Will artificial intelligence make you a better leader?

Posted in Aktuellt, Digitalisering / Internet, Leadership / Ledarskap, Technology on May 8th, 2018 by admin

Agile leadership and AI both depend on learning to let go.

Consider this real-life scene: Reflecting on the difficult moments of his week, the new CEO of a UK manufacturer felt angry. His attention kept going back to the tension in several executive-team meetings. He had an urge to shake the team and push several of its members, who were riven by old conflicts, to stop fighting and start collaborating to solve the company’s real problems. He also sensed, though, that a brute-force approach was unlikely to get very far, or to yield the creative insights that the company desperately needed to keep up with its fast-changing competitive environment. Instead, he calmed himself, stopped blaming his team, and asked himself whether he could break the logjam by pursuing truly new approaches to the company’s problems. It was then that his mind turned to, of all things, artificial intelligence.

Like many leaders, the CEO was struggling to cope with the stress induced by uncertainty, rising complexity, and rapid change. All of these are part and parcel of today’s business environment, which is different enough from the one many of us grew up with to challenge our well-grooved leadership approaches. In a recent article, we described five practices that can help you step back from the tried and true and become more inwardly agile (see “Leading with inner agility”). Here, we want to describe the relationship between some of those ideas and a technology that at first glance seems to add complexity but in fact can be a healing balm: artificial intelligence (AI), which we take to span the next generation of advanced data and analytics applications. Inner agility and AI may sound like strange bedfellows, but when you consider crucial facts about the latter, you can see its potential to help you lead with clarity, specificity, and creativity.

The first crucial fact about AI is that you don’t know ahead of time what the data will reveal. By its very nature, AI is a leap of faith, just as embracing your ignorance and radical reframing are. And like learning to let go, listening to AI can help you find genuinely novel, disruptive insights in surprising and unexpected places.

A second fact about AI is that it creates space and time to think by filtering the signal from the noise. You let the algorithms loose on a vast landscape of data, and they report back only what you need to know and when you need to know it.

Let’s return to the CEO above to see an example of these dynamics in action. The CEO knew that his company’s key product would have to be developed more efficiently to compete with hard-charging rivals from emerging markets. He urgently needed to take both cost and time out of the product-development process. The standard approach would have been to cut head count or invest in automation, but he wasn’t sure either was right for his company, which was exhausted from other recent cost-cutting measures.

All this was on the CEO’s mind as he mused about the problematic executive dynamics he’d been observing—which, frankly, made several of his leaders unreliable sources of information. It was the need for objective, creative insight that stoked the CEO’s interest in AI-fueled advanced data analytics. A few days later, he began asking a team of data-analytics experts a couple broad and open-ended questions: What are the causes of inefficiencies in our product design and development workflow? What and where are the opportunities to improve performance?

The AI team trained their algorithms on a vast variety of data sources covering such things as project life-cycle management, fine-grained design and manufacturing documents, financial and HR data, suppliers and subcontractors, and communications data. Hidden patterns in the communication networks led to a detailed analysis of the interactions between two key departments: design and engineering. Using aggregated data that didn’t identify individual communications, the team looked at the number of emails sent after meetings or to other departments, the use of enterprise chat groups and length of chats, texting volume, and response rates to calendar invites, the algorithms surfaced an important, alarming discovery. The two departments were barely collaborating at all. In reality, the process was static: designers created a model, engineers evaluated and commented, designers remodeled, and so on. Each cared solely about its domain. The data-analytics team handed the CEO one other critical fact: by going back five years and cross-referencing communications data and product releases, they provided clear evidence that poor collaboration slowed time to market and increased costs.

By liberating the AI team to follow a direction and not a destination, the CEO’s original question, “How do we improve productivity?” became a much more human, “How are we working as a team, and why?” Based on this new empirical foundation, he enlisted the engineering and design leaders to form a cross-disciplinary team to reimagine collaboration. Working with the data scientists, the team was able to identify and target a 10 percent reduction in time to market for new-product development and an 11 percent reduction in costs. But the CEO didn’t stop there. He also used the experience to ask his executive team to develop a new agility. The previously fractured team worked hard to build a foundation of trust and true listening. Regular check-ins helped them pause, formulate new questions, invite healthy opposition, and ask themselves, “What are we really solving for?” The team was growing more complex to address the company’s increasingly complex challenges.

In our experience, AI can be a huge help to the leader who’s trying to become more inwardly agile and foster creative approaches to transformation. When a CEO puts AI to work on the toughest and most complex strategic challenges, he or she must rely on the same set of practices that build personal inner agility. Sending AI out into the mass of complexity, without knowing in advance what it will come back with, the CEO is embracing the discovery of original, unexpected, and breakthrough ideas. This is a way to test and finally move on from long-held beliefs and prejudices about their organization, and to radically reframe the questions in order to find entirely new kinds of solutions. And the best thing about AI solutions is that they can be tested. AI creates its own empirical feedback loop that allows you to think of your company as an experimental science lab for transformation and performance improvement. In other words, the hard science of AI can be just what you need to ask the kind of broad questions that lay the foundation for meaningful progress.

Source: McKInsey.com
LinkAbout the author: Sam Bourton is the cofounder and chief technology officer of QuantumBlack, a McKinsey company, and is based in McKinsey’s London office; Johanne Lavoie is a partner in the Calgary office and coauthor of Centered Leadership: Leading with Purpose, Clarity, and Impact (Crown Business, 2014); and Tiffany Vogel is a partner in the Toronto office.

Few jobs can be completely replaced by new technologies

Posted in Aktuellt, Allmänt, Digitalisering / Internet on February 21st, 2018 by admin

Artificial intelligence, machine learning, and robotics can perform an increasingly wider variety of jobs, and automation is no longer confined to routine tasks. Nevertheless, the automation potential for non-routine tasks seems to remain limited, especially for tasks involving autonomous mobility, creativity, problem solving, and complex communication.

The new report The Substitution of Labor: From technological feasibility to other factors influencing job automation is the fifth report from the three-year research project, The Internet and its Direct and Indirect Effects on Innovation and the Swedish Economy under the leadership of Professor Robin Teigland.

The report examines the possibility of a number of technologies to replace labor.

Some of the key findings from the report include the following:

A majority of jobs will be affected by the automation of individual activities, but only a few have the potential to be completely substituted.
The nature of jobs will change as routine tasks will be replaced and people will work more closely together with machines.
Industries that have a large potential for job substitution are food and accommodation services, transportation and warehousing, retail trade, wholesale trade, and manufacturing.

Source: Handelshögskolan i Stockholm, hhs.se, 21 February 2018
Link

Digital trends and observations from Davos 2018

Posted in Aktuellt, Allmänt, Board work / Styrelsearbete, Digitalisering / Internet on February 6th, 2018 by admin

The massive snowfall in Davos this year certainly made getting around a little more challenging compared to years past, but that did nothing to dampen the conversation. We were fortunate to be at this year’s World Economic Forum, and after dozens of conversations with executives from around the world, we wanted to share a number of things that struck us about what we heard.

AI is growing up: Augmenting humans and social good
AI is top of mind for many executives, but the application of AI—and, more broadly, advanced analytics—is generating more thoughtful and nuanced conversations. While there are serious concerns about the social implications of AI, the reality is that it’s hard to see how machines can really be effective on their own, just as it’s hard to see how humans can work as well without machines. The most thoughtful organizations are looking to understand how AI can most effectively augment humans.

That idea of augmentation is playing through in other areas too. If you have good AI, you need processes to ensure the insights it generates are used. This is harder than it sounds. You can’t simply have a machine spitting out advice because people just won’t read it. By the same token, it doesn’t help to automate poor decisions. It’s all about finding ways to get the various technologies focused on what they do best, and then working together with humans to drive better results.

It was inspiring also to see how much focus there is on harnessing AI for social good. There is a significant opportunity for AI to help with big problems, from predicting the absence of rain in a region to managing mass immigration flows. While businesses are moving ahead quickly with AI, NGOs and regulators are far behind when it comes to the talent and capabilities needed. That may be changing, however. Increasingly there are courses on AI and social good being offered at cutting-edge technical universities, where there is strong interest from top students.

Gaining traction: Distributed ledgers (e.g., blockchain) and ecosystems
There is also a massive debate emerging around distributed ledger technology (more commonly referred to as blockchain, though that’s actually just one example of distributed ledger technology) specifically around its applications to businesses. There’s still lots of hype—often shaped by lack of true understanding of what the technology is—but also some real substance beyond its use for the cryptocurrencies that have been in the headlines. The promise of distributed ledgers lies in their ability to reliably, securely, and transparently access and share targeted sets of data.

Let’s take the example of sepsis, a dangerous but very preventable disease. Technology can help prevent sepsis by linking signals the body generates to historical health data. The analysis of this combined data could then signal danger signs before other symptoms arise and drive timely medical interventions. Distributed ledger technology could enable that kind of merging of data and analytics in a way that’s very hard to do today. Another example is banks that want to lend in emerging markets, where there is often no credit risk data, but widespread mobile phone usage. Through distributed ledgers, banks could access telco data to see potential customers’ phone bill payment records as a quick and reliable measure for loan suitability.

Distributed ledgers are also important for unlocking the cumulative power of ecoystems, which are increasingly a focus for businesses. It’s becoming clear to even the largest and most successful companies that they can’t do everything on their own. They are now concentrating much more on engaging in ecosystems of businesses, platforms, vendors, agencies, and the like through formal and informal partnerships, synergistic agreements, alliances, and other arrangements. However, ecosystems don’t happen at scale yet because of the difficulties getting different data systems to speak to each other with current technology. Distributed ledgers are the key ingredient to enable that level of communication and analysis.

Businesses are starting to put pilot teams together to understand how distributed ledgers work, and what the implications are for their businesses. We’re on the verge of some very interesting business models emerging from this.

Who’s got talent?
Almost everyone we spoke with mentioned how important the talent question has become. Of course, talent is always an issue but it’s now a CEO topic. There were three flavors of the talent challenge which we noticed:

“I need to get my hands on some quality data scientists.” There is a limited number of these kinds of people, so the competition is intense (and expensive).
“I need to train my senior people and managers to understand how to work with and lead these data scientists.”
“I need to do something about the percolating social implications.” Many leaders are concerned about the implications that displacement of jobs by automation will have on society. Added to that is the fact that much of the employment growth in Western countries is in the gig economy. Leaders are looking at re-skilling as a cheaper and more effective approach than paying to hire and train new people. But that then requires the development of the capacity to develop, administer, and adapt a constant training function, because the reality is that many employees will need to be constantly learning and adapting. That includes thinking through the skills needed in three to five years, and beginning to develop that now before it’s too late.

Bold moves and what they mean for the organization
Many business leaders are thinking much more boldly about the changes they should make. One executive at an oil services business realized that they needed excellent advanced analytics capability to help manage their pipelines (such as for maintenance). His approach was to hire the best entrepreneur he could find and set up a self-standing business to specifically build out this capability. Not only did this executive believe it was the best way to build up an important capability quickly, it was also a talent play.

These bold moves are inextricably tied to organizational issues. Building out new businesses or figuring out how (or whether) to move to full-scale agile ways of working through the business raises all sorts of thorny questions: what does the governance look like? How do you make investment decisions? These are exactly the kinds of questions that reflect a deeper commitment to transformations at the core of the business.

The tough talk: Cybersecurity and looming “Techlash”

Overall, the feeling was very positive that the business outlook was good and the economy is flying. But below the surface there were very real and potentially damaging concerns. Cybersecurity is foremost among them, with companies locked in an arms race to stay ahead of (or even catch up to) highly sophisticated cyber criminals. It’s a big issue with CEOs and boards, and some of the business world’s best minds are trying to understand how to get the upper hand.

One other undercurrent of concern was around the idea of a “techlash,” or backlash against tech companies driven by fears that they are becoming too large and monopolistic. At one level is the basic concern that tech companies are just outcompeting incumbents, but beyond that there’s a sense that large tech companies are dictating terms to the marketplace, not taking privacy concerns seriously enough, and unfocused on the social implications of technology. Yes, to some degree this is driven by jealousy at the success these new tech businesses have enjoyed and the natural discomfort that comes with disruption. But there is also real concern as well with what’s happening to our society with these changes, and a sense that not all of it is good.

Despite the complexity of some of these issues and concerns, we were encouraged to see the discussion about them. Dialog is an indication of innovation to come.

Source: McKinsey.com, 2 February 2018
Authors: Nicolaus Henke and Paul Willmott
About the authors: Nicolaus Henke and Paul Willmott are senior partners in McKinsey’s London office.
Link

Så påverkas du av artificiell intelligens

Posted in Aktuellt, Allmänt, Customer care / Kundvård, Digitalisering / Internet on January 26th, 2018 by admin

Artificiell intelligens är redan en del av vår vardag och många påverkas varje dag. Vilka konsekvenser blir det när maskinerna bli mer intelligenta? ”Vi kommer att bli alltmer bekväma med att interagera med AI”, säger Lotta Laurin, nordisk marknadschef på Salesforce.

”2020 kommer kunder att hantera 85 procent av sin relation till ett företag utan att interagera med en människa.” Det förutspådde undersökningsföretaget Gartner redan 2011. Användningen av digitala assistenter som Apples Siri och Amazons Alexa ökar snabbt liksom tillämpning av chatbots (program som simulerar en konversation) till exempel inom kundservice. En undersökning som molnföretaget Salesforce har gjort pekar i samma riktning.

– 57 procent av konsumenterna förväntar sig att röstaktiverade assistenter har stor eller måttlig inverkan på deras liv senast 2020. Allt pekar på ökad användning då allt fler produkter innehåller röststyrning och människor blir mer och mer bekväma med att interagera med AI på det sättet, säger Lotta Laurin, nordisk marknadschef på Salesforce.

AI används också för att framställa texter. I år kommer cirka 20 procent av det skrivna material som företag distribuerar att vara producerat av maskiner enligt Gartner. Aktieägarrapporter, juridiska dokument och pressmeddelanden är exempel på material som kan genereras med automatiserade skrivverktyg.

Förbättrad service och service till saker

När många olika produkter blir uppkopplade får AI en avgörande roll när det gäller att leverera underhåll och proaktiv service.

– När exempelvis en uppkopplad hiss rapporterar ett problem, kan AI göra en analys av hur allvarligt problemet är och välja rätt reparatör beroende på kunskap, plats och tillgänglighet. AI kan därefter schemalägga ärendet och informera kunden. Det här är en innovation som förhöjer kundupplevelsen samtidigt som det hjälper serviceteam att vara mer proaktiva och effektiva, säger Lotta Laurin.

Vikten av att sätta kunden i centrum är centralt för företag som vill överleva i en knivskarp konkurrens. Men det finns nu även sex miljarder föremål; hissar, bilar, maskiner och byggnader, så kallade Internet of Things (IoT), som också behöver tas omhand.

– Intelligenta hus och smarta elnät är exempel på IoT, som kräver uppkoppling och hantering av data. Den här enorma mängden av IoT kräver också support, och kanske måste företag börja betrakta dessa ”saker” som en ny form av kunder. Företag måste utveckla nya strategier för att svara upp mot de behov som sakernas internet har, jämfört med de som kunderna efterfrågar. En helt ny serviceindustri kommer växa fram, säger Lotta Laurin.

Självkörande bilar är ett bra exempel på IoT som kommer att bli vanligare på våra vägar. Enligt företaget Statista räknar 61 procent av konsumenterna med att röststyrda, uppkopplade bilar kommer att ha stor eller måttlig inverkan på deras dagliga liv senast 2020. 2015 utgjorde bilar med uppkoppling 35 procent av alla nya bilar, en siffra som förväntas stiga till 98 procent år 2020.

– Smartare, uppkopplade bilar betyder också flera applikationer som kan förbättra körupplevelsen: navigeringstjänster, trafikinformation och underhållning för att nämna några. Här har också röststyrningsfunktionen en central plats för att föraren ska kunna fokusera på körningen istället för att trycka på knappar eller skärmar, säger Lotta Laurin och tillägger:

– När de självkörande bilarna så småningom blir vanligare kommer du istället att sitta i baksätet, läsa tidningen och be bilen att köra dig hem.

AI ger fler människor arbeten

En undersökning från Narrative Science avslöjar att företagsledare, analytiker, ingenjörer med flera, inte tror att AI kommer att minska antalet jobb. Tvärtom tror 80 procent av de tillfrågade att artificiell intelligens både kommer att skapa fler arbetstillfällen och förbättra de anställdas produktivitet.

– Sedan flera år tillbaka har företag använt artificiell intelligens för att analysera Big Data och därmed få bättre beslutsunderlag. Den aktuella undersökningen visar att företag generellt menar att de skulle kunna få fram dubbelt så mycket information från sin data. De måste därför anställa fler dataanalytiker och specialister som kan tolka analyserna, avslutar Lotta Laurin.

Källa: DI.se, januari 2018
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The next-generation operating model for the digital world

Posted in Aktuellt, Allmänt, Digitalisering / Internet, Technology on December 27th, 2017 by admin

Companies need to increase revenues, lower costs, and delight customers. Doing that requires reinventing the operating model.

Companies know where they want to go. They want to be more agile, quicker to react, and more effective. They want to deliver great customer experiences, take advantage of new technologies to cut costs, improve quality and transparency, and build value.

The problem is that while most companies are trying to get better, the results tend to fall short: one-off initiatives in separate units that don’t have a big enterprise-wide impact; adoption of the improvement method of the day, which almost invariably yields disappointing results; and programs that provide temporary gains but aren’t sustainable.

We have found that for companies to build value and provide compelling customer experiences at lower cost, they need to commit to a next-generation operating model. This operating model is a new way of running the organization that combines digital technologies and operations capabilities in an integrated, well-sequenced way to achieve step-change improvements in revenue, customer experience, and cost.

A simple way to visualize this operating model is to think of it as having two parts, each requiring companies to adopt major changes in the way they work:

The first part involves a shift from running uncoordinated efforts within siloes to launching an integrated operational-improvement program organized around customer journeys (the set of interactions a customer has with a company when making a purchase or receiving services) as well as the internal journeys (end-to-end processes inside the company). Examples of customer journeys include a homeowner filing an insurance claim, a cable-TV subscriber signing up for a premium channel, or a shopper looking to buy a gift online. Examples of internal-process journeys include Order-to-Cash or Record-to-Report.
The second part is a shift from using individual technologies, operations capabilities, and approaches in a piecemeal manner inside siloes to applying them to journeys in combination and in the right sequence to achieve compound impact.
Let’s look at each element of the model and the necessary shifts in more detail:

Shift #1: From running uncoordinated efforts within siloes to launching an integrated operational-improvement program organized around journeys
Many organizations have multiple independent initiatives underway to improve performance, usually housed within separate organizational groups (e.g. front and back office). This can make it easier to deliver incremental gains within individual units, but the overall impact is most often underwhelming and hard to sustain. Tangible benefits to customers—in the form of faster turnaround or better service—can get lost due to hand-offs between units. These become black holes in the process, often involving multiple back-and-forth steps and long lag times. As a result, it’s common to see individual functions reporting that they’ve achieved notable operational improvements, but customer satisfaction and overall costs remain unchanged.

Would you like to learn more about our Digital McKinsey Practice?
Instead of working on separate initiatives inside organizational units, companies have to think holistically about how their operations can contribute to delivering a distinctive customer experience. The best way to do this is to focus on customer journeys and the internal processes that support them. These naturally cut across organizational siloes—for example, you need marketing, operations, credit, and IT to support a customer opening a bank account. Journeys—both customer-facing and end-to-end internal processes—are therefore the preferred organizing principle.

Transitioning to the next-generation operating model starts with classifying and mapping key journeys. At a bank, for example, customer-facing journeys can typically be divided into seven categories: signing up for a new account; setting up the account and getting it running; adding a new product or account; using the account; receiving and managing statements; making changes to accounts; and resolving problems. Journeys can vary by product/service line and customer segment. In our experience, targeting about 15–20 top journeys can unlock the most value in the shortest possible time.

We often find that companies fall into the trap of simply trying to improve existing processes. Instead, they should focus on entirely reimagining the customer experience, which often reveals opportunities to simplify and streamline journeys and processes that unlock massive value. Concepts from behavioral economics can inform the redesign process in ingenious ways. Examples include astute use of default settings on forms, limiting choice to keep customers from feeling overwhelmed, and paying special attention to the final touchpoint in a series, since that’s the one that will be remembered the most.

In 2014, a major European bank announced a multiyear plan to revamp its operating model to improve customer satisfaction and reduce overall costs by up to 35 percent. The bank targeted the ten most important journeys, including the mortgage process, onboarding of new business and personal customers, and retirement planning. Eighteen months in, operating costs are lower, the number of online customers is up nearly 20 percent, and the number using its mobile app has risen more than 50 percent. (For more on reinventing customer journeys, see “Putting customer experience at the heart of next-generation operating models,” forthcoming on McKinsey.com.)

Shift #2: From applying individual approaches or capabilities in a piecemeal manner to adopting multiple levers in sequence to achieve compound impact
Organizations typically use five key capabilities or approaches (we’ll call them “levers” from now on) to improve operations that underlie journeys:

Digitization is the process of using tools and technology to improve journeys. Digital tools have the capacity to transform customer-facing journeys in powerful ways, often by creating the potential for self-service. Digital can also reshape time-consuming transactional and manual tasks that are part of internal journeys, especially when multiple systems are involved.1
Advanced analytics is the autonomous processing of data using sophisticated tools to discover insights and make recommendations. It provides intelligence to improve decision making and can especially enhance journeys where nonlinear thinking is required. For example, insurers with the right data and capabilities in place are massively accelerating processes in areas such as smart claims triage, fraud management, and pricing.
Intelligent process automation (IPA) is an emerging set of new technologies that combines fundamental process redesign with robotic process automation and machine learning. IPA can replace human effort in processes that involve aggregating data from multiple systems or taking a piece of information from a written document and entering it as a standardized data input. There are also automation approaches that can take on higher-level tasks. Examples include smart workflows (to track the status of the end-to-end process in real time, manage handoffs between different groups, and provide statistical data on bottlenecks), machine learning (to make predictions on their own based on inputs and provide insights on recognized patterns), and cognitive agents (technologies that combine machine learning and natural-language generation to build a virtual workforce capable of executing more sophisticated tasks). To learn more about this, see “Intelligent Process Automation: The engine at the core of the next generation operating model.”
Business process outsourcing (BPO) uses resources outside of the main business to complete specific tasks or functions. It often uses labor arbitrage to improve cost efficiency. This approach typically works best for processes that are manual, are not primarily customer facing, and do not influence or reflect key strategic choices or value propositions. The most common example is back-office processing of documents and correspondence.
Lean process redesign helps companies streamline processes, eliminate waste, and foster a culture of continuous improvement. This versatile methodology applies well to short-cycle as well as long-cycle processes, transactional as well as judgment-based processes, client-facing as well as internal processes.

Guidelines for implementing these levers
In considering which levers to use and how to apply them, it’s important to think in a holistic way, keeping the entire journey in mind. Three design guidelines are crucial:

1. Organizations need to ensure that each lever is used to maximum effect. Many companies believe they’re applying the capabilities to the fullest, but they’re actually not getting as much out of them as they could. Some companies, for example, apply a few predictive models and think they’re really pushing the envelope with analytics—but in fact, they’re only capturing a small fraction of the potential value. This often breeds a false complacency, insulating the organizations from the learnings that would otherwise drive them to higher performance because it is “already under way” or “has been tried”. Having something already under way is a truism: everyone has something under way in these kinds of domains, but it is the companies that press to the limit that reap the rewards. Executives need to be vigilant, challenge their people, and resist the easy answer.

In the case of analytics, for example, maxing out the potential requires using sophisticated modeling techniques and data sources in a concerted, cross-functional effort, while also ensuring that front-line employees then execute in a top-flight way on the insights generated by the models.

2. Implementing each lever in the right sequence. There is no universal recipe on sequencing these levers because so many variables are involved, such as an organization’s legacy state and the existing interconnections between customer-facing and internal processes. However, the best results come when the levers can build on each other. That means, in practice, figuring out which one depends on the successful implementation of another.

Systematic analysis is necessary to guide decision making. Some institutions have started by outlining an in-house versus outsource strategy rooted in a fundamental question: “What is core to our value proposition?” Key considerations include whether the activities involved are strategic or confer competitive advantage or whether sensitive data or regulatory constraints are present.

The next step is to use a structured set of questions to evaluate how much opportunity there is to apply each of the remaining levers and then to estimate the potential impact of each lever on costs and customer experience. This exercise results in each lever being assigned an overall score to help develop a preliminary point of view on which sequence to use in implementing the levers.

There’s also a need to vet the envisioned sequences in the context of the overall enterprise. For example, even if the optimal sequence for a particular customer journey may be “IPA then lean then digital,” if the company’s strategic aspiration is to become “digital first,” it may make more sense to digitize processes first.

This systematic approach allows executives to consider various sequencing scenarios, evaluate the implications of each, and make decisions that benefit the entire business.

3. Finally, the levers should interact with each other to provide a multiplier effect. For example, one bank only saw significant impact from its lean and digitization efforts in the mortgage application journey after both efforts were working in tandem. A lean initiative for branch offices included a new scorecard that measured customer adoption of online banking, forums for associates to problem solve how to overcome roadblocks to adoption, and scripts they could use with customers to encourage them to begin mortgage applications online. This, in turn, drove up usage of online banking solutions. Software developers were then able to incorporate feedback from branch associates, which made future digital releases easier to use for customers. This in turn drove increased adoption of digital banking, thereby reducing the number of transactions done in branches.

Some companies have developed end-to-end journey “heat maps” that provide a company-wide perspective on the potential impact and scale of opportunity of each lever on each journey. These maps include estimates for each journey of how much costs can be reduced (measured in terms of both head count and financial metrics) and how much the customer experience can be improved.

Companies find heat maps a valuable way to engage the leadership team in strategic discussions about which approaches and capabilities to use and how to prioritize them.

Case example: The ‘first notice of loss’ journey in insurance
In insurance, a key journey is when a customer files a claim, known in the industry as first notice of loss (FNOL). FNOL is particularly challenging for insurers because they must balance multiple objectives at the same time: providing a user-friendly experience (for example, by offering web or mobile interfaces that enable self-service), managing expectations in real time through alerts or updates, and creating an emotional connection with customers who are going through a potentially traumatic situation—all while collecting the most accurate information possible and keeping costs in line.

Many companies have relied on Lean to improve FNOL call-center performance. One leading North American insurer, however, discovered it could unlock even more value by sequencing the buildout of three additional capabilities, based on the progress it had already made with Lean:

Digitization. This company improved response times by using digital technologies to access third-party data sources and connect with mobile devices. With these new tools, the insurer can now track claimant locations and automatically dispatch emergency services. Customers can also upload pictures of damages, and both file and track claims online. The insurer also allows some customers to complete the entire claims process without a single interaction with a company representative.

Advanced analytics. Digitization of the FNOL journey provided the insurer with more and better data faster, which in turn allowed its analytics initiative to be more effective. Now able to apply the latest modeling capabilities to better data, the company is using advanced analytics to improve decision making in the FNOL journey. For example, intelligent triage is used to close simple claims more quickly, and smart segmentation identifies claims likely to be total losses and those liable to require the special investigative unit (SIU) far earlier than before. Analytics are even being used to predict future staffing needs and inform scheduling and hiring, thereby allowing both complex and simple claims to be handled more efficiently.

Intelligent process automation (IPA). Once digital and analytics were in place, IPA was implemented. Automation tools were deployed to take over manual and time-consuming tasks formerly done by customer-service agents, such as looking up policy numbers or data from driving records. In addition to reducing costs, IPA sped up the process and reduced errors. IPA came last because the streamlining achieved by digitization and more effective use of analytics had eliminated some manual processes, so the IPA effort could focus only on those that remained.

By combining four levers—lean plus digital, analytics and IPA—this insurer drove a significant uplift in customer satisfaction while at the same time improving efficiency by 40 percent. (For more approaches to improving claims, see “Next-generation claims operating model: From evolution to revolution,” forthcoming on McKinsey.com.)2
Bringing it all together: Avoid creating new silos by thinking holistically
Senior leaders have a crucial role in making this all happen. They must first convince their peers that the next-generation operating model can break through organizational inertia and trigger step-change improvements. With broad buy-in, the CEO or senior executive should align the business on a few key journeys to tackle first. These can serve as beacons to demonstrate the model’s potential. After that comes evaluation of the company’s capabilities to determine which levers can be implemented using internal resources and which will require bringing in resources from outside. Finally, there is the work of actually implementing the model. (For more on the last topic, see “How to build out your next-generation operating model,” forthcoming on McKinsey.com.)

Transformation cannot be a siloed effort. The full impact of the next-generation operating model comes from combining operational-improvement efforts around customer-facing and internal journeys with the integrated use of approaches and capabilities.

Source: McKinsey.com
By Albert Bollard, Elixabete Larrea, Alex Singla, and Rohit Sood
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Deep learning nästa steg för AI

Posted in Aktuellt, Allmänt, Digitalisering / Internet on November 22nd, 2017 by admin

Maskiner med artificiell intelligens (AI) blir snabbt allt mer kompetenta. Nästa steg i utvecklingen är deep learning – eller djup maskininlärning på svenska – som bland annat innebär att maskinerna utbildar sig själva genom att matas med enorma datamängder.

På 1950-talet fanns det ett relativt stort intresse för Artificiell Intelligens (AI), och man lät datorer lösa matematiska problem och ta sig an schackspel. Men tiden var inte mogen för att AI skulle ta ordentlig fart, och först när en tänkande schackdator slog ut mästaren Garry Kasparov för tjugo år sedan började utvecklingen komma igång på allvar. Tio år senare hade intresset och förutsättningarna skjutit i höjden och numera kommer nya landvinningar inom området i snabb takt.

Grundläggande AI bygger på att man lär en maskin att tolka text, tal eller förändringar (som ett nytt schackdrag) för att sedan utföra något som liknar mänskligt beteende. Nästa utvecklingssteg är maskininlärning, vilket innebär att man förser en dator med information som gör att den allt mer självständigt kan förstå och hantera stora datamängder.

Ett enkelt exempel är hur datorn lär sig att känna igen bilder på katter genom att man matar in massor med olika bilder på just katter. Maskininlärning gör att den kommer att hitta sådant som är gemensamt för bilderna och på det sättet börja kunna avgöra om en bild föreställer katt eller inte. Datorn kan tränas av människor, genom att den får reda när den har fel – då läggs det in som ytterligare information och den blir allt säkrare i sina analyser.1

Liknar den mänskliga hjärnan
Det som nu har blivit högaktuellt är att istället använda maskinlärning på ett sätt som liknar hur neuronerna i den mänskliga hjärnan fungerar. Det bygger i grund och botten på att analyserna blir mer avancerade, genom att systemet består av komplexa så kallade neurala nätverk i flera lager – därav namnet ”deep learning” (djup maskininlärning).

Den här metoden har inte varit praktiskt möjlig tidigare, eftersom den bygger på att datorerna både får tillgång till och kan hantera enorma mängder med information med stor beräkningskraft. Analysföretaget Gartner konstaterade i sin stora trendanalys förra året att ”de smarta maskinernas tid är här”. De menar att detta är den trend som kommer att vara mest disruptiv – som alltså får oss att se nuvarande affärsmodeller i ett helt annat ljus – under de kommande tio åren.

Lär sig analysera själva
Man kan beskriva det som att fram till nu har datorerna varit starkt beroende av att människor har berättat hur analyserna ska gå till men också när något blir fel. Deep learning gör att maskinerna blir allt mer självständiga och kan hitta och förstå samband, och på det sättet bygga upp ett analysunderlag på egen hand. De utbildar sig själva.

Återigen är det spelapplikationer som har varit de tidiga framgångarna, inte minst 2016 när Googles programvara AlphaGo blev den första datorn att slå den mänskliga världsmästaren i brädspelet Go. Hemligheten var deep learning, något som Google satsar på enligt devisen ”AI first”.

Kan diagnosticera cancer
Men både AI i sig och utvecklingen av deep learning kan göra nytta inom långt fler områden än spel. Till exempel inom vården; IBM:s AI-dator Watson har fått läsa in 130 000 bilder för att lära sig att skilja på olika hudåkommor och cancer – och därefter kunna diagnostisera potentiell hudcancer. Vilket den lyckas göra lika bra som en hudläkare.

Överlag förväntas deep learning få stor betydelse för beslutsstöd inom sjukvården. Förutom att klassificera mutationer som kan kopplas till olika cancerformer räknar man med att bland annat kunna identifiera lungsjukdomar via bilder och att förebygga epileptiska anfall genom att upptäcka att de är på väg innan de inträffar.

Förstår vad ett hinder är
Även när det gäller självkörande fordon kommer den här tekniken ha mycket att bidra med. Dels genom bildigenkänning för att identifiera hinder i tid, men Audi räknar också med att bilarna ska lära sig att förstå hinder på ett mer abstrakt sätt för att se till att undvika även sådant som ännu inte finns inprogrammerat i bilen.

Audi beskriver själva sitt ”deep learning concept” i Q7-modellen som att bilen lär sig rutten genom upprepad körning, observationer med en förare vid ratten och kompletterande kameror. Det skapar en koppling mellan förarens reaktioner och de händelser som registreras av kameran. Därefter kan bilen förstå instruktioner, exempelvis från en tillfällig trafiksignal, tolka dem direkt och agera på det sätt som situationen kräver.

Känner igen skrot
Till sist ett exempel på en av många specialtillämpningar som lär dyka upp när tekniken blir mer spridd: förbättrad hantering av elektronikavfall, vilket är världens snabbast växande avfallsform. Det är en mångmiljardindustri och aktörerna i branschen är angelägna om att hitta snabbare sätt att bedöma och klassificera elektronikskrotet.

Med deep learning blir det möjligt att känna igen en mycket stor mängd olika objekt, trots att dessa inte alltid ser precis likadana ut. I kombination med optiska och mekaniska system för bland annat datainsamling och utsortering går det att optimera processerna – till glädje för både miljön och de företag som bäst lyckas dra nytta av systemen.

Källa: Telia.se, 17 november 2017
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Culture for a digital age

Posted in Aktuellt, Board work / Styrelsearbete, Digitalisering / Internet, Leadership / Ledarskap, Strategy implementation / Strategiimplementering on August 23rd, 2017 by admin

Risk aversion, weak customer focus, and siloed mind-sets have long bedeviled organizations. In a digital world, solving these cultural problems is no longer optional.

Shortcomings in organizational culture are one of the main barriers to company success in the digital age. That is a central finding from McKinsey’s recent survey of global executives, which highlighted three digital-culture deficiencies: functional and departmental silos, a fear of taking risks, and difficulty forming and acting on a single view of the customer.

Each obstacle is a long-standing difficulty that has become more costly in the digital age. When risk aversion holds sway, underinvestment in strategic opportunities and sluggish responses to quick-changing customer needs and market dynamics can be the result. When a unified understanding of customers is lacking, companies struggle to mobilize employees around integrated touchpoints, journeys, and consistent experiences, while often failing to discern where to best place their bets as digital broadens customer choice and the actions companies can take in response. And when silos characterize the organization, responses to rapidly evolving customer needs are often too narrow, with key signals missed or acted upon too slowly, simply because they were seen by the wrong part of the company.

Can fixes to culture be made directly? Or does cultural change emerge as a matter of course as executives work to update strategy or improve processes?1 In our experience, executives who wait for organizational cultures to change organically will move too slowly as digital penetration grows, blurs the boundaries between sectors, and boosts competitive intensity. Our research, which shows that cultural obstacles correlate clearly with negative economic performance, supports this view. So do the experiences of leading players such as BBVA, GE, and Nordstrom, which have shown what it looks like when companies support their digital strategies and investments with deliberate efforts to make their cultures more responsive to customers, more willing to take risks, and better connected across functions.

Executives must be proactive in shaping and measuring culture, approaching it with the same rigor and discipline with which they tackle operational transformations. This includes changing structural and tactical elements in an organization that run counter to the culture change they are trying to achieve. The critical cultural intervention points identified by respondents to our 2016 digital survey—risk aversion, customer focus, and silos—are a valuable road map for leaders seeking to persevere in reshaping their organization’s culture. The remainder of this article discusses each of these challenges in turn, spelling out a focused set of reinforcing practices to jump-start change.

Calculated risks

Too often, management writers talk about risk in broad-brush terms, suggesting that if executives simply encourage experimentation and don’t punish failure, everything will take care of itself. But risk and failure profoundly challenge us as human beings. As Ed Catmull of Pixar said in a 2016 McKinsey Quarterly interview, “One of the things about failure is that it’s asymmetrical with respect to time. When you look back and see failure, you say, ‘It made me what I am!’ But looking forward, you think, ‘I don’t know what is going to happen and I don’t want to fail.’ The difficulty is that when you’re running an experiment, it’s forward looking. We have to try extra hard to make it safe to fail.”

The balancing act Catmull described applies to companies, perhaps even more than to individuals. Capital markets have typically been averse to investments that are hard to understand, that underperform, or that take a long time to reach fruition. And the digital era has complicated matters: On the one hand, willingness to experiment, adapt, and to invest in new, potentially risky areas has become critically important. On the other, taking risks has become more frightening because transparency is greater, competitive advantage is less durable, and the cost of failure is high, given the prevalence of winner-take-all dynamics.

Leaders hoping to strike the right balance have two critical priorities that are mutually reinforcing at a time when fast-follower strategies have become less safe. One is to embed a mind-set of risk taking and innovation through all ranks of the enterprise. The second is for executives themselves to act boldly once they have decided on a specific digital play—which may well require changing mind-sets about risk, and inspiring key executives and boards to think more like venture capitalists.

An appetite for risk
Building a culture where people feel comfortable trying things that might fail starts with senior leaders’ attitudes and role modeling. They must break the status quo of hierarchical decision making, overcome a focus on optimizing rather than innovating, and celebrate learning from failure. It helps considerably when executives make it clear through actions that they trust the front lines to make meaningful decisions. ING and several other companies have tackled this imperative head-on, providing agile coaches to help management learn how to get out of the way after setting overall direction for objectives, budgets, and timing.

However, delegating authority only works if the employees have the skills, mind-sets, and information access to make good on it. Outside hires from start-ups or established digital natives can help inject disruptive thinking that is a source of innovative energy and empowerment. Starbucks, for example, has launched a digital-ventures team, hiring vice presidents from Google, Microsoft, and Razorfish to help drive outside thinking.

Also empowering for frontline workers (and risk dampening for organizations) is information itself. For example, equipping call-center employees with real-time analysis on account profiles, or data on usage and profitability, helps them take small-scale risks as they modify offers and adjust targeting in real time. In the retail and hospitality industries, companies are giving frontline employees both the information (such as segment and purchase history) and the decision authority they need to resolve customer issues on the spot, without having to escalate to management. Such information helps connect the front line to the company’s strategic vision, which provides a compass for decision making on things such as what sort of discount or incentive to offer in resolving a conflict or what “next product to buy” to tee up. Benefits include improvements in the customer experiences (due to faster resolution) and greater consistency across the business in spotting and resolving problems. This lowers cost at the same time it improves customer satisfaction. In addition, frontline risk taking enables more rapid innovation by speeding up iterations and decision making to support nimbler, test-and-learn approaches. These same dynamics prevail in manufacturing, with new algorithms enabling predictive maintenance that no longer requires sign-off from higher-level managers.

Regardless of industry, the critical question for executives concerned with their organization’s risk appetite is whether they are trusting their employees, at all levels, to make big enough bets without subjecting them to red tape. Many CFOs have decided to shift all but the largest investment decisions into the business units to speed up the process. The CFO at one global 500 consumer-goods company now signs off only on expenditures above $250,000. Until recently, any spend decision over $1,000 required the CFO’s approval.

Making bold bets
At the same time they are letting go of some decisions, senior leaders also are responsible for driving bold, decisive actions that enable the business to pivot rapidly, sometimes at very large scale. Such moves require risk taking, including aggressive goal setting and nimble resource reallocation.

A culture of digital aspirations. Goals should reflect the pace of disruption in a company’s industry. The New York Times set the aspiration to double its digital revenues within five years, enabled in part by the launch of T Brand Studio as a new business model. In the face of Amazon, Nordstrom committed more than $1.4 billion in technology capital investments to enable rich cross-channel experiences. The Irish bank AIB decided customers should be able to open an account in under ten minutes (90 percent faster than the norm prevailing at the time). AIB invested to achieve this goal and saw a 25 percent lift in accounts opened, along with a 20 percent drop in costs. In many industries facing digital disruption, this is the pace and scale at which executives need to be willing to play.

Embracing resource reallocation. Nimble resource reallocation is typically needed to back up such goals. In many incumbents, though, M&A and capital-expenditure decisions are too slow, with too many roadblocks in the way. They need to be retooled to take on more of a venture-capitalist approach to rapid sizing, testing, investing, and disinvesting. The top teams at a large global financial-services player and an IT-services company have been reevaluating all of their businesses with a five- to ten-year time horizon, determining which ones they will need to exit, where they need to invest, and where they can stay the course. Such moves tax the risk capacity of executives; but when the moves are made, they also shake things up and move the needle on a company’s risk culture.

The financial markets are double-edged swords when it comes to bold moves. While they remain preoccupied with short-term earnings, they are also cognizant of cautionary tales such as Blockbuster’s 2010 bankruptcy, just three years after the launch of Netflix’s streaming-video business. Companies like GE have nonetheless plunged ahead with long-term, digitally oriented strategies. In aggressively shedding some of its traditional business units, investing significantly to build out its Predix platform, and launching GE Digital, its first new business unit in 75 years, with more than $1 billion invested in 2016, GE’s top team has embraced disciplined risk taking while building for the future.

Customers, customers, customers
Although companies have long declared their intention to get close to their customers, the digital age is forcing them to actually do it, as well as providing them with better means to do so. Accustomed to best-in-class user experiences both on- and off-line with companies such as Amazon and Apple, customers increasingly expect companies to respond swiftly to inquiries, to customize products and services seamlessly, and to provide easy access to the information customers need, when they need it.

A customer-centric organizational culture, in other words, is more than merely a good thing—it’s becoming a matter of survival. The good news is that getting closer to your customers can help reduce the risk of experimentation (as customers help cocreate products through open innovation) and support fast-paced change. Rather than having to guess what’s working in a given product or service before launching it—and then waiting to see if your guess is right after the launch takes place—companies can now make adjustments nearly real-time by developing product and service features with direct input from end users. This is already taking place in products from Legos to aircraft engines. The process not only helps derisk product development, it tightens the relationship between companies and their customers, often providing valuable proprietary data and insights about how customers think about and use the products or services being created.

Data and tools
Underlying the new customer-centricity are diverse tools and data. Connecting the right data to the right decisions can help build a common understanding of customer needs into an organizational culture, fostering a virtuous cycle that reinforces customer-centricity. Amazon’s ability to use customers’ previous purchases to offer them additional items in which they might be interested is a significant element in its success. The virtuous circle they’ve created includes customer reviews (to reassure and reinforce other shoppers), along with the algorithms that share “what customers who looked at this item also bought.” Of course, Amazon has also invested heavily in automated warehouses and a sophisticated distribution model. But even those were tied to the customer desire to receive merchandise faster.

A unifying force
At its best, customer-centricity extends far beyond marketing and product design to become a unifying cultural element that drives all core decisions across all areas of the business. That includes operations, where in many organizations it’s often the furthest from view, and strategy, which must be regularly refreshed if it is to serve as a reliable guide in today’s rapidly changing environment. Customer-centric cultures anticipate emerging patterns in the behavior of customers and tailor relevant interactions with them by dynamically integrating structured data, such as demographics and purchase history, with unstructured data, such as social media and voice analytics.

The insurance company Progressive illustrates the unifying role played by strong customer focus. Progressive’s ability to persuade customers to install the company’s Snapshot device to monitor driving behavior is revolutionizing the insurance space, and not just as a marketing tool. Snapshot helps attract the good drivers who are the most profitable customers, since those individuals are the ones most likely to be attracted by the offer of better discounts based on driving behavior. It also gives the company’s underwriters actual data in place of models and guesswork. This new technology is one that Progressive can monetize into a business unit to serve other insurers as well.

Busting silos
Some observers might consider organizational silos—so named for parallel parts of the org chart that don’t intersect—a structural issue rather than a cultural one. But silos are more than just lines and boxes. The narrow, parochial mentality of workers who hesitate to share information or collaborate across functions and departments can be corrosive to organizational culture.

Silos are a perennial problem that have become more costly because, in the words of Cognizant CEO Francisco D’Souza, “the interdisciplinary requirement of digital continues to grow. The possibilities created by combining data science, design, and human science underscore the importance both of working cross-functionally and of driving customer-centricity into the everyday operations of the business. Many organizations have yet to unlock that potential.”2 The executives we surveyed appeared to agree, ranking siloed thinking and behavior number one among obstacles to a healthy digital culture.

How can you tell if your own organization is too siloed? Discussions with CEOs who have led old-line companies through successful digital transformations indicate two primary symptoms: inadequate information, and insufficient accountability or coordination on enterprise-wide initiatives.

Getting informed
Digital information breakdowns echo the familiar story of the blind men and the elephant. When employees lack insight into the broader context in which a business competes, they are less likely to recognize the threat of disruption or digital opportunity when they see it and to know when the rest of the organization should be alerted. They can only interpret what they encounter through the lens of their own narrow area of endeavor.

The corollary to this is that every part of the organization reaches different conclusions about their digital priorities, based on incomplete or simply different information. This contributes to breaks in strategic and operating consistency that consumers are fast to spot. There isn’t the luxury of time in today’s digital world for each division to discover the same insight; a digital attacker or more agile incumbent is likely to swoop in before the siloed organization even knows it should be mounting a response. So the first imperative for companies looking to break out of a siloed mentality is to inspire within employees a common sense of the overall direction and purpose of the company. Data and thoughtful management rotation often play a role.

Data-driven transparency. Data can help solve the blind-men-and-the-elephant problem. A social-services company, for instance, created a customer-engagement group to better understand how customers interact with the company’s products and brands across silos—and where customers were running into difficulty. Among other things, this required close examination of how the company collected, analyzed, and distributed data across silos. The team discovered, for example, that some customers were cancelling their memberships because of the deluge of marketing outreaches they were receiving from the company. To address this, the team combined customer databases and propensity models across silos to create visibility and centralized access rights with regard to who could reach out to members and when. Among other achievements, this team:

created segment-specific trainings that offered an integrated view of each segment’s suite of needs and offerings that would meet them
drew on information from different parts of the organization to give a more developed picture on engagement, retention, and the total number of touches associated with various segments and customers
showed the net effect of the entire organization’s activities through the customer’s eyes
embedded this information into key processes to ensure information was accessible in a cross-disciplinary way—breaking siloed viewpoints and narrow understandings of the overall business model
Management rotation. Another way to achieve better alignment on the company’s direction is to rotate executives between siloed functions and business units. At the luxury retailer Nordstrom, for example, two key executives exchanged roles in 2014: Erik Nordstrom, formerly president of the company’s brick-and-mortar stores, became president of Nordstrom Direct, the company’s online store, while Jamie Nordstrom, formerly president of Nordstrom Direct, became president of the brick-and-mortar stores. This type of rotation can be done at different levels in an organization and helps create a more consistent understanding between different business units regarding the company’s aspirations and capabilities, as well as helping create informal networks as employees build relationships in different departments.

Instilling accountability
The second distinctive symptom of a siloed culture is the tendency for employees to believe a given problem or issue is someone else’s responsibility, not their own. Companies can counter this by institutionalizing mechanisms to help support cross-functional collaboration through flexibly deployed teams. That was the case at ING, which, because it identifies more as a technology company than a financial-services company, has turned to tech firms for inspiration, not banks. Spotify, in particular, has provided a much-talked-about model of multidisciplinary teams, or squads, made up of a mix of employees from diverse functions, including marketers, engineers, product developers, and commercial specialists. All are united by a shared view of the customer and a common definition of success. These squads roll up into bigger groups called tribes, which focus on end-to-end business outcomes, forcing a broader picture on all team members. The team members are also held mutually accountable for the outcome, eliminating the “not my job” mind-set that so many other organizations find themselves trapped in. While this model works best in IT functions, it is slowly making its way into other areas of the business. Key elements of the model (such as end-to-end outcome ownership) are also being mapped into more traditional teams to try to bring at least pieces of this mind-set into more traditional companies.

Start by finding mechanisms, whether digital, structural, or process, that help build a shared understanding of business priorities and why they matter. Change happens fast and from unpredictable places, and the more context you give your employees, the better they will be able to make the right decisions when it does. To achieve this, organizations must remove the barriers that keep people from collaborating, and build new mechanisms for cutting through (or eliminating altogether) the red tape and bureaucracy that many incumbents have built up over time.

Cultural changes within corporate institutions will always be slower and more complex than the technological changes that necessitate them. That makes it even more critical for executives to take a proactive stance on culture. Leaders won’t achieve the speed and agility they need unless they build organizational cultures that perform well across functions and business units, embrace risk, and focus obsessively on customers.

Source: McKinsey.com, August 2017
By Julie Goran, Laura LaBerge, and Ramesh Srinivasan
About the authors: Julie Goran is a partner in McKinsey’s New York office, where Ramesh Srinivasan is a senior partner; Laura LaBerge is a senior practice manager of Digital McKinsey and is based in the Stamford office.
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Customers’ lives are digital—but is your customer care still analog?

Posted in Aktuellt, Customer care / Kundvård, Digitalisering / Internet, Strategy implementation / Strategiimplementering on June 20th, 2017 by admin

Digital customer care is still new territory for many companies. They can learn a lot from the natives.

Today’s customers expect digital service. More and more are getting it, too, across sectors from telecommunications to banking and from utilities to retail. For example, telco customers conduct roughly 70 percent of their purchases either partly or wholly online—and 90 percent of their service requests as well.

The rapid shift to digital customer care (or e-care) should be good for everyone. Automation and self-service cuts transaction costs for providers. When e-care is done well, customers prefer it, too. Our research among telecommunications customers shows that customers who use digital channels for service transactions are one-third more satisfied, on average, than those who rely on traditional channels. And since companies that excel in customer satisfaction also tend to create more value for their shareholders, there is even more incentive to get e-care right.

Despite e-care’s advantages, however, many companies struggle to give their customers a consistently good digital experience. The same research revealed that while more than two-fifths of service interactions with telecommunications companies begin on an e-care platform, only 15 percent are digital from start to finish. We’ve also found that use of digital service channels lags a long way behind awareness. In Europe, for example, 98 percent of mobile phone users in one survey knew their provider offered a service website, but only 37 percent made use of it. In the United States, meanwhile, only 18 percent of mobile users said they used their providers’ online service platforms.

And e-care is getting more complex to implement. Not only do customers now want access to a fully comprehensive range of online service offerings—they also want to access these offerings using a variety of platforms, including both conventional web browsers and a growing pool of mobile devices and dedicated apps. Customers expect their experience to be continuous and consistent as they migrate from one platform to another, but they also want service options that make sense in the context in which they are asking for help.

Finally, customers are getting harder to impress. The rapid rise of “digital native” companies, such as Spotify or Uber, exposes customers to simple, streamlined user experiences designed from the ground up for digital delivery. Established companies that build their e-care offerings and processes on top of, or alongside, more traditional channels often find it hard to meet the same standards.

That comparison is becoming increasingly important. When customers think about the e-care service they receive from their bank or phone company, they don’t compare it with its competitors in the same industry but with the other digital services they use every day. When the online experience doesn’t meet their expectations, customers go back to the phone. As a result, some telecoms companies have seen call-center volumes—and costs—rise as they attempt to move to a digital service model.

Making e-care work
Companies that have been able to move more customer-care services to online channels and articulate strong e-care offerings excel across seven dimensions:

Simplicity starts with a clean, clear, and intuitive design that requires few mouse clicks or screen touches to achieve the desired task. The main functionalities are easy to find and well explained. The language is concise, simple, and easy to understand. Apple offers a wide range of products aimed at very different customers, for example, but its product information and support websites use the same clean, pared-down design, with key information presented clearly and more detail available with a minimum of clicks. In financial services, companies such as PayPal have dramatically simplified online payments, in many cases requiring only the recipient’s email address or mobile-phone number as identification.

Convenience means customers are offered a wide variety of services and a choice of support channels. User interfaces are easy to navigate and critical information is not hidden within long pages or complex menu hierarchies. Even better are sites that use data intelligence to tailor page content dynamically based on who is accessing it. Similarly, biometric identification techniques using fingerprint or voiceprint technologies accelerate authentication steps and reduce the mental burden on users without comprising security. One telecom company has developed a dynamic FAQ system that suggests possible support articles as soon as a customer begins to type a query and that loads the most relevant content automatically without requiring a page refresh.

Interactivity reflects the fact that customers now expect their online experiences to be dynamic and interactive, with blogs, social-media feeds, user reviews, and customer forums all playing important roles in modern e-care. These are especially important for millennial consumers, who have grown up steeped in social media and online interactions. Accordingly, an active user community is central to UK-mobile-phone-network giffgaff’s strategy. Users receive account credit for helping others with their queries, and individual community members are regularly highlighted on giffgaff’s support website. One of the company’s core product offerings—a bundle of text messages, voice minutes, and data known as a “goodybag”—was introduced as a direct result of suggestions on user forums. Moreover, through interactive games and a cocreation system that lets users build new services for other community members, customers now help set giffgaff’s direction.

Consistency is essential: customers require that the appearance, functionality, and information available in e-care services be consistent regardless of which device or software they use. Amazon, for example, shows customers essentially the same menus, the same links, and the same tone and language across all of its mobile and website channels, giving customers the same experience as they move from one channel to the next. This commitment significantly reduces any need for relearning after each switch—and any attendant digital friction.

Value results only if e-care works for the customer. Services must be designed to reflect the user’s individual needs, rather than the company’s internal processes, and must evolve as those needs change. One insurance company, for example, uses real-time rendering technology to create a customized video presentation of the coverage included in the customer’s quotation. The video combines professionally scripted and presented content with customer-specific data drawn from multiple sources, and its content is adjusted based on the customer’s choices and responses during the presentation.

Desirability is a product not only of a consistently appealing visual design but also of the tone and presentation of the site’s content. Both usually require adaption to suit local tastes, which may require dramatically different choices depending on the specific context. For instance, Chinese websites are typically very crowded, with lots of information available, while sites in the United States and Western Europe tend toward a more streamlined aesthetic.

Brand is not just a label: it is how customers experience a company’s products and services. Given that e-care has become one of the primary ways customers interact with a business, brand reinforcement should be a primary e-care goal rather than an afterthought. The best companies therefore integrate their brand values deeply into the design of their e-care offerings.

To buttress its message of providing exactly the services its customers need, one mobile-phone company has tailored its service experience to support unique “moments of truth” in the customer journey. Once a customer logs in, the website’s navigation changes dynamically based not only on what the customer is doing but also on behavioral insights based on previous interactions with the company.

A customer who’s usually pressed for time may see just three simple plan alternatives, cutting through the clutter, while one who wants to be assured of getting the best deal will see more detail on plan options, so she can feel in control. The site then guides the customer through activation steps, offers clear instructions on how to get the most from the service, and anticipates the most common questions with detailed answers.

Measuring up
To understand how leading players measure up under this harsh scrutiny, we evaluated the e-care offerings of more than 20 major telecommunications companies across the world, covering both online services and dedicated apps. We tested half a dozen common service activities, including access to billing and consumption information, technical-support queries, and sales or upgrade queries.

Our approach looked at the way e-care platforms were designed and presented to the user, the functionalities on offer, and the information available within each of our target service activities. Under each of those three main concepts, we rated the offerings across the seven dimensions described above.

Are you ahead of the pack?
Overall, our analysis should be sobering reading in all sectors for incumbents that are digitizing their customer-service offerings. We found only one area—the presentation of information using simple, jargon-free language—where most of the companies surveyed are demonstrating best practices. Elsewhere, we did find examples of best practices, but they have not been adopted by every company, and they are not always consistently applied even when they have been adopted.

The best websites and apps in our survey sample, for example, offer a wide range of services using a clear, easily understandable architecture that requires few clicks to access relevant content. Several, but by no means all, companies provide a convenient search function to help customers access technical support. Only a few make “search” the core navigation method for technical-support information.

Indeed, not many of the surveyed companies are taking full advantage of digital platforms’ unique capabilities. Interactive features such as support wizards or explanatory videos were rare. Only the very best-performing companies managed to integrate their e-care offerings seamlessly with live channels (such as e-calling or traditional telephone support) to create a truly multichannel experience. And just a handful have deployed the most advanced e-care technologies, such as artificial intelligence or chatbots.

For many of the services we evaluated, customer experience was inconsistent between web and app platforms. Apps sometimes offered less functionality and frequently provided less information than their web counterparts, which companies tended to position as the full-service option. On further examination, differences in look and function between apps and web often arose because of the relatively recent introduction of app offerings, or the use of different design and development teams.

Best practice is not enough
As they move further into the digital world, many incumbents clearly have work to do to give their customers the best e-care experience. But that’s no reason to set their sights too low. Leading companies not only make their digital channels highly useful and consistent at every customer touchpoint—they also make them fun and emotionally appealing. They personalize the experience and keep it relevant across the entire customer life cycle. For these top digital players, e-care doesn’t just work, it builds a brand that engages and delights customers.

That’s the standard, and it’s lifting customer expectations for everyone else. To keep up, traditional companies must measure their own performance against the best of the best of best—and embrace a culture of rapid, continuous evolution and improvement. There’s no time to lose.

Source:McKinsey.com, June 2017
Authors: Jorge Amar and Hyo Yeon
About the authors: Jorge Amar is an associate partner in McKinsey’s Stamford office, and Hyo Yeon is a partner in the New York office.
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Fem trender: AI och maskininlärning

Posted in Aktuellt, Digitalisering / Internet on June 16th, 2017 by admin

AI och maskininlärning är på god väg att revolutionera samhället, det menar Fredrik Heintz som är forskare i artificiell intelligens vid Linköpings universitet. Han listar dagens starkaste trender.

1. Mer flexibel industriproduktion
Automatisering har varit avgörande för att industrier i högkostnadsländer ska kunna konkurrera på den globala marknaden. Lägre produktionskostnader genom högre produktivitet säkras nu långsiktigt med hjälp av flexibla robotar som ABB:s YuMi som med samarbets- och inlärningsförmåga ställer om produktionen efter företagets behov.

2. Digitala medarbetare
Sökmotorer och översättningstjänster är digitala stöd som blivit en del av vår vardag. Utvecklingen har nu gått så långt att det till och med går att anställa digitala medarbetare och styrelsemedlemmar. De moderna kognitiva medhjälparna för en dialog med användaren – till exempel kan programmet Watson ställa diagnos på patienter och programmet Amelia ansvara för företags kundtjänst.

3. Självkörande fordon
Tesla och Googles självkörande bilar har inspirerat hela fordonsbranschen med sin samhällsnytta. I en förarlös framtid kan väginfrastrukturen utnyttjas bättre – bilarna kan köra närmare varandra och med högre hastigheter än människor behärskar bakom ratten. Utan trafikljus och avståndsregler skulle trafikflödet bli bättre och spara både energi och pengar. Men säkerheten är fortfarande en utmaning. Hur säkerställer man att bilen fattar rätt beslut och vem bär ansvaret vid olyckor?

4. Datalogiskt tänkande
Automatisering innebär en samhällsomställning och de arbeten som inte försvinner kommer att förändras. Utvecklingen vi sett i fabrikerna sprider sig nu till tjänstemannayrkena och människan behöver anpassa sig till datalogiskt tänkande. I samarbete med intelligenta maskiner blir vi mer produktiva och konkurrenskraftiga.

5. Sociala robotar
Robotarna blir alltmer avancerade och en tydlig trend är att de även används för initiativ med mjuka värden. Sociala robotar som förstår vad som händer, fattar beslut och interagerar med människor visar positiva effekter. Till exempel hjälper de autistiska barn att kommunicera och bidrar med socialt stöd i äldreomsorgen.

Läs också ”5 trender: Social selling – sälj med sociala medier”

Källa: Dustin.se, juni 2017
Text: Moa Thorsell
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The next-generation operating model for the digital world

Posted in Aktuellt, Digitalisering / Internet on April 6th, 2017 by admin

Companies need to increase revenues, lower costs, and delight customers. Doing that requires reinventing the operating model.

Companies know where they want to go. They want to be more agile, quicker to react, and more effective. They want to deliver great customer experiences, take advantage of new technologies to cut costs, improve quality and transparency, and build value.

digi 3The problem is that while most companies are trying to get better, the results tend to fall short: one-off initiatives in separate units that don’t have a big enterprise-wide impact; adoption of the improvement method of the day, which almost invariably yields disappointing results; and programs that provide temporary gains but aren’t sustainable.

We have found that for companies to build value and provide compelling customer experiences at lower cost, they need to commit to a next-generation operating model. This operating model is a new way of running the organization that combines digital technologies and operations capabilities in an integrated, well-sequenced way to achieve step-change improvements in revenue, customer experience, and cost.

A simple way to visualize this operating model is to think of it as having two parts, each requiring companies to adopt major changes in the way they work:
•The first part involves a shift from running uncoordinated efforts within siloes to launching an integrated operational-improvement program organized around customer journeys (the set of interactions a customer has with a company when making a purchase or receiving services) as well as the internal journeys (end-to-end processes inside the company). Examples of customer journeys include a homeowner filing an insurance claim, a cable-TV subscriber signing up for a premium channel, or a shopper looking to buy a gift online. Examples of internal-process journeys include Order-to-Cash or Record-to-Report.
•The second part is a shift from using individual technologies, operations capabilities, and approaches in a piecemeal manner inside siloes to applying them to journeys in combination and in the right sequence to achieve compound impact.

Let’s look at each element of the model and the necessary shifts in more detail:

Shift #1: From running uncoordinated efforts within siloes to launching an integrated operational-improvement program organized around journeys
Many organizations have multiple independent initiatives underway to improve performance, usually housed within separate organizational groups (e.g. front and back office). This can make it easier to deliver incremental gains within individual units, but the overall impact is most often underwhelming and hard to sustain. Tangible benefits to customers—in the form of faster turnaround or better service—can get lost due to hand-offs between units. These become black holes in the process, often involving multiple back-and-forth steps and long lag times. As a result, it’s common to see individual functions reporting that they’ve achieved notable operational improvements, but customer satisfaction and overall costs remain unchanged.

Instead of working on separate initiatives inside organizational units, companies have to think holistically about how their operations can contribute to delivering a distinctive customer experience. The best way to do this is to focus on customer journeys and the internal processes that support them. These naturally cut across organizational siloes—for example, you need marketing, operations, credit, and IT to support a customer opening a bank account. Journeys—both customer-facing and end-to-end internal processes—are therefore the preferred organizing principle.

Transitioning to the next-generation operating model starts with classifying and mapping key journeys. At a bank, for example, customer-facing journeys can typically be divided into seven categories: signing up for a new account; setting up the account and getting it running; adding a new product or account; using the account; receiving and managing statements; making changes to accounts; and resolving problems. Journeys can vary by product/service line and customer segment. In our experience, targeting about 15–20 top journeys can unlock the most value in the shortest possible time.

We often find that companies fall into the trap of simply trying to improve existing processes. Instead, they should focus on entirely reimagining the customer experience, which often reveals opportunities to simplify and streamline journeys and processes that unlock massive value. Concepts from behavioral economics can inform the redesign process in ingenious ways. Examples include astute use of default settings on forms, limiting choice to keep customers from feeling overwhelmed, and paying special attention to the final touchpoint in a series, since that’s the one that will be remembered the most.

In 2014, a major European bank announced a multiyear plan to revamp its operating model to improve customer satisfaction and reduce overall costs by up to 35 percent. The bank targeted the ten most important journeys, including the mortgage process, onboarding of new business and personal customers, and retirement planning. Eighteen months in, operating costs are lower, the number of online customers is up nearly 20 percent, and the number using its mobile app has risen more than 50 percent. (For more on reinventing customer journeys, see “Putting customer experience at the heart of next-generation operating models,” forthcoming on McKinsey.com.)

Shift #2: From applying individual approaches or capabilities in a piecemeal manner to adopting multiple levers in sequence to achieve compound impact
Organizations typically use five key capabilities or approaches (we’ll call them “levers” from now on) to improve operations that underlie journeys.

Digitization is the process of using tools and technology to improve journeys. Digital tools have the capacity to transform customer-facing journeys in powerful ways, often by creating the potential for self-service. Digital can also reshape time-consuming transactional and manual tasks that are part of internal journeys, especially when multiple systems are involved.1

•Advanced analytics is the autonomous processing of data using sophisticated tools to discover insights and make recommendations. It provides intelligence to improve decision making and can especially enhance journeys where nonlinear thinking is required. For example, insurers with the right data and capabilities in place are massively accelerating processes in areas such as smart claims triage, fraud management, and pricing.
•Intelligent process automation (IPA) is an emerging set of new technologies that combines fundamental process redesign with robotic process automation and machine learning. IPA can replace human effort in processes that involve aggregating data from multiple systems or taking a piece of information from a written document and entering it as a standardized data input. There are also automation approaches that can take on higher-level tasks. Examples include smart workflows (to track the status of the end-to-end process in real time, manage handoffs between different groups, and provide statistical data on bottlenecks), machine learning (to make predictions on their own based on inputs and provide insights on recognized patterns), and cognitive agents (technologies that combine machine learning and natural-language generation to build a virtual workforce capable of executing more sophisticated tasks). To learn more about this, see “Intelligent Process Automation: The engine at the core of the next generation operating model.”
•Business process outsourcing (BPO) uses resources outside of the main business to complete specific tasks or functions. It often uses labor arbitrage to improve cost efficiency. This approach typically works best for processes that are manual, are not primarily customer facing, and do not influence or reflect key strategic choices or value propositions. The most common example is back-office processing of documents and correspondence.
•Lean process redesign helps companies streamline processes, eliminate waste, and foster a culture of continuous improvement. This versatile methodology applies well to short-cycle as well as long-cycle processes, transactional as well as judgment-based processes, client-facing as well as internal processes.

Guidelines for implementing these levers
In considering which levers to use and how to apply them, it’s important to think in a holistic way, keeping the entire journey in mind. Three design guidelines are crucial:

1. Organizations need to ensure that each lever is used to maximum effect. Many companies believe they’re applying the capabilities to the fullest, but they’re actually not getting as much out of them as they could. Some companies, for example, apply a few predictive models anddigi 2 think they’re really pushing the envelope with analytics—but in fact, they’re only capturing a small fraction of the potential value. This often breeds a false complacency, insulating the organizations from the learnings that would otherwise drive them to higher performance because it is “already under way” or “has been tried”. Having something already under way is a truism: everyone has something under way in these kinds of domains, but it is the companies that press to the limit that reap the rewards. Executives need to be vigilant, challenge their people, and resist the easy answer.

In the case of analytics, for example, maxing out the potential requires using sophisticated modeling techniques and data sources in a concerted, cross-functional effort, while also ensuring that front-line employees then execute in a top-flight way on the insights generated by the models.

2. Implementing each lever in the right sequence. There is no universal recipe on sequencing these levers because so many variables are involved, such as an organization’s legacy state and the existing interconnections between customer-facing and internal processes. However, the best results come when the levers can build on each other. That means, in practice, figuring out which one depends on the successful implementation of another.

Systematic analysis is necessary to guide decision making. Some institutions have started by outlining an in-house versus outsource strategy rooted in a fundamental question: “What is core to our value proposition?” Key considerations include whether the activities involved are strategic or confer competitive advantage or whether sensitive data or regulatory constraints are present.

The next step is to use a structured set of questions to evaluate how much opportunity there is to apply each of the remaining levers and then to estimate the potential impact of each lever on costs and customer experience. This exercise results in each lever being assigned an overall score to help develop a preliminary point of view on which sequence to use in implementing the levers.

There’s also a need to vet the envisioned sequences in the context of the overall enterprise. For example, even if the optimal sequence for a particular customer journey may be “IPA then lean then digital,” if the company’s strategic aspiration is to become “digital first,” it may make more sense to digitize processes first.

This systematic approach allows executives to consider various sequencing scenarios, evaluate the implications of each, and make decisions that benefit the entire business.

3. Finally, the levers should interact with each other to provide a multiplier effect. For example, one bank only saw significant impact from its lean and digitization efforts in the mortgage application journey after both efforts were working in tandem. A lean initiative for branch offices included a new scorecard that measured customer adoption of online banking, forums for associates to problem solve how to overcome roadblocks to adoption, and scripts they could use with customers to encourage them to begin mortgage applications online. This, in turn, drove up usage of online banking solutions. Software developers were then able to incorporate feedback from branch associates, which made future digital releases easier to use for customers. This in turn drove increased adoption of digital banking, thereby reducing the number of transactions done in branches.

Some companies have developed end-to-end journey “heat maps” that provide a company-wide perspective on the potential impact and scale of opportunity of each lever on each journey. These maps include estimates for each journey of how much costs can be reduced (measured in terms of both head count and financial metrics) and how much the customer experience can be improved.

Companies find heat maps a valuable way to engage the leadership team in strategic discussions about which approaches and capabilities to use and how to prioritize them.

Case example: The ‘first notice of loss’ journey in insurance
In insurance, a key journey is when a customer files a claim, known in the industry as first notice of loss (FNOL). FNOL is particularly challenging for insurers because they must balance multiple objectives at the same time: providing a user-friendly experience (for example, by offering web or mobile interfaces that enable self-service), managing expectations in real time through alerts or updates, and creating an emotional connection with customers who are going through a potentially traumatic situation—all while collecting the most accurate information possible and keeping costs in line.

Many companies have relied on Lean to improve FNOL call-center performance. One leading North American insurer, however, discovered it could unlock even more value by sequencing the buildout of three additional capabilities, based on the progress it had already made with Lean:

Digitization. This company improved response times by using digital technologies to access third-party data sources and connect with mobile devices. With these new tools, the insurer can now track claimant locations and automatically dispatch emergency services. Customers can also upload pictures of damages, and both file and track claims online. The insurer also allows some customers to complete the entire claims process without a single interaction with a company representative.

Advanced analytics. Digitization of the FNOL journey provided the insurer with more and better data faster, which in turn allowed its digi 1analytics initiative to be more effective. Now able to apply the latest modeling capabilities to better data, the company is using advanced analytics to improve decision making in the FNOL journey. For example, intelligent triage is used to close simple claims more quickly, and smart segmentation identifies claims likely to be total losses and those liable to require the special investigative unit (SIU) far earlier than before. Analytics are even being used to predict future staffing needs and inform scheduling and hiring, thereby allowing both complex and simple claims to be handled more efficiently.

Intelligent process automation (IPA). Once digital and analytics were in place, IPA was implemented. Automation tools were deployed to take over manual and time-consuming tasks formerly done by customer-service agents, such as looking up policy numbers or data from driving records. In addition to reducing costs, IPA sped up the process and reduced errors. IPA came last because the streamlining achieved by digitization and more effective use of analytics had eliminated some manual processes, so the IPA effort could focus only on those that remained.

By combining four levers—lean plus digital, analytics and IPA—this insurer drove a significant uplift in customer satisfaction while at the same time improving efficiency by 40 percent. (For more approaches to improving claims, see “Next-generation claims operating model: From evolution to revolution,” forthcoming on McKinsey.com.)2

Bringing it all together: Avoid creating new silos by thinking holistically
Senior leaders have a crucial role in making this all happen. They must first convince their peers that the next-generation operating model can break through organizational inertia and trigger step-change improvements. With broad buy-in, the CEO or senior executive should align the business on a few key journeys to tackle first. These can serve as beacons to demonstrate the model’s potential. After that comes evaluation of the company’s capabilities to determine which levers can be implemented using internal resources and which will require bringing in resources from outside. Finally, there is the work of actually implementing the model. (For more on the last topic, see “How to build out your next-generation operating model,” forthcoming on McKinsey.com.)

Transformation cannot be a siloed effort. The full impact of the next-generation operating model comes from combining operational-improvement efforts around customer-facing and internal journeys with the integrated use of approaches and capabilities.

Source: McKinsey.com, April 2017
Authors: Albert Bollard, Elixabete Larrea, Alex Singla and Rohit Sood
About the authors: Albert Bollard is an associate partner in McKinsey’s New York office, Elixabete Larrea is an associate partner in the Boston office, Alex Singla is a senior partner in the Chicago office, and Rohit Sood is a partner in the Toronto office.
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