Marina Gorbis's Blog, page 1363

Making Good on Your Organization’s Intentions

Why do teams and organizations often fail to achieve their goals and fall short of delivering on their good intentions?

Leaders often focus on securing commitment and buy-in, yet commitment is rarely the problem. Also, the issue isn’t just understanding the goal and knowing what needs to be done. Teams and individuals often understand and know, yet aren’t successful at doing (the “knowing-doing” gap).

In this interactive Harvard Business Review webinar, motivational expert Heidi Halvorson helps individuals and teams improve the attainment of their goals. Halvorson explains why people and organizations often don’t do what they intend, shares insights from neuroscience, describes the two key pitfalls of execution, and describes what if-then planning is and why it significantly increases the odds of executional success.

Coaching Your Employees

When you’re swamped with your own work, how can you make time to coach your employees—and do it well?

It’s a common problem. But if you don’t build your people’s own skills and capabilities, they’ll come to you for answers instead of finding their own solutions. Hand-holding kills productivity and creativity, and you can’t sustain it. In the long run, it eats up a lot more time and energy than investing in people’s development.

So you really must coach to be an effective manager. You’ll need to work with each person to agree on their goals for growth, motivate them to achieve their goals, support their efforts, and measure their progress.

In this interactive Harvard Business Review webinar, Ed Batista, experienced executive coach and co-author of the HBR Guide to Coaching Your Employees, shares insights from this Guide and from his extensive coaching experience. Batista describes how you can improve your coaching skills, create realistic and inspiring plans for people’s growth, customize your approach, and provide employees the support they need to achieve peak performance.

Capitalism’s Future Is Already Here

On September 13, 1970, The New York Times published an article by Milton Friedman castigating any managers of businesses who were “spending someone else’s money for a general social interest” – in other words, requiring customers to pay more, employees to be paid less, or owners to accept smaller profits so that the firm could exhibit some amount of social responsibility beyond the requirements of the law. Already, in his 1962 book Capitalism and Freedom Friedman had declared that “there is one and only one social responsibility of business–to use its resources and engage in activities designed to increase its profits so long as it stays within the rules of the game, which is to say, engages in open and free competition without deception or fraud.” Choices about whether and how to use money to remedy social problems should be left to individuals, he argued, who would be in better position to provide it if they were not being in effect taxed by corporate managers who thought they had better ideas for how to spend it.

The article shocked the sensibilities of many who worried about rising corporate power in the world, but for many executives struggling to chart courses through the chaos of newly globalized and deregulated markets, it offered an irresistible clarity: one need only focus on owners’ interests. In 1976, Professors Meckling and Jensen put a finer point on things with their economic rationale for maximizing shareholder value. Ronald Reagan and Margaret Thatcher gave the idea political cover. Very quickly, shareholder value became the gospel of capitalism.

The tight focus on generating returns drove many gains. It hurried along the formation of global supply chains with ever greater efficiency and economies of scale. As more firms became multinationals, fewer showed loyalty to particular communities or any hesitation to migrate their operations to wherever costs were lowest. Employees were viewed more as fungible inputs to operations, and customers viewed more as targets within more and less lucrative segments.

Yet it also began to be evident that, even if the goal was to serve the interests of a single stakeholder, the pursuit could not be so single-minded. Incentives to maximize shareholder value pushed managers toward decisions that paid off in the short term but were devastating to the long term viability of firms. As I’ve explored elsewhere, pervasive short-termism hampered the United States’ capacity to compete in international markets; encouraged a massive trend of offshoring that destroyed major segments of the US economy; generated “bad profits” that undermined customer loyalty; “financialized” the economy, making it vulnerable to increasingly severe financial crashes; undermined economic recoveries; and drastically reduced rates of return on assets and on invested capital of US firms.

These problems hardly arose overnight; they began brewing early. However, it was after the advent of the internet that the challenges to the shareholder value maximization became forceful. This is because the internet …

Shifted power in the marketplace from seller to buyer. Customers, who had access to reliable information about the available choices and a capacity to interact with other customers, were suddenly in charge.
Raised customers’ expectations. As “better, cheaper, faster, smaller, more convenient, and more personalized” became the new norm, the ability to innovate with committed employees and agile processes became critical.
Shredded vertical supply chains. Customers could buy a wider array of stuff online cheaper, and often quicker, than in a physical store. First books and music, then almost anything.
Spawned vast new horizontal value chains, in which millions of people began creating their own virtual meeting places and marketplaces with their own lateral economies of scale. First computer code, then ideas, then music, photos, and videos – and finally, physical things.
Enabled firms to create huge ecosystems of suppliers and customers that could achieve enormous scale without the sclerosis of big hierarchical bureaucracy.

As a result, a new era is emerging. Harking back to Peter Drucker’s insistence in 1973 that “there is only one valid definition of business purpose: to create a customer,” Roger Martin has declared that we are finally entering “the age of customer capitalism.”  If firms serve customers well, Martin asserts, benefits for shareholders and the community follow. Customers as stakeholders become the new center of the capitalist universe and its new gospel.

The shift in goal entails a transformation in management practices from those of hierarchical bureaucracy, including a shift from controlling individuals to enabling teams, networks, and ecosystems; a shift in the way work is coordinated from rules, plans, and reports to agile processes and dynamic linking; a shift from the values of efficiency and predictability to those of continuous improvement and transparency; and a shift from one-way, top-down communications to interactive conversations. These shifts are not just a grab-bag of unconnected management gadgets. They constitute a coherent constellation of leadership and management practices, as described by more than a score of books.

The confusing reality of the moment, however, is that there are (at least) two different systems, operating simultaneously, at different speeds and on different trajectories.

One—the Traditional Economy—is the economy that we inherited from the 20th Century. It’s a world of command and control, focused on making money through economies of scale and comprising big hierarchical bureaucracies that push out products and services and get customers to buy them with sales campaigns and advertising. This is still the larger of the two economies. It’s been in steady decline for a number of decades. It doesn’t generate net new jobs. It’s not very agile. It’s becoming steadily more efficient. But it’s not good at innovation. It’s less and less able to capture the gains of its efficiencies. It’s still a big part of what’s going on in the world. But it doesn’t have much of a future.

The other economy—the Creative Economy—is an economy of continuous innovation and transformation. This is the economy of firms and entrepreneurs that are delivering to customers what they are coming to expect, namely, “better, faster, cheaper, smaller, lighter, more convenient, and more personalized.” The Creative Economy is still relatively small but it is growing rapidly and, when implemented well, is highly profitable. It is the economy of the future. It doesn’t have to be invented: it’s already under way. Its practices represent a paradigm shift in the strict sense laid down by Thomas Kuhn: it’s a different way of thinking, speaking, and acting in the world.

The shift from the Traditional Economy to the Creative Economy isn’t just a technical wrangle about economics or management theory. It’s a shift in what society demands of the managers of its most powerful institutions: from narrow definitions of their owners and decisions that serve their short-term interests, to broad acceptance of the responsibility that comes with power and leadership concerned with what is best for society. In the shift, we are learning that an argument about the proper activities of managers can be logical, can be strongly argued, can influence decades of practice in the world’s largest corporations – and can still be plain, flat, dead wrong.

 

This post is part of a series of perspectives by leading thinkers participating in the Sixth Annual Global Drucker Forum, November 13-14 in Vienna. For more information, see the  conference homepage .

Will Tim Cook Ever Escape Steve Jobs’s Shadow?

Being the successor of a successful leader is one of the toughest challenges. How can you do more? There’s a lot to lose and few chances to win. Should you replicate the winning leadership style of your predecessor? The chances are that you will likely do worse. Should you change and build a new style? You risk destroying a well-tuned machine that works perfectly.

Three years ago Tim Cook accepted such a challenge. After a transient of three years without significant innovative product launches, yesterday he unveiled his first move into a new product category: smartwatches. He announced the Apple Watch with the well-known sentence that Steve Jobs used at the end of his speeches: “One more thing…” Regardless of what you thought of the latest smartwatch, those words are a cause for concern.

Like others’ initial reactions to the Apple Watch, mine are mixed. It has an excellent design. Apple also obviously worked intensively on the user experience. And it provides some delightful features, such as the digital touch that allow a new type of social interactions among people who wear the watch and are next to each other (e.g. by sending your heartbeat to the watch of your lucky friend).

But is the new smartwatch another example of Apple entering an emerging product category late and proposing a new interpretation that offers a more meaningful user experience? I’m not totally convinced. This time Apple seems less bold. It does not really reinvent the category; its differences from other smartwatches already on the market (e.g., Motorola’s Moto, Sony’s SmartWatch, and Samsung’s Gear) are not striking. Some commentators likened this lack of breakthrough features to other Apple copycat moves (e.g., the new iPhone 6’s bigger screens, which follows Samsung’s lead). According to some, this is a sign that Apple has lost its magical touch.

That said, the Apple Watch could be a winner. Customers (and app developers) will ultimately determine its success.

I’m more concerned about the tone of yesterday’s event and what it reveals about Apple’s leadership. The event came across like a replay of a movie I had already seen: the same format used by Steve Jobs — the same staging, colors, lighting, pace, and agenda; (almost) the same faces and voices, with the same magnifying adjectives celebrating the products features; the same “one more thing,” and the same band (U2) closing the event. In some moments, the thin silhouette of Tim Cook even reminded me of Jobs.

Apple loves art. So let me use an art-based analogy to describe my feelings. Yesterday’s extravaganza looked as if Apple’s leadership had entered a Mannerism period: In the 16th century, after the radical changes introduced by Renaissance masters like Leonardo, Raphael, and Michelangelo, many artists found it difficult to blaze a new path and instead copied and exaggerated their predecessors’ styles. It was a more sophisticated but also more artificial way of painting that lost the harmonious and natural dynamics of the Renaissance. In other words, yesterday’s extravaganza came across to me like an exaggerated celebration of Jobs’s style.

For Apple, the risk is Cook is applying a leadership recipe that has run its course. Every organization needs rituals, self-celebration, and stability, of course. But it also needs renewal. Not only because markets and competition change, but also because people in an organization — especially the youngest and freshest members of it — need new causes. They need new rituals and “manners” that they have helped create. This gives them a sense of ownership of the future and fuels new energies. As several innovation and strategy studies show, the most pernicious competitor of a successful organization is not out there in the market; it’s inside. Perhaps the strongest competitor of today’s Apple is Jobs’s Apple.

For Cook, as a leader and as a person, the risk is that by staying on the same path, he will never be “as good as Steve.” The risk is that one day, looking back to these years, he will have a feeling of having been good but never good enough. For sure, Cook’s leadership style has been forged by his closeness to Jobs. And for sure, there is a sense of emotional attachment, a sense of gratitude. But no one is the same. Perhaps Cook’s own style would be good for Apple and allow it to achieve greater heights; perhaps not. But at least he should give his organization and himself a chance to do so.

My hope is now that Tim has proven he can lead Steve’s way, he will feel free to move on and lead Tim’s way.

The Condensed October 2014 Issue

Amy Bernstein, editor of HBR, offers executive summaries of the major features. For more, see the October 2014 issue of HBR.

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Why Your Marketing Metrics Don’t Add Up

Few things can be as jarring for marketers as losing trust in your metrics. There are many sources of marketers’ trust issues with their data, but the one I hear cited more than any other is conflicting or competing data sources. Perhaps the numbers are emerging from two different reporting systems or third-party publishers, or maybe it’s a case of misattributed engagement, but all too often marketers slap down side-by-sides that simply don’t add up. When 60% of customers are coming from one channel, and the remaining 50% from another, then what? Anxiety, distress, distrust—and the cycle continues.

This discomfort is an unwelcome side effect of some welcome advances in omnichannel marketing—you’re everywhere and so is your marketing, and that’s bound to lead to at least some static. However, what we’re seeing now takes that even further, leaving marketers with significant internal and external roadblocks in the journey to become data-driven. How can you and your organization determine a single source of truth and, ultimately, leverage this to make smart, effective, efficient decisions, when the data not only doesn’t provide a clear-cut next step, but may not even make sense?

Give more sources “credit” for the sale

Attribution is one of the most common sources of confusion and distress. Even the most seemingly airtight campaigns can wind up in a situation where, at least at the surface, the numbers don’t tell a steadfast tale. One of the most common sources of data disconnect in marketing and advertising is misattribution—very simply, which platform, offer, or campaign gets the “credit” for a conversion?

Think about the last time you logged into Facebook and saw an article or video that was making the rounds. It was probably posted on a few of your friends’ walls, and maybe you saw some other people tweet about it. By the time your friend texted it to you, you’d already seen it, because you subscribe to daily updates from the site that originally posted it. Sound familiar? To which source should your click be attributed? Didn’t they all, in some way, drive you to engage with the content? Does it matter which link you clicked through when you actually consumed the content? What’s more, it’s possible you clicked through from more than one source. So who wins?

Trust issues bubble to the surface because marketers feel misled by the seemingly conflicting data when, in fact, it’s all correct—just different interpretations of what ultimately led to the conversion. Because consumers’ journeys are becoming increasingly circular, attribution can come from a variety of sources and shouldn’t incite distress signals. A greater understanding of and appreciation for this circuitous system can help dissuade some of this distrust.

Embrace multiple data sources

The best answer to data trust issues is to understand the various sources of legitimate confusion, like in the attribution example above. To accomplish this, marketers should be prepared to determine campaign key performance indicators (KPIs) ahead of launch, and ensure proper measurement across multiple internal and third party data sources.

While relying on a single data source sounds appealing, in practice digging through multiple sources is more likely to yield an accurate picture.

Global PC manufacturer Lenovo, for example, implemented a multichannel analytics strategy to measure customer satisfaction. By analyzing data from six sources—the web, postpurchase surveys, its customer relationship management (CRM) system, call center, email, and live chat—Lenovo ascertained why customers were phoning the call center and what actions they took before calling. That insight helped them improve areas of the website to reduce calls and avoid customer dissatisfaction with long wait times.

Learn to live with inflated metrics

Another common source of confusion is artificially inflated metrics. Are consumers flying through a slideshow, paying little or no mind to the images and native or promotional content? Although those aren’t great engagement experiences from a marketing perspective, they’re certainly being counted both by in-house solutions and third-party reporting systems. This doesn’t necessarily mean these are bad platform choices; but it is something you need to take into account in order to have a concrete understanding of how your marketing budget is being spent and the effectiveness of your campaigns.

And when all else fails and definitively determining the truth seems impossible, simply admit it. Decide what your organization will use to measure success, how it will be articulated and leveraged, and what you’ll look for from campaign to campaign, test to test—and make sure everyone falls in line. By having a universal set of KPIs, metrics, and vernacular, you’ll save time and key resources from ongoing debate, chatter, distress, and distrust, freeing up time for meaningful optimization activity.

Convincing Employees to Use New Technology

All of our companies are digital now – or quickly becoming that way. Almost any enterprise you can think of, no matter the industry or sector, is trying (or being pressured by competitors) to use new technology to harness the vast new oceans of data being generated by smartphones, sensors, digital cameras, GPS devices, and myriad other sources of information originating from customers and markets.

Yet how many millions of dollars have been spent on analytics technology, but with no parallel improvements – or even any changes – to the way decisions are made within a business?  How many companies have deployed internal wikis and social networks with great fanfare only to see slow take-up or a huge slow down after a few months?  Even among digital natives, adoption of things like enterprise digital tools often doesn’t live up to lofty expectations.  “We’ve spent an awful lot of money on technology, but I still see people working in the old way,” complained the CFO of a large hospitality company.  The result is often widely deployed internal applications that no one actually uses effectively. Why does this happen?

When these platforms are introduced, organizations too often focus only on deployment, not adoption. It’s remarkable how commonplace it is for leaders to lose sight of the true ROI of their digital investments: collaboration among actively engaged users, smarter decision making, increased sharing of best practices and, over time, sustained behavior change.

There are three related problems. First, CIOs and technical leaders too often take a limited “tech-implementation” view and measure success on deployment metrics like live sites or licenses. They consider business adoption someone else’s job, but in fact no one is made accountable for it. Second, platform vendors often oversell the promise of instant change through digital technology. They make their money by selling products and software, rarely by getting them used at scale. And finally, the bottom line: user adoption programs cost money.

The real return on digital transformation comes from embedding new work practices into the processes, work flows, and ultimately the culture of organizations. But even in cases where the value of adoption is understood, cost containment often takes over. Faced with limited budgets, companies focus on the most tangible part first – deploying the technology. Adoption is left for later, and often “later” never comes.

This drives negativism that can spread through the organization. Business users don’t see the value and fail to engage in the new digital platforms. The platforms are themselves blamed for the failure. Cynicism sets in. Every additional digital investment gets negatively scrutinized and the whole digital transformation program slows down.

When the process works, the benefits become obvious. Sometimes adopting a new technology can even become an irreversible movement. “We have started a ‘digital movement’ that affects all aspects of our company and requires all of our people to be engaged in the program. We will only win together,” explained Pernod Ricard’s CEO Pierre Pringuet.

Business adoption of digital tools has to be led. So what do you need to do?

Do fewer things better. Despite the myriad opportunities that exist to invest time, effort, and dollars into making your business more digital, you can’t do them all. Focus on the initiatives that you believe, once adopted by the business, will create real value — and that you believe you can actually finish. Prioritize those initiatives by both the size of the business impact and relative ease of execution. Put a time limit on execution and allocate the resource level it will take to succeed in that timeframe. And clearly communicate the value of adoption to your employees.

Plan and budget for adoption from the start. Plan for what it will take to realize the benefits beyond the technology deployment efforts. Take into account the people, process and structural changes. Budget for the  communication, training and organizational development required to succeed. And ensure that proper governance and metrics are in place to monitor progress.

Lead by example. You can influence the transition to new digital ways of working by modeling the change you want to see happen – and by encouraging your colleagues to do so. For instance, actively participating on digital platforms and experimenting with new ways of communicating, collaborating, and connecting with employees. It is the first important step to earning the right to engage your organization. Coca-Cola faced huge challenges when it deployed its internal social collaboration platform. Only when Coca-Cola’s senior executives became engaged on the platform did the community become active. As the implementation leader put it, “With executive engagement, you don’t have to mandate activity.”

Engage true believers. Drawing on influential employees in the front line is one of the most effective vehicles for promoting change in an organization. Identify your committed digital champions early – individuals who network well and can create horizontal influence to help implement behavior change across silos. Devise a program to nurture your digital champions, as they are key to transformation success and will most likely be your organization’s future digital leaders.

Engage your HR and Organizational Development people early. Encourage them to take a leadership role in the transformation. It will be essential for them to adapt management and HR processes to ensure the new practices get institutionalized – for instance, designing a new digital competency model or formalizing a reverse mentoring program. They also need to ensure that the metrics and goals that describe adoption success are monitored, as well as to provide regular analytics on progress.

Align rewards and recognition. Transformation goals and measures are inextricably linked. It’s natural that conflicts in your reward structures will occur as you get into the depth of your digital conversion, and this can slow down execution. Many retailers, for instance, have had to align their online and in-store sales incentives to avoid channel conflicts. Use all available reward structures to foster adoption, not just financial ones. And consider new forms of employee engagement, such as games, which can also yield positive results.

Remember, creating a digital organization is not just about implementing new technology. If you want to see true and lasting value from your technology investments, people need to change their mindsets and behaviors, and you need to lead that change.

When Star Talent Grew More Powerful than Capital

Any mention of the Sixties elicits an immediate response: hippies, Vietnam protests, Woodstock, sexual freedom, and psychedelic drugs. But along with all of that something quite dramatic happened to corporate America as well, and it started showing up in the data in 1960. It was the first salvo in an economic revolution, one that was largely in keeping with the above phenomena: the rise of talent.

As I describe in my article in this month’s HBR, up to 1960, the U.S. economy had evolved at a glacial pace and had exhibited remarkably narrow and limited creative intensity.   At the turn of the 20th century, the proportion of workers who had to exercise significant independent judgment and decision-making in their jobs had been just 13%, and the remaining 87% of workers across all job classifications worked in routine-oriented jobs in which their superior determined what they were supposed to do all day and, to a great extent, how they were supposed to do it. By 1960, the percentage of creativity-oriented jobs had risen to just 16%, representing a change in job content for only 3 in every 100 workers over a 60-year period.   In 1960, therefore, 84% of all jobs held by Americans involved minimal independent judgment and decision-making.

This was truly an era in which to prosper a company had to have capital and to own natural resources; but really didn’t need much in the way of uniquely talented employees. Rather they needed lots and lots of competent and compliant ones.

From 1960, however, the economy started morphing in a fashion that required more and more workers to express meaningful judgment and decision-making. The growth in these jobs accelerated during 1960-2010 at a rate four times that of 1910-1960 – so much so that by 2010, the proportion of creative jobs had more than doubled to comprise 33% of the workforce.

This came as no surprise to Peter Drucker, who repeatedly predicted fundamental changes in the U.S. economy during the 20th century. As early as 1959 he was arguing argued that the economy was changing from one in which the key assets were strong legs, arms and backs to one in which the most important muscle was the one between an employee’s ears. These knowledge workers would be different, he suggested, they would not be able to distance themselves from their day-to-day work because their work was a product of their brain — they were their work. This meant that they needed to be treated more like volunteers than employees, an amazingly prescient observation.

The growing creativity of work of the past five decades has shown up in stock values as well. In 1960, just eight of the top 50 market capitalization companies owed their position to creative talent. Predictably, perhaps, the largest was IBM, which in 1960 stood at #4. But there were also Eastman Kodak (#11), P&G (#15), General Foods (#19), Coca Cola (#34), American Home Products (#40), Campbell Soup (#48), and RCA (#49). They were still outliers; far more common were firms that owed their position to their control of natural resources, such as oil or minerals, or real estate.

But these heralds were soon joined by many more and moved from being a small minority to the dominant force in the economy, comprising 28 of the top 50 companies.

It is hard to think about this transformation as anything but a positive thing. Twenty of every 100 American workers who would have had a routine-intensive job a century ago have a creativity-intensive one now. But it has come with a cost to the capitalists who own these companies. Competent and compliant workers also meant cheap workers. Workers who must demonstrate meaningful independent judgment and decision-making don’t come cheap and they certainly don’t exhibit compliance as one of their leading characteristics.

Capitalists used to spend their time battling unionized labor for the spoils of their joint economic pie — and generally capitalists were successful. Now their battle is with the high-end talent upon whom capital is entirely dependent to make the decisions that will make the company they own profitable or not. That talent has a hell of a lot more bargaining power than organized labor ever had. (And labor itself is now essentially friendless.)

Drucker was right on two fronts: talent-laden knowledge workers would become a dominant force and they would have to be treated with kid gloves as if they were volunteers. But it is unclear whether Drucker realized that they would need to be treated as ultra-highly paid volunteers and become in the 21st Century capitalists’ principal economic adversary.

The Reason Your Team Won’t Take Risks

Most senior managers agree that taking risks is important for innovation, but in far too many cases, they don’t act like they believe this. For example, one global organization, where one of us conducted a culture survey several years ago, considered itself to be highly supportive of developing new products, services, and practices. Yet when several hundred professionals were asked what would happen if they developed and tried “new and untested ideas,” only 17% said that such behavior would be rewarded or approved – 47% said that the reaction from their superiors would be “unpredictable.”

In other words, the reality in many organizations today is that despite the public emphasis on innovation, the underlying culture may be strongly risk-averse. As one senior manager in a large financial institution said to one of us (only partially tongue-in-cheek), “The key to success here is to never make the same mistake once.”

Unfortunately, this kind of attitude is anathema to successful innovation, which does indeed require a tolerance for risk-taking and learning from periodic failure. So how can you break out of this mode and create an environment that is more conducive to innovation? In our experience, one of the starting points is to be more explicit about what risk-taking really means, and what is acceptable and what is not. Here are four tactics for doing this:

Publicly define a smart risk. The better innovation companies distinguish the areas where risk is encouraged, and where it is not. One of our clients, for example, makes it clear that there should be minimal “execution risk” regarding customer commitments and financial results, but encourages “discovery risk” in developing new solutions to customer problems. These guardrails define the “safe zone” for innovation, and they should include specific parameters such as time (must show progress after x months) or financial impact (has the potential to generate xx revenue or costs no more than xxx).

Use the right words to encourage the right culture. Language drives behavior and creates a mindset around what’s acceptable and what’s not. For example, using terms like “experiment” or “scouting mission,” instead of “successful vs. unsuccessful project,” will signal a more open attitude toward risk. Centering innovation activities on the concept of “exploration” eases the tension associated with trying new things. That’s why Amazon’s Jeff Bezos encourages an “explorer mentality” rather than a “conqueror mentality” in his teams, so that their focus is on forging new paths rather than just doing better than their competitors. The beverage company Pernod Ricard established a division called the “Breakthrough Innovation Group” to experiment with new ideas. The group has a similar spirit to a Silicon Valley start-up, in that it brings an entrepreneurial, exploring mindset into the larger company.

Keep it nimble and small. Size matters, and when it comes to innovation risk, smaller – and faster – experiments are often better. Tesla keeps teams small, so they maintain an entrepreneurial mindset with a higher tolerance toward risk than older firms in the automotive industry that rely on larger teams. A similar example comes from the Defense Advanced Research Projects Agency (DARPA). Unlike other government agencies, it is a lean organization with only two management layers, which enables them to move ideas and decisions with speed, because as they say, “urgency inspires greater genius.” DARPA also employs small teams on projects that move quickly and have clear autonomy – which has led to incredible innovations.

Establish clear phases and criteria for funding projects. If you currently have risky (and expensive) innovations in your pipeline, stop providing blank checks. Instead, fund each project in clearly defined phases. If it passes one phase, give it additional funding. At Google, teams have timelines of three to four months to prove a concept’s viability. If the idea they’re working on doesn’t prove itself sufficiently in that timeframe, teams are disbanded. Teams are expanded only if ideas have demonstrable potential.

Successful innovation is never guaranteed – it always entails a certain amount of risk. If employees don’t understand the types and amounts of risks that are acceptable, they might not be willing to get into the innovation game. In the long term, that could put your company at even greater risk.

How to Clone Your Best Decision-Makers

Any company’s decisions lie on a spectrum. On one end are the small, everyday decisions that add up to a lot of value over time. Amazon, Capital One, and others have already figured out how to automate many of these, like whether to recommend product B to a customer who buys product A or what spending limit is appropriate for customers with certain characteristics.

On the other end of the spectrum are big, infrequent strategic decisions, such as where to locate the next $20 billion manufacturing facility. Companies assemble all the data and technology they can find to help with such decisions, including analytic tools such as Monte Carlo simulations. But the choice ultimately depends on senior executives’ judgment.

In the middle of the spectrum, however, lies a vast and largely unexplored territory. These decisions — both relatively frequent and individually important, requiring the exercise of judgment and the application of experience — represent a potential gold mine for the companies that get there first with advanced analytics.

Imagine, for example, a property-and-casualty company that specializes in insuring multinational corporations. For every customer, it might have to make risk-assessment decisions about hundreds of facilities around the world. Armies of underwriters make these decisions, each underwriter more or less experienced and each one weighing and sequencing the dozens of variables differently.

Now imagine that you employ advanced analytics to codify the approach of the best, most experienced underwriters. You build an analytic model that captures their decision logic. The armies of underwriters then use that model in making their decisions.  This is not so much crunching data as simulating a human process.

What happens? The need for human knowledge and judgment hasn’t disappeared — you still require skilled, experienced employees. But you have changed the game, using machines to replicate best human practice. The decision process now leads to results that are:

Generally better. The incorporation of expert knowledge makes for more accurate, higher-quality decisions.
More consistent. You have reduced the variability of decision outcomes.
More scalable. You can add underwriters as your business grows and bring them up to speed more quickly.

In addition, you have suddenly increased your organization’s test-and-learn capability. Every outcome for every insured facility feeds back into the modeling process, so the model gets better and better. So do the decisions that rely on it.

Using analytics in this way is no small matter. You’ll find that decision processes are affected. And not only do you need to build the technological capabilities, you’ll also need to ensure that your people adopt and use the new tools. The human element can sidetrack otherwise promising experiments.

We know from extensive research that decisions matter. Companies that make better decisions, make them faster, and implement them effectively turn in better financial performance than rivals and peers. Focused application of analytic tools can help companies make better, quicker decisions — particularly in that broad middle range — and improve their performance accordingly.

Predictive Analytics in Practice

An HBR Insight Center




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