True!

Posted in Aktuellt, Allmänt on March 30th, 2013 by admin

Utvärdering av min föreläsning i Luleå …

Posted in Aktuellt, Lectures / Föreläsningar on March 29th, 2013 by admin

Förra veckan fick jag möjlighet att föreläsa i en och halv timme för stora delar av det norrländska näringslivet i Luleå. Så kul att det landade rätt!

Läs mer om mina olika föreläsningar här.

10 things to do every workday

Posted in Aktuellt, Allmänt, Leadership / Ledarskap on March 28th, 2013 by admin

I’ve always been focused on performance. I’m a list person. I love the feeling of crossing things off. It makes me feel productive. Plus, consistent productivity has the wonderful byproduct of accomplishing more. Jeff Haden’s recent article on Linkedin summarizes the value of having a daily to-do list beautifully: You don’t wait to do the work until you get the dream job – you do the work in order to get the dream job.

I’ve never shared this list with anyone until now.

It’s the list of ten things I try to do every workday. Yes, there are days when I don’t get them all done, but I do my best to deliver. It has proven very effective for me. They are:
1.Read something related to my industry.
2.Read something related to business development.
3.Send two emails to touch base with old colleagues.
4.Empty my private client inbox by responding to all career coaching questions within one business day.
5.Check in with each team member on their progress.
6.Have a short non-work related conversation with every employee.
7.Review my top three goals for my company that are focused on it’s growth.
8.Identify and execute one task to support each of my top three goals.
9.Post five valuable pieces of content on all my major social media accounts.
10.Take a full minute to appreciate what I have and how far I’ve come.

This list could be longer. BUT…

If it was longer, I wouldn’t be as good at getting them all done. This list is manageable to me. Of course, I do more than these ten things every day. But, these are the ten I choose to do with consistency. Why? Over the years, they’ve proven the best way for me to grow my career and my business. The collective results have made completing these tasks consistently; even when I don’t feel like it, well worth it.

Source: J.T. O´Donell, Linkedin.com, 28 March 2013
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More from J.T. O´Donell here

LinkedIn now #1 tool of top sales reps

Posted in Aktuellt, Försäljning / Sales on March 27th, 2013 by admin

Just when you thought LinkedIn.com was a career site for finding your next job, along comes new, primary research from Jill Konrath and Ardath Albee showing that LinkedIn is now the most important sales tool in a sales rep’s arsenal.

3,094 sales professionals, representing a broad base of outside and inside sales reps, sales managers, consultants and entrepreneurs from small, midsize and large companies, participated in this survey. Of this total, 4.9 percent were categorized as Top Sellers.

Top Sellers are those individuals who attribute the majority of their new business opportunities and revenue to their use of LinkedIn.
“That means there’s a whole slew of people out there who don’t have a clue about what these people are doing. You can say these Top Sellers are spending time on groups or they have a really good profile, but you don’t know how they’re working or anything about their thought-process and their mindset,” said Jill Konrath, co-author of Cracking The LinkedIn Sales Code, an eBook which summarizes the research findings.
“I’m hoping that after reading our research, people who aren’t in this top 4.9 percent will say, ‘I never thought it could be done this way… and I can do it, too,'” Konrath added.

What is it these Top Sellers are doing on LinkedIn that others are not? Here’s a summary of the research:
•Top Sellers are strategic. There’s a whole strategy behind everything they do.
•They spend much more time on LinkedIn than their colleagues (six hours or more per week).
•LinkedIn is viewed as essential to their business. It’s literally part of their sales DNA and they can’t imagine how they’d live without it.
•They’re not just dabbling and looking people up. Instead, they have a targeted database and they use LinkedIn to create strategic searches to identify the people that they want to go after.
•They join the groups their target market belongs to and start participating in the groups, thereby allowing them to initiate conversations with group members.
•Top Sellers are very deliberate about building their profiles so that it reads nothing like a resume or sell-sheet. Rather, their profile is about the challenges a client might face and the kind of results clients can expect.
•Because LinkedIn lets you upload content into your profile, they’ll also include relevant case studies, webinars or SlideShare presentations.

As we learned from The Challenger Sale, the biggest differentiator in making a sale is often how well the sales rep is able to create the perception as a relevant, knowledgeable, problem-solver. It seems this new LinkedIn research supports the idea that LinkedIn may very well be a Challenger Sales Rep’s favorite sales tool.

Source: John Fox, The Blog, March 2013
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Är kvinnor bättre lämpade som chefer?

Posted in Aktuellt, Leadership / Ledarskap on March 27th, 2013 by admin

I tolv av 18 europeiska länder anser anställda att kvinnor är bättre lämpade som chefer än män. I Sverige föredrar något fler en manlig chef än en kvinnlig. Det visar en undersökning från bemanningsföretaget Randstad.

Globalt är det något fler som anser att män är bäst lämpade som ledare jämfört med hur många som föredrar kvinnliga ledare. Men europeiska anställda föredrar kvinnliga chefer om de vore tvungna att välja mellan en kvinnlig eller manlig chef, enligt undersökningen från Randstad.
– Vi ser en konsekvent tendens i hela Europa, vilket visar att europeiska arbetstagare tror kvinnor är kapabla ledare. I bara två europeiska länder tänker över hälften av de anställda att män är bäst lämpade att leda. Det är också värt att påpeka att en stor andel av de anställda inte verkar bry sig om chefens kön, säger Camilla Grana, VD för Randstad Norge, till den norska tidningen Dagens Næringsliv.

Arbetskraftsundersökningen bygger på intervjuer i 32 länder där minst 400 anställda är intervjuade i varje land. Undersökningen visar att det är vanligast att föredra manliga chefer i Kina, Hong Kong, Indien och Malaysia, men Kina och Indien har också störst andel anställda som vill se fler kvinnor på chefspositioner. I Spanien, Chile och Mexiko är det däremot fler som anser att kvinnliga chefer är bättre på att leda än manliga chefer.

I Sverige är det färre än globalt som anser att män är bättre lämpade än kvinnor att leda. Var tredje anställd i Sverige föredrar en manlig chef, och i stort sett lika många föredrar en kvinnlig chef. Enligt undersökningen tror två tredjedelar av de anställda i Sverige att det är svårare för en kvinna än en man att nå en chefsposition och det är fler i Sverige än i andra länder som anger att deltidsarbete är ett hinder i karriären.

Mer än hälften, 54 procent, tror enligt undersökningen att kvotering skulle få fler företag att anställa kvinnliga chefer.

Läs om Årets Chef i Sverige – Sarah McPhee – här.

Källa: SvD.se, 23 mars 2013
Länk
Hur fungerar ledarskapet i er organisation? Vet du, eller tror du att du vet? Läs mer här om hur vi på 3S hjälper våra uppdragsgivare att säkerställa ett Faktabaserat underlag för att utveckla sitt ledarskap och säkerställa affärsmålen.

Varför sitter en del kollegor och mailar under ledningsgruppsmötet?

Posted in Aktuellt, Executive Team / Ledningsgruppsarbete, Fact Based Management, Leadership / Ledarskap, Strategy implementation / Strategiimplementering on March 24th, 2013 by admin

Känner du igen dig i denna frågeställning?
Ja, då är Du inte ensam! Enligt Ledningsgrupps Barometern™ anser 59% av alla chefer att arbetet i ledningsgruppen är ”kortsiktigt och operativt inriktat”. Kanske är det ett av skälen till att intresset och engagemanget inte är tillräckligt högt?

Hur väl fungerar er ledningsgrupp, hur arbetar ni förhållande till andra svenska ledningsgrupper och framförallt var finns potentialen till en ökad effektivitet i ert ledningsgruppsarbete?

I ett näringsliv där marknads- och konkurrensförutsättningarna förändras snabbare än någonsin tidigare (enligt StrategiBarometern™ håller 88% av svenska företagsledningar med om detta) ökar betydelsen av en väl fungerande ledningsgrupp.
Känner Du / ni igen er i följande utmaningar:
– Hur säkerställer vi att alla verkligen fokuserar helheten i ledningsgruppens arbete?
– Hur når vi rätt balans mellan operativ och strategiska frågor?
– Hur kan jag som VD (eller motsvarande) veta att alla i gruppen verkligen förstår och accepterar min ambition med
ledningsgruppen?
– Varför sitter en del kollegor och skickar mail och sms under pågående ledningsgruppsmöte?
– Hur kan vi effektivisera vårt arbete för att skapa rätt förutsättningar för att nå de övergripande affärsmålen?
– Varför kan vi aldrig hålla oss inom avsatt tid?
– Hur fungerar vi förhållande till andra ledningsgrupper?
– Är detta verkligen en fråga för ledningsgruppen att lägga sin gemensamma tid på?
– Fattade vi verkligen ett beslut i frågan, eller …?
– Får vi verkligen den ”pay-off” på investerad tid som vi kan förvänta oss av ledningsgruppens arbete?
– Hur säkerställer jag (som VD eller motsvarande) att mina kollegor verkligen förankrar ledningsgruppens beslut inom sina respektive ansvarsområden? Och att det sker tillräcklig snabbt?

Vill du ha svar på dessa frågor, och kanske få ytterligare idéer om hur ni kan ytterligare utveckla ert ledningsgruppsarbete, tveka inte att återkomma så berättar jag mer om hur vi under de senaste 15 åren hjälpt ledningsgrupper i ett tjugotal länder att effektivisera sitt arbete, utveckla sin lång- och kortsiktiga affärsplanering med ett enda syfte – att öka sin affärsmässiga framgång!

Läs mer om ökad effektivitet i ert ledningsgruppsarbete här.

Big data: What’s your plan?

Posted in Aktuellt, Allmänt, Fact Based Management on March 24th, 2013 by admin

The payoff from joining the big-data and advanced-analytics management revolution is no longer in doubt. The tally of successful case studies continues to build, reinforcing broader research suggesting that when companies inject data and analytics deep into their operations, they can deliver productivity and profit gains that are 5 to 6 percent higher than those of the competition.1 The promised land of new data-driven businesses, greater transparency into how operations actually work, better predictions, and faster testing is alluring indeed.

But that doesn’t make it any easier to get from here to there. The required investment, measured both in money and management commitment, can be large. CIOs stress the need to remake data architectures and applications totally. Outside vendors hawk the power of black-box models to crunch through unstructured data in search of cause-and-effect relationships. Business managers scratch their heads—while insisting that they must know, upfront, the payoff from the spending and from the potentially disruptive organizational changes.

The answer, simply put, is to develop a plan. Literally. It may sound obvious, but in our experience, the missing step for most companies is spending the time required to create a simple plan for how data, analytics, frontline tools, and people come together to create business value. The power of a plan is that it provides a common language allowing senior executives, technology professionals, data scientists, and managers to discuss where the greatest returns will come from and, more important, to select the two or three places to get started.

There’s a compelling parallel here with the management history around strategic planning. Forty years ago, only a few companies developed well-thought-out strategic plans. Some of those pioneers achieved impressive results, and before long a wide range of organizations had harnessed the new planning tools and frameworks emerging at that time. Today, hardly any company sets off without some kind of strategic plan. We believe that most executives will soon see developing a data-and-analytics plan as the essential first step on their journey to harnessing big data.

The essence of a good strategic plan is that it highlights the critical decisions, or trade-offs, a company must make and defines the initiatives it must prioritize: for example, which businesses will get the most capital, whether to emphasize higher margins or faster growth, and which capabilities are needed to ensure strong performance. In these early days of big-data and analytics planning, companies should address analogous issues: choosing the internal and external data they will integrate; selecting, from a long list of potential analytic models and tools, the ones that will best support their business goals; and building the organizational capabilities needed to exploit this potential.

Successfully grappling with these planning trade-offs requires a cross-cutting strategic dialogue at the top of a company to establish investment priorities; to balance speed, cost, and acceptance; and to create the conditions for frontline engagement. A plan that addresses these critical issues is more likely to deliver tangible business results and can be a source of confidence for senior executives.

What’s in a plan?
Any successful plan will focus on three core elements.

Data
A game plan for assembling and integrating data is essential. Companies are buried in information that’s frequently siloed horizontally across business units or vertically by function. Critical data may reside in legacy IT systems that have taken hold in areas such as customer service, pricing, and supply chains. Complicating matters is a new twist: critical information often resides outside companies, in unstructured forms such as social-network conversations.

Making this information a useful and long-lived asset will often require a large investment in new data capabilities. Plans may highlight a need for the massive reorganization of data architectures over time: sifting through tangled repositories (separating transactions from analytical reports), creating unambiguous golden-source data,2 and implementing data-governance standards that systematically maintain accuracy. In the short term, a lighter solution may be possible for some companies: outsourcing the problem to data specialists who use cloud-based software to unify enough data to attack initial analytics opportunities.

Analytic models
Integrating data alone does not generate value. Advanced analytic models are needed to enable data-driven optimization (for example, of employee schedules or shipping networks) or predictions (for instance, about flight delays or what customers will want or do given their buying histories or Web-site behavior). A plan must identify where models will create additional business value, who will need to use them, and how to avoid inconsistencies and unnecessary proliferation as models are scaled up across the enterprise.

As with fresh data sources, companies eventually will want to link these models together to solve broader optimization problems across functions and business units. Indeed, the plan may require analytics “factories” to assemble a range of models from the growing list of variables and then to implement systems that keep track of both. And even though models can be dazzlingly robust, it’s important to resist the temptation of analytic perfection: too many variables will create complexity while making the models harder to apply and maintain.

Tools
The output of modeling may be strikingly rich, but it’s valuable only if managers and, in many cases, frontline employees understand and use it. Output that’s too complex can be overwhelming or even mistrusted. What’s needed are intuitive tools that integrate data into day-to-day processes and translate modeling outputs into tangible business actions: for instance, a clear interface for scheduling employees, fine-grained cross-selling suggestions for call-center agents, or a way for marketing managers to make real-time decisions on discounts. Many companies fail to complete this step in their thinking and planning—only to find that managers and operational employees do not use the new models, whose effectiveness predictably falls.

There’s also a critical enabler needed to animate the push toward data, models, and tools: organizational capabilities. Much as some strategic plans fail to deliver because organizations lack the skills to implement them, so too big-data plans can disappoint when organizations lack the right people and capabilities. Companies need a road map for assembling a talent pool of the right size and mix. And the best plans will go further, outlining how the organization can nurture data scientists, analytic modelers, and frontline staff who will thrive (and strive for better business outcomes) in the new data- and tool-rich environment.

By assembling these building blocks, companies can formulate an integrated big-data plan similar to what’s summarized in the exhibit. Of course, the details of plans—analytic approaches, decision-support tools, and sources of business value—will vary by industry. However, it’s important to note an important structural similarity across industries: most companies will need to plan for major data-integration campaigns. The reason is that many of the highest-value models and tools (such as those shown on the right of the exhibit) increasingly will be built using an extraordinary range of data sources (such as all or most of those shown on the left). Typically, these sources will include internal data from customers (or patients), transactions, and operations, as well as external information from partners along the value chain and Web sites—plus, going forward, from sensors embedded in physical objects.

To build a model that optimizes treatment and hospitalization regimes, a company in the health-care industry might need to integrate a wide range of patient and demographic information, data on drug efficacy, input from medical devices, and cost data from hospitals. A transportation company might combine real-time pricing information, GPS and weather data, and measures of employee labor productivity to predict which shipping routes, vessels, and cargo mixes will yield the greatest returns.

Three key planning challenges
Every plan will need to address some common challenges. In our experience, they require attention from the senior corporate leadership and are likely to sound familiar: establishing investment priorities, balancing speed and cost, and ensuring acceptance by the front line. All of these are part and parcel of many strategic plans, too. But there are important differences in plans for big data and advanced analytics.

1. Matching investment priorities with business strategy
As companies develop their big-data plans, a common dilemma is how to integrate their “stovepipes” of data across, say, transactions, operations, and customer interactions. Integrating all of this information can provide powerful insights, but the cost of a new data architecture and of developing the many possible models and tools can be immense—and that calls for choices. Planners at one low-cost, high-volume retailer opted for models using store-sales data to predict inventory and labor costs to keep prices low. By contrast, a high-end, high-service retailer selected models requiring bigger investments and aggregated customer data to expand loyalty programs, nudge customers to higher-margin products, and tailor services to them.

That, in a microcosm, is the investment-prioritization challenge: both approaches sound smart and were, in fact, well-suited to the business needs of the companies in question. It’s easy to imagine these alternatives catching the eye of other retailers. In a world of scarce resources, how to choose between these (or other) possibilities?

There’s no substitute for serious engagement by the senior team in establishing such priorities. At one consumer-goods company, the CIO has created heat maps of potential sources of value creation across a range of investments throughout the company’s full business system—in big data, modeling, training, and more. The map gives senior leaders a solid fact base that informs debate and supports smart trade-offs. The result of these discussions isn’t a full plan but is certainly a promising start on one.

Or consider how a large bank formed a team consisting of the CIO, the CMO, and business-unit heads to solve a marketing problem. Bankers were dissatisfied with the results of direct-marketing campaigns—costs were running high, and the uptake of the new offerings was disappointing. The heart of the problem, the bankers discovered, was a siloed marketing approach. Individual business units were sending multiple offers across the bank’s entire base of customers, regardless of their financial profile or preferences. Those more likely to need investment services were getting offers on a range of deposit products, and vice versa.

The senior team decided that solving the problem would require pooling data in a cross-enterprise warehouse with data on income levels, product histories, risk profiles, and more. This central database allows the bank to optimize its marketing campaigns by targeting individuals with products and services they are more likely to want, thus raising the hit rate and profitability of the campaigns. A robust planning process often is needed to highlight investment opportunities like these and to stimulate the top-management engagement they deserve given their magnitude.

2. Balancing speed, cost, and acceptance
A natural impulse for executives who “own” a company’s data and analytics strategy is to shift rapidly into action mode. Once some investment priorities are established, it’s not hard to find software and analytics vendors who have developed applications and algorithmic models to address them. These packages (covering pricing, inventory management, labor scheduling, and more) can be cost-effective and easier and faster to install than internally built, tailored models. But they often lack the qualities of a killer app—one that’s built on real business cases and can energize managers. Sector- and company-specific business factors are powerful enablers (or enemies) of successful data efforts. That’s why it’s crucial to give planning a second dimension, which seeks to balance the need for affordability and speed with business realities (including easy-to-miss risks and organizational sensitivities).

To understand the costs of omitting this step, consider the experience of one bank trying to improve the performance of its small-business underwriting. Hoping to move quickly, the analytics group built a model on the fly, without a planning process involving the key stakeholders who fully understood the business forces at play. This model tested well on paper but didn’t work well in practice, and the company ran up losses using it. The leadership decided to start over, enlisting business-unit heads to help with the second effort. A revamped model, built on a more complete data set and with an architecture reflecting differences among various customer segments, had better predictive abilities and ultimately reduced the losses. The lesson: big-data planning is at least as much a management challenge as a technical one, and there’s no shortcut in the hard work of getting business players and data scientists together to figure things out.

At a shipping company, the critical question was how to balance potential gains from new data and analytic models against business risks. Senior managers were comfortable with existing operations-oriented models, but there was pushback when data strategists proposed a range of new models related to customer behavior, pricing, and scheduling. A particular concern was whether costly new data approaches would interrupt well-oiled scheduling operations. Data managers met these concerns by pursuing a prototype (which used a smaller data set and rudimentary spreadsheet analysis) in one region. Sometimes, “walk before you can run” tactics like these are necessary to achieve the right balance, and they can be an explicit part of the plan.

At a health insurer, a key challenge was assuaging concerns among internal stakeholders. A black-box model designed to identify chronic-disease patients with an above-average risk of hospitalization was highly accurate when tested on historical data. However, the company’s clinical directors questioned the ability of an opaque analytic model to select which patients should receive costly preventative-treatment regimes. In the end, the insurer opted for a simpler, more transparent data and analytic approach that improved on current practices but sacrificed some accuracy, with the likely result that a wider array of patients could qualify for treatment. Airing such tensions and trade-offs early in data planning can save time and avoid costly dead ends.

Finally, some planning efforts require balancing the desire to keep costs down (through uniformity) with the need for a mix of data and modeling approaches that reflect business realities. Consider retailing, where players have unique customer bases, ways of setting prices to optimize sales and margins, and daily sales patterns and inventory requirements. One retailer, for instance, has quickly and inexpensively put in place a standard next-product-to-buy model3 for its Web site. But to develop a more sophisticated model to predict regional and seasonal buying patterns and optimize supply-chain operations, the retailer has had to gather unstructured consumer data from social media, to choose among internal-operations data, and to customize prediction algorithms by product and store concept. A balanced big-data plan embraces the need for such mixed approaches.

3. Ensuring a focus on frontline engagement and capabilities

Even after making a considerable investment in a new pricing tool, one airline found that the productivity of its revenue-management analysts was still below expectations. The problem? The tool was too complex to be useful. A different problem arose at a health insurer: doctors rejected a Web application designed to nudge them toward more cost-effective treatments. The doctors said they would use it only if it offered, for certain illnesses, treatment options they considered important for maintaining the trust of patients.

Problems like these arise when companies neglect a third element of big-data planning: engaging the organization. As we said when describing the basic elements of a big-data plan, the process starts with the creation of analytic models that frontline managers can understand. The models should be linked to easy-to-use decision-support tools—call them killer tools—and to processes that let managers apply their own experience and judgment to the outputs of models. While a few analytic approaches (such as basic sales forecasting) are automatic and require limited frontline engagement, the lion’s share will fail without strong managerial support.

The aforementioned airline redesigned the software interface of its pricing tool to include only 10 to 15 rule-driven archetypes covering the competitive and capacity-utilization situations on major routes. Similarly, at a retailer, a red flag alerts merchandise buyers when a competitor’s Internet site prices goods below the retailer’s levels and allows the buyers to decide on a response. At another retailer, managers now have tablet displays predicting the number of store clerks needed each hour of the day given historical sales data, the weather outlook, and planned special promotions.

But planning for the creation of such worker-friendly tools is just the beginning. It’s also important to focus on the new organizational skills needed for effective implementation. Far too many companies believe that 95 percent of their data and analytics investments should be in data and modeling. But unless they develop the skills and training of frontline managers, many of whom don’t have strong analytics backgrounds, those investments won’t deliver. A good rule of thumb for planning purposes is a 50–50 ratio of data and modeling to training.

Part of that investment may go toward installing “bimodal” managers who both understand the business well and have a sufficient knowledge of how to use data and tools to make better, more analytics-infused decisions. Where this skill set exists, managers will of course want to draw on it. Companies may also have to create incentives that pull key business players with analytic strengths into data-leadership roles and then encourage the cross-pollination of ideas among departments. One parcel-freight company found pockets of analytical talent trapped in siloed units and united these employees in a centralized hub that contracts out its services across the organization.

When a plan is in place, execution becomes easier: integrating data, initiating pilot projects, and creating new tools and training efforts occur in the context of a clear vision for driving business value—a vision that’s unlikely to run into funding problems or organizational opposition. Over time, of course, the initial plan will get adjusted. Indeed, one key benefit of big data and analytics is that you can learn things about your business that you simply could not see before.

Here, too, there may be a parallel with strategic planning, which over time has morphed in many organizations from a formal, annual, “by the book” process into a more dynamic one that takes place continually and involves a broader set of constituents.4 Data and analytics plans are also too important to be left on a shelf. But that’s tomorrow’s problem; right now, such plans aren’t even being created. The sooner executives change that, the more likely they are to make data a real source of competitive advantage for their organizations.

Source: McKinsey Quaterly, March 2013
Authors: Stefan Biesdorf, David Court, and Paul Willmott
About the authors: Stefan Biesdorf is a principal in McKinsey’s Munich office, David Court is a director in the Dallas office, and Paul Willmott is a director in the London office.
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For more information about how to collect data that will help you to drive and improve your business – read more at www.3s.se

Tio trender företagsledare måste ha koll på

Posted in Aktuellt, Allmänt on March 23rd, 2013 by admin

Det är lätt hänt att man blir så upptagna med det dagliga arbetet att man missar vad som är på väg att hända i omvärlden. Duktiga företagsledare ska inte följa flocken utan ligger oftast minst ett par steg före alla andra. Men det kan vara svårt göra sig tid till att stanna upp och försöka se alla möjligheter och potentiella problem i det allt mer frenetiskt blinkande medieutbudet. Det finns dock hjälp. Trendspanare, prognosmakare och framtidsforskare håller ett öga på statistiken och ett på marknaden och samhället samtidigt som de med ett finger i luften försöker förutser hur morgondagen kommer att te sig.

För en tid sedan publicerade två av de mest namnkunniga framtidsbedömarna, professor Thomas Malnight på IMDs handelshögskola i Lausanne och hans kollega Keys Tracey från Strategy Dynamics Global, en prognos för 2013. De kokade sedan ned rapporten till tio punkter av vad företagsledare bör ha koll på under året:

1. Socialisera allt: De ”sociala generationerna” och deras digitala värld
Den sociala tekniken är nu en central del av vårt arbete och dagliga liv. De ”sociala generationerna” kommer att omforma företagen från insidan; hjälpa dem att bygga upp bredare och mer flexibla nätverk för att skapa och leverera nya värden till kunderna. Rörlighet och samhörighet kommer att stå i centrum för den framtida affärsmiljön: kommunikation och marknadsföring går från att fokusera på en-till-en relationer, till många-till-många.

2. Omdefiniera värde: Konsumenten vinner kampen om ”den nya konsumenten”
Begreppet värde omdefinieras för 2100-talet. Konsumenterna vill ha mer personligt deltagande i värdeskapandet och ändra hela tankesättet bakom produktionen till “gjort med mig.” Värdet kommer också att handla om “delat med mig” när den ”herrelös ekonomin” expanderar. Detta kommer i synnerhet att drivas av de yngre generationerna som värdesätter upplevelser som de kan dela med andra, som är bra för hela samhället, framför ägodelar.

3. Distribuera allt: Rörlighet i produktion och konsumtion

Rörligheten går in i en ny fas. Det inte bara konsumeras var och när som helst, utan även verktygen och resurserna som krävs för att skapa värde fördelades även de allt bredare. Arbetet blir alltmer fördelat. Småskalig tillverkning, inklusive 3D-utskrifter, kommer att förändra produktionen. Förnybar teknik distribuerar energiproduktion, medan massundervisningsplattformar håller på att revolutionera utbildningen. Frågan är vad som inte kan distribueras, inte vad som kan.

4. Nästa “industriella” revolution: Robotar och smarta maskiner omforma arbetet
Smarta maskiner och robotar kommer att omdefiniera samhället. Det finns redan nu robotar som ”arbetar” som receptionister, bankassistenter och även fängelsevakter, samtidigt som dagens teknik gör det möjligt för amatörer att göra det vad endast proffsen en gång kunde göra. Fördel: att tekniken kan ta itu med saker som att ta hand om en åldrande befolkning. Nackdel: många arbetstillfällen går förlorade. Men nästa våg av smarta maskiner kommer också att skapa nya typer av jobb. Utmaningen blir att se att det finns en arbetsstyrka som är redo för dem.

5. Den nya rymdkapplöpningen: Gränssprängande teknik igen?
Vetenskapliga framsteg från rymdprogrammen har haft en betydande inverkan på hur vi lever och arbetar, från avancerade material till den globala telekommunikationen. Nu är kommersiella rymdresor och -utforskning en realitet, samtidigt som en ny rymdkapplöpning är på gång, särskilt mellan USA, Kina och Europa. Detta kommer säkert leda till nya framsteg, liksom frågor över ägande rättan över ”rymdtillgångar” och om framstegen ska användas till allas nytta.

6. Geopolitiskt krig: Kampen om framtiden
Det kommer att vara i BRICS och N11 (BRICS: Brasilien, Ryssland, Indien och Kina (China) – N11, Next eleven: Bangladesh, Egypten, Indonesien, Iran, Mexico, Nigeria, Pakistan, Filippinerna, Sydkorea, Turkiet och Vietnam) samt andra snabbt växande tillväxtmarknader som slaget om herraväldet över framtidens ekonomiska tillväxt och sociala utveckling kommer stå. Det är ett multipolärt marknadslandskap, baserat på väsensskilda ekonomiska, sociala och politiska system. Politiker, tillsammans med företag, försöker fortfarande hitta sina platser i den nya världsordningen, även när förtroendet för regeringarna faller, nationalism stiger och vi ser ett tidligt maktskifte gentemot folket. Möjligheten för radikala politiska förändringar inom och mellan nationer ökar.

7. Resurskriget eskalerar: Från en värld av överflöd till en värld i brist
Samtidigt som världens befolkning kommer vara runt 9 miljarder år 2050 börjar planetens resurser att sina, något som bara förvärras av klimatförändringarna. År 2030 kommer det att krävas dubbelt så mycket resurser än vad planeten har tillgång till att producera, vilket medför stora risker för social oro och konflikter när människor och nationer börjar tvingas konkurrerar om allt knappare resurser. De knappa resurserna styr redan pris, volatilitet och gränsöverskridande investeringar. Ny teknik och nytänkande konsumtion kommer att vara avgörande i framtiden, där företag snarare än regeringar kan komma att leda vägen.

8. Företagen stiger fram: Från vinst till ändamål
Många företag har börjat försöka, ofta med partners, ta itu med sociala och ekonomiska utmaningar. Företagen försöker anpassa sig till de krav som konsumenter, medarbetare och investerare har på de företag som de väljer att associeras med. Men det kan också vara bra rent affärsmässigt när företag inser den ömsesidiga nyttan med samhället.

9. Information är makt: Säkerhetsutmaningen
Internet är den nya frontlinjen för säkerhet. Kunskap och information är en källa till makt och ger konkurrensfördelar till organisationer, nationer och individer som vet hur man använder den. Men det är en allt större utmaning att behålla kontrollen när rörlighet och demokratiseringen av allt (handel, politik och samhällen) ökar parallellt med IT-brottslighet och IT-krig. Rättsprocesserena och lagstiftningarna ökar säkerheten och kontrollen vilket minska rörligheten och demokratisering. Digital frihet eller ett storebrorssamhället?

10. Vem behöver bankerna? Omforma det finansiella systemet
Det finansiella systemet är trasigt. Tillsynsmyndigheterna vill ha förändring, företagen vill ha nya finansieringsmöjligheter och konsumenterna vill ha alternativ. Framtidens “banker” kommer bestå av statligt ägda verksamheter och företag som inte använder kontanter: utan istället byteshandel och community-valutor. Digitala plånböcker och mobila banktjänster öppnar dörren för telekombolag och software-aktörer, medan förtroende är inkörsporten för återförsäljare och gräsrotsfinansierade samhällen. I en allt trängre och kontantlöst finansiella systemet behöver inte bankerna längre vara några nyckelaktörer.

Liksom alla stora nya trender utgör spridningen av ekonomisk makt såväl utmaningar och möjligheter. Är du och ditt företag är redo att dra fördel av dessa?

Källa: Talarforums månadsbrev mars 2013
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