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Case Study: When Two Leaders on the Senior Team Hate Each Other

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The feedback in the 360-degree reviews was supposed to be anonymous. But it was crystal clear who’d made the negative comments in the assessment of one executive.

Lance Best, the CEO of Barker Sports Apparel, was meeting with Nina Kelk, the company’s general counsel, who also oversaw human resources. It had been a long day at the company’s Birmingham, England, headquarters, and in the early evening the two were going over the evaluations of each of Lance’s direct reports. Lance was struck by what he saw in CFO Damon Ewen’s file. Most of the input was neutral, which was to be expected. Though brilliant and well respected, Damon wasn’t the warmest of colleagues. But one person had given him the lowest ratings possible, and from the written remarks, Lance could tell that it was Ahmed Lund, Barker’s head of sales. One read: “I’ve never worked with a bigger control freak in my life.”

Editor's Note

This fictionalized case study will appear in a forthcoming issue of Harvard Business Review, along with commentary from experts and readers. If you’d like your comment to be considered for publication, please be sure to include your full name, company or university affiliation, and email address.

“These comments are pretty vicious,” Lance said.

“You’re surprised?” Nina asked.

“I guess not,” Lance acknowledged.

His CFO and his sales chief had been at loggerheads  for a while. Ahmed’s 360 also contained a few pointed complaints about his working style — no doubt from Damon.

Lance sighed. Five years earlier, when he’d stepped into his role, he’d been focused on growing the company that his father, Eric — the previous CEO — had founded. Barker had licensing deals with sports leagues to make merchandise with their logos and partnered with large brands to produce it for retail markets, and when Lance took the company over, its revenues were about £100 million. Soon after, he’d landed the firm’s biggest partner, Howell. Negotiating the deal with the large global brand had been a challenge, but it increased business so much that Lance and his direct reports still felt as if they didn’t have enough hours in the day to get everything done. They certainly didn’t have time for infighting like this.

“So what do we do with this info?” Lance asked.

Nina shrugged. “This is the first time I’ve been through this process myself.”

“Right. Clearly I’ve got to do something, though. I know that Ahmed and Damon aren’t mates, but I do expect them to be civil.”

Nina nodded, but Lance sensed she was biting her tongue. “You can be honest with me, Nina. I need your counsel.”

“Well, if I’m honest,” she said tentatively, “I think that’s part of the problem. The expectation is that we’re civil, but that doesn’t translate to collaboration. We all trust you, but there isn’t a whole lot of trust between the team members.”

“So does everyone think Damon is awful?” he asked, pointing to the report.

Nina shook her head. “It’s not just about him. You can see from the feedback that Ahmed isn’t a saint, either. He picks fights with Damon, and the tension between them — and their teams — has been having a ripple effect on the rest of us. You see the finger-pointing. It seems like everyone is out for themselves.”

Although Lance hated hearing this, it wasn’t news. He’d just tried to convince himself that the problems were growing pains and would sort themselves out. After all, sales and finance were often at odds in organizations, and the conflict hadn’t had a big impact on Barker’s revenues. They’d grown 22% the previous year and 28% the year before that.

Of course, none of that growth had come easily, and opportunities had certainly been missed. The team had dropped the ball on inquiries from several retailers interested in its products by failing to coordinate getting them into the company’s system quickly. Now, Lance realized that might be a sign of more fallout to come. He needed to fix this. “My dad always wanted to do one of those team-building retreats,” he said, smiling. This had been a running joke among Barker’s executives for years. Whenever Eric had sensed tension, he’d mention the idea, but he never followed through.

Nina laughed. “Unfortunately, I think we’re beyond that.”

This Mess

The next morning, Lance was in his office when he got a text from Jhumpa, the head of product and merchandising: Can you talk?

Knowing this couldn’t be good, Lance called her immediately.

Skipping the formalities, she launched in: “You need to get them on the same page.” Lance didn’t have to ask who “them” was. “Ahmed has promised samples for the new line on the Clarkson account, but his order exceeds the limits accounting set, so we need Damon’s signoff, and he won’t give it.”

This was a recurring fight. Ahmed accused Damon of throwing up roadblocks and using his power to undermine the sales department. Damon retorted that Ahmed was driving Barker into the ground by essentially giving products away. Lance went back and forth on whose side he took, depending on which of them was behaving worse. But he didn’t want to intervene again. Why couldn’t they just find a compromise?

Practically reading his mind, Jhumpa said, “They’ll stay in this standoff forever if you let them. It’s as if they’re in their own little fiefdoms; they act like they’re not even part of the same team.”

“Have you talked to them about this?”

“The holdup with Clarkson? Of course I have. But it doesn’t help. This situation is a mess.”

The last comment stung. The team wasn’t perfect, but it was still operating at a pretty high level.

“It would really help if you talked to them,” Jhumpa gently pleaded.

Lance thought back to the last time he’d sat down with Ahmed and Damon. Each had brought a binder filled with printouts of the e-mails they’d exchanged about a missed sale. Lance had marveled at how long it had probably taken each of them to prepare — never mind the wasted paper.

“Let me look into it,” Lance said. This had become his default response.

“Can I tell you what I’d do if I were in your shoes?” Jhumpa said. “Fire them both.” Though Lance had always appreciated her straightforwardness, he was taken aback. “Just kidding,” she added hastily. “What about having them work with a coach? I mean, we could all benefit from someone to help us talk through how we handle conflicts and from establishing some new norms.”

Lance wondered if the firing comment had really been a joke, but he let it pass. “I did talk to that leadership development firm last year,” he said. “They had some coaching packages that seemed appealing, but we all agreed we were too busy with the new accounts.”

“Well, maybe we should make time now,” Jhumpa replied.

After they hung up, Lance was still thinking about the idea of letting Ahmed and Damon go. Terrifying as the thought was, it might also be a relief. He’d heard of CEOs who’d cleaned house and replaced several top execs at once. He could keep Jhumpa, Nina, and a few others and bring in some fresh blood. It would be one surefire way to reset the team dynamics.

Doing Just Fine

Later that afternoon, at the end of a regular meeting with the finance team, Lance asked Damon to stay behind.

“I heard there’s a holdup on the Clarkson samples,” he said.

“The usual. Sales needs to pare back the order. As soon as Ahmed does that, I can sign off,” Damon said calmly.

“It doesn’t sound like Ahmed’s budging.”

“He will.”

Lance decided to wade in.

“Is everything OK with you guys?”

“Same as usual. Why? What’s going on? The numbers look great this quarter. We’re doing just fine.”

“I agree on one level, but I have concerns on another. It’s taking six months to onboard new customers at a time when everyone is fighting for them.”

“Is this about those 360 reviews? I tried to be fair in my feedback,” Damon said a bit defensively.

“The input is anonymous, so I don’t know who said what, but the tension between you and Ahmed is obvious.”

“Of course it is. I’m the CFO and he’s in charge of sales. If we’re both doing our jobs well, there’s going to be conflict. And that’s what I’m doing: my job. I’m the keeper of the bottom line, and that means I’m going to butt heads with a few people.” Lance had heard him say this before, but Damon took it one step further this time. “Your discomfort with conflict doesn’t make this any easier.”

They both sat quietly for a minute. Lance knew that as part of this process he’d need to examine his own leadership. Indeed, his 360 had been eye-opening. His people had described him as a passionate entrepreneur and a visionary, but they’d also commented on his preference for managing one-on-one, instead of shepherding the team, and his tendency to favor big-picture thinking over a focus on details.

“OK. I hear you on that,” Lance finally said. “That’s on me. But you also need to think about what you can do to improve this situation. There’s a difference between productive and unhealthy conflict, and right now it feels like we’ve got too much of the latter.”

Our Vision Might Crumble

“Have you considered one of those team-building retreats?” Lance’s father asked when they spoke that night. “I know you all never took me seriously — ”

Lance chuckled. “Because you never booked it!”

“ — but I still think it’s a good idea,” Eric continued. “No one really knows how to have a productive fight at work. It’s not a skill you’re born with. You have to learn it.”

“I’m considering it, Dad. But I’m not sure it would be enough at this point.”

“What about the comp?” This was another thing that Eric had brought up routinely. During his tenure as CEO, he’d realized that the C-suite compensation wasn’t structured to encourage collaboration. Bonuses were based on individual, functional unit, and company performance at respective weightings of 25%, 70%, and 5%.

“Maybe it’s time to bump up that 5% to at least 10% or even 20%,” Eric said.

“I’d like to make those changes, but I need Damon’s help to do it, and he’s swamped,” Lance said. “Besides, lots of experts say that too many people view comp as a hammer and every problem as a nail. CEOs expect comp to fix anything, but usually you need other tools. I may have to do something more drastic.”

“You’re not considering firing anyone, are you?” Eric had personally hired all the senior executives now on Lance’s team and was almost as loyal to them as he was to his own family.

“To be honest, it’s been on my mind. I’m not sure what I would do without Ahmed or Damon. They’re an important part of why we make our numbers each year. They help us win. But I look back  and wonder how we did it playing the game this way. I need a team that’s going to work together to reach our longer-term goals.” When Eric had retired, he and Lance set a target of reaching revenues of £500 million by 2022. “This group feels as if it could disintegrate at any moment. And our vision might crumble along with it.”

“I’m sorry,” Eric said. “Do you feel like you inherited a pile of problems from your old dad?”

“No, I feel like I’ve somehow created this one — or at least made it worse.”

“Well, one thing is certain:  You’re the boss now. So you’ll have to decide what to do.”

What should Lance do about the conflict between Damon and Ahmed?

If you’d like your comment to be considered for publication in a forthcoming issue of HBR, please remember to include your full name, company or university affiliation, and email address.

I Ran 4 Experiments to Break My Social Media Addiction. Here’s What Worked.

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Social media can connect us to new ideas, help us share our work, and allow previously unheard voices to influence culture. Yet it can also be a highly addictive time-sink if we’re not careful about our goals, purpose, and usage.

Over the last two years, I conducted four different experiments to monitor my own behavior, implementing trackers and blockers in order to better understand how social media usage affected my productivity. My goal was to see if by interrupting my daily behavior I could change my “default settings” and have more time for deep, focused work.

In the end, these four experiments opened my eyes about my relationship to social platforms, and taught me effective strategies to maximize the benefit of these social tools while limiting the downsides.

The first step was collecting data. Before beginning my experiments, I tracked my daily behavior to better understand where my time and energy was going, which gave me insight into what I could change to produce more satisfying deep work. I used RescueTime for tracking my computer usage, and Moment to track my cell phone behaviors.

Experiment #1: Complete Removal of Social Sites For 30 Days

My first experiment was a complete removal of all social aspects from my routine: no Facebook, Instagram, Twitter, YouTube, or LinkedIn for 30 days. Leading up to it, I raised objections—“but I need Facebook for my work!”, my brain sputtered, in a testament to the addictive power of the apps.

I logged out of each site and deleted all the apps from my phone. Then, I used Freedom, a website blocking tool, to restrict the social sites from my browser and phone. Finally, I had my partner take over my phone and install parental restrictions on browser sites with a password unknown to me. (I wasn’t taking any chances.)

The Results. Once I decided to go all-in, it was surprisingly easier to do than expected. There was a relief in being offline and deciding, once and for all, to do it. Here’s what I learned:

There were a few technical hassles: Facebook, in particular, is embedded in a lot of other applications, which created a problem any a tool required Facebook as a login. Going forward, I’ll create email-based logins only (which is also better for security).
My book-reading skyrocketed. In a month, I read more books than I had in the combined three months prior. Whenever I craved a break, I turned to my Kindle, instead of social or news sites.
I used social sites a surprising amount for research and discovery—when I’m thinking of a person I want to connect with, or a project I want to follow-up on, I would quickly type the social site for ease. Not having access created more friction in the short-term, but didn’t ultimately delay the work I was doing. There was a tension between instant access and carving out boundaries for deeper creative work that I found useful, albeit annoying.

After the experiment was over, I went back to allowing myself unlimited social media access and continued to track my usage using RescueTime. With a fresh perspective after a month away, I was able to more clearly see a pattern emerge around how I used the various sites, both for better and for worse. My key finding was the marked difference in my behaviors across devices: My laptop wasn’t the biggest culprit for addictive behavior: when I was at my desk, working, I spent the majority of my time actually working. My phone was the biggest culprit for addictive behavior.

Further, it was very clearly time-based. My social media usage (or cravings) clearly spiked at certain times. Most of my bad habits were tied up in late-night tiredness, early-morning mindlessness, and craving “The Scroll” whenever I was tired. It also became fairly predictable that I wanted a mid-morning break (around 11am) and an afternoon break (around 3 or 4pm). By far, the worst time was late evening, after dinner, when my brain felt like complete mush.

By all-out blocking the social feeds for thirty days, I saw where in the day my tiredness emerged and when I wanted to use the platforms for research or actual connection.

Experiment #2: Carving Out Daily Time Blockers

I wanted to learn whether or not I could limit, but not eliminate, social media and have equally effective results. This next experiment involved a daily restriction on websites based on the known “tired times” I’d identified in the first experiment.

For two weeks, I limited social access during certain periods of the day using the blocking app like Freedom. I allowed social sites on my computer in the afternoons only — not in the mornings, or after dinner. I also blocked all news websites, television sites, and installed Newsfeed Eradicator for Facebook, a social plug-in that helps prevent the scrolling nature of the newsfeed.

Results: Keeping the mornings social-media and news free was a game changer. I got so much more done on my biggest projects by having dedicated focus hours, and also knowing that there was a scheduled break in my day coming up.

The long-term effects of this change became apparent by day four or five. In the mornings, if I succumbed to impulsivity (a quick check here, an Amazon purchase there, firing off a couple of emails), it was far more difficult for me to throttle back into the realm of deep work.
By carving out chunks of the day to focus on specific work projects (moving one big project forward before 11am), I radically improved my personal productivity.
Temptation was strong, but waned over time: by overcoming the biggest pull to check first thing in the morning, I was much more focused and clear throughout the rest of the morning.

This proved to be a very effective strategy for me. Time-based internet blockers helped me increase my productivity. But now the reverse question came up: instead of blocking out times when I’d never use social, what if I dedicated a particular slot of time to it?

Experiment #3: The Social “Happy Hour”

The next experiment I tried was dedicating a specific hour of my day completely for use on social sites. I set up a calendar invitation from 4-5pm: a “happy hour” at the end of the work day to connect, enjoy, and run across new people and ideas after nearly 12 hours of working or parenting.

Results: Creating a built-in stress relief hour where I know that I can slide into “social research and browsing” (“The Scroll”), helped me avoid temptation at other hours of the day. It was easier to replace a bad habit with a better one than to focus all my energy on eliminating the bad habit.

Strangely, consolidating all of my social media use into a single hour made it seem less exciting. I noticed that I’d be finished scrolling within 20 minutes, or 30 minutes on a long day. There’s only so much sustained reading and commenting that I can do.
I was much more efficient at responding to all of the requests that come my way—rather than have metered out conversations trickling through the day, I buckled down, opened up new browser tabs for each meaningful mention or request, and whipped through it.
My content creation went way down. Instead, I began to plan ahead with a loose Evernote file for social media status updates and things I wanted to share, and the 12-hour delay between composing and pressing “publish” gave me a better chance to reflect on whether instant-sharing was really still necessary.

The biggest insights were that (1) social media usage dripped throughout the day drains the energy and focus I have for writing and other work, and (2) that there’s something insidiously satisfying about pressing publish on a status update, and each time I do it, I get the dopamine hit of satisfaction and response. But each tiny posting saps energy, and that adds up.

Experiment #4: 24 Hours To Break the Cycle

One of my favorite methods for resetting my brain is taking a full weekend day without my phone or my laptop, an idea I originally got from Tiffany Shlain’s “tech shabbat.” Back when I used to train for triathlons and open-water swims, Saturdays were spent largely outdoors, and it’s rather difficult to spend time scrolling the web while biking or swimming. So I used Freedom and a mesh wifi network to block the internet from midnight on Friday evening until Saturday at 3pm from all of my machines.

Results. Having something to do—going on a hike, going to the beach, meeting friends for coffee—helps tremendously.

The hardest part is walking out the door without the phone. From there, the freedom begins. The best way to block the internet is to physically leave devices elsewhere.
On days when I stay inside, I set my Freedom App to a weekend schedule of “no social media or email” until 3pm on Saturdays. The mornings can be lazy and slow. I’m not a doctor, I’m not an emergency worker, and we can all make it through the day if I’m not on email at 6am on a Saturday morning. By the time 1pm rolls around, I’m usually so involved in some other activity that I don’t notice.
I found I needed to be flexible about this experiment. On days when I have article deadlines or want to work a few hours on the weekend, I’ll set parameters for how and when to log on to get a chunk of work done.

Today, even with kids (and no triathlons currently), I still notice the effect of taking a Saturday away each week to disrupt the pattern of connection. A day free of the Internet is a great way to do a pattern reset if you notice (as I have) personal productivity dips by Friday.

Shifting From Subtraction to Addition

By and large, my first experiments were based on control and elimination. Sometimes, instead of focusing on constriction and willpower, however, it’s actually a better strategy to focus on the thing I want more of: more reading, more unplugged time with my family, space to think. One of the reasons diets don’t work very well is because most of them focus what you restrict, rather than what you add. My later experiments opened my eyes to the power of addition: planning ahead for dedicated social time, or a Saturday spent outdoors.

Today, I use Freedom to block social websites and news in the mornings nearly every day. I deleted Facebook and email from my phone, I will manually re-install them from 4pm to 5pm and then delete them again (yes, daily). I take regular 24-hour breaks. And I track my usage with RescueTime, which sends me an alert when I’ve hit 45 minutes of total “distracting” time.

With social media, many of us want to reduce our consumption, but we miss an important piece of the puzzle: we’re craving something that we want, and we think that social media has a quick answer. These experiments helped me realize that at the heart of my cravings around the social internet are deep connections with friends, access to new ideas and information, or time to zone out and relax after a hard day. Each of these components can be satisfied with other things beyond social media, and more effectively. As with many tools, it’s not an all or nothing, good-versus-bad conversation. I will continue to experiment in the future, especially now that Apple has introduced it’s “Screen Time” feature. Just because the apps are available, doesn’t mean our current default behaviors are the best ways to use them or get what we want. By limiting my access to social sites, I created a pattern disrupt that allowed me to reach out to more friends, read more books, and go deeper into work that mattered.

Debating Minimum Wage, and Reflections on a Year of #MeToo

Youngme Moon, Mihir Desai, and Felix Oberholzer-Gee are back with Season 2 of After Hours! In this episode, they debate whether the federal minimum wage should be raised, offer their personal reflections on a year of the #MeToo movement, and share their picks for the week.

Download this podcast

For interested listeners:

Early evidence on the $15 minimum wage, including the research cited by Felix
Slides from a recent classroom discussion Mihir led on #MeToo

HBR Presents is a network of podcasts curated by HBR editors, bringing you the best business ideas from the leading minds in management. The views and opinions expressed are solely those of the authors and do not necessarily reflect the official policy or position of Harvard Business Review or its affiliates.

To Land a Great Job, Talk About Why You Love Your Work

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When interviewing for your next job, how can you impress your recruiter and increase your chances of securing a job offer? Of course you may wish to emphasize your ambitions and goals you hope to achieve as a result of working at the company — your extrinsic motivation for the job. But to what extent should you also emphasize your love for your work and what you hope to achieve as part of the process of working at the company? This comprises your intrinsic motivation for the job, and most of us understand how important it can be to sustained engagement at work; but do recruiters care to hear this?

Our research suggests that they do — and that job applicants aren’t taking advantage of that. Indeed, we have found that people fail to predict the power of such a statement of intrinsic motivation on the impression they make.

To examine this prediction problem — the discrepancy between what candidates think will impress recruiters and what recruiters actually find impressive — we surveyed 1428 full-time employees and MBA students across five studies. Some provided their predictions, guessing what recruiters would find impressive when hiring a job candidate. Others told us what they actually valued when making hiring decisions.

As a first test, we asked full-time employees to view several statements that they could make during a job interview. Some statements emphasized intrinsic motivation, for example, wanting a job that is interesting and meaningful. Other statements emphasized extrinsic motivation, for example, caring for career advancement and financial security. Candidates indicated how impressive they thought each statement was for recruiters. Another group of employees viewed these same statements and told us how impressed they would be by a job candidate who expressed each of these during an interview. Whereas job candidates accurately predicted how impressed recruiters would be by statements of extrinsic motivation, these individuals failed to realize how much recruiters would be impressed by expressions of intrinsic motivation. Emphasizing love for a particular job was more important for recruiters than candidates anticipated.

We found this same pattern — that people fail to predict the value of expressing intrinsic motivation — when the roles were reversed. In this study, recruiters predicted what recruits find appealing in a company and what would convince them to accept a job offer. Specifically, we asked MBA students to view statements about company culture, including current employees’ intrinsic and extrinsic motivation, and predict how useful each one is in convincing an admitted candidate to join the company. Other MBAs viewed these same statements and told us whether they would accept a job offer from a company who expressed each of these in its culture. Whereas recruiters correctly predicted that recruits wanted to work at a company where the culture emphasized extrinsic motivation, they underestimated how much recruits valued working at a company where the culture emphasized intrinsic motivation. Emphasizing that employees find their job interesting and meaningful impressed job candidates more than those in the role of recruiter anticipated.

Why do candidates, and recruiters, underestimate how much others value intrinsic motivation? We found that although people know that they care about intrinsic motivation, they don’t know that others also care about this just as much. People’s lack of awareness that others value intrinsic motivation influences what they say when trying to impress others.

This failure to appreciate that others care to be intrinsically motivated has consequences for what we say in job interviews. In one study, we asked MBA students to choose a pitch for a job interview: One pitch emphasized intrinsic motivation (e.g., “I love doing my work”) and the other pitch emphasized extrinsic motivation (e.g., “the position would be a great place for me to advance my career”). If students chose the pitch that the majority of recruiters (another group of MBAs) selected as more convincing, they could be eligible to win a prize. We found that while only 43% of the candidates chose the intrinsic pitch, 69.5% of the recruiters thought it was superior and more likely to land the job.

How can job seekers ensure they emphasize motivations that recruiters care for? One tip is to take the recruiter’s perspective. We asked employees to view two job pitches that emphasized either intrinsic or extrinsic motivation, and to the choose one that would impress a recruiter. Before choosing, we instructed one group to take the recruiter’s perspective. This group first considered who they would hire if they were the recruiter, before choosing a pitch they believed would impress a recruiter. The other group did not take the recruiters’ perspective before choosing. Perspective-taking helped those in the role of job candidate better intuit that recruiters are impressed by intrinsic motivation, leading 45.9% of them to choose this message compared with only 31.7% who did not take the recruiters’ perspective.

The takeaway is clear: candidates interviewing for a job should highlight the meaning they derive from their work, and recruiters looking to attract job candidates should emphasize that their employees do work they love. Engaging in perspective taking — putting yourself in the other person’s shoes — is one way to ensure intrinsic motivation is emphasized.

Help Your Team Measure Customer Experience Data More Accurately

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Customer experience (CX) goes beyond measuring the relationship between customers and companies; it is also about quantifying the hundreds of regular interactions and residual memories that influence future behavior. Specific tools like journey mapping and touchpoint management are keys that employees can use to unlock the code for many in-store and in-person experiences. But it’s important for your team to understand the context in which data is being used to make company-wide decisions.

The balanced scorecard was initially popularized in the early 1990s as a way for companies to look at varying aspects of the business, from customer satisfaction, to financial well-being to operational outcomes, all in one simple read-out. It looks like this:

 

A shortcoming with the balanced scorecard is that it gives companies a “false sense of data.” When leaders have even small amounts of data, it can be easy to assume they know enough to make aggressive decisions, all based on information with sources they don’t control or fully understand. In some cases, a little data can be worse than having no data at all; it can invite hubris.

For example, customer satisfaction scores are influenced by population density — a factor which does not translate into balanced scorecards. Urban environments have peak-time “rushes” when higher volumes of people are all trying to do the same things. Someone entering a pharmacy in the heart of New York City during rush hour will unquestionably have longer-than-desired wait times. This increases both the “perceived wait time” and the likelihood of a negative experience.

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Customer scores for stores like this could indicate that the locations need more attention, but they are also typically among the best performing, financially. The long lines frustrate customers, but also indicate that there is a lot of business happening.

A similar store in a part of the country where interactions have less time pressure may have higher scores, despite not performing as well for the company as its New York City counterpart. The reality is that balanced scorecards, as they were originally published, have the potential to punish some of the most economically-valuable businesses.

An “equitable scorecard” (sometimes called a “weighted scorecard”) is an established mathematical process which accounts for environmental and uncontrollable factors in customer experience scores. It can create a relative “pound for pound” benchmark for each individual location of a business, anywhere in the world.

When your team understands how to use equitable scorecard techniques, they’ll be able to calculate a more accurate score for any given location by accounting for variables like clientele composition, location, environment and other localized factors.

For example, a fast food restaurant might serve customers in five different ways:

A customer walks in, orders, waits, and takes their order out.
A customer walks in, orders, waits, and sits down at a table with their order.
A customer orders through the drive-through. The customer drives off and eats elsewhere.
A customer orders through the drive-through. The customer eats in their car in the parking lot.
A customer orders through an app or by phone, then picks up their order.

The needs of the customer and the job to be done by the restaurant in each of these instances are very different. Some value speed above all else. Others require good ambiance to enjoy their meal. Some want a clear and efficient layout of the location. These needs can vary widely from location to location and a single customer can have different needs at different times during the same visit.

So, when the exact same team serves the exact same food in the exact same three minutes, each customer segment will have different reactions. Those identical efforts may yield five very different customer satisfaction scores because expectation is a key determinant of experience.

Companies routinely find that locations rated low-performing by balanced scorecards are actually outperforming reasonable expectations after accounting for uncontrollable operating conditions. The reverse is sometimes also true, where high-scoring stores should, statistically, be performing at an even higher level. The result of equitable scorecarding is a true reflection of staff effort, engagement, efficiency, and efficacy.

When good store managers and employees come under increased scrutiny because of incomplete scorecard data, it can quickly decrease the overall sense of employee appreciation. This tends to increase turnover rates, compound unnecessary replacement costs, impacts business efforts because of additional ramp times and, ultimately, slows revenue growth.

Equitable scorecards measure performance based on expectations, eliminating the engagement-killing notion that people are quantified by decontextualized, inhumane numbers. By establishing reasonable benchmarks for each individual location, companies will also improve the allocations of time and money needed to help a business grow.

Creating a level playing field within a company establishes trust and motivates teams to drive for higher success. By measuring and accounting for uncontrollable factors, companies can promise management and staff that their work will be judged individually based on the cards they are dealt. Fair CX measurements lead to improved experiences, financial growth, and greater engagement, keeping both the customers and the company happy.

Why Doctors Need Leadership Training

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Medicine involves leadership. Nearly all physicians take on significant leadership responsibilities over the course of their career, but unlike any other occupation where management skills are important, physicians are neither taught how to lead nor are they typically rewarded for good leadership. Even though medical institutions have designated “leadership” as a core medical competency, leadership skills are rarely taught and reinforced across the continuum of medical training. As more evidence shows that leadership skills and management practices positively influence both patient and healthcare organization outcomes, it’s becoming clear that leadership training should be formally integrated into medical and residency training curricula.

In most professions, the people who demonstrate strong leadership skills are the ones who take on greater leadership responsibilities at progressive stages of their careers. In medicine, physicians not only begin managing and directing teams early in their careers, but they rise through the ranks uniformly.

Within the first years of graduate medical training, or residency, resident physicians in all specialties lead teams of more junior residents, as well as other care personnel, without undergoing any formal training or experience in how to manage teams. It is rare for first-year resident physicians (interns) to not become second-year residents, for second-year residents to not become third-year residents, and for senior residents to not become fellows or attending physicians, although each step involves more management. And the span of leadership and responsibility grows once physicians enter independent practice.

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Although medical trainees spend years learning about physiology, anatomy, and biochemistry, there are few formal avenues through which trainees learn fundamental leadership skills, such as how to lead a team, how to confront problem employees, how to coach and develop others, and how to resolve conflict. Some residency programs across the country are developing career tracks specifically for those interested in management and leadership careers, but these paths are often targeted towards individuals explicitly seeking management positions or healthcare management projects in their training, missing the fact that to be a physician is to lead. The set of individuals who would benefit from leadership skills in daily practice is much wider than those with specific career interests in management.

Despite this lack of focused attention toward development of leadership capabilities in trainees, evidence suggests that leadership quality affects patients, healthcare system outcomes, and finances alike. For example, hospitals with higher rated management practices and more highly rated boards of directors have been shown to deliver higher quality care and have better clinical outcomes, including lower mortality. Enhanced management practices have also been associated with higher patient satisfaction and better financial performance. Effective leadership additionally affects physician well-being, with stronger leadership associated with less physician burnout and higher satisfaction.

These benefits are crucial in a healthcare landscape that is increasingly focused on measuring and achieving high care quality, that is characterized by high rates of burnout across clinical personnel, and that is asking physicians to lead larger, multidisciplinary teams of nurses, social workers, physician assistants, and other health professionals.

Medical schools and residency programs should modify curricula to include leadership skill development at all levels of training — and this should be as rigorous as development of clinical reasoning or procedural skills. Leadership curricula should focus on two key sets of skills. First, interpersonal literacy is crucial for effective leadership in modern healthcare. This includes abilities related to effectively coordinating teams, coaching and giving feedback, interprofessional communication, and displaying emotional intelligence. The centrality of these skills has been recognized by healthcare institutions globally, including the American Medical Association, the National Health Service, and the Canadian College of Health Leaders.

A second, separate set of necessary skills deals with systems literacy. In today’s healthcare landscape, physicians need to understand the business of healthcare organization, including concepts such as insurance structure and costs that patients encounter. Physicians are also increasingly responsible for understanding and acting on quality and safety principles to correct and enhance the systems they work in. Finally, given the sensitive nature of their work, physicians must be comfortable with recognizing, disclosing, and addressing errors, and helping their teams do so as well.

Formal education on these topics could take the form of dedicated didactics during medical school and residency training, orientation sessions, and skill-building retreats, which are common in other occupations that require managerial development. At least some teaching should be delivered longitudinally over multiple years. This is important, because as trainees rise in the medical ranks and gain more responsibility (i.e. supervising medical students for the first time as interns, overseeing teams for the first time as junior residents), their ability to engage with leadership content changes.

Trainee performance evaluations should explicitly assess for adequate progression of leadership capabilities, with targeted remediation available for those not demonstrating competency. Residents should not be allowed to progress in training without achieving pre-specified proficiency in these areas. Assessment systems should also be developed to mitigate biases that downplay or disregard women’s and minorities’ leadership capabilities. And importantly, longitudinal studies will be needed to rigorously assess effectiveness of programs for teaching and measuring leadership skills. A 2015 systematic review of physician leadership development programs found that few reported negative outcomes or system level effects (i.e. impact of training on quality metrics) of their interventions.

While these changes may seem daunting given the vast amount of information trainees are already responsible for and the time-constrained nature of training, studies have found that trainees want to formally develop leadership skills. And several programs stand out as examples of how this can be done.

As first described in a 2013 Harvard Business Review article, Vanderbilt’s Otolaryngology program developed a 4-year program for residents consisting of Naval ROTC topics, public speaking training, a micro-MBA course, and a capstone leadership project. This program, which is delivered over morning conferences or dinner sessions (when residents are excused from the operating room), exposes trainees to health care policy, finance, conflict resolution, checklist and debriefing programs, public speaking, and one-on-one communication simulation sessions. Trainees ultimately use the skills they gain for collaborating with Vanderbilt undergraduates, primary care physicians, and others on a population health project during one of their four training years. The program’s founder and Vanderbilt Otolaryngology’s Chair, Dr. Roland Eavey notes that delivering similar content to faculty is key for gaining buy-in regarding the educational importance of leadership and to ensure appropriate modeling of effective leadership.

Meanwhile, at the Uniformed Services University, medical students undergo a 4-year curriculum focused on leadership attribute development. The Military Medical Practice and Leadership didactic curriculum is delivered in preclinical years and focuses on self-awareness, communication skills, and team dynamics. Subsequently, students take part in four multi-day “medical field practicum” experiences, during which they are introduced to their responsibilities as military officers and undergo both lecture and simulation modules focused on patient care, operations, and crisis management. Fourth-year medical  students are ultimately evaluated on medical knowledge and leadership abilities in a simulated tactical field setting.  Although centered in undergraduate medical education, this program is notable for its longitudinal mix of didactic and practical experiences and its evaluative nature, and could with reductions in time intensity be tailored to the graduate medical education setting.

Undoubtedly, enhancing leadership training in medicine will increase the costs of training and assessment. Yet, as we seek to optimize the therapeutics and procedures we perform to reduce mortality and enhance care quality, we should also seek to optimize the skills of the physicians leading all corners of healthcare system. For as the evidence shows, it can make an important difference for healthcare outcomes, experiences, and financial sustainability alike.

How Competition Is Driving AI’s Rapid Adoption

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Artificial intelligence (AI) is engendering all kinds of breathless headlines, from being able to play Go to spotting rare cancer tumors. But how will AI impact the economy in broad terms? The answer hinges on both on what AI can be used for and the dynamics of a competitive race to adopt AI that’s set to unfold between firms.

New research from the McKinsey Global Institute simulates the potential global macroeconomic impact of five powerful technologies (computer vision, natural language, virtual assistants, robotic process automation, and advanced machine learning). It finds that AI could (in aggregate and netting out competition effects and transition costs) deliver an additional $13 trillion to global GDP by 2030, averaging about 1.2% GDP growth a year across the period. This would compare well with the impact of steam during the 1800s, robots in manufacturing in the 1900s, and IT during the 2000s.

The average effect on GDP depends on multiple factors. At the industry level they include (a) the extent of AI diffusion in economies; (b) the build-up of corporate profit; and (c) labor market dynamics.

The modeling and simulation relies on two important features. The first is high-quality data from two corporate surveys conducted by MGI and McKinsey in 2007, one of around 1,600 executives across industries globally on digital technologies and AI to ascertain the causes of economic impact and the likely pace of that impact, and one of more than 3,000 corporations in 14 sectors in ten countries. The second feature of the simulation is micro-estimates of the pace of adoption and absorption of AI technologies.

A faster pace of adoption

We know that technologies often take a long time to diffuse and to deliver benefits. It took more than 30 years for electricity to diffuse and enable industrial plant design that could generate significant productivity growth. It took several decades for steam to drive the rollout of railways services and create a large market of exchanges in the United States. Amazon, born 24 years ago, had captured about 45% of online retail commerce in the United States by 2017, but still stood for just about 5% of total US retail gross merchandise volume in that year.

How does AI diffusion compare with the absorption of the early set of digital technologies such as web, mobile, cloud, and big data? Those technologies started to be used about ten to 25 years ago, and the average level of absorption of these technologies was about 37% in 2017. Our simulation suggests that it may reach 70% by 2035. In comparison, absorption of AI might reach today’s level of digital absorption by 2027—in roughly ten years.

There are two stand-out reasons why AI adoption and absorption could be more rapid this time. One is the breadth of ways in which AI is used, including in areas where digitization is still under-penetrated, such as the automation of services and smart automation of manufacturing processes. Second is that returns for front-runners tend to be large. They will benefit from innovations enabling them to serve (and perhaps create) new markets and, at the same time, gain share from non-AI adopters in existing markets. Perception of cannibalization is high among firms surveyed, in line with their experience of early digitization and the emergence of many new business models.

We simulate that about 70% of companies might adopt some AI technologies by 2030, up from today’s 33%, and about 35% of companies might have fully absorbed AI, compared with only 3% today. The econometrics demonstrate that peer competitive pressure is the largest influencer of the decision to adopt AI and make it work across all enterprise functions. The peer pressure effect on adoption incentive is an order of magnitude larger than the expected profitability impact of AI, or perception of the impact it has had in recent years.

A race between firms

Even if a technology race develops, some companies will adopt rapidly, but others less so—and the benefits of AI will vary accordingly. The pace could be enhanced by sector dynamics and by characteristics of firms such as the size and extent of their globalization, but could also be held back by constraints such as early capabilities in digitization, or by organizational rigidities.

We simulated the economic impact of AI for three groups of companies: “front-runners,” “followers,” and “laggards.” The first group experiences the largest benefits from AI, and the second benefits but only by a fraction of the general AI productivity uplift. Laggards (many of them nonadopters) may witness a shrinking market share, and may have no choice but exit the market in the long term.

Regarding front-runners, our average simulation suggests that about 30% of companies might have absorbed the full set of AI technologies in their operations by 2030. About half of those will do so in half the time, and may more than double their operating cash flows by 2030. This is equivalent to sustaining a long-term growth rate of 6% per year through AI. These companies would typically be growing at the rate of high-growth performing firms. Cash generation is not linear as the impact of AI scales up over time—it might be negative in the early years and only becomes positive and accelerates after a period of five to seven years. In this initial period, front-runners could experience cash outflows as they invest in, and scale up, AI. Over time, however, front-runners will tend to slowly concentrate the profit pool of their industry in a winner-takes-most phenomenon.

Followers are firms that are cautiously starting to adopt and absorb AI technologies, having seen the tangible impact enjoyed by front-runners and having realized the competitive threat of not adopting and absorbing. We simulated that 20% to 30% of firms would be in this group by 2030. For these companies, the pace and degree of change in cash flow are likely to be more moderate, and typically below the average productivity uplift witnessed by their economy. On the one hand, front-runners have already triggered some spillovers that spread some benefits to followers; on the other hand, followers lose market share to front-runners.

Laggards are companies that are not investing in AI seriously, or not at all. Why do laggards not jump into AI? The answer is that they may face short-term constraints and may bet—wrongly—that time is on their side. The cost of investment in and implementation of AI means that the divergence among firms on their stance toward AI adoption may only affect their economics after a few years. This may dissuade them from acting. These companies could lose around 20% of cash flow by 2030 compared with today. Laggards may have major capability issues that prevent them from joining the AI race, and therefore they may need to respond in other ways such as limiting costs and cutting investment. The drop in cash flow arrives last, but it is a major slide when it comes.

A fierce competitive race among companies appears to be in prospect with a widening gap between those investing in AI and those that are not. This divide can facilitate “creative destruction” and competition among firms so that the reallocation of resources toward higher-performing companies improves the vibrancy of overall economies. But there is no doubt that the transition may cause disruption and shock in the economy. These tradeoffs need to be understood and managed appropriately in order to capture the potential of AI for the world economy.

Brands Shouldn’t Believe Everything They Read About Themselves Online

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“Don’t believe everything you hear” is good advice — especially in an era of fake news and alternative facts. The same goes for managers who often rely on social-sentiment analysis to get a handle on what consumers think of their brands.

Social-sentiment analysis is the process of algorithmically analyzing social posts, comments, and behaviors and categorizing them into positive, negative, or neutral. Many companies use it to understand how their customers are feeling about their brands.

We recently conducted an extensive social-sentiment analysis with a team of researchers at Boston University’s Emerging Media Studies program as part of our Experience Brand Index research this past spring. In that research, we asked 4,000 consumers in the United States and United Kingdom about their actions and interactions with a wide range of brands over the last six months. These experiences were rated across more than a dozen dimensions, and we rolled up the results into a single Brand Experience score from 1 to 100.

The index graded nearly 100 different brands on how well consumers believed they were fulfilling the promises they make, how well they stood out from their competitors, and how likely consumers were to recommend them to friends and to stay loyal. Overall, our top 10-rated brands have a 200% better net promoter score (NPS) than the bottom 10, and have consumers who are 25% more likely to say they’re going to stay loyal.

To round out the research, we enlisted a group of graduate students in Boston University’s Emerging Media Studies program to run social sentiment analysis against the brands, fully expecting to see high-scoring brands receive high levels of positive sentiment and low-scoring brands receive high negatives.

We were wrong.

There appears to be very little predictive power between how people appear to feel online and how consumers who have experiences with those brands rate them.

We think social-sentiment analysis has value as a part of a brand’s consumer intelligence plan, but we have some advice for those using it or about to embark on the journey:

1. React, but don’t over-react. The type of consumers moved to post and share statements about brands (or about anything, for that matter) are not necessarily representative of the entirety of your customer base. Social-media users tend to be younger and more female than overall online audiences, and emerging research into social behavior suggests that people who post on social media tend to hold more extreme positions — they tend to be motivated by strong feelings, either positive or negative.

A recent study by Engagement Labs in the Journal of Advertising Research pointed out that online conversations about brands and offline conversations (as measured by their TalkTrack tracking study) were not strongly related.

In a recent interview, the lead investigator pointed out that online reaction to the Dick’s Sporting Goods decision to stop selling assault rifles and require all gun buyers to be 21 was met with a large degree of negative sentiment online but more positive sentiment offline.

More recently, Forbes did an in-depth analysis of the social reaction to Nike’s decision to feature Colin Kaepernick in an advertising campaign. It found a significant spike in negative sentiment online in the hours after the ad was first released. But, within two days, the sentiment shifted to positive.

So, while it’s important for your brand to react to specific negative customer-service posts immediately and address any specific issues consumers are having, we don’t recommend you react immediately to spikes in sentiment you see on a given day — especially if it’s in reaction to something new, like an ad campaign. If you do, you run the risk of over- or under-correcting for issues that just aren’t there.

2. Drill into specifics. What exactly does the sentiment analysis say and how does the tool you use define sentiment? In our experience, different tools — whether it’s NetBase or Brandmonitor or Hootsuite — will give you vastly different results for the same brand over the same period of time. Every platform defines sentiment differently and scores words and phrases in unique ways. And, despite significant advances in AI and sentiment algorithms, all of the platforms continue to have problems recognizing and correctly categorizing sarcasm, irony, jokes and exaggerations.

For example, a sarcastic post that says, “Great product, right?” and contains a picture of a broken cell phone is likely to be mischaracterized as positive.

As a result, it’s important to use your tool to listen for the right things. Again, the Nike example is instructive here. Rather than just look at the overall sentiment, the company examined tweets that had any purchase-intent statements — either positive (“going to buy”) or negative (“will never buy”) and found that positive outnumbered negative by 5 to 1. And the sales numbers appear to bear that out — with Thomson Reuters reporting a 61% increase in the amount of sold-out merchandise at Nike stores in the 10 days after the campaign launched compared to the 10 days before the ad appeared.

So, specifics matter. Look for spikes in volume and sentiment around specific hashtags to understand what might be going on.

3. Compare to what (and who) you know. The point of sentiment analysis is to give you a quick, directional perspective on what online chatter about your brand is all about. We believe it’s crucial to utilize other ways of tracking how consumers feel about your brand — whether it’s a brand tracker, tracking surveys, or analysis of customer service logs. It’s always best to have a mix of methods that deliver a well-rounded understanding of the voice of your customer.

It’s also best to have a sense of the cultural context during the time you’re measuring sentiment. Online sentiment can be driven by the negative actions of a specific brand — like a large retail bank illegally creating savings and checking accounts without customers’ consent — or it can be influenced by broader conversations in the culture that have little to do with a specific brand. For example, around the time we fielded our survey in the United States and United Kingdom, consumer tech leaders were testifying about privacy practices in the two countries, impacting the online conversation about that entire category of brands.

So, while there’s a ton of discussion about fake news and the role of bots and trolls in political news, we found an equally cautionary tale for brands. When it comes to social sentiment, listener beware.

How to Decide Which Data Science Projects to Pursue

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In 2018, every organization has a data strategy. But what makes a great one?

We all know what failure looks like. Resources are invested, teams are formed, time goes by — but nothing comes of it. No one can necessarily say why; it’s always Someone Else’s Fault.

It’s harder to tell the difference between a modest success and excellence. Indeed, in data science they can they look very similar for perhaps a year.  After several years, though, an excellent strategy will yield orders of magnitude more valuable results.

Both mediocre and excellent strategies begin with a series of experiments and investments leading to data projects. After a few years, some of these projects work out and are on their way to production.

In the mediocre strategy, one or two of these projects may even have a clear ROI for the business. Typically, these projects will be some kind of automation for cost savings, or applying machine learning to an existing process to improve its efficiency or performance. This looks a lot like success, and it may suffice, but it’s missing out on the unique advantages of an excellent data strategy.

In an excellent strategy, more data projects have worked out, and they were surprisingly cost-effective to develop. Further, the process of building the first few projects inspires new project ideas. In an excellent strategy, the projects will include automation and efficiency and performance improvements, but they will also include projects and ideas for new revenue generation and entirely new businesses driven by your unique data assets. The data teams work well together, build on each other’s work, and collaborate smoothly with their business partners. There’s a clear vision of what the machine-learning driven future of the business can look like, and everyone is working together to achieve it.

Building an Excellent Data Strategy

Crafting a data strategy requires many parties at the table, including data experts, technology leadership, and business and subject-matter experts. It also requires leadership support that goes beyond just wanting to check off a “machine learning” box.

Here’s how most companies decide which data projects to pursue, which alone is a recipe for the mediocre data strategy. Management identifies a set of projects it would like to see built and creates the ubiquitous prioritization scatterplot: one axis represents a given project’s value to the business and the other axis represents its estimated complexity or cost of development. Each project is given a spot on the chart, and management allocates the company’s limited resources to the projects that they believe will cost the least and have the highest business value.

This is not wrong, but it is also not optimal. An excellent data strategy moves beyond a straightforward evaluation of each project in isolation to consider a few additional dimensions.

First, an excellent data strategy includes a well-coordinated organizational core. It’s built on a centralized technology investment and well-selected and coordinated defaults for the architecture of data applications. This centralization of defaults allows for each application to make different decisions if necessary while maintaining maximum compatibility across the organization and flexibility over time by default.

For example, one global media company I worked with had grown dramatically through acquisitions. Each business line had a different technology stack and independent IT group, leading to challenges integrating data that already existed, and different architectures for all future investments. Centralizing this practice was key to their ongoing success.

Second, an excellent data strategy is specific in the short term and flexible in the long term. We know quite a lot about what the machine learning capabilities of tomorrow look like, but less about what the capabilities of next year will look like. We can only guess what will be possible in five years. Similarly, the business landscape is transforming, leading to new competition and new opportunities. Organizations that engage in five-year planning cycles will miss the opportunities that emerge in the meantime. An excellent strategy is one that is adaptable and considered to be a living document.

The best strategies are strong in directional conviction, but flexible in the details. You want to know where you want to end up, but not necessarily pre-define each step you need to take to get there.

Finally, an excellent data strategy takes into account one key insight: data science projects are not independent from one another. With each completed project, successful or not, you create a foundation to build later projects more easily and at lower cost.

Choosing Between Data Science Projects

Here’s what project selection looks like in a firm with an excellent data strategy: First, the company collects ideas. This effort should be spread as broadly as possible across the organization, at all levels. If you only see good and obvious ideas on your list, worry — that’s a sign that you are missing out on creative thinking. Once you have a large list, filter by the technical plausibility of an idea. Then, create the scatterplot described above, which evaluates each project on its relative cost/complexity and value to the business.

Now it gets interesting. On your scatterplot, draw lines between potentially related projects. These connections exist where projects share data resources; or where one project may enable data collection helpful to another project; or where foundational work on one project is also foundational work on another. This approach acknowledges the realities of working on such projects, like the fact that building a precursor project makes successor projects faster and easier (even if the precursor fails). The costs of gathering data and building shared components are amortized across projects.

This approach makes higher-value projects — those that would perhaps have seemed too ambitious — look less like an aggressive, expensive push forward. Instead, it reveals that such projects may indeed be more efficient and safer to proceed with than other lower-value projects that looked attractive in a naive analysis.

Put differently, an excellent data strategy acknowledges that projects play off of one another, and that the costs of projects change over time in light of other projects undertaken (and new technology, as well). This allows more accurate planning and may expand the organization’s capabilities more than expected. You can revisit this planning process quarterly, which is in line with how quickly machine learning technologies are changing.

We’re currently at a moment in the development of machine learning, AI, and data where the technology isn’t commoditized and it’s not entirely obvious where to invest. Companies with excellent data strategies will be more likely to choose well.

How to Blow a Presentation to the C-Suite

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Divya, a director who leads a large engineering team, was invited to a two-day retreat with the CEO and senior executives of her Fortune 50 company. She and 30 of her high-potential peers were excited to rub shoulders with the leadership team.

The purpose of the retreat was to expose up-and-coming leaders to broader challenges, expand their network across silos, and, of course, give them an opportunity to connect personally with C-suite executives.

The session kicked off with participants dividing into small teams to tackle company-wide strategic challenges. This was a rare opportunity to present directly in front of the CEO, so Divya and her teammates worked hard to research their assigned topic, frame the specific challenge, and debate different ideas and solutions. Instead of hanging out at the bar after dinner, they worked far into the night finalizing their presentation. Divya was selected as the spokesperson for her group, and the next morning, she made their pitch.

The team’s idea was met with a lukewarm reaction and what, at best, could be called a polite round of applause. Naturally, they were disappointed in the tepid response.

Divya and her team are all smart, do great work in their current jobs, and have promising careers ahead of them. So, what went wrong?

Based on my experience watching hundreds of presentations made by high-potential leaders, I can tell you that Divya and her colleagues are not alone in failing to land a key pitch. When presenting ideas to the CEO, even seasoned leaders who don’t regularly interact with the C-suite fall into a few common traps that can be easily avoided.

Trap #1: An Idea Without Its Problem

Smart, successful people tend to have great ideas. It’s natural for you to be excited about your ideas and eager to share them with your executives. But place yourself in your CEO’s shoes: She’s on the receiving end of endless smart ideas. For yours to stand out and be useful to the CEO, it must solve a problem.

Begin the presentation with the problem you’ve identified and spend time upfront creating context, surfacing the pain points, and building a sense of urgency around addressing the challenge. Many presenters often move straight to solution and neglect to build a sound case for immediate action. It’s the problem, not the idea, that executives want to hear first. Spend the first quarter of your allotted time calling out the problem and the next quarter on the idea. The more urgent the problem appears, the more eager your audience will be for the solution.

Unfortunately, in Divya’s case, her presentation started with an idea. She didn’t realize that pitching a solution outside the context of its founding problem left it wide open to criticism. In a world where executives have a host of responsibilities and crises to manage, they need to triage which ones they’ll act on. They’ll be more motivated to prioritize your idea if they can see a direct connection to a problem that won’t go away or that will become more significant without their attention.

Trap #2: An Idea Without a Clear ROI

Once you’ve established the problem in your presentation, the next step is to prove that your idea will not only solve it, but do so in ways that grow the business. First, show how your initiative will self-fund within a short period of time. Next, project how it will grow in revenue to support both its expansion and begin to fund other parts of the organization. Make sure you include estimates for the often-overlooked money needed for infrastructure and setup.

Divya’s team started with an idea and proceeded to explain the way they would implement it. They were excited about the technical merits of this idea but didn’t mention how the solution might be helpful to the company in the marketplace or against the competition. What’s more, the idea would require a heavy investment in tools that currently didn’t exist.

Trap #3: A Presentation Without Interaction

As with all good presentations, you want to meet your audience where they are. But when speaking with the C-suite, presenters often overexplain obvious things and don’t leave enough time for interaction.

Divya spent four minutes out of their allotted 20-minute slot reviewing their research process and what the group learned. Since none of this was new information to the executives, she lost their attention. The entire presentation took 17 minutes, leaving a precious few minutes for questions and follow-up.

Reserve the second half of your allotted time for questions. While that seems like an outsized chunk, used well, it can be the most valuable part of your talk. Rapid-fire, blunt questions are a sign that executives are interested in your idea. They’re processing what you said, testing various angles and hypotheses, and generally want to know more. A common misconception is that if there are no questions, then things went well. The opposite is usually true. The more questions you receive, the better the presentation.

One word of caution: Don’t count critiques framed as questions as healthy interaction. For example, “How can this possibly work? You haven’t accounted for extra headcount.” That’s not really a question. If your audience is curious and engaged a genuine question will sound more like, “How would you deal with headcount if your growth projections are accurate?”

Trap #4: Data Without Attention to Detail

Even when you set aside enough time for interaction, you can run into trouble if you don’t have the correct answer to an executive’s question. Presenters can be imprecise or sloppy with details when questioned, especially when it comes to numbers.

During the Q&A, Divya’s teammate Josh made a claim about the number of current customers using a particular product. He missed the actual number by 12% because of a calculation error.

Once you present an incorrect number, your executives will tend to write off the rest of your data. Be sure of your facts, be prepared with the source of your information, and, if there’s an error, be ready to quickly follow up with a correction. And if you don’t know the answer, don’t waste time. Simply admit to that, and tell them you’ll look into it and follow up.

If you’re in a position to present to the most senior executives in your organization, you’re already considered smart and capable. You don’t need to prove it by launching directly into your idea and sharing endless details. Instead, give your audience what it really wants: an overview of the problem and how you think it can be solved for the benefit of the company. Give them plenty of time to interact with you, and you’ll prove that you’re as smart and capable as they thought.