Five moves to make during a digital transformation

Posted in Aktuellt, Board work / Styrelsearbete, Digitalisering / Internet, Executive Team / Ledningsgruppsarbete on May 20th, 2019 by admin

Surveyed executives confirm that digital transformations rarely achieve success. But in those that do lie the structural elements that may help organizations overcome the odds.

Despite the abundance of digital and analytics transformations underway across the business landscape, few companies are achieving the results envisioned. Our latest McKinsey Global Survey on the topic confirms that the rate of success is alarmingly low.1 About eight in ten respondents say their organizations have begun digital transformations in recent years, but just 14 percent say their efforts have made and sustained performance improvements.2 What’s more, only 3 percent report complete success at sustaining their change. (Explore the survey results in our data visualization, “An interactive look at digital transformations.”)

That companies find difficulty turning in successful digital transformations is not surprising, since we know from previous research that digital transformations are harder than more traditional ones to get right. But a look at the structure of digital and analytics transformations points to five key moves at particular stages of a transformation that set successful change efforts apart. These actions suggest ways that other organizations can plan and execute digital transformations successfully.

For starters, respondents who report the greatest levels of success in pursuing digital transformations say their organizations ruthlessly focus on a handful of digital themes tied to performance outcomes. In defining their transformations’ scope, these successful organizations boldly establish enterprise-wide efforts and build new businesses. They also create an adaptive design that allows the transformation strategy and resource allocation to adjust over time. In addition, they adopt agile execution practices and mind-sets by encouraging risk taking and collaboration across parts of the organization. Moreover, in successful efforts, leadership and accountability are crystal clear for each portion of the transformation.

Ruthlessly focus on a clear set of objectives

When considering a response to digital disruptions, organizations face many critical choices. Should they transform their existing business model or build a new one? Should they drive down costs or focus on customer engagement? Which areas of the business will require more investment in digital initiatives, and which will need to defund their own initiatives to free up resources for the ones that perform well or reflect higher-priority objectives? Getting leaders to agree upon the best way forward can be challenging, but the survey results suggest a need for consensus.Would you like to learn more about McKinsey Digital?Visit our Digital Organization page

With successful digital transformations, respondents say their organizations keep efforts focused on a few digital themes—that is, the high-level objectives for the transformation, such as driving innovation, improving productivity, or reshaping an end-to-end customer journey—that are tied to business outcomes, rather than pursuing many different agendas (Exhibit 1). At successful organizations, accountability for those objectives also spans the organization. These respondents are 3.7 times more likely than others to report a shared sense of accountability for meeting their transformations’ objectives. They also say their organizations have been clear about the financial effects of their initiatives; for example, they estimate impact based on the company’s current business momentum and models of near- and long-term scenarios.

Exhibit 1

Be bold when setting the scope

We know from previous research that digital strategies should be bold in magnitude and scope,3and the survey results show that this also holds true for digital transformations. The successful digital and analytics transformations are about 1.5 times more likely than others to be enterprise-wide in scale (Exhibit 2). This result aligns with earlier research, which found that companies making digital moves often use new digital technologies at scale to capture the full benefits from their technology investments.4 Respondents at successful organizations are also 1.4 times more likely than others to report the creation of new digital businesses during their transformations.

Exhibit 2

Create an adaptive design

The fast pace at which digital drives change explains why so many companies are launching digital transformations and why the transformations themselves must be flexible. Defining a multiyear transformation’s investment requirements and performance targets up front—and not revisiting them as the transformation progresses—has perhaps never been a sound approach. But digital transformations require monthly, if not weekly, adjustments. We see this adaptability ingrained in the design of successful transformations: respondents reporting success are almost three times more likely than others to say their efforts involve at least monthly adjustments to their strategic plans, based on business leaders’ input on the state of the transformation (Exhibit 3).

Exhibit 3

Along with the need for adaptable transformation targets, flexible talent allocation is a differentiator in a transformation’s success. Respondents at successful organizations are more than twice as likely as others to strongly agree that their allocation of talent to digital initiatives has been dynamic during their transformations. Finally, a larger share of respondents reporting success say their organizations have reallocated their operating expenditures to fund the transformation. Earmarking resources for initiatives that span organizational silos can help ensure that a transformation is properly funded and that initiatives aren’t partially funded by one part of the organization only to be deprioritized by another.

Adopt agile execution approaches and mind-sets

Just as the transformation’s design must be adaptable, so must the execution of its initiatives. Successful digital and analytics transformations are likelier than others to employ more agile ways of working, such as encouraging risk taking, innovation, and collaboration across parts of the business, during a transformation.5 Agility’s importance to transformation success is clear when we look at the agile characteristics of companies’ organizational culture. Respondents at successful organizations are more than twice as likely as their peers elsewhere to strongly agree that employees are rewarded for taking risks of an appropriate level and 2.6 times likelier to say their organizations reward employees for generating new ideas (Exhibit 4). Additionally, these respondents are three times likelier to say employees collaborate effectively across business units, functions, and reporting lines. These findings align with previous research on successful digital cultures, which found that being risk averse and too siloed often prevents incumbents from realizing business impact from their digital activities.

Exhibit 4

Of course, organizations can rely on employees to be innovative, take appropriate risks, and work collaboratively only if they have the right digital talent. Talent is another aspect in which successful digital and analytics transformations differ notably from the rest. A larger share of success-group respondents than their peers strongly agree that their organizations are focused on attracting and developing highly talented individuals. They are 1.8 times likelier than others to say their organizations have hired new employees with strong digital and analytics capabilities during their transformations. What’s more, these respondents report that an average of 53 percent of employees have been trained in new digital and analytics capabilities since their transformations began—1.7 times greater than the share of employees reported at other organizations.

Make leadership and accountability crystal clear

Who owns the digital and analytics transformation is often a hotly contested question, since the initiatives that organizations pursue will affect how company resources are prioritized and might even change the entire direction of the organization. A look at responses describing leadership roles shows significant differences between the success group and others in how certain roles lead the transformation’s strategy and its execution. Respondents reporting successful transformations are likelier than others to say their leaders—from the board and CEO down to the leaders of specific initiatives—engage materially in the efforts (Exhibit 5). For example, leaders at these organizations are more likely to communicate their transformations’ progress regularly to the markets. There also is greater clarity at successful organizations about who is responsible for which portion of the transformation, whether it’s the ownership of a specific initiative or a particular stage in the process.

Exhibit 5

Clarity about ownership is critical, since responsibility often shifts among different groups as the digital transformation progresses, and the handoffs must be well-defined. The survey results show how successful companies manage ownership over time during their digital and analytics transformations (Exhibit 6). For setting strategy and measuring impact, the largest shares of respondents from successful organizations say responsibility lies with the corporate strategy function, which has visibility across the entire business and broader ecosystem. By contrast, respondents at all other organizations are more likely than the success-group respondents to say individual business units or functions are responsible for these steps. Meanwhile, respondents from successful organizations say business units most often oversee the actual execution of initiatives—that is, building and refining them.

Exhibit 6

Looking ahead

While most respondents say their organizations have not fully sustained the improvements made during transformations, lessons can be learned from the approaches of the organizations that did succeed. The results from those efforts point to moves companies can make to keep their transformations on a path toward success:

  • Raise the bar on leadership alignment and commitment. The broader scope of successful transformations further underscores the importance of having buy-in and alignment across the full organization to keep efforts coordinated and prioritized. Lack of leadership alignment around objectives often leads to many subscale and misaligned initiatives. One way to encourage commitment to a transformation’s initiatives is to show leaders, using pilots and proof-of-concept exercises, that the strategy will work, followed by investment in a single cross-cutting initiative. Building these proof points can galvanize support for the change effort. The same is true of increasing leaders’ digital fluency. These steps help make leaders comfortable with dedicating operating and capital expenditures at an enterprise level, which shows executive commitment and reduces the risk of wasting resources on incomplete initiatives.
  • Build in flexibility with clearly defined handoffs. Not only are successful transformations more likely than others to span large parts of the organization, but the ownership of each transformation will evolve over time as it moves from ideation through execution. The results suggest that there must be a clear plan for how these shifts in accountability will occur. Handoffs and overlap are notorious friction points that are critical to manage and define. Leaders should gather the pertinent groups across the business and provide a clear plan for each transition, to avoid duplication, misalignment, and dropped balls.
  • Enforce survival of the fittest among digital initiatives. Like ownership, funding for initiatives requires clarity: there should be clear criteria for reallocation of resources, whether operating or capital expenditures, based on performance. All digital initiatives should be expected to meet their targets to continue to receive funding. When initiatives fail to do so, organizations should defund them without delay to free up capital for new ones and quickly move on to the next approach. Seeking out M&A and partnership opportunities to quickly build out missing capabilities for new initiatives has been shown to be an important differentiator for success,6  and this seems likely to continue to hold true as the pace of digital transformations continues to increase.

About the author(s)

The survey content and analysis were developed by Jonathan Deakin, a partner in McKinsey’s London office; Laura LaBerge, a senior expert in the Stamford office; and Barbara O’Beirne, an associate partner in the Dublin office.

They wish to thank Jacques Bughin, Tanguy Catlin, Oisin O’Sullivan, and Soyoko Umeno for their contributions to this work.

Link

AI-trenderna som boomar 2019

Posted in Allmänt, Digitalisering / Internet, Technology on April 18th, 2019 by admin

Tillsammans med ett flertal experter har Bisnode sammanställt en rapport om innovationer inom artificiell intelligens, AI, som kommer påverka näringslivet närmaste framtiden.jälvkörande bilar, smarta assistenter och prediktiv analys.
Här är AI-trenderna vi pratar om – redan i år.
– Vi är mitt inne i en digital transformation som kommit längre än vi tror, säger Rikard Candell, analyschef hos Bisnode.

Tillsammans med ett flertal experter har Bisnode sammanställt en rapport om innovationer inom artificiell intelligens, AI, som kommer påverka näringslivet närmaste framtiden.

– I undersökningen har vi lyft fram de områden som håller på att utvecklas i snabb takt. Vår förhoppning är att den ska ses som ett sätt att ”titta in” i framtiden inom flera områden, säger Rikard Candell.

”Mer pricksäkra analyser”
Rapporten fokuserar på totalt nio områden (se faktaruta), där Candell framförallt pekar på affärsnyttan för två av dessa: prediktiv analys och Natural Language Processing/AI-assistenter.

– Det som kopplar ihop dessa är möjligheten att bland annat personligt anpassa kommunikation till dig som individ. Tack vare AI får man mer pricksäkra analyser, redan idag kan exempelvis musikappar rekommendera musik baserat på vad du lyssnat på tidigare. Det är en typ av maskinell inlärning som vi själva är med och förbättrar genom våra beteenden och återkoppling hur bra rekommendationen var.

 ”Skapar en konkurrensfördel”
Hos Bisnode är den prediktiva analysen en central del i hanteringen av kunddata. Genom möjligheten att sammanställa datan utifrån en mängd olika källor kan man dra långtgående slutsatser kring kundernas beteenden:

– Tack vare AI har vi kunnat bygga analysmodeller som nyttjar nya datamängder för att styra kommunikationen och säljinsatser mot sannolika köpare, säger Candell och fortsätter:

– Det skapar en konkurrensfördel eftersom dessa bolag vet mer om kunden och kundens behov. I genomsnitt har företag som arbetar datadrivet sex procents högre lönsamhet.Redan idag finns alltså konkreta tillämpningsområden där AI kan nyttja stora mängder data och göra rekommendationer bättre, alternativt där arbetet tar mycket tid och där AI kan förenkla stora delar av detsamma.

 Assistenter på frammarsch
AI-assistenterna, högtalarna som redan har börjat ta plats i vår vardag, är ett annat område som inom kort kommer se en snabb utveckling. Det tror Rikard Candell:

– Även om de i nuläget fungerar som en sorts avancerad chattrobot förenklar de vardagen. Framåt kommer de ha samlat in mängder med intressant data som kan analyseras för en bättre kundupplevelse och förenklade köpprocesser.

 Finns det några risker med den ökande användningen av AI, som du ser det?

– Risken som målas upp i media är existentiell risk från AI. Vi behöver absolut ha diskussionen kring vilken typ av AI vi vill ha men inom översiktlig tidshorisont påverkas vi snarare av snäv AI. Snäv AI är designade för att lösa ett specifikt problem – likt filtrering av spammail eller att hitta sannolika köpare, säger Candell och fortsätter:

– Riskerna kring snäv AI är därför främst kopplade till våra jobb. Men samtidigt också fördelarna: Tekniken har kommit långt men AI är inte en naturlig del av verksamheten hos de flesta bolag. På kort sikt kommer det underlätta vårt arbete och förbättra de tjänster vi konsumerar. En läkare kan få hjälp i diagnosticering och lägga tid på patienter, en riskavdelning kan få hjälp att identifiera möjlig penningtvätt och fokusera på att undersöka dessa vidare, en marknadsförare kan identifiera sannolika kunder och fokusera på konvertering av dessa till köp.

 

9 AI-trender 2019

• Autonomous things
Robotar och datorer som på egen hand kan sköta avancerade uppgifter, exempelvis självkörande fordon.

 Robotic Process Automation (RPA)
Monotona processer som kan skötas av mjukvara – medan den mänskliga hjärnan frigörs för att fatta strategiska och kreativa beslut.

 • Prediktiv analys
Att förutspå framtiden baserat på historiska data, där AI kan hantera enorma mängder data på en nivå som människan inte klarar av.

 Natural Language Processing / AI-assistenter
Redan idag finns enklare assistenter du kan styra med rösten därhemma för att lyssna på nyheter eller hitta ett kakrecept. Snart kommer de även kunna sköta exempelvis bokning av tider via telefon och mer avancerade uppgifter.

 • Augmented Reality (AR)
Teknik som på ett enklare sätt kan visualisera stora mängder data. Redan idag kan forskare granska projekt utifrån olika perspektiv tack vare tekniken.

 • Augmented Analytics
Kan utföra analyser och automatiskt generera affärsinsikter, där AI ersätter den mänskliga analytikern.

 • Blockchain
En sorts datastruktur där information, som exempelvis en transaktion, färdas via en kedja av krypterade ”block”. Möjliggör överföring av data utan mellanhänder – och kan säkerställa att till exempel digital annonsering exponeras för mänskliga ögon.

 • Biohacking
I grunden handlar det om att modifiera biologiska egenskaper, där DNA-programmering och biochip kan hjälpa oss att exempelvis få ökad intelligens, bättre sömn, mindre stress och tåligare fysik.

 Artificiell Superintelligens (ASI)
En artificiell intelligens som kan göra allt en människa kan göra – fast bättre. Är i nuläget en framtidsvision men flera företag tittar redan på detta, som kallas ”AI på steroider”.

Källa: DI.se, 18 april 2019
Link

Trögt för företagen som missat digitaliseringsvågen

Posted in Aktuellt, Digitalisering / Internet, Strategy implementation / Strategiimplementering, Technology on January 17th, 2019 by admin

Hur man ska hantera digitaliseringen är en av detaljhandelns absolut största frågor just nu. Men de företag som säljer produkter till andra företag är tröga ur startblocken. Än så länge har bara vart fjärde B2B-företag e-handel.

Vanans makt är stor. I en säljkår som upparbetat kontakter och genomfört kundbesök på samma sätt under många år, vill många fortsätta på samma sätt som tidigare. Enligt undersökningen Convert 2019 som Collector Bank har gjort tror ungefär en fjärdedel av partihandelsbolagen att de inte kommer att utveckla e-handel som ny försäljningskanal.

“För säljaren fungerar de gamla säljmötena bra. Men köparna vill kanske inte lägga en timme på att fika med en säljare. De kanske hellre vill lägga sina beställningar på kvällen när de sitter i soffan”, säger Jonas Ogvall som är ansvarig för rapporten.

Digitaliseringen av B2B-försäljningen ligger långt efter digitaliseringen i konsumenthandeln. Även bland de företag som kommit igång med e-handel är det bara ungefär en sjättedel av omsättningen som kommer från digitala kanaler.

“Många inom företagshandeln tycker att just deras bransch är speciell och tror att kunderna vill handla som de alltid har gjort”, säger Jonas Ogvall.

Utvecklingen på konsumentsidan visar dock enligt honom att det argumentet inte håller. Bolag som resonerat på det sättet har fått se sig omsprungna av konkurrenter och digitala uppstickare.

“Då kanske kunden i stället upptäcker att produkten går att köpa på nätet i Tyskland istället och lägger ordern där. Dessutom går man miste om en exportmöjlighet”, säger Jonas Ogvall.

“Risken finns att svenska företag vänder sig utomlands för sina inköp i stället.”

På köpsidan är situationen en helt annan. Där handlar sju av tio företag på nätet. I många fall begränsas dock möjligheterna att handla på nätet av att varorna helt enkelt inte finns tillgängliga via e-handel.

“Glasstillverkaren Lejonet och Björnen är ett bra exempel. De köper förbrukningsmaterial på nätet, men kan inte köpa mjölk, smör och grädde. Men det vill de såklart”, säger Jonas Ogvall.

Det leder till att en hel del företag, cirka 30 procent, handlar på sajter som riktar sig mot konsumenter i stället.

En stor anledning till att förändringen i företagshandeln inte går snabbare är att kompetens saknas. För att nå ut på nätet krävs dessutom ganska stora investeringar och det är inte självklart att de återbetalar sig med en gång.

“Man kanske inte kan räkna med att få tillbaka pengarna man lägger ner redan första året, men man får en chans att vara kvar på marknaden”, säger Jonas Ogval.

Källa: DI.se, 15 januari 2019
Länk

Underskattar vi “robothotet”?

Posted in Aktuellt, Allmänt, Digitalisering / Internet on November 28th, 2018 by admin

Svenskarna eniga: Robotar inget hot mot våra jobb!

Svenskarna har en stark tilltro till sin egen arbetsförmåga – i alla fall i jämförelse med robotar. Inte en enda av över 1.000 tillfrågade tror att deras arbetsuppgifter helt eller delvis kan ersättas av automatiserad arbetskraft inom de närmaste tre åren.

Det visar en Sifo-undersökning som molntjänstbolaget Citrix beställt. Enligt svaren tror lejonparten – tre av fyra – att deras arbeten inte alls kan komma att påverkas av robotar.

Det är att underskatta kraften i digitaliseringen, enligt Citrix.

“Nya tekniska framsteg inom bland annat artificiell intelligens, maskinlärning och automatisering skapar helt nya förutsättningar för innovation och ritar om spelplanen för hur vi i dag gör affärer på. Jag tror att automatiseringen kommer göra människors arbete både bättre och mer intressant”, säger Mats Ericson, Sverigechef på Citrix.

Undersökningen visar att svaren skiljer sig stort mellan olika åldersgrupper, där de yngre är mer benägna att se sig ersättningsbara än de äldre. 21 procent i åldrarna 16–34 år tror att vissa arbetsuppgifter kan komma att automatiseras jämfört med endast 8 procent bland sysselsatta i åldrarna 56 år och uppåt. Totalsiffran oberoende av ålder är 14 procent. 10 procent tror att robotarnas intåg kan göra deras yrkesroller mer utvecklande.

Det är också fler bland de yngre som tror att deras yrkesroll kommer förändras inom tre år. I åldrarna 16–34 år är siffran 33 procent, jämfört med 22 procent i åldrarna 35–55 år och 16 procent bland personer som är över 56 år.

Källa:DI.se, 27 november 2018
Länk

Digital strategy: The four fights you have to win

Posted in Aktuellt, Allmänt, Digitalisering / Internet, Technology on November 19th, 2018 by admin

Yesterday’s tentative approaches won’t deliver; you need absolute clarity about digital’s demands, galvanized leadership, unparalleled agility, and the resolve to bet boldly.

If there’s one thing a digital strategy can’t be, it’s incremental. The mismatch between most incumbents’ business models and digital futures is too great—and the environment is changing too quickly—for anything but bold, inventive strategic plans to work.

Digital strategy: The four fights you have to win
Unfortunately, most strategic-planning exercises do generate incrementalism. We know this from experience and from McKinsey research: on average, resources don’t move between business units in large organizations. A recent book by our colleagues, Strategy Beyond the Hockey Stick, seeks to explain what causes this inertia (strategy’s social side, rooted in individual interests, group dynamics, and cognitive biases) and to suggest a way out (understanding the real odds of strategy and overhauling your planning processes to deliver the big moves that can overcome those long odds).

All this holds doubly true for digital strategy, which demands special attention. Leaders in many organizations lack clarity on what “digital” means for strategy. They underestimate the degree to which digital is disrupting the economic underpinnings of their businesses. They also overlook the speed with which digital ecosystems are blurring industry boundaries and shifting the competitive balance. (For more on why companies often fall short, see “Why digital strategies fail.”) What’s more, responding to digital by building new businesses and shifting resources away from old ones can be threatening to individual executives, who may therefore be slow to embrace (much less drive) the needed change.

In our experience, the only way for leaders to cut through inertia and incrementalism is to take bold steps to fight and win on four fronts: You must fight ignorance by using experiential techniques such as “go-and-sees” and war gaming to break leaders out of old ways of thinking and into today’s digital realities. You must fight fear through top-team effectiveness programs that spur senior executives to action. You must fight guesswork through pilots and structured analysis of use cases. And you must fight diffusion of effort—a constant challenge given the simultaneous need to digitize your core and innovate with new business models.

In this article, we will describe how real companies are winning each of these fights—overcoming inertia while building confidence about how to master the new economics of digital. You can join these companies in that effort, thereby giving your digital strategy a jolt and accelerating the shift of your strategy process as a whole, from old-fashioned annual planning to a more continuous journey yielding big moves and big gains even when the end point isn’t entirely clear.

1. Fighting ignorance

Many senior executives aren’t fully fluent in what digital is, much less up to speed on the ways it can change how their businesses operate or the competitive context. That’s problematic. Executives who aren’t conversant with digital are much more likely to fall prey to the “shiny object” syndrome: investing in cool digital technologies (which might only be relevant for other businesses) without a clear understanding of how they will generate value in the executives’ own business models. They also are more likely to make fragmented, overlapping, or subscale digital investments; to pursue initiatives in the wrong order; or to skip foundational moves that would enable more advanced ones to pan out. Finally, this lack of grounding slows down the rate at which a business deploys new digital technologies. In an era of powerful first-mover advantages, winners routinely lead the pack in leveraging cutting-edge digital technologies at scale to pull further ahead. Having only a remedial understanding of trends and technologies has become dangerous.

Raising your technology IQ
For inspiration on how to raise your company’s collective technology IQ, consider the experience of a global industrial conglomerate that knew it had to digitize but didn’t think its leadership team had the expertise to drive the needed changes. The company created a digital academy to help educate its leadership about relevant digital trends and technologies and to provide a forum where executives could ask questions and talk with their peers. Academy leaders also brought in external experts on a few topics the company lacked sufficient internal expertise to address.

Supplementing the academy effort (aimed at leaders) was an organization-wide assessment of digital capabilities and an evaluation of the company’s culture. This provided a fact base, which everyone could understand, about what the organization needed to build over the course of the digital transformation. As business leaders developed digital plans, they were accountable for explaining and defending them to other executives. They also had to help gather those plans into an enterprise-wide digital strategy that every business leader understood and had helped to create.

Overcoming competitive blind spots
If your company resembles many we know, it’s still stuck in some old ways of thinking about where money gets made and by whom. You’re also likely to be overlooking ways digital is changing both the economics of the game and the players on the field in your industry. If any of this sounds familiar, you probably need a jolt—something that forces you to think differently about your business. More specifically, you need to start thinking about it as digital disruptors do. In our experience, this demands a process that begins with a sprint to get everything moving, to see what your industry (and your company’s role in it) could look like if you started from scratch, and to redraw your road map.

The financing division of a European financial-services company went through such a process when it tried to understand digital’s impact on its current lines of business. For example, a conversation began in the auto-loans division with the question “how can we make it easier for people to get their loans online?” It turned into a deeper examination of “how does our business model change if people stop buying cars and start buying mobility?” Similarly, an auto insurer might move from asking “how can I sell car insurance online better” to “what does car insurance mean in the context of autonomous vehicles?” There’s no substitute for exploring such questions, which emerge when digital, regulatory, and societal trends collide with today’s value chain (for more on these collisions, see “Digital strategy: Understanding the economics of disruption”).

Once the new realities are discovered, companies should speed up the process of understanding how other players—including nontraditional ones—will respond. The financial-services provider jump-started things by holding a series of war-gaming workshops. It divided its leadership team into groups and assigned them to role-play potential attackers such as Amazon, Google, or small, cherry-picking start-ups. Seeing through the eyes of “baggage-free” attackers inspires an awareness of how players with very different core competencies are likely to act in the new landscape. It can also propel a shared sense of urgency to change the old ways of thinking and acting.

These sessions radically changed the way the company’s leaders thought about their business, their industry, and the digital shifts remaking both. The end result was a set of leading-edge ideas for deploying digital to make the current operating model faster and more effective, for investing in new digital offerings, for designing and launching a new digital ecosystem to meet the emerging needs of digital consumers, and for partnering with start-ups beginning to emerge as leading players in advanced mobility.

2. Fighting fear
Getting left behind by digital first movers can be hazardous to your company’s future. But many of your executives may perceive responding to digital—making the big bets, building new businesses, shifting resources away from old ones—as hazardous to their own future. As we’ve noted, that exacerbates the social side of strategy and breeds strategic inertia. If you want to make big digital moves, you must fight the fear that your top team and managers will inevitably experience.

From what we have seen, this kind of fight doesn’t happen organically. You need to design a programmatic effort with the same rigor you would insist on to redesign key processes across your organization. This typically involves making a clear case that executives can’t hide from the changes digital is bringing and that encouraging and accelerating change—rather than chasing it—can create more value. Then you need to give executives the tools and support network they must have to succeed as leaders of that journey. Many companies focus on the extensive detailing of digital-initiative plans but skip the critical step of building an equally rigorous program to sustain the leaders driving change.

Honest dialogue
At the industrial company we discussed earlier, the move to digital implied significant change in the characteristics leaders required to be effective. Naturally, concerns about waning influence, or worse, followed for many of the company’s 20 or so business-unit leaders. The industrial conglomerate confronted these fears head-on by organizing a top-team effectiveness program to surface anxieties, build awareness of how they were affecting decision making, and define how leaders could remain relevant. In workshops, executives discussed the specific mind-sets and behavioral shifts needed to gain “ownership” of digital initiatives as a group and to become role models for their organizations.

Support networks
Leaders also formed communities that cut across their businesses, initially to share best practices and coordinate the timing of implementation. Over time, the role of these communities grew to include skill-building activities, such as bringing in speakers with specialized capabilities and motivational messages and organizing Silicon Valley go-and-sees that reinforced the importance of leading digital change. The communities also provided peer support to help teams navigate the new landscape.

We have seen other organizations similarly coalesce around digital-leadership training (sometimes supported by digital advisory boards) that helps executives to become comfortable with—even embrace—the uncertainty of the destination and the career trade-offs needed for a well-executed digital strategy. These support networks dovetail with, and bolster, the digital IQ–raising efforts we described earlier. Indeed, we find that leaders who understand the shifting economics also understand that their careers will be affected one way or another.

3. Fighting guesswork

Pursuing an aggressive digital strategy involves leaps into the unknown: simultaneously, you are likely to be moving into new areas and overhauling existing businesses with new technologies. What’s more, in many digital markets, the premium of being a first mover makes it necessary not only to shift direction but also to do so faster than your peers. The combination of ambiguity and the need for speed sometimes gives rise to guesswork and moves that are hasty or poorly thought out—and to anxiety about whether a move isn’t going to work or just needs more time.

Building the proof points as you go
One way to fight guesswork is to anchor your strategy decisions to a thesis about the business outcomes that different digital investments will produce. This is less about elaborate business-school modeling and more about thinking that draws fast, ground-level lessons from the data to determine whether your business logic is correct. Put another way, it means figuring out if there is sufficient value to make it worthwhile to invest something—as part of a process of learning even more. This approach increases the odds of successful implementation: a well-articulated view of the outcomes means that you can track how well the strategy is working. It also makes it easier to assess whether the new direction is worth it in terms of both financial capital and organizational pain.

Those proof points must be grounded in digital reality. Consider the experience of a global oil and gas company investigating the potential impact of several advanced technologies on its business. Rather than develop theoretical value-creation scenarios, the company’s digital center of excellence got busy exploring: How might sensors, robots, and artificial intelligence improve productivity and safety in unmanned operations? What operating hurdles, such as skill gaps among managers and frontline workers, would need to be overcome?

“Skunkworks” efforts began to give the company sharper insights into the timetables and financial profiles of different investments, so it avoided both the “finger in the air” syndrome (which dooms some digital efforts) and excessive modeling (which bogs down others). The end result was a value-thesis projection of a pretax cash-flow improvement exceeding 20 percent by 2025. That built the confidence of senior leaders and the board alike.

Pilots and stage gates
A second way to reduce the need for guesswork is to take full advantage of real-time data and the opportunities they provide for experimentation. Digital does amplify the gut-wrenching uncertainty by multiplying the strategic choices leaders face while reducing the time frame for making and implementing those decisions. But it also contains a silver lining: the potential for gaining rapid, data-driven insights into how things are going. Information on the progress of a product launch, for example, is available in days rather than months. That makes rapid course corrections possible and, ultimately, considerably improves the chances of success.

The oil and gas company mentioned earlier got a rapid bead on the impact that its digital initiatives were having on its business performance when it automated the evaluation of several business cases. Testing was more or less continuous, which reduced the level of anxiety about the investments, because executives had hard data on how things were performing rather than relying on guesses or intuition in realms they didn’t know extremely well. It also gave them more confidence to push cutting-edge solutions: they didn’t need to see how other oil and gas companies did things when they could move first and see, in near real time, what worked and what didn’t.

An important element of this nimble approach was breaking up big bets into smaller, staged investments. While the oil and gas company was ready to invest in digital, it was decidedly uncomfortable with throwing money at a problem and hoping for the best. It therefore developed a series of rigorous stage gates for investments managed by a new, central digital-transformation office. The office was charged with overseeing the portfolio of digital investments to ensure that the most promising projects were funded and others defunded before they soaked up valuable resources. In tandem, the head of the company’s digital efforts was vested with the responsibility for approving which ideas would move to initial development, basing these decisions on the organization’s overall vision for digital.

The ideas, which originated mostly with the business units, included clear requirements for testing. The “fail fast” mind-set was embedded from the outset because it allowed the company to learn quickly from mistakes and to minimize wasted funding. Another payoff was that the central team could identify synergies, which allowed the development costs of some investments to be shared rather than borne by a single business. These processes helped temper some of the risks of the bold investments the company was making, gave leaders the confidence to venture ahead as first movers, and kept open the option to correct course quickly when the data pointed in another direction.

4. Fighting diffusion
Effective strategy requires focus, but responding to digital inevitably risks diffusion of effort, or “spreading the peanut butter too thinly.” Most companies we know are trying, and struggling, to do two things at once: to reinvent the core by digitizing and automating some of its key elements, for example, and to create innovative new digital businesses. The challenge is acute because of the dizzying pace of digital change and the uncertainty surrounding the adoption of new technology. Even if the technology for autonomous vehicles pans out, for instance, when will the majority of people really begin to use them? Given the impossibility of knowing, it’s easy to wind up with an unfocused hodgepodge of digital initiatives—a far cry from a strategy.

Two concepts can help you navigate. First, view your company as a portfolio of initiatives at different stages of seeding, nurturing, growing, or pruning. Our colleague Lowell Bryan championed this view upward of 15 years ago, and it is more relevant than ever in our digital age because the opportunities, time frames, and economics of core businesses can be very different from those of new ones—so resources and efforts shouldn’t be applied uniformly.

Second, embrace the necessity of “big moves,” such as the dramatic reallocation of resources, sustained capital investment, radical productivity improvements, and disciplined M&A. As our colleagues have shown, successful market-beating strategies nearly always rest on such moves. Making them mutually reinforcing, so that developments in the core help to support new digital businesses and vice versa, is a critical part of managing the risks of diffusion.

To understand what the application of these ideas looks like in practice, consider the experience of a global IT-services company wrestling with how much to invest in digital over the next five years (rather than use standard R&D funding across all of the company’s business lines). That meant scrutinizing which traditional businesses faced obsolescence as a result of digital, whether digital could stretch any of those lifetimes (or if immediate divestment was preferable), which new digital businesses to invest in, and how much to invest.

A portfolio approach
As a first step, the company went through its portfolio business by business, focusing on three questions: Which emerging digital products and services were missing from the portfolio? Which product offerings and elements of the existing operating model should be digitized or fully digitally reengineered to improve customer journeys? And what areas should be abandoned? The answers for the company’s healthcare markets differed from those for banking, but the company became comfortable with hard choices and more attuned to new opportunities by tying all decisions to clear use cases.

As part of this exercise, the company developed scenarios for how the value pools in each of its industry verticals would probably shift across component customer value chains. It wanted to get a sense of the types of services that clients and potential clients were likely to demand and thus might try to obtain from new suppliers or IT outsourcers. For businesses where more revenue would be likely to shift, the company was comfortable placing bigger bets on new digital offerings, in contrast with its approach to businesses where the revenue at stake wasn’t changing as much.

Big, mutually reinforcing moves
This systematic evaluation of value-pool opportunities across the portfolio generated a frank discussion of how the organization’s risk appetite had to change. It also catalyzed a greater willingness to invest in new digital businesses—which the company did, to the tune of more than $1.5 billion. As part of this strategic evolution, the company launched an aggressive program to better leverage foundational digital capabilities, such as automation, advanced analytics, and big data. These capabilities, to be sure, were key building blocks for the new digital businesses. Just as important, however, by deploying the capabilities at scale across existing businesses, the company was better able to stretch the life of its core offerings.

The portfolio strategy paid dividends both in revenue gains and cost reductions. For example, investing in a balanced fashion between core and new businesses led to faster than expected revenue streams from new offerings. The company estimated that 40 percent of its revenues would flow from them within two to three years. Moreover, its digitally improved core businesses, with a sizable base of existing customer revenues, provided additional funding for the new digital portfolio. That increased the leadership’s commitment to the strategy, bolstering confidence that the new portfolio offerings would provide growth more than compensating for the eventual decline of core businesses.

Your best digital competitors—the ones you really need to worry about—aren’t taking small steps. Neither can you. This doesn’t mean that a digital strategy must be designed or put to work with any less confidence than strategies were in the past, though. Strategy has always required closing gaps in knowledge about complex markets, inspiring executive teams (and employees) to go beyond their fears and reluctance to act, and calibrating risks when you bet boldly.

The good news is that the digital era, for all its stomach-churning speed and volatility, also serves up more information about the competitive environment than yesterday’s strategists could ever imagine. Simultaneously, analytically backed, rapid test-and-learn approaches have opened up new avenues to help companies correct course while staying true to their strategic goals. Today’s leaders need to step up by persuading their organizations that digital strategies may be tougher than other strategies but are potentially more rewarding—and well worth the bolder bets and cultural reforms required, first, to survive and, ultimately, to thrive.

Source: McKinsey.com, October 2018
Link
By Tanguy Catlin, Laura LaBerge and Shannon Varney
About the authors: Tanguy Catlin is a senior partner in McKinsey’s Boston office, where Shannon Varney is an associate partner; Laura LaBerge is a senior practice manager of Digital McKinsey and is based in the Stamford office.

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
Länk

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
Link