Finding the right candidates to work for your company is a process that every entrepreneur ultimately faces if they want their business to grow. But finding top candidates is challenging.
In fact, a Glassdoor report revealed that attracting quality candidates is a problem for about 75 percent of companies. Thus, it's important to know how to spot A-listers. Here's how you can hone that skill.
1. Determine if they meet (or exceed) your preferences.
It's a given that potential job candidates should meet your basic requirements. But if you want to have an army of A-lister employees, you have to go for the ones that have the preferred skills you desire.
That means you should also have an open mind about the process and consider candidates who offer skills you may not have listed but can use in the future as your company grows, such as UI/UX designer candidates who also have experience with machine learning or marketing.
2. Pay attention to challenges.
Challenges and contests are some great ways to hire A-lister employees. For example, you can find great software developers for your startup by attending coding challenges and hackathons. If you notice contests and challenges listed on the resume, consider selecting these candidates for your interview pool.
3. Note their achievements.
An A-lister candidate lists not only the tasks she has done for her past jobs but the goals she's achieved as well. She's able to quantify her performance using key metrics. Look for accomplishments that sum up the candidate's performance in a numerical format. For instance, look for a percentage of revenue increase for a project or how many readers the candidate grew her company's blog readership based on a marketing campaign she spearheaded.
4. Look for a modern resume template.
Top candidates leverage great resume templates that are hybrids of traditional and modern resume formats to better showcase their skills. It's crucial to use a good resume template, because it helps optimize the resume to use keywords that fit the job description and it works across different industries.
This makes it easier for recruiters that use tracking systems to find a better selection of candidates during the initial resume-screening process. Leverage this technology when looking for top candidates so you can save time. It'll help you be more effective with your search so you can avoid candidates who don't meet your basic requirements.
5. Consider their patterns.
A-lister talent often demonstrates patterns that show consistency in career progression and skills development. Pay attention to how often the candidate switches jobs and the responsibilities they list.
For example, a top marketing candidate may not work at the same job for 10 years, but he may demonstrate a natural career progression that provides extensive experience, such as going from marketing coordinator to marketing manager and progressing to a director role. Consider the last role they had and determine if their experience is relevant to the position you're trying to fill.
Also, consider the versatility of the candidate and whether or not they are a fit for the organization. Look at the candidate's past job responsibilities and titles and determine if they have worked in different industries as these can be good indications of their potential to drive value and impact for your business as it grows.
Running a business is never going to be easy, and most entrepreneurs know they will have to put in long hours to get their business off the ground. But can creativity and leadership be sustained for 60, 70 or 80 hours a week?
Of course, business owners have a different relationship with their work compared to their staff – everything they do is for themselves, after all. So rather than suggest a reduction in hours, it is perhaps better to think of ways to ensure the leader remains on top of his or her game. How should business leaders structure their day in order to stay fresh while still getting everything done?
Many creatives have a hobby or place they specifically use in order to clear their mind for the next task, or find inspiration. For example, going to the beach or jogging through the local park. Personally, I like to go for a bike ride in order to find inspiration for my copywriting and thought-leadership work. But it's not just about looking for ideas: I find a 45-minute ride gives me a boost in productivity that more than offsets the time spent in the saddle!
Leadership is also creative, so you have to completely change your surroundings in order to get the clean break needed to focus on the next task. But, unlike the carefree freelance writer, you need to structure your day.
Build a routine
Despite having the freedom to work when and where they want, most successful business people follow a daily routine. Otherwise, they know they would constantly get sidetracked with urgent but minor matters rather than concentrating on what is strategically important.
This is very different from merely having a to-do list – it entails the business leader setting out time for all the key business tasks she or he must fulfill. To paraphrase Stephen Covey, author of The Seven Habits of Highly Effective People, don't prioritize what's on your schedule, schedule your priorities.
Scheduling your priorities for the business ensures that you don' putt off the tasks you don't like. This approach also encourages productivity by ensuring that individual tasks are completed and accounted for in a set timescale.
Now shake it up!
No one can be productive, efficient or even polite to staff and clients if they are working nonstop for 10 hours at a time. Therefore, it's essential to change things within your routine and to give yourself a few minutes to unwind a little on a regular basis throughout the day.
You'll need to actively take your mind off things, so go and make a cup of a coffee, ideally getting out of the office for a few minutes.
For the small business owner, adding a gym session into your daily routine is a great way to recharge mentally while also keeping the body in shape. You might think you don't have time, but a targeted, 30-minute workout can unleash far greater levels of productivity than merely brewing a cup of tea.
Remember, you're the boss – there's nothing stopping you from installing a rowing machine or exercise bike in your office, or the foyer. Some bosses go even further and incorporate a spot of team building into the workday by inviting their staff to join in on a jog, ride or even a spot of lunchtime soccer!
Helping employees select and activate the benefits that fit into their work-life balance is a critical, annual responsibility for internal communications teams. Getting employees' attention, providing appropriate education, and inspiring action – before open enrollment deadlines – can be a heavy task for internal communicators and HR professionals.
The subject matter and process for open enrollment are often complicated and difficult to understand, especially when teams rely on dense, jargon-filled collateral produced by insurance and benefits providers. To help you and your employees get the most from your open enrollment campaign, this two-part series focuses on two key aspects: preplanning and then execution of your campaign.
How can your program break through overloaded inboxes, improve employee understanding and participation? When should you start your campaign and what should your messages say? As an organization that provides communications analytics, at PoliteMail, we wanted to do a better job ourselves. Before you send any open enrollment messages, take time to do these five helpful things.
1. Select your audience segments. A one-size-fits-all open enrollment campaign is easier to create and run but not the most effective. Different employee groups have different needs and educational requirements. Common segments include age, tenure, location, engagement and participants versus nonparticipants. Before open enrollment begins, your communications and HR teams can collaborate to create segmented distribution lists.
My company looked at a breakdown of participants by age group and benefit participation. We noticed the majority of young, single employees opted for the cheapest available healthcare plan, even if it wasn't the most appropriate option for them. Without fully understanding total cost, they simply opted into the lowest premium. Based on this data, we decided it was important to provide more educational materials to clarify total out-of-pocket (OOP) cost versus monthly premium.
2. Identify your key touch points. Early in the open enrollment process, most employees need minimal information. If an introductory email contains too much information, employees may feel overwhelmed and tune out. Start with basic must-knows (e.g., an infographic with key steps, deadlines and links to helpful resources). It is important to segment by new participants, experienced participants and nonparticipants. Each group should receive different information at different cadences. By making an outline of touch points for each segment, you will know exactly how many communications you need to create and what information you want to cover during each phase.
In our case, based on employee feedback from the prior year's campaign, we knew employees were confused by the dense PDFs provided by our insurance company. Our employees wanted information that was easier to digest and summarized. In response, we made a color-coded, bulleted list of the highlights for each plan. Then we went one step further.
We found that certain cohorts of employees – young singles, people starting a family, people whose dependents had chronic conditions – chose a specific plan for a distinct reason. We found employees in each category who were willing to create a short video explaining why they selected their plan and why it worked for them. These communications garnered the highest levels of engagement.
3. Engage family decision-makers. Frequently, benefits elections are not just employee decisions but family decisions. To expedite the process, we make benefits information directly available to spouses (if elected). Early in the program, we offered employees the ability to ask their spouse to opt into the open enrollment email campaign. Rather than bundle this question in with the initial open enrollment announcement, this was most effective as a separate message.
4. Consider your communication channels. While email is the go-to channel for most open enrollment campaigns, it pays to deploy a multi-channel approach. To account for generational differences, learning styles and accessibility, deploy a multi-channel approach. Individuals want and need to receive information in different ways.
Beyond email and provider documents available on your intranet, consider live webinars or open discussion groups with Q&A. In addition, use digital signage, bathroom posters and table-top stands to remind people of decision points and campaign deadlines. Since many employees learn best from in-person orientations and face-to-face meetings, arm your direct managers and employee ambassadors with well-organized talking points and access to reference materials.
5. Plan your measurements. What are your program goals? Do you want to increase participation in certain benefits, have more satisfied employees or have people feel more informed? By establishing goals upfront, you can collect the appropriate data at the best time, whether it's coming from the HR system, your email system or from surveys and polls. You may also find this data useful mid-campaign. If you notice a high click-through to your open enrollment form, but a low completion percentage, you may be able to take corrective action before it's too late.
Keep in mind that new participants in your company's benefits plan will need more communication touch points, clearly explaining what is expected of them (and when), as well as additional reminders. Meanwhile, more experienced participants need to know what is new or different this year and the deadlines. And nonparticipants will need different messaging based on age and tenure. They also may need the ability to opt out of the campaign.
Our planet seems to be run by economics. Every day, we're faced with financial requirements and limitations. That being said, when one of those limitations (like how much you can save in your retirement account) is increased, it's really great news!
Each year, the Internal Revenue Service (IRS) reviews the maximum contribution limits for retirement accounts for the following tax year. Sometimes they make changes, sometimes they don't. These are accounts, such as the 401(k), 403(b), most 457 plans, and the federal government’s Thrift Savings Plan (a 401(k) style plan for military members).
On November 1, 2018, the IRS announced changes to employee contribution limits for 401(k) and other retirement accounts for the 2019 tax year. As we close out these last couple of months in 2018, let's keep these contribution limits in mind for how much we want to save for retirement in 2019.
Here are some of the changes to 401(k) contribution limits for 2019.
Basic limits for 2019
The IRS imposes limitations as to how much employees can contribute to their 401(k) plan each year. That contribution limit was increased from $18,500 to $19,000 for the 2019 tax year.
This includes all elective employee salary deferrals as well as any after-tax contributions made with a 401(k), 403(b), most 457 plans and the federal government’s Thrift Savings Plan.
The IRS encourages people nearing retirement to save more by offering an allowance called "catch-up" contributions. What this means is that if you're 50 years of age or older, you're allowed to make an additional $6,000 catch-up contribution to your 401(k) each year.
While the catch-up contribution limit hasn't changed for 2019, this still affords the opportunity to catch up if you had to dedicate resources to paying for your children's college education in the past.
This pushes the maximum contribution limits to $25,000 for those ages 50 and older.
Total contribution limits for 2019
Without imposed limitations, people could take advantage of retirement accounts. High-income earners could hide all of their income by deferring it to a 401(k) plan.
To avoid this, the IRS sets a maximum allowable contribution. That's your elected deferral plus any after-tax contributions you may make, plus your employer’s match.
That maximum allowable contribution to a 401(k) increased in 2019 from $55,000 to $56,000.
Take the first step
A retirement plan is an excellent way for small business owners to save for retirement, retain good employees and offset some tax liabilities. However, there are many different types of employer-sponsored retirement plans.
If you need help figuring out which is best for your business, check out this article.
A wave of corporate breakups has rippled through industry after industry over the past several years. This has happened in consumer goods, for instance, with Kraft Foods’ spin-off of its North American grocery business; in materials, with Alcoa’s split into separate aluminum and engineering businesses; in technology, with HP’s separation of services and software from printers and PCs; in energy, with Danish industrial conglomerate A.P. Moller-Maersk’s divestiture of its oil businesses; and in health care, with Siemens’ spin-off of its medical technology division. The trend started in the 1980s in the United States and reached Europe in the late 1990s, but it has intensified in recent years, as more vocal investors have pressed for more focused business structures.1
You might wonder if we are finally seeing the long-anticipated demise of the diversified public corporation.2 After all, both finance and strategy scholars, while recognizing that a little diversification can be a good thing, have argued for years that greater amounts of it are detrimental to performance and value creation, particularly when businesses in a portfolio aren’t clearly linked.3 This idea that the relationship between diversification and performance follows an inverted U-shaped curve has become established wisdom in management literature.4 It continues to permeate leading textbooks on corporate strategy.5
Why would companies diversify beyond optimal levels? Because, traditional thinking suggests, managers have enjoyed higher compensation levels and faced fewer takeover risks in large, diversified corporations than in smaller, more specialized companies. So, many observers assume that it was in response to greater capital and product market pressures in more liberalized, well-developed economies that companies de-diversified and focused on one or a narrow range of activities that they know best.6
Though this tidy narrative is intuitively appealing, two aspects of it don’t hold up. First, some companies continue to be highly diversified — and do well. Many private equity groups and conglomerates such as Alphabet or the Mahindra Group are thriving in multiple lines of business. This observation resonates with recent research7 suggesting that the capacity to manage diversification differs more widely between companies than previously thought. Second, if the decline in diversification were due to external pressures, we would expect the worst diversifiers (those with the most detrimental performance effects) to have refocused or gone out of business, thus raising the average returns of diversification across all companies.8
Clearly, there is a richer story to explore, and that is why we embarked on two lines of inquiry: To examine the relationship between diversification and corporate performance, we performed a meta-analysis of five decades’ worth of empirical research. And to identify factors that account for successful diversification, we conducted case studies of more than 30 large, diversified corporations, in a range of industries, that have consistently outperformed their more focused competitors. We complemented publicly available materials with interviews of managers, analysts, and industry experts. (See “About the Research.”)
About the Research
For our meta-analysis, we searched 50 leading business, finance, economics, and management journals, as well as databases of doctoral studies, for primary studies on the diversification-company performance relationship. With a total of 267 studies published between 1962 and 2016, including about 150,000 company-level observations from 28 countries, our data set is considerably larger than previous meta-analyses in this area.
We took into account a range of diversification measures that capture both the number of different business lines and their revenue share in a company’s portfolio. These measures also allowed us to distinguish between related and unrelated diversification. We included both studies that used accounting-based measures of corporate performance (such as return on assets) and those that used capital-market-based performance measures (such as Tobin’s q).
Applying several meta-analytic methods, we found that the negative effect of unrelated diversification on performance has lessened noticeably over time. The mean effect of unrelated diversification on performance was negative and strongly significant from the 1970s through the late 1990s. However, by the turn of the millennium, this negative effect had declined so much that it was no longer statistically different from zero. The effect of related diversification on company performance stayed positive throughout.
We found that high levels of diversification aren’t necessarily bad for performance — and that diversified companies aren’t a dying breed. While average levels of diversification have declined over time, levels of related diversification have begun to increase since the late 1990s. Companies are considered related diversifiers if they operate in similar product or service markets. Our research further suggests that this kind of diversification strategy continues to enhance companies’ operational performance and capital market valuations.
We also found that the average effect of unrelated diversification on performance, while still negative, is now so small that it is not significantly different from zero anymore. (That’s a big shift from the past; between the 1970s and the 1990s, companies experienced strong negative effects.) This result holds with respect to various measures of performance, including capital market measures, such as market value and risk-adjusted market returns, and accounting indicators, such as profitability and sales growth.9
How might these changes be explained? Perhaps by the apparent decline in the costs of diversification. In recent years, the risk of value-destroying behavior seems to have been reduced by new trends such as the increased efficiency of capital markets, a stronger focus on corporate governance, the proliferation of value-oriented key performance indicators (KPIs) and incentives, and improved transparency due to advances in information and communications technology. And meanwhile, the benefits of diversification persist. During the financial crisis, many diversified companies benefited from easier and cheaper access to capital.10 Companies may also realize financial and organizational advantages if they can tap into richer internal markets for knowledge and managerial talent. As the recent examples of Amazon and Alphabet show, a diversified company can be a strong organizational model for transferring technology into new businesses in the pursuit of growth. Diversification here has less to do with whether a company is active in many industries but rather with the range of applications for knowledge, technology, and other intangible assets.
If diversification strategies no longer harm performance across the board, when do they pay off? Our research indicates that companies tend to reap the rewards when they take three steps:
1. Limit the number of business models in the portfolio and support each one with a strong, cohesive operating model. The challenge of managing diversification is not driven by the number of business units, products, or industries that a company covers but rather by the diversity of its business models. A business model explains how an organization will create value, deliver it to customers, and capture it for the company itself.11 Successful diversified companies tend to have a dominant logic governing their portfolio that allows them to leverage expertise and experience across a wide range of businesses. They avoid the trap of being lured into businesses that seem related from a product or market perspective (for instance, because they target the same customers or use the same raw materials) but actually require different capabilities.
For example, consider easyGroup, the British private holding company best known for its low-cost airline, easyJet. The group’s portfolio has expanded significantly to include a collection of businesses with the “easy” brand, ranging from travel-related ventures, such as easyBus, easyCar, and easyHotel, to offerings geared toward daily living, like easyCoffee, easyGym, and easyMoney. From a product perspective, the group looks like a highly diversified conglomerate. However, its subsidiaries have something critical in common: All of them offer “no frills” services.
Describing the activities of the original business, easyJet, in terms of its business model rather than its industry enabled the company to expand into a whole range of other services that follow a similar logic. All the businesses benefit from a strong group brand that signals a clear value proposition to price-sensitive consumers across product markets. A stringent focus on standardization of products and services and cost discipline provide the operational foundations for this low-cost positioning. And the online distribution system leads to further synergies and learning across businesses.
As the easyGroup example illustrates, shared characteristics among businesses in a portfolio may relate to different aspects of the business model. They may address value creation (degree of product customization, share of revenues from product sales versus services), value delivery (capital intensity, distribution models), or value capture (revenue mechanism, competitive differentiation). The similarities are essential for competitive advantage in each industry. That’s how the group can best support the success of its business units.
A company’s operating model is central to its business model and, empirical evidence shows, a chief driver of performance.12 It’s the sum total of the capabilities that enable the business to effectively and efficiently create, deliver, and capture value. The operating model is also a philosophy, a consistent set of management processes and practices that defines how decisions get made and objectives are set.
A company can leverage a strong, cohesive operating model to manage and improve performance across a diversified portfolio of businesses. A familiar example is Danaher, a science and technology company with $18 billion in revenue from more than 25 operating companies in industries as diverse as diagnostics, life sciences, dental care, and environmental and applied solutions. While these businesses involve different products and serve diverse markets, they share a number of important characteristics. They offer small, medium-priced, performance-critical components of high-value systems that are difficult to substitute. Products are typically assembly-manufactured, with low customization and at medium volumes. The businesses are active in relatively small markets with high growth and low volatility, and with a fragmented customer base, which are less attractive for large, sophisticated competitors such as Siemens or General Electric.13
The company’s operating model, called the Danaher Business System (DBS), is consistently applied to all business units. It includes four components that support the broad objective of “helping realize life’s potential”:
People: A corporate talent funnel allows the company to carefully manage the development “journeys” of 2,000 high-potential employees through monthly reviews and extensive training in DBS principles and tool kits.
Plan: An annual strategic planning process focuses on challenging business units’ management thinking and identifying five to seven strategic priorities for each business.
Process: A Kaizen-inspired continuous improvement process is supported by more than 50 tool kits, the DBS Office (which consists of about 20 members who rotate into the various businesses), and business unit experts.
Performance: The company translates strategic plans into specific targets, actions, and owners, and conducts monthly reviews of each unit’s 15 KPIs. DBS measures performance in four areas: quality, delivery, cost, and innovation. Performance assessments are linked to the strategic plan, occur at frequent and regular intervals, and include objectives with varying time horizons.
The operating model creates value by emphasizing discipline and continuous improvement. This is particularly important for the Danaher businesses with high average gross margins. Unless well-managed, such margins have a habit of being self-destructive, as they tend to encourage lax management practices. Danaher also generates considerable value by applying DBS to newly acquired businesses. The company has repeatedly improved operating margins by seven percentage points or more in what were already high-margin businesses at the time of acquisition. For example, after Danaher’s acquisition of Tektronix in 2007, its sales grew by 14.9% and margins increased to 15.8% in 2008.
2. Tailor the corporate parenting strategy to the portfolio. If the value of a diversified company is to be greater than the sum of its parts, the corporate parent needs a strategy for adding value.14 Its role and activities must be aligned with the needs and opportunities of the portfolio businesses.
A corporate center can add value in many ways, of course. It may offer financial advantages by providing easier and cheaper access to external and internal funds, treasury management, and tax optimization. It may provide expertise and tool kits for strategy analysis and execution (for example, in mergers and acquisitions) to help business units with long-term value creation. The businesses may further benefit from corporate functions and resources that offer distinct capabilities or cost advantages. And in some companies, the center adds value through even deeper operational engagement — for instance, by fostering cooperation among portfolio businesses, closely monitoring operational performance, leading improvement initiatives, or even restructuring struggling business units.
But a sound parenting strategy is more than just a random collection of value-adding activities. You need to sort out the right level of involvement. Sometimes a light touch is best; sometimes units need more hands-on guidance.15 (See “What Kind of Parent Do Your Business Units Need?”)
What Kind of Parent Do Your Business Units Need?
Here are six common approaches, ranging from light to heavy involvement.
Our experience tells us that corporate parents ought to take three factors into account when deciding how much to intervene: the portfolio strategy, the needs of the businesses, and the corporate center’s capabilities. Those elements should all be in sync.
Portfolio strategy. The size, homogeneity, synergy potential, and stability of the corporate portfolio should all influence parenting strategy. In general, the higher the number of business units and the more diverse they are, the less involved the parenting strategy should be. This is simply a result of limited managerial capacity for closely monitoring a diverse set of businesses and the increasing cost of complex corporate processes in larger portfolios. But if the business units are closely related and have high synergy potential, it may make sense for the parent to get more involved and actively foster collaboration among units. Strong parent involvement and centralization of activities require a stable portfolio, however. If significant changes in portfolio composition are expected and the corporate center needs to move assets around, the parenting strategy should not involve too many rigid structures or fixed linkages among business units.
Units’ needs. The industry, business model, and strategic priorities of each business in the portfolio will help determine which parenting style to use. For example, in highly dynamic industries where players must make fast decisions close to the market, light corporate involvement often makes sense, whereas a business unit in a highly regulated environment may benefit from the political clout and support of the parent. If a portfolio business has a strong focus on exploiting existing capabilities and reducing costs, a centralized model may be favorable, whereas a focus on exploration and innovation may call for decentralization. When business units’ needs vary widely — for example, when very different skill sets must be managed — it can be difficult for a parent to accommodate them all, since activities that add value for one unit may destroy value for another. A responsible corporate parent should understand why the needs of the business units differ, rather than impose standard recipes on all units indiscriminately.
Corporate capabilities. When choosing a parenting strategy, the corporate center should also consider the capabilities at its disposal. Those may be found in people (individuals with relevant expertise or experience), processes (for strategic planning, mergers and acquisitions, due diligence, investment valuation, and so on), or systems (for risk management, say, or talent development). Different parenting strategies demand different corporate capabilities. While less-involved parents mainly require strong business judgment and financial capabilities, parents that want to act as a strategic or operational guide need a superior understanding of the relevant markets and sharp business development skills. If the corporate center wants to provide functional leadership to improve units’ performance, it must, of course, excel in key functions (particularly in finance, strategy, and human resources).
Corporate parents should maximize their net — not total — value contributed. That’s important to remember, because an overly (or ham-handedly) involved parent can unwittingly destroy units’ value in many ways. For example, managers at the corporate center who do not understand the specific requirements and success factors of particular business units may impose policies and services that are inappropriate. Inefficient corporate processes can add costs and delays, not to mention considerable confusion over objectives and expectations on the part of hard-pressed business-unit managers. Businesses may waste time and resources on internal coordination with other business units, in an attempt to influence corporate policies or compete for power.
Successful multi-business companies, well aware of such risks, are constantly on the lookout for ways to limit the costs, complexity, and bureaucracy that corporate involvement can induce. For example, Danaher manages its large portfolio and adds significant parenting value with a corporate staff of fewer than 100 employees. In our experience, many corporate parents would be well-served by doing less rather than more.
3. Allocate resources based on clear portfolio roles. Once the portfolio and parenting strategies are set, disciplined capital allocation is the most effective instrument for translating these strategies into action. A Boston Consulting Group study of some 7,000 large global companies showed that companies in the top third of stock market valuation relative to their peers invested approximately 50% more in capital expenditure than their peers and achieved 55% higher returns on assets and 65% higher sales growth.16 They achieved this by consistently focusing their investments on their most attractive businesses and by being disciplined in investment project selection and governance.
Successful diversified companies invest in the best businesses, not just in the best projects. While they do assess the potential of individual investment proposals, their first consideration is the strategic attractiveness of a business and the extent to which future investments can strengthen its competitive advantage and sustain high returns. That approach helps them avoid common capital-allocation pitfalls, such as the maturing-business trap (not reducing capital expenses after a business’s growth plateaus or starts to decline), the egalitarian trap (giving every unit its “fair share,” regardless of potential), or the myopia trap (sacrificing long-term value creation for short-term financial performance).
Assigning clear roles to the businesses in a portfolio is a good way to link investments to strategic potential. A diversified energy company that one of us has worked with classifies its various units as development, growth, base, or harvesting businesses. Depending on their portfolio role, units have different strategic priorities. Development businesses focus on building a defendable position in their market; growth businesses focus on exploiting growth opportunities and improving their market position; base businesses concentrate on securing a foothold in the market but not necessarily on extending their reach or chasing new growth opportunities; and harvesting businesses explicitly do not search for growth but instead focus on extracting the maximum remaining value.
The role of each business determines the guidelines that apply to capital allocation. For example, coal power generation is classified as a mature harvesting business, so its capital expenses are limited to mandatory investments, effectively shrinking its asset base. This approach frees up money for investments in the renewables segment, which, as a growth business, is allowed to invest up to three times its own cash flow from operations.
Additionally, a corporate center can manage its investment program by regularly analyzing the overall risk-return profile of its portfolio of initiatives. By doing this, the energy company discovered that it had too many low-risk, low-return activities and just a few big, risky ones with high potential returns. As a result, management changed its investment strategy and encouraged units to pursue more small investments with high-risk and high-reward potential to improve the balance across the portfolio. A tech company that undertook a similar analysis realized that it was spending too much on maintaining legacy systems and incremental, low-risk user experience improvements, and was not investing sufficiently in new platforms and customer journey transformations.
Beyond strategic capital budgeting, thriving multi-business companies also excel in the selection of major investments and apply rigorous governance mechanisms to support and track them. Over time, such strategic and financial diligence in capital allocation tends to pay off in terms of superior growth rates and return on capital. Those results come from strong corporate-level judgment skills, learning, and adaptation. While post-completion audits for large initiatives are common in many companies, few companies regularly feed the learnings back into the selection process. To do that effectively, they need to review not only past activities but also past decisions about which ideas to pursue. As the head of corporate strategy at a large, successful industrial conglomerate told us, “We made our biggest losses from moves not made. So, we also explicitly review opportunity cost mistakes.”
Evidence suggests that the corporate diversification discussion should not revolve around whether the conglomerate model is dead or out of fashion, because in many cases it is working quite well. The more useful question to ask is how multi-business companies can manage their portfolios for success. Limiting the number of business models, choosing the right parenting strategy, and allocating capital based on strategic portfolio roles may well make the difference between a diversification premium and a diversification discount.
Productivity in the United States’ health care industry is declining — and has been ever since World War II. As the cost of treating patients continues to rise, life expectancy in America is beginning to fall. But there is mounting evidence that artificial intelligence (AI) can reverse the downward spiral in productivity by automating the system’s labyrinth of labor-intensive, inefficient administrative tasks, many of which have little to do with treating patients.
Administrative and operational inefficiencies account for nearly one third of the U.S. health care system’s $3 trillion in annual costs. Labor is the industry’s single largest operating expense, with six out of every 10 people who work in health care never interacting with patients. Even those who do can spend as little as 27% of their time working directly with patients. The rest is spent in front of computers, performing administrative tasks.
Using AI-powered tools capable of processing vast amounts of data and making real-time recommendations, some hospitals and insurers are discovering that they can reduce administrative hours, especially in the areas of regulatory documentation and fraudulent claims. This allows health care employees to devote more of their time to patients and focus on meeting their needs more efficiently.
To be sure, as we’ve seen with the adoption of electronic health records (EHR), the health care industry has a track record of dragging its feet when it comes to adopting new technologies — and for failing to maximize efficiency gains from new technologies. It was among the last industries to accept the need to digitize, and by and large has designed digital systems that doctors and medical staff dislike, contributing to warnings about burnout in the industry.
Adopting AI, however, doesn’t require the Herculean effort electronic health records (EHRs) did. Where EHRs required billions of dollars in investment and multi-year commitments from health systems, AI is more about targeted solutions. It involves productivity improvements made in increments by individual organizations without the prerequisite collaboration and standardization across health care players required with EHR adoption.
Indeed, AI solutions dealing with cost-cutting and reducing bureaucracy — where AI could have the biggest impact on productivity — are already producing the kind of internal gains that suggest much more is possible in health care players’ back offices. In most cases, these are experiments launched by individual hospitals or insurers.
Here, we analyze three ways AI is chipping away at mundane, administrative tasks at various health care providers and achieving new efficiencies.
Faster Hospital Bed Assignments
Quickly assigning patients to beds is critical to both the patients’ recovery and the financial health of hospitals. Large hospitals typically employ teams of 50 or more bed managers who spend the bulk of their day making calls and sending faxes to various departments vying for their share of the beds available. This job is made more complex by the unique requirements of each patient and the timing of incoming bed requests, so it’s not always a case of not enough beds but rather not enough of the right type at the right time.
Enter AI with the capability to help hospitals more accurately anticipate demand for beds and assign them more efficiently. For instance, by combining bed availability data and patient clinical data with projected future bed requests, an AI-powered control center at Johns Hopkins Hospital has been able to foresee bottlenecks and suggest corrective actions to avoid them, sometimes days in advance.
As a result, since the hospital introduced its new system two years ago, Johns Hopkins can assign beds 30% faster. This has reduced the need to keep surgery patients in recovery rooms longer than necessary by 80% and cut the wait time for beds for incoming emergency room patients by 20%. The new efficiencies also permitted Hopkins to accept 60% more transfer patients from other hospitals.
All of these improvements mean more hospital revenue. Hopkins’s success has prompted Humber River Hospital in Toronto and Tampa General Hospital in Florida to create their own AI-powered control centers as well.
Easier and Improved Documentation
Rapid collection, analysis and validation of health records is another place where AI has begun to make a difference. Health care providers typically spend nearly $39 billion every year to ensure that their electronic health records comply with about 600 federal guidelines. Hospitals assign about 60 people to this task on average, one quarter of whom are doctors and nurses.
This calculus changes when providers use an AI-powered tool developed in cooperation with electronic health record vendor Cerner Corporation. Embedded in physicians’ workflow, the AI tool created by Nuance Communications offers real-time suggestions to doctors on how to comply with federal guidelines by analyzing both patient clinical data and administrative data.
By following the AI tool’s recommendations, some health care providers have cut the time spent on documentation by up to 45% while simultaneously making their records 36% more compliant.
Automated Fraud Detection
Fraud, waste, and abuse also continues to be a consistent drain. Despite an army of claims investigators, it annually costs the industry as much as $200 billion.
While AI won’t eliminate those problems, it does help insurers better identify the claims that investigators should review — in many cases, even before they are paid — to more efficiently reduce the number of suspect claims making it through the system. For example, startup Fraudscope has already saved insurers more than $1 billion by using machine learning algorithms to identify potentially fraudulent claims and alert investigators prior to payment. Its AI system also prioritizes the claims that will yield the most savings, ensuring that time and resources are used where they will have the greatest impact.
Getting Ready for AI
When it comes to cutting health care’s administrative burden through AI, we are only beginning to scratch the surface. But the industry’s ability to amplify that impact will be constrained unless it moves to remove certain impediments.
First, healthcare organizations must simplify and standardize data and processes before AI algorithms can work with them. For example, efficiently finding available hospital beds can’t happen unless all departments define bed space in the same terms.
Second, health care providers will have to break down the barriers that usually exist between customized and conflicting information technology systems in different departments. AI can only automate the transfer of patients from operating rooms to intensive care units (ICU) if both departments’ IT systems are able to communicate with each other.
Finally, the industry’s productivity will not improve as long as too many health care personnel continue in jobs that don’t add value to the business by improving outcomes. Health care players need to begin reducing their workforces by taking advantage of the industry’s 20% attrition rate and automating tasks, rather than filling positions on autopilot.
The task of improving productivity in health care by automating administrative tasks with AI will not be completed quickly or easily. But the progress already achieved by AI solutions is encouraging enough for some to wonder whether re-investing savings from it might also ultimately cut the overall cost of health care as well as improve its quality. For an industry known for its glacial approach to change, AI offers more than a little light at the end of a long tunnel.
When innovations threaten to disrupt an industry by replacing an old business model with a new one, incumbents need to invest in that model in order to survive. That’s the conventional wisdom, and it’s given rise to popular mantra “Disrupt or Be Disrupted.”
If you’re dealing with innovations that have led to a dominant new business model in your industry, that advice is sound, as the leaders of Netflix and Blockbuster can tell you. But what if an innovation poses a threat, and you can’t yet tell whether it has genuinely transformative potential? What are the costs and benefits of self-disruption at that uncertain stage?
These are rarely studied questions. Almost all of the research available on the topic of self-disruption focuses on how well incumbents respond to innovations after the they have enabled new models to take over an industry. They might focus on how incumbents cope with the rapid obsolescence of their old capabilities, say, or with internal resistance to inevitable change. That’s valuable information, of course, but it shines no light on the problem of how to respond to potentially threatening innovation that has yet to generate a dominant new business model.
Getting at that problem isn’t easy, because the data about what works are hard to come by. A few years ago, though, we realized that a valuable set of data was available about the U.S. electric utility sector, in which the existing model for energy generation, which has prevailed for a century, may now be giving way to a new one. We studied the sector for three years and came up with some very interesting findings about the “adjustment costs” that companies incur when they disrupt themselves. We’ll soon publish our findings in full in the journal Organizational Science, but below we’ll recap the highlights of the article, in which we identify adjustment costs in a variety of situations for self-disrupters, provide a framework for how companies can identify the locus and intensity of those costs, and lay out different strategies to mitigate them.
A rare natural experiment
In the traditional model of electricity generation, large power plants produce power at a centralized location, which operates at a considerable distance from the points of consumption. Since the mid-2000s, however, this model has been under threat from a new, decentralized model, in which electricity is generated on a much smaller scale near or at the point of use, often through a combination of rooftop solar photovoltaic (PV) systems, batteries, and the digital management of the electricity grid.
For our study, we collected data on 512 strategic initiatives, both centralized and decentralized, launched by 48 leading U.S. electric utilities during 2008-2015—a period in which the decentralized model was in its uncertain, nascent phase. We examined how each initiative impacted a company’s value through changes in its stock price, which we used as a proxy for determining the short-term costs of self-disruption. We validated our quantitative approach by reviewing several case studies of U.S. and international electric utilities.
As we studied the data, we realized that we were looking at the results of a rare natural experiment on the costs of adjustment to self-disruption. The information presented itself along two big dimensions: the power-generation assets owned by the centralized power plants (i.e., their production capacity, which obviously varied considerably) and the competitive intensity of their markets (which ranged from perfect competition to near monopoly, because of regulatory differences between states).
We looked in particular at firms with generation assets that would become redundant if the disruptive model became dominant. Firms with generation assets above the median, we found, incurred adjustment costs that were approximately $800M higher than those below the median. And firms operating in more competitive markets incurred approximately $600M higher cost of self-disruption than those in less competitive markets. These factors—the generation asset base and the external competitive environment in which they operated—are two of the main drivers of value for electric utilities. And when they’re high, we discovered, they make the adjustment costs of self-disruption high, at least in the short term.
We summarized our findings on this point in a simple but revealing two-by-two.
Quadrant 1, not surprisingly, is the danger zone. Companies here operate in highly competitive environments and have high stocks of assets that they have accumulated to support the traditional business. They’re the ones potentially most threatened by innovation—and, ironically, as the figure makes clear, they’re also the ones who pay the highest adjustment cost for disrupting themselves in response to that innovation.
What companies fall into this quadrant? Traditional automotive manufacturers certainly do. Competition in the industry is fierce, and many of these manufacturers’ key assets, such as factories, technology expertise, and distribution networks, are only useful in the traditional business, which is predicated on widespread vehicle ownership. These companies now have to contend with the rising threat to their traditional business model from autonomous vehicles and ride sharing, but, as this figure shows, the adjustment costs for pursuing these innovations will be high. The Ford Motor Company has learned this hard way in recent years.
Companies in Quadrant 4 are likely to have the lowest cost of self-disruption, because they operate in markets that are not especially competitive, and because their key assets can easily be deployed in new places and for new purposes. Think here of enterprise-software developers such as Microsoft, SAP, and Oracle, who are undergoing the disruptive change to cloud-based software service. These companies have high market share in their specific application domains, which means competition isn’t a major concern, and many of their key assets—in software development and enterprise-customer relationships, for example—can easily be adapted to fit the new model.
Companies in Quadrants 2 and 3 bear intermediate costs of self-disruption, but the locus of those costs varies between, respectively, the external competitive environment and the internal asset base.
Companies in Quadrant 2 tend to bear relatively high indirect costs, because of the greater threat of cannibalization and the greater conflict for resources in a highly competitive environment. Traditional game developers facing the emergence of mobile gaming fall into this quadrant—Electronic Arts and Nintendo, for example. Their stock of assets—intellectual property, game-development capabilities—is compatible with mobile gaming, but they operate in a highly competitive market where product life is short and consumer preferences change quickly.
Companies in Quadrant 3 tend to bear relatively high direct costs, because they need to develop new assets and lack expertise in implementing the new business. Companies in the satellite TV industry fall into this quadrant. The industry has been dominated by DirecTV (AT&T) and Dish Network, both of which have prospered without much competition by selling packages of channels at relatively high prices. Their key asset stocks are networks of satellites that very specifically serve the existing business—which is a problem now that companies such as Netflix and Amazon are threatening to disrupt the satellite model by offering low-cost video-on-demand packages via the Internet.
Companies in the same industry can fall into different quadrants, of course, depending on their asset configurations and their competitive positioning. This is the case in the retail sector, where brick-and-mortar operations are under threat from online commerce. The cost of self-disruption is high in this environment for retailers such as JC Penney and Sears, whose asset base consists of a vast array of stores that they operate in a highly competitive market. It is considerably lower, on the other hand, for luxury retailers such as Louis Vuitton and Gucci, which face much less competition and whose greatest asset is often their brand.
Costs and benefits
The framework we’ve outlined above can be very useful to leaders who are considering the costs and benefits of self-disruption.
Companies in Quadrant 4 are well positioned to embrace disruption. A case in point is Microsoft, which—drawing on many of its existing assets in software development, customer relationships, and networking technologies—has embraced a shift from on-premise software licensing to cloud-based software and infrastructure services, where it faces low competitive intensity within the enterprise market.
Companies in Quadrant 1, on the other hand, are not well positioned. They are the most threatened by disruptive innovations and have to adapt—but they have to do this in a highly competitive environment, without the benefit of leveraging their existing asset stocks. If the companies in this quadrant rush to self-disrupt during the nascent period of innovation and change, they are likely to incur significant adjustment costs, which may doom their prospects. These companies would do much better to adopt a wait-and-see approach, in which they shy away from taking on major initiatives on their own until the initial uncertainty around disruptive innovations is resolved. Alternatively, they might explore disruptive initiatives via strategic alliances with partners from outside the industry—as GM is doing with Lyft, for example, by pursuing an alliance to help manage the shift toward ride sharing and autonomous vehicles.
Companies in Quadrant 2 can benefit from dividing their assets between their existing and disruptive business models, but in doing so they have to mitigate the indirect adjustment costs that accompany such sharing of assets, and the conflicts that will arise from cannibalization and resource allocation in a highly competitive environment. Here a viable approach is to pursue self-disruption only in niche market, so as to avoid cannibalization and to leverage asset bases such as brand and pre-existing IP, which are less constrained by the traditional business. Nintendo opted for this approach when it began investing in mobile gaming, by focusing on games that are unique to smartphones.
Companies in Quadrant 3 don’t operate in as competitive an environment as those in Quadrant 2, but they incur greater adjustment costs when it comes to developing asset bases that support the new business. A viable approach here is to pursue an active M&A and alliance strategy to build new asset stocks, as Dish Network has managed to do successfully.
Our study has a number of obvious limitations. We examined data from only one industry; we used stock-price data as a proxy for long-term performance; and we were unable, because of a lack of data, to explore how firms might lower adjustment costs internally. Still, we feel our findings represent an important contribution to the strategy literature, because they help explain why some incumbents are able to adapt successfully to disruptive innovation while others are not.