Informatyka w Firmie Archive

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Solving the digital and analytics scale-up challenge in consumer goods

Many consumer-goods companies have entered the digital and analytics race, but very few are scaling impact. Here’s what leaders are doing right.

Ask any consumer-goods executive if his or her company has invested in digital and analytics, and you’ll almost certainly get an affirmative response. But ask whether those investments have yielded the desired results—and more than half of the time the answer will be no. Our research shows that only 40 percent of consumer-goods companies that have made digital and analytics investments are achieving returns above the cost of capital. The rest are stuck in “pilot purgatory,” eking out small wins but failing to make an enterprise-wide impact. The value at stake isn’t trivial: our analysis suggests that a company’s aptitude at scaling up digital and analytics programs is correlated with its financial performance. In this article, we describe the most common pitfalls that companies encounter in their journey toward digital and analytics scale-up. We also explore an emerging recipe for sustained success.

Measuring digital and analytics maturity—and its value

The consumer-goods industry has some catching up to do when it comes to digital maturity. Among 11 industries analyzed in the latest McKinsey Digital Quotient 1 survey, consumer goods ranks third lowest (Exhibit 1). The industry does much better in a comparison of analytics maturity, coming in at fifth place. This isn’t surprising: most consumer-goods companies have focused on established analytical areas (such as pricing) that require relatively little direct consumer data. Sectors with more direct consumer connections, such as retail, have focused more on digital capabilities to enable an omnichannel consumer experience. Within the consumer-goods industry, the companies with the highest levels of digital and analytics maturity are creating significant value. Between 2010 and 2018, the compound annual growth rate (CAGR) for the total shareholder returns (TSR) of the most mature digital and analytics performers—those in the top quintile—was 19.2 percent, approximately 60 percent higher than the 12.3 percent CAGR for bottom-quintile companies. While that analysis doesn’t prove causality, the correlation is compelling. And in light of the growth challenge that the industry is up against, the call to action is loud and clear: either fully tap into the power of digital and analytics, or get left behind.

Drawing on our experience working with consumer-goods players around the world, we have identified the four most common failure modes—the mistakes that hinder organizations from capturing value at scale from digital and analytics:

  • Neglecting to connect digital and analytics programs to the enterprise strategy. Laggards tend to treat digital and analytics efforts as side projects rather than important enablers of enterprise-wide priorities. Not surprisingly, these efforts struggle to get the attention and resources they require to succeed.
  • Making big investments prematurely. Some companies, enamored of having the latest technology, invest in digital and analytics before they thoroughly understand what the business truly needs and what will deliver significant impact. This failure mode tends to come in two flavors: a company either pursues a costly, all-encompassing “data lake,” without carefully thinking through exactly what that data lake will enable, or invests in a new technology stack in efforts to simplify or harmonize core platforms (such as enterprise-resource-planning systems), only to find that today’s best-in-class tech stack becomes outdated just two years later.
  • Holding out for “perfect” hires. Laggards spend as much as six months searching for two or three data scientists or wait until they feel they’ve found the “perfect” hire to lead the team. While it’s not wrong to look for the best data scientists, data engineers, designers, and other skilled people to fill critical roles, there are several ways to accelerate progress while building your technical bench—such as training internal talent, disaggregating roles, or partnering for new capabilities.
  • Underinvesting in change management. Executives often tell us that they wish they’d spent as much or more on change management as they did on technology. Without senior business leaders committed to role modeling the changes and a comprehensive plan for encouraging adoption by frontline employees, new techniques won’t stick. As a rule of thumb, digital and analytics leaders should allocate their energy and investment as follows: 25 percent on data, 25 percent on technology, and 50 percent on change management.

MORE: www.mckinsey.com

About the authors: Ford Halbardier is an associate partner in McKinsey’s Dallas office, where Brian Henstorf is a partner; Robert Levin is partner in the Boston office; and Aldo Rosales is an associate partner in the Mexico City office.

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How to Turn AI into ROI

After several decades of progress, AI technology is now poised to become a sig-nificant source of value for a wide range of businesses. In the 2019 MIT Sloan Management Review and Boston Consulting Group (BCG) Artificial Intelligence Global Executive Study and Research Report, 9 out of 10 respondents agree that AI represents a business opportunity for their company.In addition, a growing number of leaders view AI as not just an opportunity but also a strategic risk: “What if competitors, particularly unencumbered new entrants, figure out AI before we do?” In 2019, 45% perceived some risk from AI, up from an already substantial 37% in 2017. This shift suggests an increasing awareness of and concern with competitors’ use of AI. In China, perceived risk from AI is even higher.Significant challenges remain, however. Many AI initiatives fail. Seven out of 10 companies surveyed report minimal or no impact from AI so far. Among the 90% of companies that have made at least some investment in AI, fewer than 2 out of 5 report obtaining any business gains from AI in the past three years. This number improves to 3 out of 5 when we include companies that have made signifi-cant investments in AI. Even so, this means 40% of organizations making significant investments in AI do not report business gains from AI.The crux is that while some companies have clearly figured out how to be successful, most compa-nies have a hard time generating value with AI. As a result, many executives find themselves facing a set of AI realities: AI is a source of untapped opportunity, it is an existential risk, and it is difficult. Above all, it is an urgent issue to address. How can executives exploit the opportunities, manage the risks, and minimize the difficulties associated with AI? How should they navigate all three factors?

Our findings — based on a survey of more than 2,500 executives and 17 interviews with leading experts — provide a data-driven view of what organizations that succeed with AI are doing and what real success with AI looks like. Companies that cap-ture value from their AI activities exhibit a distinct set of organizational behaviors. They:•Integrate their AI strategies with their overall business strategy.•Take on large, often risky, AI efforts that priori-tize revenue growth over cost reduction.•Align the production of AI with the consump-tion of AI, through thoughtful alignment of business owners, process owners, and AI ex-pertise to ensure that they adopt AI solutions effectively and pervasively.•Unify their AI initiatives with their larger busi-ness transformation efforts.•Invest in AI talent, data, and process change in ad-dition to (and often more so than) AI technology. They recognize AI is not all about technology. More: www.bcg.com

More: Winning With AI. Pioneers Combine Strategy, Organizational Behavior, and Technology. OCTOBER 2019RESEARCH REPORT, By Sam Ransbotham, Shervin Khodabandeh, Ronny Fehling, Burt LaFountain, and David Kiron.

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Badanie Ericsson: Polscy przedsiębiorcy otwarci na 5G

71,5% przedsiębiorców uważa, że technologia 5G zwiększy konkurencyjność polskiej gospodarki wobec innych krajów Unii Europejskie, a tylko 3% zdecydowanie wyklucza taką możliwość. Polskie firmy oczekują szybkiego wdrożenia technologii 5G i wykorzystania jej potencjału do rozwoju własnego biznesu i krajowej gospodarki – wynika z badania Ericsson Polska zrealizowanego we współpracy z Łódzką Specjalną Strefą Ekonomiczną oraz Skandynawsko-Polską Izbą Gospodarczą.

Największe nadzieje na polepszenie koniunktury dzięki technologii 5G widzą managerowie średnich przedsiębiorstw (50-249 pracowników). 75,6% respondentów z tej grupy uważa, że sieć 5G zdecydowanie zwiększy lub będzie miała pozytywny wpływ na konkurencyjność polskiej gospodarki. Uczestnicy badania Ericsson bardzo optymistycznie podchodzą do prognoz wprowadzenia technologii 5G na terenach miejskich i głównych szlakach transportowych w Polsce do 2025 roku. 62,3% jest przekonanych o realności wprowadzenia technologii 5G w Polsce w tym terminie, w tym 13% uznaje taki scenariusz za wielce prawdopodobny.

„Przejście z 4G na 5G przyniesie korzyści zarówno konsumentom, jak i wielu branżom. 5G zapewnia większą wydajność sieci, wychodząc naprzeciw potrzebom w zakresie większej przepustowości. Technologia ta umożliwi również rozwój nowych usług, ekosystemów i nowych źródeł przychodów dla polskich firm. Jednak wykorzystanie tego potencjału wymaga inwestycji w technologie, a także rozwoju przedsiębiorstw, wypracowania nowych modeli wprowadzania produktów i usług na rynek oraz dostosowań o charakterze organizacyjnym w skali infrastruktury krajowej” – mówi Martin Mellor, szef firmy Ericsson w Polsce.

Potrzeba współpracy oraz edukacji

Większość badanych managerów interesuje się nowymi technologami i potrafi wskazać wyzwania stojące przed wdrożeniem technologii 5G w Polsce. Ponad połowa (51,3%) średniej wielkości przedsiębiorstw jako główne bariery wskazała obawy związane z bezpieczeństwem i kontrolą danych. 46% ogółu respondentów główną barierę, która mogłaby opóźnić wprowadzenie technologii 5G w Polsce widzi w budowie infrastruktury sieci. Za równie problematyczne respondenci uznali obawy związane z negatywnym wpływem technologii 5G na zdrowie (42%). Tymczasem według Światowej Organizacji Zdrowia nie ma wystarczających dowodów potwierdzających szkodliwe oddziaływanie pola elektromagnetycznego o natężeniu wykorzystywanym w telekomunikacji na ludzkie zdrowie.

„Wyniki te pokazują, że tylko ścisła współpraca na polu edukacji pomiędzy wszelkimi zaangażowanymi podmiotami na rynku, począwszy od instytucji unijnych, przez administracje lokalne, operatorów, przedsiębiorstwa czy organizacje pozarządowe może przyczynić się do uwolnienia realnego potencjału 5G” – mówi Carsten Nilsen, prezes Skandynawsko-Polska Izba Gospodarcza, partner badania Ericsson Polska.

Wyróżnikiem łączności 5G jest kilkudziesięciokrotnie wyższa szybkość transmisji danych w porównaniu do LTE oraz praktycznie brak opóźnień (0,1 ms). Dzięki temu przedsiębiorstwa inwestując w nowoczesne narzędzia np. łączące sztuczną inteligencję, analizę danych i 5G, będą w stanie realizować równolegle wiele procesów w czasie rzeczywistym, co pozwoli na zdobycie realnej przewagi konkurencyjnej w swojej branży.

„Polscy przedsiębiorcy mogą już testować swoje pomysły w Łódzkiej Specjalnej Strefie Ekonomicznej. W tym roku zainaugurowaliśmy pierwszy w kraju przemysłowy Akcelerator Technologii 5G, czyli program wspierający rozwój innowacyjnych rozwiązań wykorzystujących w sposób znaczący technologię 5G lub 5G ready (architekturę Non-Standalone 5G) w przedsiębiorstwach. Program stwarza startupom możliwość pracy na żywym organizmie: instalowanie urządzeń przy liniach produkcyjnych, na pojazdach czy w halach magazynowych wybranych partnerów, pracę z realnymi danymi, a nawet działania z klientem końcowym” – komentuje Agnieszka Sygitowicz, Wiceprezes Łódzkiej Specjalnej Strefy Ekonomicznej S.A., partner badania Ericsson Polska.

Polska u progu technologii 5G

Technologia 5G jest już dostępna w Polsce, ale konieczne jest przydzielenie odpowiednich pasm i częstotliwości. Od 2015 r. Ericsson zainstalował niemal 5 mln stacji bazowych gotowych obsłużyć 5G, które czekają na zdalne uruchomienie. Spora część z tych stacji znajduje się w Polsce. Komisja Europejska oczekuje, że do 2025 r. kraje członkowskie będą posiadać szerokie pokrycie siecią 5G. Ericsson ogłosił uruchomienie komercyjnej sieci 5G z operatorem Polkomtel.  Wiosną planowane jest uruchomienie badawczej sieci 5G firmy Ericsson
na Politechnice Łódzkiej. Ericsson prowadzi testy 5G z innymi operatorami i podmiotami w Polsce.

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Badanie “Technologia 5G w polskich przedsiębiorstwach” zostało zrealizowane w terminie 11.2019-02.2020 na zlecenie Ericsson Polska przez agencję SW Research metodą wywiadów on-line (CAWI) na grupie 109 przedstawicieli wyższej kadry zarządzającej firm polskich lub posiadających przedstawicielstwo w Polsce.

Katarzyna Pąk

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Notes from the AI frontier: Applications and value of deep learning

Artificial intelligence (AI) stands out as a transformational technology of our digital age—and its practical application throughout the economy is growing apace. For this briefing, Notes from the AI frontier: Insights from hundreds of use cases (PDF–446KB), we mapped both traditional analytics and newer “deep learning” techniques and the problems they can solve to more than 400 specific use cases in companies and organizations. Drawing on McKinsey Global Institute research and the applied experience with AI of McKinsey Analytics, we assess both the practical applications and the economic potential of advanced AI techniques across industries and business functions. Our findings highlight the substantial potential of applying deep learning techniques to use cases across the economy, but we also see some continuing limitations and obstacles—along with future opportunities as the technologies continue their advance. Ultimately, the value of AI is not to be found in the models themselves, but in companies’ abilities to harness them.

It is important to highlight that, even as we see economic potential in the use of AI techniques, the use of data must always take into account concerns including data security, privacy, and potential issues of bias.

  1. Mapping AI techniques to problem types
  2. Insights from use cases
  3. Sizing the potential value of AI
  4. The road to impact and value

[…]

Implications for stakeholders

As we have seen, it is a company’s ability to execute against AI models that creates value, rather than the models themselves. In this final section, we sketch out some of the high-level implications of our study of AI use cases for providers of AI technology, appliers of AI technology, and policy makers, who set the context for both.

  • For AI technology provider companies: Many companies that develop or provide AI to others have considerable strength in the technology itself and the data scientists needed to make it work, but they can lack a deep understanding of end markets. Understanding the value potential of AI across sectors and functions can help shape the portfolios of these AI technology companies. That said, they shouldn’t necessarily only prioritize the areas of highest potential value. Instead, they can combine that data with complementary analyses of the competitor landscape, of their own existing strengths, sector or function knowledge, and customer relationships, to shape their investment portfolios. On the technical side, the mapping of problem types and techniques to sectors and functions of potential value can guide a company with specific areas of expertise on where to focus.
  • Many companies seeking to adopt AI in their operations have started machine learning and AI experiments across their business. Before launching more pilots or testing solutions, it is useful to step back and take a holistic approach to the issue, moving to create a prioritized portfolio of initiatives across the enterprise, including AI and the wider analytic and digital techniques available. For a business leader to create an appropriate portfolio, it is important to develop an understanding about which use cases and domains have the potential to drive the most value for a company, as well as which AI and other analytical techniques will need to be deployed to capture that value. This portfolio ought to be informed not only by where the theoretical value can be captured, but by the question of how the techniques can be deployed at scale across the enterprise. The question of how analytical techniques are scaling is driven less by the techniques themselves and more by a company’s skills, capabilities, and data. Companies will need to consider efforts on the “first mile,” that is, how to acquire and organize data and efforts, as well as on the “last mile,” or how to integrate the output of AI models into work flows ranging from clinical trial managers and sales force managers to procurement officers. Previous MGI research suggests that AI leaders invest heavily in these first- and last-mile efforts.
  • Policy makers will need to strike a balance between supporting the development of AI technologies and managing any risks from bad actors. They have an interest in supporting broad adoption, since AI can lead to higher labor productivity, economic growth, and societal prosperity. Their tools include public investments in research and development as well as support for a variety of training programs, which can help nurture AI talent. On the issue of data, governments can spur the development of training data directly through open data initiatives. Opening up public-sector data can spur private-sector innovation. Setting common data standards can also help. AI is also raising new questions for policy makers to grapple with for which historical tools and frameworks may not be adequate. Therefore, some policy innovations will likely be needed to cope with these rapidly evolving technologies. But given the scale of the beneficial impact on business the economy and society, the goal should not be to constrain the adoption and application of AI, but rather to encourage its beneficial and safe use.

About the author(s) : Michael Chui is a partner of the McKinsey Global Institute, where James Manyika is chairman and a director; Mehdi Miremadi is a partner in McKinsey’s Chicago office; Nicolaus Henke is a senior partner in the London office; Rita Chung is a consultant in the Silicon Valley office; Pieter Nel is a specialist in the New York office, where Sankalp Malhotra is a consultant.

More: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/an-executives-guide-to-ai

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Smart Homes 2020

Thanks to internet of things (IoT) devices, voice assistants and high-speed networking, smart homes are on the cusp of mainstream adoption. As sophisticated networks of door locks, vacuum cleaners and baby monitors become more common, brands see a gold mine of user data. But the rules of marketing engagement are changing. Privacy-conscious consumers don’t want interruptive advertising in their personal spaces. They’ll be more likely to roll out the welcome mat for brands that ask permission and help them solve their problems.

What are the most popular smart-home products, and why are people buying them?

Connected TVs, smart speakers, security systems and cameras, door locks, lighting and thermostats are among the top smart-home products. Consumers purchase these products—and related services—to make their lives easier, save time and money, improve energy efficiency and stay healthy and safe.

Who are the major players in the smart-home ecosystem?

Amazon, Google, Apple and Samsung have created some of the largest IoT and smart-home platforms in the West, while Baidu, Alibaba and Xiaomi are among top providers in China. Other players—including hardware manufacturers, security and telecom providers, utilities, software firms and startups—are also jockeying for position. The market is consolidating and more products are becoming cross-compatible.

Why should marketers be excited about smart homes?

Smart homes, cars and other interconnected IoT environments present new paradigms for human-computer interaction and opportunities for marketers to gain deeper insights about their audiences. They will enable responsible marketers to engage with consumers in more immediate, meaningful and personalized ways.

What will marketing within smart homes look like?

There is no blueprint for marketing or advertising on smart-home devices and no major ecosystems that support it. While some advertisers are serving ads on addressable TVs and within audio streams on voice-controlled devices, this interruptive messaging will give way to permission-based engagements that fit seamlessly into daily routines.

WHAT’S IN THIS REPORT? This report provides an overview of the state of smart homes. It also explains four forces that are guiding this market and how they will change traditional marketing models. More: eMarketer