information via smartphones to physicians, thereby providing new opportunities to manage health and disease.