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Appendix A: Case Studies

List of case studies, case study 1: handling roommate conflicts, case study 2: salary negotiation at college corp, case study 3: oecollaboration, case study 4: the ohio connection, case study 5: uber pays the price, case study 6: diverse teams hold court.

Chapter Reference: Section 2.2 Approaches to Conflict

Whether you have a roommate by choice, by necessity, or through the random selection process of your school’s housing office, it’s important to be able to get along with the person who shares your living space. While having a roommate offers many benefits such as making a new friend, having someone to experience a new situation like college life with, and having someone to split the cost on your own with, there are also challenges. Some common roommate conflicts involve neatness, noise, having guests, sharing possessions, value conflicts, money conflicts, and personality conflicts (Ball State University, 2001). Read the following scenarios and answer the following questions for each one:

  • Which conflict management style, from the five discussed, would you use in this situation?
  • What are the potential strengths of using this style?
  • What are the potential weaknesses of using this style?

Scenario 1: Neatness. Your college dorm has bunk beds, and your roommate takes a lot of time making their bed (the bottom bunk) each morning. They have told you that they don’t want anyone sitting on or sleeping in the bed when they are not in the room. While your roommate is away for the weekend, your friend comes to visit and sits on the bottom bunk bed. You tell your friend what your roommate said, and you try to fix the bed back before your roommate returns to the dorm. When they return, your roommate notices that the bed has been disturbed and confronts you about it.

Scenario 2: Noise and having guests. Your roommate has a job waiting tables and gets home around midnight on Thursday nights. They often brings a couple friends from work home with them. They watch television, listen to music, or play video games and talk and laugh. You have an 8 a.m. class on Friday mornings and are usually asleep when they returns. Last Friday, you talked to your roommate and asked them to keep it down in the future. Tonight, their noise has woken you up and you can’t get back to sleep.

Scenario 3: Sharing possessions. When you go out to eat, you often bring back leftovers to have for lunch the next day during your short break between classes. You didn’t have time to eat breakfast, and you’re really excited about having your leftover pizza for lunch until you get home and see your roommate sitting on the couch eating the last slice.

Scenario 4: Money conflicts. Your roommate got mono and missed two weeks of work last month. Since they have a steady job and you have some savings, you cover their portion of the rent and agree that they will pay your portion next month. The next month comes around and your roommate informs you that they only have enough to pay their half of the rent.

Scenario 5: Value and personality conflicts. You like to go out to clubs and parties and have friends over, but your roommate is much more of an introvert. You’ve tried to get them to come out with you or join the party at your place, but they’d rather study. One day your roommate tells you that they want to break the lease so they can move out early to live with one of their friends. You both signed the lease, so you have to agree or they can’t do it. If you break the lease, you automatically lose your portion of the security deposit

Works Adapted

“ Conflict and Interpersonal Communication ” in Communication in the Real World  by University of Minnesota is licensed under a  Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Ball State University. (2001). Roommate conflicts. accessed June 16, 2001, from  http://cms.bsu.edu/CampusLife/CounselingCenter/VirtualSelfHelpLibrary/RoommateIssues.asx.

Chapter Reference:  Section 2.4 Negotiation

Janine just graduated college, she’s ready to head out on her own and get that first job, and she’s through her first interviews. She receives an offer of a $28,000 salary, including benefits from COLLEGE CORP, from an entry-level marketing position that seems like a perfect fit. She is thrown off by the salary they are offering and knows that it is lower than what she was hoping for. Instead of panicking, she takes the advice of her mentor and does a little research to know what the market range for the salary is for her area. She feels better after doing this, knowing that she was correct and the offer is low compared to the market rate. After understanding more about the offer and the rates, she goes back to the HR representative and asks for her preferred rate of $32,500, knowing the minimum that she would accept is $30,000. Instead of going in for her lowest amount, she started higher to be open to negotiations with the company. She also sent a note regarding her expertise that warranted why she asked for that salary. To her happy surprise, the company counter offered at $31,000—and she accepted.

  • What key points of Janice’s negotiation led to her success?
  • What could have Janice done better to get a better outcome for her salary?

“ Conflict and Negotiations ” in Organizational Behaviour by OpenStax is licensed under a Creative Commons Attribution 4.0 International License .

“Good & Bad Salary Negotiations,”  Salary.com , April 19, 2018, https://www.salary.com/articles/good-bad-examples-of-salary-negotiations .

Herner, M. (n.d). 5 things HR wishes you knew about salary negotiation. Payscale.com, accessed October 21, 2018, https://www.payscale.com/salary-negotiation-guide/salary-negotiation-tips-from-hr .

Chapter Reference:  Section 3.2 Creating, Maintaining, and Changing Culture

At OECollaboration, a technology company that develops virtual collaboration software for new companies, Mike Jones is a new manager. One of the biggest challenges he has faced is that the team that he is managing is well established and because he is an outsider, the team members haven’t yet developed trust in him.

Two weeks into his new employment, Mike held a meeting and discussed all of the changes to the remote work agreements as well as implementing new meeting requirements for each employee to have a biweekly meeting scheduled with him to discuss their projects. The team was outraged, they were not excited, and the following days he wasn’t greeted in a friendly way; in addition, his team seemed less engaged when asked to participate in team functions.

Tracy James is also a new manager at OECollaboration who started at the same time as Mike, in a similar situation where she is a new manager of an existing team. Tracy was able to hold a meeting the first day on the job to listen to her team and get to know them. During this meeting she also told the team about herself and her past experiences. Additionally, she held one-on-one meetings to listen to each of her team members to discuss what they were working on and their career goals. After observation and discussion with upper management, she aligned her own team goals closely with the skills and experiences of her new team. She met with the whole team to make changes to a few policies, explaining why they were being changed, and set the strategy for the team moving forward.

Because she got her team involved and learned about them before implementing her new strategy, this was well received. Her team still had questions and concerns, but they felt like they could trust her and that they were included in the changes that were being made.

  • What challenges can a new manager encounter when starting to manage an existing team?
  • What strategies can a new manager implement to ensure that their new team is engaged with them and open to change and growth?

Adapted Works

“ Organizational Power and Politics ” in Organizational Behaviour by OpenStax is licensed under a Creative Commons Attribution 4.0 International License .

Giang, V. (2013, July 31). The 7 types of power that shape the workplace. Business Insider. https://www.businessinsider.com/the-7-types-of-power-that-shape-the-workplace-2013-7

Morin, A. (2018, June 25). How to prevent a workplace bully from taking your power. Inc. https://www.inc.com/amy-morin/how-to-prevent-a-workplace-bully-from-taking-your-power.html

Weinstein,  B. (n.d.). 10 tips for dealing with a bully boss,” CIO , accessed October 13, 2018, https://www.cio.com.au/article/198499/10_tips_dealing_bully_boss/.

Chapter Reference:  Section 4.1 Power

Janey worked as an executive assistant to a product manager at her company: Ohio Connection. Overall, she loved her job; she was happy to work with a company that provided great benefits, and she and found enjoyment in her day-to-day work. She had the same product manager boss for years, but last year, her manager left Ohio Connection and retired. Recently her new manager has been treating her unfairly and showcasing bullying behavior.

Yesterday, Janey came into work, and her boss decided to use their power as her manager and her “superior” to demand that she stay late to cover for him, correct reports that he had made mistakes on, and would not pay her overtime. She was going to be late to pick up her son from soccer practice if she stayed late; she told him this, and he was not happy.

Over subsequent days, her boss consistently would make comments about her performance, even though she had always had good remarks on reviews, and created a very negative work environment. The next time she was asked to stay late, she complied for fear of losing her job or having other negative impacts on her job. Janey’s situation was not ideal, but she didn’t feel she had a choice.

  • What type of power did Janey’s boss employ to get her to do the things that he wanted her to do?
  • What negative consequences are apparent in this situation and other situations where power is not balanced in the workplace?
  • What steps should Janey take do to counteract the power struggle that is occurring with her new manager?

Chapter Reference:   Section 5.1 Interpersonal Relationships at Work

Uber revolutionized the taxi industry and the way people commute. With the simple mission “to bring transportation—for everyone, everywhere,” today Uber has reached a valuation of around $70 billion and claimed a market share high of almost 90% in 2015. However, in June 2017 Uber experienced a series of bad press regarding an alleged culture of sexual harassment, which is what most experts believe caused their market share to fall to 75%.

In February of 2017 a former software engineer, Susan Fowler, wrote a lengthy post on her website regarding her experience of being harassed by a manager who was not disciplined by human resources for his behavior. In her post, Fowler wrote that Uber’s HR department and members of upper management told her that because it was the man’s first offense, they would only give him a warning. During her meeting with HR about the incident, Fowler was also advised that she should transfer to another department within the organization. According to Fowler, she was ultimately left no choice but to transfer to another department, despite having specific expertise in the department in which she had originally been working.

As her time at the company went on, she began meeting other women who worked for the company who relayed their own stories of harassment. To her surprise, many of the women reported being harassed by the same person who had harassed her. As she noted in her blog, “It became obvious that both HR and management had been lying about this being his ‘first offense.’” Fowler also reported a number of other instances that she identified as sexist and inappropriate within the organization and claims that she was disciplined severely for continuing to speak out. Fowler eventually left Uber after about two years of working for the company, noting that during her time at Uber the percentage of women working there had dropped to 6% of the workforce, down from 25% when she first started.

Following the fallout from Fowler’s lengthy description of the workplace on her website, Uber’s chief executive Travis Kalanick publicly condemned the behavior described by Fowler, calling it “abhorrent and against everything Uber stands for and believes in.” But later in March, Uber board member Arianna Huffington claimed that she believed “sexual harassment was not a systemic problem at the company.” Amid pressure from bad media attention and the company’s falling market share, Uber made some changes after an independent investigation resulted in 215 complaints. As a result, 20 employees were fired for reasons ranging from sexual harassment to bullying to retaliation to discrimination, and Kalanick announced that he would hire a chief operating officer to help manage the company. In an effort to provide the leadership team with more diversity, two senior female executives were hired to fill the positions of chief brand officer and senior vice president for leadership and strategy.

Critical Thinking Questions

  • Based on Cox’s business case for diversity, what are some positive outcomes that may result in changes to Uber’s leadership team?
  • If the case had occurred in Canada, what forms of legislation would have protected Fowler?
  • What strategies should have been put in place to help prevent sexual harassment incidents like this from happening in the first place?

“ Diversity in Organizations ” in Organizational Behaviour by OpenStax is licensed under a Creative Commons Attribution 4.0 International License .

Della Cava, M. (2017, June 13). Uber has lost market share to Lyft during crisis. USA Today. https://www.usatoday.com/story/tech/news/2017/06/13/uber-market-share-customer-image-hit-string-scandals/102795024/

Fowler, T. (2017, February 19). Reflecting on one very, very strange year at Uber. https://www.susanjfowler.com/blog/2017/2/19/reflecting-on-one-very-strange-year-at-uber.

Lien,  T. (2017, June 6). Uber fires 20 workers after harassment investigation. Los Angeles Times.  http://www.latimes.com/business/la-fi-tn-uber-sexual-harassment-20170606-story.html

Uber (2017, February). Company info. https://www.uber.com/newsroom/company-info/

Chapter Reference:  Section 5.3 Collaboration, Decision-Making and Problem Solving in Groups

Diverse teams have been proven to be better at problem-solving and decision-making for a number of reasons. First, they bring many different perspectives to the table. Second, they rely more on facts and use those facts to substantiate their positions. What is even more interesting is that, according to the Scientific American article “How Diversity Makes Us Smarter,” simply “being around people who are different from us makes more creative, diligent, and harder-working.”

One case in point is the example of jury decision-making, where fact-finding and logical decision-making are of utmost importance. A 2006 study of jury decision-making, led by social psychologist Samuel Sommers of Tufts University, showed that racially diverse groups exchanged a wider range of information during deliberation of a case than all-White groups did. The researcher also conducted mock jury trials with a group of real jurors to show the impact of diversity on jury decision-making.

Interestingly enough, it was the mere presence of diversity on the jury that made jurors consider the facts more, and they had fewer errors recalling the relevant information. The groups even became more willing to discuss the role of race case, when they hadn’t before with an all-White jury. This wasn’t the case because the diverse jury members brought new information to the group—it happened because, according to the author, the mere presence of diversity made people more open-minded and diligent. Given what we discussed on the benefits of diversity, it makes sense. People are more likely to be prepared, to be diligent, and to think logically about something if they know that they will be pushed or tested on it. And who else would push you or test you on something, if not someone who is different from you in perspective, experience, or thinking. “Diversity jolts us into cognitive action in ways that homogeneity simply does not.”

So, the next time you are called for jury duty, or to serve on a board committee, or to make an important decision as part of a team, remember that one way to generate a great discussion and come up with a strong solution is to pull together a diverse team.

  • If you don’t have a diverse group of people on your team, how can you ensure that you will have robust discussions and decision-making? What techniques can you use to generate conversations from different perspectives?
  • Evaluate your own team at work. Is it a diverse team? How would you rate the quality of decisions generated from that group?

Sources: Adapted from Katherine W. Phillips, “How Diversity Makes Us Smarter,” Scientific American, October 2014, p. 7–8.

“ Critical Thinking Case ” in  Organizational Behaviour by OpenStax is licenced under a Creative Commons Attribution 4.0 International License .

Conflict Management Copyright © 2022 by Laura Westmaas, BA, MSc is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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  • v.3(1); 2019 Mar

When Team Conflicts Threaten Quality of Care: A Study of Health Care Professionals' Experiences and Perceptions

Stéphane cullati.

a Quality of Care Service, University Hospitals of Geneva, Switzerland

b Department of General Internal Medicine, Rehabilitation and Geriatrics, University of Geneva, Switzerland

c Institute of Sociological Research, University of Geneva, Switzerland

Naike Bochatay

d Unit of Development and Research in Medical Education, University of Geneva, Switzerland

Fabienne Maître

e Division of General Internal Medicine, University Hospitals of Geneva, Switzerland

Thierry Laroche

f Division of Anaesthesiology, University Hospitals of Geneva, Switzerland

Virginie Muller-Juge

Katherine s. blondon.

g Interprofessional Simulation Centre, University of Geneva, Switzerland

Noëlle Junod Perron

h Department of Community Medicine, Primary and Emergency Care, University Hospitals of Geneva, Switzerland

Nadia M. Bajwa

i Department of General Paediatrics, University Hospitals of Geneva, Switzerland

j Department of Surgery, University of Washington, Seattle

Georges L. Savoldelli

Patricia hudelson, pierre chopard, mathieu r. nendaz.

To explore professionals' experiences and perceptions of whether, how, and what types of conflicts affected the quality of patient care.

Patients and Methods

We conducted 82 semistructured interviews with randomly selected health care professionals in a Swiss teaching hospital (October 2014 and March 2016). Participants related stories of team conflicts (intra-/interprofessional, among protagonists at the same or different hierarchical levels) and the perceived consequences for patient care. We analyzed quality of care using the dimensions of care proposed by the Institute of Medicine Committee on Quality of Health Care in America (safety, effectiveness, patient-centeredness, timeliness, efficiency, and equity).

Seventy-seven of 130 conflicts had no perceived consequences for patient care. Of the 53 conflicts (41%) with potential perceived consequences, the most common were care not provided in a timely manner to patients (delays, longer hospitalization), care not being patient-centered, and less efficient care. Intraprofessional conflicts were linked with less patient-centered care, whereas interprofessional conflicts were linked with less timely care. Conflicts among protagonists at the same hierarchical level were linked with less timely care and less patient-centered care. In some situations, perceived unsatisfactory quality of care generated team conflicts.

Based on participants' assessments, 4 of 10 conflict stories had potential consequences for the quality of patient care. The most common consequences were failure to provide timely, patient-centered, and efficient care. Management of hospitals should consider team conflicts as a potential threat to quality of care and support conflict management programs.

Workplace conflicts among professionals are frequent in health care. A majority of health care professionals has witnessed disruptive behaviors 1 or perceived conflicts on a weekly basis. 2 , 3 In the United States, 20% of residents reported serious conflicts with other staff members, 4 and 43% of surgeons reported experiencing conflicts about postoperative goals of care with intensivists either sometimes or always. 5

Conflicts within teams can involve harsh language (threats, yelling, profanity), blaming, breakdown in communication, or disruptive conduct. 6 , 7 Whereas team conflicts may be viewed constructively when used to clarify misunderstandings and disagreements about roles and tasks, 8 , 9 they can alter team dynamics and communication, decrease trust and team performance, 10 , 11 and lead to poor mental health among professionals. 12 , 13 , 14 Team conflicts may draw health care professionals' attention away from patient care and drain their personal resources, 3 , 6 , 15 , 16 posing a threat to the team safety climate 17 and, ultimately, the quality of patient care. 4 , 18

Two qualitative studies specifically analyzed the consequences of conflicts for patient care. 19 , 20 The study of Walrath et al used focus groups to explore disruptive behaviors observed or experienced by 96 registered nurses in an acute care hospital in the Northeastern United States. The authors noted that disruptive behaviors affected the nurses, the nurses' practice settings, and the patients. The impact on patient care could take 2 forms: first, nurses could be distracted from patient care; second, the patient could become the witness of disruptive behaviors from health care professionals. Nurses also expressed the general concern that disruptive behaviors could decrease the quality of care and create risks to patient safety. 19 The study of Aberese-Ako et al used ethnographic methods to explore the influence of conflicts among health care professionals working in the departments of obstetrics and gynecology of a referral hospital in Ghana. They found that team conflicts could affect quality of care in 2 ways: directly (delays in provision of health care, not providing “essential care” to patients) and indirectly (health care professionals feeling demotivated and exhibiting negative attitudes toward patients). The authors also found that conflicts may have positive effects on quality of care,by preventing “medical complacency and negligence.” 20 However, these studies had 2 main limitations. First, they mainly discussed the consequences of conflicts for patients care by considering quality as a general concept. Quality of care is, however, not a single dimension, 21 and which of the multifaceted nature of quality of care (safety, effectiveness, patient-centeredness, timeliness, efficiency, and equity) 22 are affected by team conflicts remains, until now, an unexplored area.

Second, these studies focused on team conflicts in general, disregarding the fact that conflicts are not similar in their form. 23 For example, team conflicts differ depending on whether the protagonists (the health care professionals involved in a conflict) belong to the same profession or not. 24 Interprofessionality of care can lead to team conflict as a result of areas of overlapping competencies and shared responsibilities, 25 , 26 such as when determining the goals of care. 5 Interprofessional conflicts may affect the safety climate 17 and lead to medical errors. 4 Team conflicts also differ in their form when involving protagonists within a professional group but with different statuses (nurse vs nurse manager, resident vs chief resident, etc). 27 In acute care hospitals, hierarchy is often inherent to medical and clinical decision making and health care professional cultures have fostered a hierarchical power structure that is now challenged by the interprofessional processes of patient care. 28 Few studies have looked at the interplay between hierarchical differences and conflicts. 29 We therefore know little about how it might affect aspects of quality of care.

Given this gap in the literature, the objective of this study was to explore health care professionals' experiences of team conflicts and their perceptions of whether and how conflicts affected the multifaceted quality of patient care. We also sought to understand whether different forms of conflicts (intraprofessional vs interprofessional and same vs different levels of hierarchy) might affect different aspects of quality of care.

Design and Setting

This study reports on a large-scale qualitative research project on health care professionals' experiences of team conflicts. 23 The study was conducted at the University Hospitals of Geneva, Switzerland, a 1700-bed tertiary care hospital. The study was approved by the Regional Research Ethics Committee of Geneva. Given the sensitivity of the topic, chief medical officers and chairs of the departments involved in the study independently reviewed and approved the project.

Recruitment and Interviews

Between October 2014, and March 2016, we invited a randomly selected sample of professionals involved in first-line patient care to participate in a semistructured interview: residents, chief residents, certified nursing assistants, nurses, and nurse supervisors. We did not include attending physicians because they represent second-line physicians in the process of care at our hospital, whereas chief residents are involved in direct supervision. We selected participants from 4 clinical departments with different levels of acuity, types of patients, and work organization to have a range of experiences (internal medicine, family medicine, pediatrics, and 2 surgical units). An approximately equal number of physicians and nurses were invited from each department. Four social scientists conducted the interviews (N.Bo., V.M.J., P.H., S.C.). Two of these scientists worked at the University of Geneva, and 2 worked at the University Hospitals of Geneva; none was involved with the participants' hospital department.

After accepting to participate in the study, each participant received a written description of the research project, with instructions to think about conflict stories they had experienced or witnessed with coworkers. The definition of conflict stories was left up to the participants. The interview guide, available elsewhere, 23 was informed by a previous study on professional conflicts in health care. 30 During the interviews, participants were invited to recount 1 or more conflict stories. We also asked about the sources, consequences, and responses to conflicts. At the beginning of the interview, we gave participants time to sign consent forms and to ask questions. Participants could withdraw from the study at any time; we also gave them the opportunity to read and request removal of any content from their interview transcript. Average duration of interviews was 38 minutes (minimum 23, maximum 69).

Interviews were audio-taped, transcribed verbatim, and anonymized. Transcripts were analyzed using a thematic approach. 31 All authors read 6 interviews to familiarize themselves with the interviews, discussed codes that were derived from the data, and developed an initial list of codes. This list of codes was inductively grounded in the data and deductively derived from the literature on work conflicts. 15 , 32 , 33 The codes were then tested on a sample of 15 interviews and refined. For each conflict story, we coded protagonists' characteristics (gender, professional group and status, specialty) and the form of the conflict story (intraprofessional vs interprofessional conflict, hierarchical differences among professionals involved in the conflict, and whether or not the conflicts had been solved). Hierarchical levels (same vs different) were defined based on whether protagonists had, or not had, a management role in our hospital such as nurse manager (vs nurse and certified nurse) or chief resident (vs resident). Although we had not specifically prompted participants to talk about interprofessionality and professional hierarchies, these were spontaneously mentioned in their conflict stories, and therefore we included these dimensions in our analyses. All data were then coded (N.Bo.) using Atlas.ti Scientific Software Development (version 7.5).

Potential consequences of conflict stories for patient care were based on what participants reported. In the interview guide, 1 questioner asked: “What were the consequences of this situation?” We selected conflict situations in which participants reported consequences for patient care and analyzed these responses using the 6 dimensions of quality of care defined by the Institute of Medicine Committe on Quality of Health Care in America: safety, effectiveness, patient-centeredness, timeliness, efficiency, and equity. 10 We adapted the 6 attributes and their definitions for this study ( Table 1 ). Two of the interviewers (N.Bo., S.C.) independently coded the reported consequences of conflict stories for patient care, assigning either 1 or several dimensions of quality of care to each situation. They then met to compare their coding and reach a consensus in case of discrepancy. When more than 1 quality-of-care dimension was coded in a conflict story, we distinguished between the primary dimension and secondary dimensions. The primary dimension was the main consequence in participants' discourses, and secondary dimensions were less important. The current analyse focused on the primary dimension. Two nurse supervisors and 2 physicians (F.M., T.L., P.C., M.N.) reviewed all coded data. Discrepancies were solved by consensus.

Table 1

Dimensions of Quality of Care a

Characteristics of Health Care Professionals and of Team Conflict Stories

A total of 82 semistructured interviews with health care professionals (participants' characteristics are reported in Table 2 ) provided 130 team conflict stories. Of the 82 interviewees, 41 shared 1 story, 34 shared 2 stories, and 7 shared 3 stories. Seventy-five percent (98 of 130) of conflicts stories were experienced first-hand, whereas others were witnessed; 84% (109 of 130) referred to specific situations, whereas 16% (21 of 130) were generic situations; 67% (87 of 130) involved protagonists at the same level of hierarchy, and 33% (43/130) involved different levels of hierarchy among protagonists. Fifty-seven percent (74 of 130) involved intraprofessional conflicts, whereas 43% (56 of 130) were interprofessional.

Table 2

Characteristics of the 82 Health Care Professionals

Consequences of Conflict Stories on Quality of Care

Health care professionals were asked about the consequences of conflict stories they reported. Based on their assessment, 59% (77 of 130) of team conflict stories had no consequences for patient care and 41% (53 of 130) had potential consequences for patient care.

We categorized these consequences into the 6 dimensions of quality of care: safety, effectiveness, patient-centeredness, timeliness, efficiency, and equity ( Table 3 ). Among the 53 conflict stories that involved potential consequences for patient care, 28 (53%) had a single consequence on quality of care (1 quality dimension), whereas 25 (47%) had multiple consequences (several quality dimensions). Among conflict stories with multiple consequences, we distinguished the primary (reported above and in Table 4 , column A) and other consequences. We reported the primary consequence in order of frequency (highest to lowest). The main dimension involved in conflict stories was care not being provided in a timely manner (34% of the 53 stories that had consequences for patient care): participants reported delays in providing medical treatment or performing surgical interventions.

We try to provide good patient care, we don't want to do anything that could harm patients; that's obvious. If I think of my family, I wouldn't want anyone to be anesthetised longer just because the surgeon is poorly organized. So, even if I don't know our patients, I don't think they should just be lying down in the OR for no reason. (Nurse, Surgical Units)

Table 3

Quality Dimensions Involved in the Consequences of Team Conflicts on Patient Care, as Reported by Participants

Table 4

Frequency of Quality Dimensions Involved in 53 Team Conflict Stories a

Lack of patient-centeredness was the second most mentioned consequence for care (30% of the situations): Teams did not fully meet patient and family needs; they failed to listen to patient requests because they were distracted by conflicts.

Of course, it affected patients! I work night shifts, and as I got to work one evening, people told me that the schedule for the Christmas break was up. I checked it, and it drove me mad. I was in for a bad night, and obviously all the patients had bad nights, too. (…) I was really upset, so I was less available for my patients. I was not able to focus on their needs as much as usual. (Nurse, Internal Medicine)

The third most mentioned consequence was a lack of efficiency (25%). Teams did not communicate in an optimal way, resulting in a loss of information, counter-orders, and a lack of consistency in decisions, team disorganization, and deteriorated communication regarding medical orders.

We keep having to look for nurses when we have questions about our patients, and when we ask someone, they always say: “Oh, she just left to go get lunch.” “Okay, so who is in charge of her patients while she's away?” “Well, I don't know.” So we waste a huge amount of time. (Resident, Pediatrics)

The other 3 dimensions were less frequently mentioned in our interviews (see quotes in Table 3 ). Less effective care (3 conflict stories) was related to surgical interventions performed by inexperienced and unsupervised surgeons or failure to use best available techniques. In terms of safety (2 conflict stories), participants described environments in which errors had occurred (or were more likely to occur) because of conflicts. Finally, lack of equitable care (1 conflict story) was mentioned in an end-of-life situation. Because of the patient's state, a respiratory therapist refused to perform what had been asked to alledgely make the patient more comfortable.

Of note, during the interviews, some participants reported difficulty in assessing whether the conflict story had had consequences for the patient:

I do not think this conflict changed anything in the patient's care, but it's hard to say. Yeah, I would say that we probably managed to keep it among ourselves without affecting our patient. (Resident, Internal Medicine)

Forms of Team Conflicts and Quality of Patient Care

Distribution of conflict stories across the 2 forms of conflicts (intraprofessional vs interprofessional conflict, same vs different levels of hierarchy) is reported in Table 5 . Distribution of consequences for quality of care with respect to these 2 forms of conflicts is provided in Table 4 .

Table 5

Forms of Team Conflict Stories With and Without Consequences for Patient Care

Timeliness and patient-centredness consequences differed by form of conflicts ( Table 4 , columns B and C). Timeliness consequences were mostly linked with interprofessional conflicts (61%) and with conflicts among protagonists at the same level of hierarchy (78%): patient-centeredness consequences with intraprofessional conflicts (63%) and with conflicts among protagonists having the same hierarchical level (69%).

Efficiency consequences were linked with conflicts among protagonists at the same level of hierarchy (62%) and were roughly similar between intraprofessional vs interprofessional conflicts (54% and 46%, respectively).

Effectiveness, safety, and equity of care consequences also differed by form of conflicts; however, sample sizes across strata were too small to draw conclusions.

Through interviews with a random sample of physicians, nurses, and certified nursing assistants working at a teaching hospital, we have examined whether and how team conflicts could affect quality of patient care. Our study illustrates how conflicts among health care professionals are not circumscribed in teams and may lead to suboptimal patient care. This result is in line with previous studies 19 , 20 ; however, our study goes further by identifying which aspects of quality of care were affected by team conflicts. We used the framework of the Institute of Medicine Committee on Quality of Health Care in America as a template to go beyond general statements about quality of care. 10 The most common consequences were failure to provide care in both a timely and a patient-centered manner and less efficient care. This finding is comforting, as it suggests that when team conflicts spill over to patient care, safety may be affected only rarely. Nevertheless, our results suggest that team conflicts are detrimental to patient-centeredness. This result echoes an ethnographic study of morning interprofessional rounds in intensive care, which showed that conflicts prevented teams from involving patients in their own care. 34

Our study assessed whether forms of team conflicts can be linked with different dimensions of quality of care. We observed that intraprofessional conflicts were linked with impaired patient-centered care and that interprofessional conflicts were related with more delays. These results are new and would need further—in particular, quantitative— studies to better understand these phenomenons and to determine the impact of these problems. However, they may help attending physicians, nurse managers, and quality management programs identifying the domains in which quality of patient care may be threatened by team conflicts and implement appropriate counter-measures more efficiently.

How can we explain the effect of team conflicts on quality of patient care? The provision of safe patient care rests on good clinical knowledge but also on good communication and collaboration skills. More specifically, effective collaborative practice and teamwork represent cornerstones for high quality of care. 35 , 36 , 37 , 38 However, conflicts can lead health care professionals to exhibiting poor collaborative attitudes. 20 Failure to communicate within a team may mean that decisions for patient care are made in isolation and do not include all team members' perspectives on patients. 39

Finally, based on participants' assessments of 130 conflicts stories, we showed that more than 4 of 10 (41%) team conflicts stories had potential consequences for quality of care. To our knowledge, this is the first study to document the proportion of team conflicts potentially affecting quality of patient care. Two caveats must, however, be emphasized: First, the observed proportion (4 of 10) is indicative and should be confirmed by quantitative observational studies based on larger sample sizes; second, because consequences for patient care were based on participants' perceptions, we cannot assess the true impact of these conflict stories, and thus we prefer to speak of potential consequences, possibilities of something affecting patient care.

Taking the above into account, we can say that a non-negligible proportion of team conflict stories was perceived by professionals to have potentially negative consequences on patient quality of care. It is possible that this proportion may have been underestimated in our study; indeed, during interviews, some participants had difficulty assessing the consequences of team conflict on patient care. While waiting for more studies on this topic, our results led us to believe that when addressing team conflicts, patient care should be taken into consideration and that quality management of acute care hospitals should consider team conflicts as a potential threat for the quality of patient care. Improvement programs may need to strengthen health care professionals' ability to identify and respond to team conflicts.

Limitations

This study has several limitations. First, the data-collection technique (semistructured interviews) may have favored information bias, and we did not triangulate data from interviews with field observations or hospital records. Second, team conflict stories were selected using participants' self-assessment, allowing selection bias in identifying conflicts stories. Third, during interviews, some participants had difficulties evaluating whether and how conflicts had affected patient care; even if interviewers helped participants identifying these consequences, we may have missed some patient-care consequences. Fourth, we interviewed a limited number of health care professions. Integrating additional professions (midwives, physiotherapists, for example) may enrich our description of team conflict stories, but we cannot assert whether it would enrich the description of consequences of team conflict on patient care. Fifth, based on participants' discourses, it was not always entirely clear whether quality of care was the consequence or the cause of conflict stories. Some participants referred to the chicken-and-egg conundrum to describe how distinguishing what had started a conflict from its consequences could be tricky, as unaddressed conflicts can lead to further tensions. 23 Despite multiple reviews of these ambiguous situations by all co-authors, we cannot avoid the possibility that some conflicts stories may have been misclassified as cause of impaired quality of care or as cause of team conflict.

Despite these limitations, we feel confident in the robustness of our results. We conducted a significant number of interviews in different clinical settings, increasing confidence in our capacity to capture sufficient heterogeneity in conflict stories. Random sampling allowed a smooth recruitment of participants, avoiding participants' concerns with the reasons of their selection for the study. Finally, interviews were conducted by social scientists to minimize barriers related to peer interviewing.

In a tertiary hospital, conflicts among health care professionals can potentially affect patient care. When team conflicts have consequences for patient care, they mostly influence timeliness, patient-centeredness, and efficiency. Quality managers of care hospitals should consider team conflicts as potential barriers to quality care. Quality management should consider preventive actions and support programs for management of conflicts.

Acknowledgments

The authors wish to thank all participants who agreed to share their stories and experiences as well as the heads of departments who supported this project. They also extend their gratitude to Ms. Nuria Scherly for her transcription work.

Grant Support: This study was funded in part by funds from the University of Geneva.

Potential Competing Interests: The authors report no competing interests.

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Case Study #7: Empowered Process---Skilled Leadership: Diffusion, Party Capacity & Speaking Truth to Power

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Case Study #2: Intrapersonal Approaches to Conflict: Cognitive & Perceptual Biases

This is the second case study in the series Holding These Truths: Empowerment and Recognition in Action. This series presents case studies for a future conflict resolution textbook. It has been successfully piloted with several international classes. Those, who benefit most, stress the importance of carefully studying the introduction. (See Introduction to Conflict Case Studies, Nancy D. Erbe). Because the case study format is intentionally unique, written in an interactive and non-linear workbook style, unlike many introductions, the information provided there is required for understanding the case studies. Readers are encouraged to send comments and critiques directly to the author. Because of the deliberate one-of-a-kind format of the text, detailed page-by-page comments and questions are welcome. A list of the entire series is included below.

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This is the first case study in the series Holding These Truths: Empowerment and Recognition in Action. This series presents case studies for a future conflict resolution textbook. It has been successfully piloted with several international classes. Those, who benefit most, stress the importance of carefully studying the introduction. (See Introduction to Conflict Case Studies, Nancy D. Erbe). Because the case study format is intentionally unique, written in an interactive and non-linear workbook style, unlike many introductions, the information provided there is required for understanding the case studies. Readers are encouraged to send comments and critiques directly to the author. Because of the deliberate one-of-a-kind format of the text, detailed page-by-page comments and questions are welcome. A list of the entire series is included below.

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This series, Holding These Truths: Empowerment and Recognition in Action, presents case studies for a future conflict resolution textbook. It has been successfully piloted with several international classes. Those, who benefit most, stress the importance of carefully studying this introduction. Because the case study format is intentionally unique, written in an interactive and non-linear workbook style, unlike many introductions, the information provided here is required for understanding the case studies. Readers are encouraged to send comments and critiques directly to the author. Because of the deliberate one-of-a-kind format of the text, detailed page-by-page comments and questions are welcome. A list of the entire series is included below.

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Conflict Management in Multiethnic Communities: a Case Study in Southeast Sulawesi, Indonesia

  • Published: 02 December 2021
  • Volume 23 , pages 1963–1985, ( 2022 )

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  • Jamaluddin Hos   ORCID: orcid.org/0000-0002-6871-518X 1 ,
  • Siti Kusujiarti 2 ,
  • Jumintono 3 ,
  • Ambo Upe 1 ,
  • Muhammad Arsyad 1 ,
  • Hasniah 4 ,
  • Firdaus Yuni Dharta 5 &
  • Jemma Natanson 6  

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This study aims to identify physical conflict management and avoidance between ethnic groups in transmigration settlements. Using a qualitative approach with data collection techniques comprised of observation and in-depth interviews, this research uses interpretive understanding for analyzing the data. Both macro-level and micro-level theories are applied in this research to analyze the interconnections of structure and agency. The fundamental question to be answered in this study is what causes conflicts between ethnic groups in transmigration settlement sites and how ethnic group citizens manage conflicts so that they do not become open conflicts and remain coexistent. Results show that the sources of conflict include the legal uncertainty of land ownership and the lack of social platforms for different ethnic groups to interact and discuss the problems. Social disparities between ethnic groups and differences in values and norms also contribute to the conflict. However, there was a potential for cooperation between ethnic groups. The different ethnic groups tend to restrain themselves and prevent violent conflict. The ethnic communities managed the conflict by ignoring disputes and making compromises to avoid open disagreements. These compromises involved lowering the demands and expectations of the object of conflict; the strategy manages the disputes and helps to reduce open disagreements and support multiethnic communities. In order for the ethnic groups involved in the conflict to negotiate and compromise, the availability of social space plays a pivotal role.

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The research was financed by the Directorate of Research and Community service, Directorate General of Research and Development of Ministry of Research, Technology, and Higher Education of the Republic of Indonesia.

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Jamaluddin Hos, Ambo Upe & Muhammad Arsyad

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Siti Kusujiarti

Faculty of Technical and Vocational Education, University of Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

Faculty of Cultural Sciences, University of Halu Oleo, Kendari, Indonesia

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Hos, J., Kusujiarti, S., Jumintono et al. Conflict Management in Multiethnic Communities: a Case Study in Southeast Sulawesi, Indonesia. Int. Migration & Integration 23 , 1963–1985 (2022). https://doi.org/10.1007/s12134-021-00923-0

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case study of conflict in organization

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Team-Building Strategies: Building a Winning Team for Your Organization

case study of conflict in organization

Discover how to build a winning team and boost your business negotiation results in this free special report, Team Building Strategies for Your Organization, from Harvard Law School.

Conflict and Negotiation Case Study: Long-Term Business Partnerships and Negotiated Agreements

Business negotiations: the signing of the contract is only the beginning of the negotiation relationship..

By PON Staff — on September 26th, 2019 / Conflict Resolution

case study of conflict in organization

To protect the future interests of their organization, negotiators sometimes must accept fewer benefits or absorb greater burdens in the short run to maximize the value to all relevant parties during negotiation – including future employees and shareholders – over time.

Suppose that the operations VPs of two subsidiaries of an energy company are preparing to negotiate the location of a new energy source within the company. Beta , the energy source, is limited in supply, but it is inexpensive and efficient to use in the present and grows in potency over time.

One subsidiary would reap short-term gains by using Beta immediately, while the other is generating a technology that would make even greater use of Beta in the future.

The New Conflict Management

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Retaining Beta for future use would create more long-term value for the company overall.

But because the future consequences of our decisions often appear remote, it could be difficult for the executives involved to negotiate this wise decision.

In negotiations , a temporal delay often exists between our decisions and their consequences, a situation that becomes complicated when ‘others’ – rather than ourselves – will be affected by our decisions.

In negotiations concerning long-term concerns, a strong symmetry exists between powerful present organizational actors and powerless future generations. Because those with control over the decision process have less at stake, the dependency of future generations on the present generation intensifies.

How do you maximize value in negotiations? Leave a comment.

Related Conflict Resolution Article:  The Deal is Done, Now What?  –  After a long round of business negotiations with a partner from Silicon Valley in a joint venture to manufacture devices using your tech and their know-how. The contract is unambiguous and its terms exact – all contingencies are covered and strong enforcement mechanisms are in place to insure compliance with the negotiated agreement. The foundation for the new partnership is solid and the dealmaking negotiation to arrive at this day is what helped build that foundation. But now what? As any experienced business negotiator knows, it takes more than a perfect contract to have a successful agreement. To work together with a partner and others, you need an effective working relationship based upon trust and mutual respect. In reality, business negotiators know that the signing of the contract is only the beginning of business negotiations between the two parties and that the building of a successful relationship between the two firms relies upon those same communication skills and negotiation skills that developed the foundational contract in the first place. A successful relationship with a partner, whether a domestic negotiator or an international negotiator, is, in many cases, the difference between success and failure. In this article, the Program on Negotiation (PON) at Harvard Law School takes negotiation strategies first published in the Negotiation Briefings   newsletter to show you how to transform a contract into a successful, win-win relationship.

Dealmaking – Relationship Rules and Business Negotiations  –  Here are some concrete guidelines for fostering a productive, value-creating relationship with your negotiating counterpart – in business and in daily life. These negotiation strategies and negotiation tips come from  The Global Negotiator: Making, Managing, and Mending Deals Around the World in the 21st Century   by Program on Negotiation faculty member Jeswald Salacuse .

Originally posted in May 2013.

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A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Never just tech

Creating value beyond the hype

Let’s deliver on the promise of technology from strategy to scale.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

  • “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
  • “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
  • “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

Jessica Lamb and Gayatri Shenai

McKinsey Live Event: Unlocking the full value of gen AI

Join our colleagues Jessica Lamb and Gayatri Shenai on April 8, as they discuss how companies can navigate the ever-changing world of gen AI.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

  • data scientist:
  • prompt engineering
  • in-context learning
  • bias detection
  • pattern identification
  • reinforcement learning from human feedback
  • hyperparameter/large language model fine-tuning; transfer learning
  • data engineer:
  • data wrangling and data warehousing
  • data pipeline construction
  • multimodal processing
  • vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

  • Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
  • Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
  • Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

  • Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
  • Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
  • Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

Eric Lamarre

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

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Why do women go through menopause? Scientists find fascinating clues in a study of whales.

case study of conflict in organization

The existence of menopause in humans has long been a biological conundrum, but scientists are getting a better understanding from a surprising source: whales.

Findings of a new study suggest menopause gives an evolutionary advantage to grandmother whales’ grandchildren. It's a unique insight because very few groups of animals experience menopause.

A paper published Wednesday in the journal Nature looked at a total of 32 whale species, five of which undergo menopause. The findings could offer clues about why humans, the only land-based animals that also goes through menopause, evolved the trait.

“They’ve done a great job of compiling all the evidence,” said Michael Gurven, a professor of anthropology at the University of California, Santa Barbara who studies human evolution and societies. “This paper quite elegantly gets at these very difficult issues.”

Whales might seem very distant from humans, but they have important similarities. Both are mammals, both are long-lived, and both live in family and social groups that help each other.

How long does menopause last? Menopause questions and concerns, answered.

Studying these toothed whale species offers a way to think about human evolution, said Gurven, who was not involved in the study.

In five species of toothed whales – killer whales, beluga whales, narwhals, short-finned pilot whales and false killer whales – the researchers’ findings suggest menopause evolved so grandmothers could help their daughters' offspring, without competing with them for mates.

Only daughters' offspring are aided because in these whales, while the males stay with their family group, they mate with females in other groups. But mothers do tend to give more support to their male offspring than to their female offspring.

Post-reproductive-age females help their family group in many ways. Off the coast of Washington state and British Columbia in Canada, grandmother killer whales catch salmon and "break the fish in half and share that catch with their families. So they're actively feeding their families,” said Darren Croft, a professor of behavioral ecology at the University of Exeter in the United Kingdom and senior author on the paper.

The whale grandmothers also store ecological knowledge about when and where to find food in times of hardship by using the experience they have gained over the lifetime of their environments.

“We see just the same patterns in (human) hunter-gatherer societies,” Croft said. “In times of a drought or in during times of social conflict, the people would turn to the elders of that community. They would have the knowledge.”

The 'grandmother hypothesis'

The researchers’ findings support what’s known as “the grandmother hypothesis .” It states that menopause is evolutionarily useful because while older women are no longer able to have children, they can instead focus their efforts on supporting their children and grandchildren. This means their family lines are more likely to survive, which has the same effect as having more children.

“What we showed is that species with menopause have a much longer time spent to live with their grand offspring, giving them many more opportunities for intergenerational health due to their long life,” said Samuel Ellis, an expert in human social behavior at the University of Exeter and the paper’s first author.

The difference in humans, Gurven said, is that both grandmothers and grandfathers contribute to the well-being of their children and grandchildren.

“In the human story, I think it’s multigenerational cooperation on steroids,” he said.

Though the study doesn’t prove once and for all that the grandmother hypothesis is the reason for menopause in women, it does lay out the evidence, he said. “It’s part of the story, but no one would say it tells the whole story,” Gruven said.

Does menopause lead to a longer life in humans?

There are two proposed pathways for how menopause evolved in humans: the live-long hypothesis and the stop-early hypothesis.

The live-long hypothesis suggests menopause increased total life span, but not how long a woman could have children. That leads to a prediction that species with menopause would live longer but have the same reproductive life span as species without menopause.

In the stop-early hypothesis, the theory is that menopause evolved by shortening the reproductive life span while the total life span remained unchanged. For this to be true, it would be likely that similar species without menopause would have the same life span as those that have menopause, but a shorter reproductive life span.

In looking at species of toothed whales that don’t have menopause and five that do, the researchers' findings make the long-life hypothesis seem most likely.

“This comparative work we’ve been able to do shows that females minimize this competition over reproduction by not also lengthening their reproductive period. Instead, they've evolved a longer lifespan while keeping a shorter reproductive life span,” Croft said.

This appears to be exactly what humans did.

“One of the striking features of this work is the fact that we find this really incredible and rare life-history strategy that we see human societies and in the ocean, but not elsewhere in mammal societies,” he said.

Whale study doesn't reflect men's life spans

The similarities with humans are not across the board, which is good news for men.

No one knows why in humans only females undergo menopause even though both sexes live to be approximately the same ages.

That’s not the case in some of these whales species, where male life spans are typically much shorter than those of females.

“In the killer whale population, for example, females regularly live into their 60s and 70s," Croft said. "The males are all dead by 40.”

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