For all the ink spilled on the concept of strategy, it continually proves to be a surprisingly slippery idea. In practice, leaders often struggle to define and communicate strategies to their team. In their 2008 article, “Can You Say What Your Strategy Is?” David Collis and Michael Rukstad don’t mince words: “Most executives cannot articulate the objective, scope, and advantage of their business in a simple statement. If they can’t, neither can anyone else.”

Here, I’m going to describe a document that you can think of as a bridge between your strategy and the seemingly endless communications you need to make to get various stakeholders comfortable with backing it. I call it a “strategy spine” because it describes how the strategy translates into specific flows of resources. Much as your own spine serves to tie together many different parts of your body to create capability for movement, a strategy spine shows key stakeholders exactly how all the pieces fit.

Back from the Future

Before you can start building your strategy spine, you need to be clear on objectives, scope, and key advantages, for, say, a five-to-six-year timeframe. An exercise that I recommend is for those who are involved with creating the strategy to write a story from the future. Write it as though an admiring reporter for a leading publication in your sector were telling the story of your success.

What would that world look like? What experiences would customers be having that they would be willing to pay for? Which ecosystem partners will you be collaborating with? What kind of environment is it going to be like for your employees? If you are in a mission-driven organization, what kind of impact will you have had and for whom? Make sure that you mention the key decisions and resource allocation choices you have made along the way to that future success.

Through this process, what you will find is that you are going to be, intentionally or not, making choices about what things led to that success and what things could have detracted from it. Then, coming back to today, you can start to fill out your strategy spine document.

Creating the Strategy Spine

The strategy spine document consists of six elements, as follows:

To show what an actual spine looks like, I’m going to look at a well-known firm tackling a big new opportunity. I’ll begin by describing the firm’s strategy and I’ll then fill out what could have been that company’s strategy spine. I should note that I am not working with the company referred to nor do I have any insider information.

Unilever’s Positive Beauty Growth Platform

In September of 2021, consumer packaged goods giant Unilever kicked off a new venture, called the Positive Beauty Growth Platform. It’s part of a larger strategy for the firm which is aimed at linking together the twin issues of inequality and environmental sustainability. Sustainable brands already represent high-growth opportunities for the firm, as it reported in 2019 that its purpose-led brands have outperformed the others in its portfolio in terms of growth.

The platform seeks to fund startups and scaleups to give it a cutting edge look at the changing way in which people are buying, most notably through mechanisms involving social connections. Grandview research finds that this way of buying is worth about $474.8 billion in size today but is slated to grow rapidly to some $3.4 trillion by 2028. So this is a classic investment in a set of options — investments with huge upside potential that you can access with a relatively contained downside.

One would want to know, when doing this for real, what the objectives are for the project to contribute to the personal care division.  Although I’m not privy to this information, we can play around a bit with what we do know. The division’s revenue in 2019 was some $24.5 billion and it has been growing successfully, according to Statista.

Clearly, one of the goals of this strategy is to meet customers where they are — which increasingly is not in conventional retail outlets for the kind of products Unilever sells. Indeed, Seb Joseph reports in Digiday that 8% of Unilever’s total sales in 2020 were coming from e-commerce channels, as opposed to 6% the year before. If we figure that by the time the strategy is mature, e-commerce of some kind will represent 10% of the Division’s sales, that implies investments to capture some $2.5 billion of consumer spending by those engaged in social activities combined with commerce.

A Hypothetical Strategy Spine

The exhibit The Strategy Spine shows what it might look like, all filled out. Without insider knowledge, it’s hard to put specific numbers and goals in the boxes, but I’ll offer some illustrative examples.

How should you use the document? You can think of this as a living document bringing the principles of agile working to strategy — you can create one, learn more, get feedback, and update it as new information comes in. What it allows you to see, in a simple format, is how all the different pieces of revenue and investment fit together — or don’t. You can now use it to work backward to establish the annual metrics and goalposts that becomes part of your operating plan. This way, you can modify the plan as you test your assumptions and learn more about what is viable or not. Good luck!

This content was originally published here.

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The labor mismatch has pushed private-sector wages to increase at more than double the long-term pre-COVID-19 growth rates, yet positions remain unfilled. There are several underlying factors for this imbalance. Some are directly related to the impact of the COVID-19 pandemic and are therefore likely to be temporary. There are indications, however, that deeper structural shifts are at play that could have a longer-lasting impact on labor supply and demand. On the supply side, evolving work preferences and accelerated retirement may continue for some time; likewise, demand shifts from services to goods also appear to have some staying power.

Addressing the challenges is not easy, and focusing on recruitment and pay may not be sufficient to resolve the issue. Successfully navigating the current labor mismatch requires a comprehensive set of coordinated actions that address labor issues and their effects across the value chain. Nevertheless, there are actions executives can take to respond.

The 2021 labor mismatch has had a profound impact on US businesses

The United States’ post-COVID-19 economic recovery has seen an unusual reduction in labor-force participation. Jobs are available—the job-openings rate is around 50 percent above prepandemic levels—but the workforce to fill them has contracted. About four million people have left the civilian workforce (Exhibit 1).

Despite wage increases, logistics operations are still having difficulty hiring and retaining frontline workers, while also seeing increased absenteeism, causing knock-on effects across the supply chain. Suppliers’ on-time delivery rates are falling, a situation exacerbated by supply shortages. “On orders” are being cut at greater rates and experiencing significant delays, driving even further volatility in order patterns. Companies that employ third-party logistics services are also experiencing considerable challenges, such as transport rates increasing by up to 30 percent.

The labor mismatch is unlikely to dissipate on its own

What’s striking about the current labor challenge is that, unlike in the past, higher wages alone have not led to positions being filled. There are several underlying factors for this imbalance—some may be temporary, while others are long lasting. There are also regional differences, and in some cases labor availability varies significantly at different zip-code and skill-level combinations.

Some factors related to the COVID-19 pandemic are beginning to dissipate. For example, the federally enhanced unemployment-benefits program wound down in September. Workers who left their jobs because of health concerns or to take care of family members or children at home due to school or childcare-facility closures may return to work. ¹ 1. A McKinsey survey found that among respondents who had left their jobs, 45 percent cited the need to take care of family as an influential factor in their decision. See “‘Great Attrition’ or ‘Great Attraction’? The choice is yours,” McKinsey Quarterly, September 8, 2021. And training programs that were suspended due to the pandemic, such as those provided by driving schools, have largely resumed.

Last, a change in mindset toward work may also be an underlying factor of long-term shifts in labor supply. McKinsey research indicates a disconnect between why employers think their staff are leaving and why employees are actually leaving their jobs. Employers are looking at transactional factors, such as compensation or alternative job offers, but these are not the primary drivers of attraction or attrition. Employees place greater value on relational elements, such as a sense of belonging or having caring and trusting teammates at work.

4. Infrastructure Investment and Jobs Act draft, August 2021; EMSI; US Bureau of Labor Statistics. Since the logistics and construction industries typically attract similar pools of labor supply, the impact of such legislation would extend multiple years into the future. Additionally, the shift in consumer spending from services toward goods during the COVID-19 pandemic, which added supply-chain pressure to refill fast-selling products, may also stick.

Several industries are also experiencing drastic changes in demand. The travel and food-services industries, for example, saw severe demand drops and responded by furloughing or laying off workers and accelerating early retirements. These measures may have contributed to structural shifts in the labor market for these industries. The trucking industry was facing falling numbers of drivers before the pandemic because of multiple factors, including generational demographics, age limits, time away from home, and drug tests. The pandemic compounded the problem: on one hand, more people ordered goods to their homes, which changed how the deliveries were made and further increased the demand for truck drivers—and on the other, the closure of truck-driving schools, combined with a pull of labor supply away from driving toward construction, reduced the supply of labor.

A meaningful intervention for the mismatch

Together, these factors mean that the labor mismatch in US supply chains is unlikely to dissipate quickly, with imbalances in supply and demand persisting. So what can companies do to address this imbalance now? In this unprecedented environment, companies may have to look beyond the traditional levers of recruitment and retention, and also implement a comprehensive set of coordinated actions to address the labor shortage. For interventions to be meaningful, they need to address the full value chain.

This seems challenging, but there are reasons to be optimistic. Companies are seeing meaningful shifts in their labor-supply profiles by taking the following steps.

Ensuring viability of the supplier base. Companies can engage suppliers with large labor forces—for example, temporary labor, food services, janitorial services, and third-party transportation—to ensure operational viability or identify alternative suppliers that can reduce first- and second-tier supplier risk.

Reimagining the job of a driver and warehouse worker

One logistics company used advanced analytics, including machine-learning techniques and web scraping more than 50,000 reviews, to identify causes of worker attrition among its drivers and distribution-center employees. It found that the physical nature of the job, lack of work–life balance, and scheduling issues were key drivers of attrition.

The company then designed a range of interventions to mitigate these issues, including a leadership training program for supervisors and managers to address frontline grievances. It also provided greater flexibility in scheduling and pay, and collaborated with customers to solve the root causes of employee-satisfaction problems—such as SKUs that were difficult to pick and deliveries that were scheduled for inconvenient times.

Finally, the company developed an implementation structure and stood up a project-management office to ensure that initiatives were successfully implemented. In distribution centers where changes had been implemented, worker retention improved by about 10 to 15 percent; the company sought to scale those gains across the organization.

Reimagine the employee value proposition—beyond wages. Companies that solved for competitive wages and built attractive value propositions for employees have found it easier to retain their workforces. In addition to proactively adjusting wages to stay ahead of competitors (especially in highly competitive markets), or embarking on aggressive recruitment campaigns, companies can deploy analytics to pinpoint drivers of attrition—and make bold changes where it matters most (see sidebar “Reimagining the job of a driver and warehouse worker”).

Create capability to identify the stressed nodes and adjust labor flows. Companies can take measures to shift network flow away from labor-stressed nodes, especially where labor supply varies across regions. For example, orders could be rerouted to other warehouses, or products could be manufactured in locations that are less stressed from a labor-supply standpoint. Reformulating or redesigning products can help as well by reducing the need for labor-constrained components and ingredients.

Increasing output by reducing complexity

A consumer-goods company was able to increase productivity by cutting 30 percent of its product portfolio with limited impact on sales. It achieved this by defining the labor cost and complexity of each product, deploying advanced analytics to estimate the substitutability of each product, and conducting an assortment and optimization simulation to identify which SKUs to delist (exhibit).

Explore lean management and automation. Companies may reduce reliance on labor across the supply chain over the long term through product reengineering, lean-management transformation, and automation. Furthermore, automation could help companies improve employee engagement and satisfaction. More than 40 percent of employees spend at least a quarter of their time performing manual and repetitive tasks. In some cases, automation can help not just reduce labor demand, but also allow employees to spend more of their time on higher-value, meaningful work.

Engage customers and suppliers on cost and service. Companies can engage customers on value-based offerings. They can also engage suppliers through cleansheet—based negotiations that build in complete cost-to-serve estimates, such as cost differences for labor-intensive activities, and service factors such as lead times and delivery windows.

Unlock new sources of labor supply. Companies can explore new sources of labor supply—for example prison-, juvenile-, or veteran-transition programs—or adapt roles for non-English speakers and reskill workers from declining industries or roles.

Bolster HR processes. They can also streamline and strengthen interview and onboarding processes—for example, by setting up “talent war rooms” to focus on such interventions.

Leveraging people analytics to improve frontline retention

A trucking company successfully deployed people analytics to improve frontline retention. First, it identified the top quartile of drivers who were most likely to leave the company. Analysis of this high-risk population allowed the company to identify the key drivers of employee dissatisfaction and implement targeted interventions. These interventions led to an improvement of more than 20 percent in new-driver retention, a 15 percent increase in the number of driver applications, and a more than 30 percent increase in the number of new hires, which translated to a 10 percent-plus increase in revenue potential (exhibit).

Develop agile management across functions. Companies can deploy digital performance-management tools, such as control towers, to manage labor flows. Daily cross-functional war rooms can increase visibility around labor availability and help the organization to plan and adjust accordingly.

The labor mismatch is a complex challenge, one that may be here to stay for a while—and it is clear there is no silver-bullet solution.

The labor mismatch is a complex challenge, one that may be here to stay for a while—and it is clear there is no silver-bullet solution. Companies looking to embark on a labor-resilience transformation can take the following three steps. First, employers need to understand how labor shortages impact their suppliers, internal labor, and customers—starting with the size and impact of labor risk across operations; the severity of the labor gap by location, roles, and suppliers; and a forecast of labor dynamics in each relevant market. Second, companies could design bold interventions that structurally change both the demand and supply of the organization’s labor. Third, companies may require strong executive-level support to ensure that cross-functional initiatives are implemented effectively.

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For many brands, it has been a devastating year to say the least.

Over half of the most valuable global brands have experienced a decline in brand value, a measure that takes financial projections, brand roles in purchase decisions, and strengths against competitors into consideration. But where some have faltered, others have asserted their dominance and stepped up for their customers like never before.

The visualization above showcases the top 50 most valuable global brands from a study conducted by , which calculates brand value across hundreds of companies.

As consumers move cautiously into 2021, which brands have they chosen to keep by their side?

The Heavy Hitters

With an eye-watering brand value of $323 billion, Apple is the most valuable global brand in the world, followed closely by Amazon in second place, and Microsoft in third. Average growth in brand value across all three of these tech brands in 2020 was roughly 50%.

In particular, Microsoft—who overtook Google in this year’s ranking—has increased its brand value by $100 billion in just one decade. The tech giant has reinvented itself over the years by focusing not just on how its products impact consumers’ lives, but instead on how they impact the planet. The company is promising to become carbon negative by 2030.

However, other brands that sit at the top of the global brands list have not had the same recent success. Coca-Cola for example sits in sixth place, but has seen a decline in brand value of over $13 billion since 2010.

Here is the full list of the most valuable global brands in 2020:

It is clear that brands that went above and beyond during the COVID-19 pandemic not only benefit from more meaningful connections with their customers; it also pays financially—with brand value for all 100 companies included in the study totaling $2 trillion.

Movers and Shakers

When it comes to 2020’s fastest risers, Amazon, Microsoft, Spotify, and Netflix lead the way.

Not too far behind these brands is PayPal, which saw 38% growth in the last year due to some major strategic pivots. More recently, the brand announced it would be redirecting capital from shareholders and investing in low-level employees who have been essential during the pandemic.

Other brands making their mark in 2020 are Instagram, Tesla, and YouTube—all of which are new to the ranking and are experiencing significant growth in brand value. In fact, electric vehicle company Tesla experienced a 769% increase in market capitalization in just twelve months, making it the world’s most valuable automaker.

The Great Brand Shift

As pharmaceutical companies begin distributing vaccines across the globe, consumer optimism is starting to build again. However, the future of brands remains uncertain.

Only 41 out of 100 most valuable global brands remain in the ranking today from the study conducted in 2000. With almost 60 hugely influential brands falling out of favor in the last two decades, there are several ways in which today’s brands can build economic resilience and thrive in an anxious world:

1. Leadership: The degree to which a brand has a clear purpose that is executed seamlessly across the entire organization.
2. Engagement: Creating meaningful and collaborative relationships with consumers based on the brand’s unique story and reason for being.
3. Relevance: Being omnipresent for customers and delivering on their expectations by going beyond selling products or services.

Although the impacts of 2020 will be felt for years to come, brands that stay ahead of consumers’ changing expectations will be in a better position to weather the storm.

This content was originally published here.

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Robert Warren/Getty Images

Why are so many meetings so unproductive?

Many professionals, fed up with calendars chock-full of long, disorganized, soul-bruising sessions, resort to uncharitable, even cynical explanations:

But if we want an accurate, rather than merely cathartic answer to the question, we’d be wise to consider Hanlon’s Razor: Never attribute to malice that which can be adequately explained by misunderstanding.

After more than a decade of working with prominent organizations to fix broken meetings, I’ve come to learn that many bad meetings are more adequately explained by a simple, flawed assumption. We assume that intuitive problem solving, a highly effective approach for individuals, will, in the context of meetings, prove just as effective for groups. But often, it does not.

When Individuals Solve Problems Intuitively the Result Is Magic

To understand what intuitive problem solving is, we need to recognize first that when working out any problem, from picking out a necktie to solving a quadratic equation, we make our way through five stages:

You might assume that we move through these stages sequentially to solve problems. But in the past several decades, psychologists have discovered the opposite to be true. Rather than advance through the stages in order, we tend to do so in a manner that is rather unsystematic.

For example, pretend you’re ordering food online. You begin by quickly generating a solution — Mexican (stage 2) — but as soon as the thought enters your mind, you evaluate (stage 3) and remember that you had Mexican the day before, so you generate another solution (stage 2) — Indian. Upon evaluation (stage 3), however, you fear your hefty Chicken Tikka Masala go-to might outsize your appetite. At this point, you take a step back and define the problem (stage 1), asking yourself “What kind of meal would leave me feeling satisfied but not overly stuffed?” A better question leads to a better answer: sushi (stage 2). You do a quick gut check to make sure sushi is truly what you desire (stage 3), and you move forward with your order (stages 4 and 5).

This is called intuitive problem solving, and it comes so naturally to us that, when we solve problems in this way, we’re wholly unaware that we are doing it. All we have to do is place our attention on the problem and, much like a car’s automatic transmission, our brain shifts gears for us. As a result, intuitive problem solving is remarkably efficient. Magical, even.

When Groups Solve Problems Intuitively the Result Is Often Chaos

Intuitive problem solving is so magical for us as individuals that we assume it should fare just as well for groups. When we hold a meeting, we gather around a table, place our collective attention on the problem, and let our automatic transmissions take over. But, all too often, this turns out to be a mistake.

In order for groups to collaborate effectively and avoid talking past one another, members must simultaneously occupy the same problem-solving stage. But because intuitions are private to their owners, attendees in group meetings are unable to easily discern what problem-solving stage they each are on. Consequently, members unknowingly begin the meeting on different stages.

Imagine a software team who gathers to discuss a disgruntled VIP customer, threatening, quite publicly, to jump ship to a competitor. While one attendee thinks the path forward is obvious and focuses on crafting an implementation plan (stage 5), another is intent on generating alternative solutions (stage 2), while yet another attendee is still trying to figure out whether the exit of this pompous hellion is, in fact, even a problem (stage 1). Perhaps it’s a blessing!

As the meeting progresses, things get even more chaotic. Without realizing it, each attendee continues to switch stages without notifying others. The result is a disorganized meeting that traverses many stages, yet conquers none.

To solve problems as a group, we need to jettison the assumption that intuitive problem solving is sufficient, and instead embrace a more methodical approach — one that homes in on just one problem-solving stage. In other words, we need to stop with the automatic, and start learning to drive stick.

The Power of Methodical Meetings

In a methodical meeting, for each issue that needs to be discussed, members deliberately and explicitly choose just one problem-solving stage to complete.

To convert an intuitive meeting into a methodical one take your meeting agenda, and to the right of each agenda item, write down a problem-solving stage that will help move you closer to a solution, as well as the corresponding measurable outcome for that stage. Then, during that part of the meeting, focus only on achieving that outcome. Once you do, move on.

A Template for Conducting a Methodical Meeting

Pair each agenda item with a problem solving stage and a measurable outcome.

If you don’t know which problem-solving stage to choose, consider the following:

Do you genuinely understand the problem you’re trying to solve? If you can’t clearly articulate the problem to someone else, chances are you don’t understand it as well as you might think. If that’s the case, before you start generating solutions, consider dedicating this part of the meeting to defining the problem (stage 1) and ending it with a succinctly written problem statement.

Do you have an ample list of potential solutions? If the group understands the problem, but hasn’t yet produced a set of potential solutions, that’s the next order of business. Concentrate on generating as many quality options as possible (stage 2). Even if you end your discussion with only a slightly longer list than with which you began, you’ve made important progress.

Do you know the strengths and weaknesses of the various solutions? Suppose you have already generated potential solutions. If so, this time will be best spent letting the group evaluate them (stage 3). Free attendees from the obligation of reaching a final decision—for which they may not yet be ready—and let them focus exclusively on developing a list of pros and cons for the various alternatives.

Has the group already spent time debating various solutions? If the answer is yes, use this part of the meeting to do the often difficult work of choosing (stage 4). Make sure, of course, that the final choice is in writing.

Has a solution already been selected? Then focus on developing an implementation plan (stage 5). If you’re able to leave the conversation with a comprehensive list of actions, assigned owners, and due dates, you can celebrate a remarkably profitable outcome.

Most bad meetings are not caused by lazy, power-tripped leaders, or entitled, self-centered attendees. Instead, they are caused by a simple mistake made by everyone involved. We assume our go-to way for solving problems alone, intuitively, can be effectively deployed to solve problems together. But more often than not, it can’t. Instead, we should hold methodical meetings, discussions that deliberately and explicitly aim to conquer just one stage at a time.

Granted, conquering just one problem-solving stage, I’ve come to learn, sounds a bit underwhelming to some — like taking a small bite out of large woolly mammoth. But those who try methodical meetings are met with an often profound revelation: thoroughly conquering any individual problem-solving stage, even an earlier one, frequently allows you to leap frog ahead, sometimes to the very end of the problem-solving life cycle. As the famously methodical Steve Jobs once noted: “If you define the problem correctly, you almost have the solution.”

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Historically, in a buoyant economic environment with ever-growing multiples, active management hasn’t always been required for private-equity (PE) firms to make healthy returns. Today, the economic underpinnings of PE deals have changed. There is a palpable sense in the market that deal prices have likely reached their peak. As competition intensifies, secondary and tertiary buyouts have become the norm and value creation for PE investors is less straightforward than it once was.

While for more than a decade PE experts have extoled the virtues of active management, few investors have made strides in building the mind-set or the muscle needed to actively manage their assets. What explains the discrepancy between PE firms’ acknowledgement that active management is essential and the fact that so few seem committed to doing it? It might simply be that the industry has essentially been successfully applying the same investment processes for decades and change is hard. It could also stem from the fact that they’re unsure of the key roles—both on their assets’ management teams and in their own organizations—that will create value. And not enough portfolio companies or PE firms have people with the right backgrounds and skills in those roles.

To create real value, high-performing firms must essentially become more entrepreneurial—that is, they need to take initiative, shape strategy, be willing to assume risks with the promise of bigger rewards, and take the long view on creating value. High-performing firms play an active role in the transformation journey of their assets. They also demonstrate that value-generating activities are not just onetime exercises but part of a larger commitment to improving a company’s health and effecting a real transformation. In addition, PE firms that successfully take their investments from good to great also transform themselves by ensuring that the right skills and structures are in place within their own organizations.

Are many PE firms actively involved in creating value?

To gauge the prevalence of an entrepreneurial approach in PE, we analyzed the results of the McKinsey 2018 Private Equity Operating Group Benchmarking Survey, where 45 operating partners across a range of countries described how they support their firms’ investment strategies and create alpha in their portfolios. The survey results do support a strategic shift toward active management. The time spent on “driving measurable performance improvement” increased to 49 percent from 40 percent in 2015. In return, the time spent on “monitoring and reporting” decreased to 19 percent from 29 percent. ¹ 1. For more on the survey, see Jason Phillips and Dhruv Vatsal, “Private equity groups and the pursuit of ‘portfolio alpha,’” November 2018.

We found that 59 percent of respondents claim that their PE firm has a well-defined model for value creation. Of those firms, 75 percent (up from 50 percent in 2015) report that the value-creation model is being consistently used across their portfolios. ² 2. For more on the survey, see Jason Phillips and Dhruv Vatsal, “Private equity groups and the pursuit of ‘portfolio alpha,’” November 2018. These findings support an emphasis on taking more initiative (exhibit).

We have seen the most successful PE firms build trust-based relationships with their assets’ management teams from the start. They work together with management teams to cocreate a comprehensive and detailed operational plan and, even before closing the acquisition, set priorities for improvements that support both a transformation and exit plan. Then they allocate adequate resources throughout the holding period to deliver real value and continuously work together with management teams.

Creating value: An entrepreneurial approach

Of course, investors need to concern themselves with the day-to-day-business of their assets. Occasionally firms will acquire assets that are in obvious and desperate need of transformation. But in our experience, even in cases where assets are performing well, it is crucially important for PE investors to have a transformational mind-set from the start. Those who take ownership and think like entrepreneurs are much more likely to create real value. In addition to installing a transformation function to head the change, ³ 3. For more, see Olivier Gorter, Richard Hudson, and Jesse Scott, “The role of the chief transformation officer,” November 2016. our work shows three key factors that support successful value creation in PE-owned assets.

Creating early trust-based relationships with the asset’s management

For a PE firm to be prepared for fast course changes, it pays off to invest in understanding the asset’s operations and its management team early on. In our experience, rapid decision making and execution are closely linked to the level of trust among key stakeholders in an organization. Therefore, creating strong, trust-based relationships is critical for successful value creation.

This trust is typically achieved when investors become considerably more involved and learn all that they can about their potential new assets. For example, the head of operations of one European-based fund accompanied their deal team to visit all seven of an asset’s production sites, including those overseas, prior to closing a deal. They brought along experts on equipment effectiveness and lean manufacturing, among others, to advise them on the details of all the processes and fuel a meaningful conversation with management about operational improvements in production.

At this stage of the process, such a hands-on approach requires more resources than what have been typically allocated in the past. While many PE firms are willing to invest in upgrading the digital capabilities of their assets, they might balk at heightened resource requirements in other areas, particularly those related to HR such as talent, change management, and organizational health. In our experience, however, we have found these to be worthy investments.

Another way to foster trust with a management team is to ensure clear alignment on the cornerstones of the asset’s strategy, priorities, and timing—even when everyone doesn’t see eye-to-eye. Take the case of one PE firm that invested in an automotive supplier of components for light vehicles and heavy trucks. The ambitious growth plan of the management team required large investments into the company’s asset base. While both the PE investor and management were in full agreement on that growth plan, the investor had a different view of how to create the most value for shareholders: separating the light vehicle and heavy truck businesses to give both divisions a clearer strategic profile.

At first, the management team resisted the idea of separating the business. Through an early alignment on corporate strategy and full underwriting of an extensive capital-expenditure plan, both sides built a trusted relationship. All along, however, the investor persisted in expressing the notion that a separation of the business should be considered later. After two years of working together extensively, the management team had fully bought into the separation, executed on it flawlessly, and sold the heavy truck components business to a strategic buyer that operated only in that market segment. The light vehicle business continued to grow on the back of its investment program and was sold two years later to a Chinese investor.

Establishing a comprehensive and granular operational plan that leads toward the targeted exit

Alignment with an asset’s management team is also critical when it comes to exit planning. It is paramount for management and PE owners to jointly formulate future plans that are designed with the exit scenario and the required timeline in mind.

The most successful plans share two common characteristics. First, they are fully comprehensive: our experience suggests that, regardless of the circumstances, real transformation happens only when a leadership team embraces the idea of comprehensive change in how the business operates—tackling all the factors that create value for an organization, including top line, bottom line, capital expenditures, and working capital, as well as the business model and the overall strategy. Second, they are granular: detailed milestones, rigorous meeting cadence, and clear responsibilities enable tight management of the progress and drive execution.

A telecommunications company in the United Kingdom, for example, undertook a thorough back-office benchmarking exercise and identified cost-reduction opportunities—its savings plans were included in their general and administrative expense targets. But a couple of months in, they were not making any progress. The chief transformation officer determined the reasons why: the initiatives were too high level, with insufficient information on approach, timeline, milestones, and underlying key performance indicators to be addressed. For example, most initiatives—such as “Reduce headcount by 10 percent”—were focused only on output, with no further detail on how that should be achieved.

The potential was thoroughly quantified but the initiative was simply not actionable. Rather, the analysis should have been coupled with tangible changes such as new processes or automation tools that would free up the targeted headcount and deliver the full potential.

Of course, best practices for organizational transformation also apply to PE firms looking to transform their portfolio companies and are often even more important in the PE context. Unlike strategic investors, PE firms have holding periods of between four and seven years and a shorter time frame to materialize an ambitious, full-potential plan. Hence, PE firms must rely on well-proven and fast transformational practices to beat the odds of successful transformation that McKinsey research estimates at only 26 percent. ⁴ 4. For more, see “The T-word,” McKinsey Quarterly Five Fifty.

PE teams need to think about value drivers in waves. They must consider the right timing for implementation and when they need to show evidence for an EBITDA-uplift (which may be considerably later than the implementation, particularly for digitization initiatives). Certain tools, while promising, may eventually be deferred into the holding period of the next owner.

Moreover, as with any transformation, organizational health and talent are key. ⁵ 5. Organizational health is broadly defined as a group’s ability to agree on and achieve strategic goals. For more, see Lili Duan, Rajesh Krishnan, and Brooke Weddle, “The yin and yang of organizational health,” McKinsey Quarterly, November 2017. McKinsey research shows that organizations with top-quartile health achieve 3.0 times higher returns compared with those in the bottom quartile and that health programs can result in tangible performance gains after as little as six months. ⁶ 6. For more, see Chris Gagnon, Elizabeth John, and Rob Theunissen, “Organizational health: A fast track to performance improvement,” McKinsey Quarterly, September 2017. Talent is equally important for performance. McKinsey research shows that organizations that align talent with their value agenda are more than 2.0 times more likely to outperform their peers and achieve 2.5 times the ROI in their first year. ⁷ 7. For more, see “Winning with your talent-management strategy,” August 2018, and Mike Barriere, Miriam Owens, and Sarah Pobereskin, “Linking talent to value,” McKinsey Quarterly, April 2018.

PE investor, become an entrepreneurial owner

Some PE firms have indeed experienced success in applying this more hands-on, entrepreneurial attitude to their portfolio companies. But those firms that sustain their ability to create value by leading their portfolio companies through transformations repeatedly and consistently share three key traits: these firms have the right resources in place to truly be active, entrepreneurial owners. They focus on securing the right skills for the job, which include deep financial acumen and operational and change-management skills. And lastly, they set up internal structures to facilitate value creation. Primarily, this involves ensuring that the deal team and the operating partners work well together from the early stages of the investment.

Allocate enough resources to create value

When value creation becomes a material part of a firms’ investment thesis, additional in-house resources are necessary to drive operational improvements. Back when multiples were lower, investment professionals used to be deeply involved only in the three to 12 months leading up to a deal closing, and again shortly before the exit. But entrepreneurial owners need to allocate resources throughout the entire holding period.

For example, a midmarket fund acquired an Italy-based IT systems integrator. Before the acquisition, the Italian company had already conducted multiple acquisitions and established a process that integrated one company after the other over the course of several months. The fund’s plans for the company involved ambitious growth through a buy-and-build strategy, a plan that the management team could not fully execute with existing resources. So, to complement its strategy and management skill set, six months after acquiring the base company the fund hired a team to serve a new function: postmerger integration and corporate development. This team consisted of a postmerger integration expert and other dedicated resources with a background in M&A and postmerger management consulting. The team spent the first months getting up to speed on the situation and existing integration strategy before defining a new strategy that prioritized generating the highest-value synergies—all while maintaining the business momentum.

From the pre-acquisition phase until the integration was complete, the team was deeply involved in assessing every potential new acquisition. Within a short time, they acquired and successfully integrated 12 companies, doubling revenues to almost €500 million from 2017 to 2019. The team sees the clear potential to double revenues again to €1 billion by 2022. During this time frame the company also expects to generate EBITDA synergies of €15 million or 3 percent of combined sales by systematically integrating processes and product portfolios of the acquisitions and realizing economies of scale. Since the arrival of the new manager and his postmerger integration team, the company has developed the ability to integrate several acquisitions in parallel and at a much faster pace than before. By dedicating resources to a new role and filling it with the right expertise, the company effectively became a well-oiled integration machine.

Another way to foster trust with a management team is to ensure clear alignment on the cornerstones of the asset’s strategy, priorities, and timing—even when everyone doesn’t see eye-to-eye.

Secure the right skills for the job

While investment professionals need to be equipped with deep financial acumen, a focus on value creation also requires a more diverse set of operational and change-management skills. PE firms might consider hiring people from more diverse professional backgrounds. COOs of their assets’ competitors, industry advisers, and company founders, for example, might all have more valuable hands-on experience than the usual hiring targets for PE firms.

Also, it is essential for those PE professionals who are driving the value-creation program to have strong interpersonal skills. Specifically, they need the ability to influence others both to have an impact on portfolio companies and to convince the right people to join the assets in the first place. Particularly when PE professionals tend to be ten to 20 years younger than those on the management teams of portfolio companies, it is critical that they take the time to understand and be empathetic, respectful, and thoughtful about how to build good working relationships.

Ensure that internal structures facilitate value creation

In our work, we have seen two distinct models that allow PE firms to focus more strongly on value creation. The first is the extended deal team. Smaller firms tend to favor this model, where they hire professionals with financial expertise but also backgrounds in operational and strategic functions. This background allows them to assess potential acquisition targets, determine what resources are needed for the implementation, and then oversee that implementation.

The second is a model where the dedicated operating team takes on a more meaningful role after an acquisition is completed. Larger firms tend to favor this model, where a dedicated operating partner supports the deal phase, and a larger operating team fully takes over post-acquisition. The key is for this team to work closely with the company’s management and take on a more active role in implementing value-creation initiatives.

More so than what model they choose, the degree of interaction among all those involved throughout the investment period is critical for creating value. In our experience, close collaboration between all PE professionals—from due diligence to exit—is key to executing a defined goal successfully. Specifically, conflicts tend to arise in models with a dedicated team of operating partners, due to different compensation models or strict distinctions of responsibilities across the different stages of the investment cycle. These potential conflicts need to be considered and actively managed. Close cooperation among fund managers and management teams from the early stages of a deal onward, coupled with harmonized incentive schemes, should help.

To some degree, PE professionals sense a change in the air. That change might not have arrived for all investors—some continue to cling to the old ways of focusing almost solely on finding deals, closing them, and then beginning their search for the next deal. But in the coming months, as multiples have likely reached their peak, those firms will indeed find it increasingly difficult to extract value from their investments. Investors who take an early lead on the path toward entrepreneurial ownership can boost their ROIs as well as their firm’s reputation.

Christian Behrends is a senior vice president in McKinsey’s Düsseldorf office, Michael Lange and Lukas Schäfer are partners in the Frankfurt office, and Annika Rahm is a consultant in the Munich office.

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As someone born between the years of 1980-2000, I’m a millennial myself. I have begun to notice this exciting movement among my demographic; we are making changes, and forming new systems and expectations of government.

Larry Harvey, cofounder of Burning Man, has a theory about why the event is a success: “…[Burners’] abilities and gifts should and must be shared with others, and merge with the world— and the world will answer to that.” And the world has answered. There were over 50 official local Burn events around the world that further extend the commitment of Burning Man’s 10 principles.

On a local level, the US cities of Houston, Grand Rapids, Philadelphia and Omaha have been early movers in the creation of millennial boards and commissions. This means more young people are aware of these volunteer roles they can fill in their town in order to learn, understand and inform their city councils, mayors, and other departments. Some boards and commissions also work to promote other open positions within the city, making sure to not only to think of diversity in terms of gender and race, but in terms of age, experience and perspective. This builds a more accurate and up-to-date demographic representation in government that creates a new pipeline of people ready to lead.

Technology has also proven to be this generation’s way to change government. Millennial founders and CEOs of companies like Uber, Facebook and scooter sharing company Bird have pushed the boundaries of government through innovation. The City of San Francisco is currently having to shape city laws around Bird scooters and their safety. The United States Congress had to work to understand how Facebook operates and its massive influence. The European Union needed to decide how to officially classify Uber (as a taxi service or a digital company) in order to start creating and applying laws. Various levels of authority around the world are creating new policies in response to disruptive young entrepreneurs.

Alexandria Ocasio-Cortes, a 29-year-old from New York City, recently unseated 56-year-old 10-term politician and incumbent Joe Crowley, making her the youngest person to serve in the United States Congress. Vogue magazine wrote: “If Trump is the last gasp of baby boomers, Ocasio-Cortez is the first emphatic cry of the millennial.” Ocasio-Cortez has inspired a new wave of young people to believe that they too can run for office, and possibly win.

Ocasio-Cortes doesn’t just rely on the standard news and policy sites to share her work in government. She actively posts to a combined viewership of millions of followers, largely from the millennial demographic, via Instagram, Twitter and more. She produces a steady stream of video and picture “stories” to share her journey each day. It’s an eye-opening experience to have witnessed her progress from humble beginnings on the campaign trail to the floor of Congress. Ocasio-Cortes will continue to be a major influence to younger generations who value relevant, instantaneous and authentic exchanges on the internet.

Government is something that eventually adapts with the times. I look forward to discovering news ways millennials can engage and impact government. And for those apprehensive about change, I’ll leave you with a positive note shared by the World Economic Forum about a survey from my fellow Global Shapers: that if millennials are happy, the world will prosper.

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AI-enabled weapons may offer superhuman speed and precision (see article). But they also have the potential to upset the balance of power. In order to gain a military advantage, the temptation for armies will be to allow them not only to recommend decisions but also to give orders. That could have worrying consequences. Able to think faster than humans, an AI-enabled command system might cue up missile strikes on aircraft carriers and airbases at a pace that leaves no time for diplomacy and in ways that are not fully understood by its operators. On top of that, AI systems can be hacked, and tricked with manipulated data.

Deterrence rested on the consensus that if nuclear bombs were used, they would pose catastrophic risks to both sides. But the threat posed by AI is less lurid and less clear. It might aid surprise attacks or confound them, and the death toll could range from none to millions. Likewise, cold-war arms-control rested on transparency, the ability to know with some confidence what the other side was up to. Unlike missile silos, software cannot be spied on from satellites. And whereas warheads can be inspected by enemies without reducing their potency, showing the outside world an algorithm could compromise its effectiveness. The incentive may be for both sides to mislead the other. “Adversaries’ ignorance of AI-developed configurations will become a strategic advantage,” suggests Henry Kissinger, who led America’s cold-war arms-control efforts with the Soviet Union.

That leaves the last control—safety. Nuclear arsenals involve complex systems in which the risk of accidents is high. Protocols have been developed to ensure weapons cannot be used without authorisation, such as fail-safe mechanisms that mean bombs do not detonate if they are dropped prematurely. More thinking is required on how analogous measures might apply to AI systems, particularly those entrusted with orchestrating military forces across a chaotic and foggy battlefield.

The principles that these rules must embody are straightforward. AI will have to reflect human values, such as fairness, and be resilient to attempts to fool it. Crucially, to be safe, AI weapons will have to be as explainable as possible so that humans can understand how they take decisions. Many Western companies developing AI for commercial purposes, including self-driving cars and facial-recognition software, are already testing their AI systems to ensure that they exhibit some of these characteristics. The stakes are higher in the military sphere, where deception is routine and the pace is frenzied. Amid a confrontation between the world’s two big powers, the temptation will be to cut corners for temporary advantage. So far there is little sign that the dangers have been taken seriously enough—although the Pentagon’s AI centre is hiring an ethicist. Leaving warfare to computers will make the world a more dangerous place.■

This content was originally published here.

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When we think AI, we think of pure automation. This is not true anymore. Did you think, for example, that AI could never replace portrait photographers? Think again; it doesn’t even need a model. Today we have AIs that can ‘imagine’ – that is, according to the Merriam-Webster definition, form an image of “something not present to the senses or never before wholly perceived in reality”.

The video above shows the results from an AI that has learned to generate photos of people who don’t exist. At a qualitative level, only a few artists are capable of inventing faces with photographic precision.

The mechanism that gifts AI with imaginative powers has a name: Generative Adversarial Networks (GAN). GANs were partly inspired by neuroscience research. In essence, GANs consist of two entities which compete against – and learn from – each other: one learns to generate fakes, while another one learns to detect fakes. As the fake detector becomes more effective, so does the fake generator. Neuroscientists have discovered that we use a related mechanism, the actor-critic model, which is believed to be located pretty much in the middle of our brains.

Imagination has a direct application: guessing the representation of a subject in a different way, or in other words, translating an image from one representation to another. For instance, this AI imagines what the sketch of a photograph would look like, or what the colour version of a black and white photograph would look like.

This AI, meanwhile, simulates day from a night picture. This is valuable, as creating self-driving cars that work and can locate themselves precisely in all conditions – day, night, fog, rain, snow and so on – requires a lot of data that covers all scenarios. Collecting large amounts of data in all conditions is practically very difficult, as certain conditions (such as snow) occur very rarely in some areas. Instead of collecting more data, scientists have come up with this night-to-day workaround. This could also lead to better night vision for the military, airplane pilots and human drivers.

When something is not directly visible, GANs are used to make educated guesses. Take the case of the BodyNet AI (see above), which estimates the body shape of a person given a picture of them fully-clothed. The digital mannequin is useful for designing tailored clothing without either taking measurements by hand or the help of sophisticated body scanners.

The ability to make educated guesses is not limited to image generation or translations. Imagination is, in a broader sense, a tool for discovery, and has applications in diverse domains such as cybersecurity or drug design.

Modern cybersecurity tools feature AIs that can detect threats by looking at their characteristics. Researchers have designed a GAN which learns to generate pieces of malignant code that can bypass these cybersecurity detectors. That may sound scary, but the good news is we could also use it to perfect detectors of malignant code – or, if you think about it, sometimes deception can be a good thing, such as when we engineer drugs to fight diseases.

The 4th Industrial Revolution is not just about automation, but about human-machine collaboration and symbiosis. GANs are a turning point in the development of AI, and will help us supercharge our mental capabilities.

They are also a tool with which we can study the mechanisms of imagination and help us to better understand the role of imagination in domains such as translation or discovery. Even though imagination does not equal creativity, it is one of the tools we use to invent new things. What are the missing pieces, yet to be discovered, that will enable us to build machines that can outsmart us in the creative realm as well?

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Many companies have adapted to a “data-driven” approach for operational decision-making. Data can improve decisions, but it requires the right processor to get the most from it. Many people assume that processor is human. The term “data-driven” even implies that data is curated by — and summarized for — people to process.

But to fully leverage the value contained in data, companies need to bring artificial intelligence (AI) into their workflows and, sometimes, get us humans out of the way. We need to evolve from data-driven to AI-driven workflows.

Distinguishing between “data-driven” and “AI-driven” isn’t just semantics. Each term reflects different assets, the former focusing on data and the latter processing ability. Data holds the insights that can enable better decisions; processing is the way to extract those insights and take actions. Humans and AI are both processors, with very different abilities. To understand how best to leverage each its helpful to review our own biological evolution and how decision-making has evolved in industry.

Just fifty to seventy five years ago human judgment was the central processor of business decision-making. Professionals relied on their highly-tuned intuitions, developed from years of experience (and a relatively tiny bit of data) in their domain, to, say, pick the right creative for an ad campaign, determine the right inventory levels to stock, or approve the right financial investments. Experience and gut instinct were most of what was available to discern good from bad, high from low, and risky vs. safe.

It was, perhaps, all too human. Our intuitions are far from ideal decision making instruments. Our brains are inflicted with many cognitive biases that impair our judgement in predictable ways. This is the result of hundreds of thousands of years of evolution where, as early hunter-gatherers, we developed a system of reasoning that relies on simple heuristics — shortcuts or rules-of-thumb that circumvent the high cost of processing a lot of information. This enabled quick, almost unconscious decisions to get us out of potentially perilous situations. However, ‘quick and almost unconscious’ didn’t always mean optimal or even accurate.

Imagine a group of our hunter-gatherer ancestors huddled around a campfire when a nearby bush suddenly rustles. A decision of the ‘quick and almost unconscious’ type needs to be made: conclude that the rusting is a dangerous predator and flee, or, inquire to gather more information to see if it is potential prey – say, a rabbit, that can provide rich nutrients. Our more impulsive ancestors–those that decided to flee– survived at a higher rate than their more inquisitive peers. The cost of flight and losing on a rabbit was far lower than the cost of sticking around and risking losing life to a predator. With such asymmetry in outcomes, evolution favors the trait that leads to less costly consequences, even at the sacrifice of accuracy. Therefore, the trait for more impulsive decision-making and less information processing becomes prevalent in the descendant population.

In modern context, survival heuristics become myriad cognitive biases pre-loaded in our inherited brains. These biases influence our judgment and decision-making in ways that depart from rational objectivity. We give more weight than we should to vivid or recent events. We coarsely classify subjects intro broad stereotypes that don’t sufficiently explain their differences. We anchor on prior experience even when it is completely irrelevant. We tend to conjure up specious explanations for events that are really just random noise. These are just a few of the dozens of ways cognitive bias plagues human judgment and for many decades, it was the central processor of business decision-making. We know now that relying solely on human intuition is inefficient, capricious, fallible and limits the ability of the organization.

Data-Supported Decision Making
Thank goodness, then, for data. Connected devices now capture unthinkable volumes of data: every transaction, every customer gesture, every micro- and macroeconomic indicator, all the information that can inform better decisions. In response to this new data-rich environment we’ve adapted our workflows. IT departments support the flow of information using machines (databases, distributed file systems, and the like) to reduce the unmanageable volumes of data down to digestible summaries for human consumption. The summaries are then further processed by humans using the tools like spreadsheets, dashboards, and analytics applications. Eventually, the highly processed, and now manageably small, data is presented for decision-making. This is the “data-driven” workflow. Human judgment is still the central processor, but now it uses summarized data as a new input.

While it’s undoubtedly better than relying solely on intuition, humans playing the role of central processor still creates several limitations.

Alas, we are accommodating our biases when we process the data.

Bringing AI into the Workflow
We need to evolve further, and bring AI into the workflow as a primary processor of data. For routine decisions that only rely on structured data, we’re better off delegating decisions to AI. AI is less prone to human’s cognitive bias. (There is a very real risk of using biased data that may cause AI to find specious relationships that are unfair. Be sure to understand how the data is generated in addition to how it is used.) AI can be trained to find segments in the population that best explain variance at fine-grain levels even if they are unintuitive to our human perceptions. AI has no problem dealing with thousands or even millions of groupings. And AI is more than comfortable working with nonlinear relationships, be they exponential, power laws, geometric series, binomial distributions, or otherwise.

This workflow better leverages the information contained in the data and is more consistent and objective in its decisions. It can better determine which ad creative is most effective, the optimal inventory levels to set, or which financial investments to make.

While humans are removed from this workflow, it’s important to note that mere automation is not the goal of an AI-driven workflow. Sure, it may reduce costs, but that’s only an incremental benefit. The value of AI is making better decisions than what humans alone can do. This creates step-change improvement in efficiency and enables new capabilities.

Leveraging both AI and Human processors in the workflow
Removing humans from workflows that only involve the processing of structure data does not mean that humans are obsolete. There are many business decisions that depend on more than just structured data. Vision statements, company strategies, corporate values, market dynamics all are examples of information that is only available in our minds and transmitted through culture and other forms of non-digital communication. This information is inaccessible to AI and extremely relevant to business decisions.

For example, AI may objectively determine the right inventory levels in order to maximize profits. However, in a competitive environment a company may opt for higher inventory levels in order to provide a better customer experience, even at the expense of profits. In other cases, AI may determined that investing more dollars in marketing will have the highest ROI among the options available to the company. However, a company may choose to temper growth in order to uphold quality standards. The additional information available to humans in the form or strategy, values, and market conditions can merit a departure from the objective rationality of AI. In such cases, AI can be used to generate possibilities from which humans can pick the best alternative given the additional information they have access to. The order of execution for such workflows is case-specific. Sometimes AI is first to reduce the workload on humans. In other cases, human judgment can be used as inputs to AI processing. In other cases still, there may be iteration between AI and human processing.

They key is that humans are not interfacing directly with data but rather with the possibilities produced by AI’s processing of the data. Values, strategy and culture is our way to reconcile our decisions with objective rationality. This is best done explicitly and fully informed. By leveraging both AI and humans we can make better decisions that using either one alone.

The Next Phase in our Evolution
Moving from data-driven to AI-driven is the next phase in our evolution. Embracing AI in our workflows affords better processing of structured data and allows for humans to contribute in ways that are complementary.

This evolution is unlikely to occur within the individual organization, just as evolution by natural selection does not take place within individuals. Rather, it’s a selection process that operates on a population. The more efficient organizations will survive at higher rate. Since it’s hard to for mature companies to adapt to changes in the environment, I suspect we’ll see the emergence of new companies that embrace both AI and human contributions from the beginning and build them natively into their workflows.

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