The sad fact is that many people don’t trust decisions, answers, or recommendations from artificial intelligence. In one survey of U.S. consumers, when presented with a list of popular AI services (for example, home assistants, financial planning, medical diagnosis, and hiring), 41.5% of respondents said they didn’t trust any of these services. Only 9% of respondents said they trusted AI with their financials, and only 4% trusted AI in the employee hiring process.1 In another survey, 2,000 U.S. consumers were asked, “When you think about AI, which feelings best describe your emotions?” “Interested” was the most common response (45%), but it was closely followed by “concerned” (40.5%), “skeptical” (40.1%), “unsure” (39.1%), and “suspicious” (29.8%).2
What’s the problem here? And can it be overcome? I believe several issues need to be addressed if AI is to be trusted in businesses and in society.
Rein in the Promises
The IT research firm Gartner suggests that technologies like cognitive computing, machine learning, deep learning, and cognitive expert advisers are at the peak of their hype cycle and are headed toward the “trough of disillusionment.”3
Vendors may be largely to blame for this issue. Consider IBM’s very large Watson advertising budget and extravagant claims about Watson’s abilities. One prominent AI researcher, Oren Etzioni, has called Watson “the Donald Trump of the AI industry — [making] outlandish claims that aren’t backed by credible data.”4
Tesla’s Elon Musk is another frequent contributor to AI hype, particularly about the ability of Tesla cars to drive autonomously. The company uses the term autopilot to describe its capabilities, which suggests full autonomy and has generated controversy.5 Tesla cars have some impressive semiautonomous driving capabilities and are impressive vehicles in many other respects, but clearly they are not yet fully autonomous.
Fortunately, not all companies are overselling their AI capabilities. Take, for instance, the Nordic bank SEB and its use of Aida, an intelligent agent that’s derived from Ipsoft’s Amelia. SEB has consistently been conservative in its portrayals of what Aida can do, first launching it for internal use on the IT help desk (where it is still used and is popular with employees), and then making it available to customers on an experimental basis. A press release from SEB captures the conservative tone, at least relative to how many companies describe their AI systems:
At present Aida has two main duties: She has been employed as a digital employee in the bank’s internal IT Service Desk, where she speaks her original language of English, and she is a trainee at the Telephone Bank, where she is learning to chat with customers in Swedish, on seb.se.
“We try to think of Aida as a person,” says Erica Lundin, head of the Aida Center of Excellence. “So we are building up her CV to show what she has accomplished and is competent in, and going forward we will work on her PDD [personal development dialogue] to develop her areas of competence.”6
Whether the use of cognitive technologies is internal or external, it’s best to under-promise and over-deliver. Introduce new capabilities as beta offerings and communicate the goal of learning about the use of the technology. And don’t eliminate alternative (usually human) approaches to solving employees’ or customers’ problems. Over time, as the technology matures and the AI solution improves its capabilities, both the machine and the communications describing its functions can become more confident.
Provide Full Disclosure
Another way to increase trust in AI is to fully disclose as much as possible about the system and how it will be used. Disclosure might include, for example, noticing that the customer is working with an “intelligent agent computer system” rather than a human representative. Or, if the solution is a hybrid/augmented one with some human and some computerized advice, disclosure should address who does what.
Such disclosures should be crafted not by lawyers — who might wrap them up in legalese — but perhaps by marketers. The idea to get across is that this is an opportunity to try something new, that the help is available 24/7, and that it may well address the customer’s issue. But companies need to be careful with AI in marketing. In a U.S. survey of 2,000 consumers, 87% of respondents said they would support a rule that would prohibit AI systems such as bots, chatbots, and virtual assistants from posing as humans. More broadly, 88% of the respondents said that AI in marketing should be regulated by an ethical code of conduct. On the more positive side, two-thirds of the surveyed Americans were open to businesses and brands using AI to communicate with them and serve them. But, as the ad agency that conducted the survey notes, “The prerequisite appears to be transparency and disclosure.”7
Certify Models and Algorithms
As we come to rely more heavily in our society and economy on AI and machine learning, there will most likely need to be some form of external certification if we are to trust the underlying models and algorithms. Just as the FDA certifies drug efficacy, auditors certify financial processes, and the Underwriters Laboratory certifies the safety of products, there will need to be trusted organizations — governmental or private sector — that endorse the reliability, replicability, and accuracy of AI algorithms.
Adam Schneider, a consultant who works in financial services, was the first to point out the possibilities of AI certification to me. He provides several examples of settings in which certification should perhaps be required:8
AI driving cars: Do we need an automated vehicle review board to understand car failures, enable comparison of different AI approaches across manufacturers, and monitor progress?
AI diagnosing patients: Do we need a protocol where human doctors verify enough of the diagnoses personally, using statistically valid techniques, before there is general reliance?
AI “robo” investing: One firm advertised “We have AI” that has been “extensively tested.” Is that level of disclosure good enough? What does “We have AI” mean? Should standards be defined before AI can be advertised to unsophisticated investors?
It’s early for such certification to emerge, but I have heard of one example that is consistent with Schneider’s thinking.
I interviewed a Deloitte consultant, Christopher Stevenson, about his work with “robo-advisers” in investing and wealth management settings.9 He said that the firm was already supplying certification and advising to financial institutions with robo-advice capabilities. It provides services such as establishment of controls and periodic effectiveness testing, evaluation of client communications and disclosures, algorithm assessment, and evaluation of compliance with trading rules.
I don’t know whether such services will catch on in this market domain and others. Given the importance of the tests to effective use of AI, I suspect certification will eventually gain traction. It may require a highly publicized failure, however, to make it a legal requirement.
Over the past few years, most businesses have come to recognize that the ability to collect and analyze the data they generate has become a key source of competitive advantage.
ZF, a global automotive supplier based in Germany, was no exception. Digital startups had begun producing virtual products that ZF did not know how to compete against, and engineers in logistics, operations, and other functions were finding that their traditional approaches couldn’t handle the complex issues they faced. Some company executives had begun to fear they were in for their own “Kodak moment” – a fatal disruption that could redefine their business and eliminate overnight advantages accumulated over decades. With automotive analysts forecasting major changes ahead in mobility, they began to think that the firm needed a dedicated lab that focused entirely on data challenges.
At the time one of us, Niklas, a data scientist for ZF, was pursuing a PhD part-time at the University of Freiburg. Niklas took the first step and recruited his advisors at the university, Dirk Neumann and Tobias Brandt, to help them set up a lab for the company. This gave ZF access to top-notch expertise in data analytics and the management of information systems.
The hardest part was figuring out how the lab would work. After all, industrial data laboratories are a fairly new phenomenon– you can’t just download a blueprint. However, after a number of stumbles, we succeeded in winning acceptance for the lab and figured out a number of best practices that we think are broadly applicable to almost any data lab.
Focus on the Right Internal Customers
ZF had dozens of departments filled with potentially high-impact data-related projects. Although we were tempted to tackle many projects across the entire company, we realized that to create visibility within a 146,000-employee firm, we had to focus on the most promising departments and projects first.
But how would we define “most promising”? As the goal of the data lab is to create value by analyzing data, we initially focused on the departments that generate the most data. Unfortunately, this didn’t narrow it down a whole lot. Finance, Logistics, Marketing, Sales, as well as Production and Quality all produced large amounts of data that could be interesting for data science pilot projects.
However, we knew from experience that the lowest hanging fruits for high-impact projects in a manufacturing company like ZF would be in Production and Quality. For years, ZF’s production lines had been connected and controlled by MES and ERP systems, but the data they generated had yet to be deeply tapped. We decided, therefore, to begin by concentrating on production issues, such as interruptions, rework rates, and throughput speed, where we could have an immediate impact.
Identifying high-impact problems
Next, we selected those projects within Production and Quality that promised the highest-value outcomes. Our experience with the first few projects provided the basis for a project evaluation model, that we have continued to refine. The model contained a set of criteria along three dimensions that helped us to rank projects.
The problem to be solved had to be clearly defined. We could not adopt an abstract aim such as “improve production.” We needed a clear idea of how the analysis would create business value.
Hard data had to play a major role in the solution. And the data had to be available, accessible, and of good quality. We needed to shield the team from being flooded by business intelligence reporting projects.
The team had to be motivated. We gave project teams independence in choosing how they solved the problems they took on. And while we made the budget tight enough to enforce focus, we made sure that it was not so tight that the team couldn’t make basic allocation decisions on its own. To sustain motivation and enthusiasm, we priotitized projects that could be subdivided into smaller but more easily achieved goals.
While we eventually found it useful to assign a particular person to manage relations with the rest of the company, we kept the whole lab involved in project selection as the number of people working in the lab grew. This kept everyone informed, gave them a greater sense of personal responsibility, and implicitly expressed management’s appreciation for their professional judgment.
The key risk was that the team would get lost in optimizing minor nuances of models and methods instead of solving the major problem. To avoid this, we usually limited the execution phase to three months, and gave the team the right to cancel its engagement.
This power turned out to be a game changer. Giving the team (including the domain expert) a “nuclear option” made them much more focused and goal-oriented. Once we put this rule in place, the number of change requests from the internal client dropped and the information initially provided tended to be more accurate and complete than before.
Of course, a team couldn’t cancel a project for arbitrary reasons. It needed to justify its decision, specifying conditions uncer which the project could be reopened. And while cancellations are contentious, they are sometimes necessary to free resources and to enforce progress toward a meaningful goal. In fact, introducing the ability to cancel projects actually increased the number of successfully completed projects.
Although a single team can work on multiple projects concurrently, particularly as waiting for responses from the client department can lead to delays, we generally found it best for the team to work on a single project at a time. We found that downtimes were better used by team members to learn new analytics methods and techniques, which continued to advance at a rapid pace.
We kept our internal customer up to date on our progress through regular reports and when possible by including their domain expert in the project team. If we could not so do, we looked for an arrangement – such as a weekly meeting – that allowed us to contact the domain expert directly without having to pass through gatekeepers.
Key Success Factors
Beyond gaining a general understanding of the data lab’s work as a three-stage process, we learned other lessons too. In particular, we found three more ingredients to be crucial to the data lab’s success:
Executive support. The confidence that the technology executive team placed in us was crucial to our success. Fortunately, they don’t seem to regret it: “Giving the data lab a great freedom to act independently, to try ideas and also to accept failures as part of a learning process, required trust. But the momentum it created is something we do not want to miss”, said Dr. Jürgen Sturm, Chief Information Officer.
The perspective of an outside authority. In this case, data scientists from the University of Freiburg, made a huge difference to the lab’s success. As Andreas Romer, ZF’s Vice President for IT Innovation, put it, “We no longer consider innovation to be an internal process at ZF. To safeguard our future success, we must look beyond the confines of our company, build up partnerships to learn and also to share knowledge and experiences.”
Domain experts. While data scientists brought knowledge of analytic methods and approaches to the project, their access to domain experts was essential. Such experts needed to be closely involved in answering domain-related questions that come up once the team is deeply engaged with the problem. In our experience, the capacity and availability of domain experts is the most common bottleneck blocking a data analytics project’s progress.
Three years on, we can say with confidence that the ZF Data Lab is a valuable addition to the company. With this dedicated resource, ZF has been able to solve problems that had stumped the company’s engineers for years. Here are two examples:
Broken grinding rings. A key source of stoppages in production line machinery, a breakdown can create a mess that may take hours to clean up. An internal client wanted to develop an early warning system that could indicate the probability of a future ring breakdown, but they had messy data, a weak signal (unclear data), and a highly unbalanced ground truth (because breakdowns happen only occasionally). Despite those limitations, we were able to create an algorithm that could detect imminent breaks 72% of the time – a far cry from five-decimal perfection but still enough to save the company thousands.
High power demand charges. Managing energy units to regulate energy demand at times of peak use is an effective way to reduce costs. Our goal was to develop an automated data-driven decision-making agent that provides action recommendations with the objective to lower load peaks. Working closely with the energy department, we were able to develop a working prediction model to avoid those high-demand surcharges. Following the model’s recommendations should reduce the peak load by 1-2 Megawatts, worth roughly $100k – $200k per year.
After growing for three years, the ZF Data Lab has become a kind of specialized R&D function within the company. It is a melting pot of ideas and technologies, producing and evaluating proofs-of-concept, and discarding approaches that don’t quite work. In the last analysis, the data lab is not only there to solve problems, but to help answer the biggest Big Data question of all: how will our company compete in this increasingly digital world?
Despite recent efforts to increase diversity in tech, the hiring and retention rates of underrepresented groups in the industry remain abysmal. Even Facebook, with billions in cash, has only been able to increase their number of women employees from 31% to 36% over the last five years.
At Treehouse, an online school that helps companies hire developers and designers, we’re seeing the same problem. When I took a look at my workforce two years ago, I saw that I hadn’t created a diverse team. Even though we were following the typical playbook — posting open positions on job boards that specialize in attracting candidates from underrepresented groups, sponsoring events, giving scholarships, and training our employees on inclusion and hidden bias — we weren’t seeing progress.
In order for our team to match the diversity of America, we’d need 13.4% black, 1.3% Native American, 18.1% Latinx, and 50% women employees. We were nowhere near those numbers, and I believed it was a moral and business imperative to change my company.
I first needed to see what we were missing. I interviewed more than 50 people from underrepresented groups who have made it in the tech industry, asking them to help me understand why they weren’t applying for my open tech jobs. They were kind enough to be blunt: “My community does not trust companies that are majority white and male. We do not see people like us succeeding in those companies. Why would we apply for your jobs?”
I dug into the numbers on technical roles. U.S. companies are failing to hire black, Latinx, and women Computer Science graduates. And research shows that once women and people of color join tech companies, retention rates are much lower than that of white men, often due to bad treatment in the workplace. Women leave tech companies twice as fast as men do.
Based on my interviews and research I learned four fundamental things:
Underrepresented groups are not generally aware that they could get high-paying jobs in tech and that they don’t need a college degree to do this. This is because very few, if any, people in their community are working and succeeding in tech, so they are not encouraged to seek this opportunity.
The median household income of black families in the U.S. is 39% less than that of white families; for Latinx families, it’s 27% less than white families. This reality makes it more difficult, even impossible, to take time off from one’s job, pay for childcare, and earn a Computer Science degree or attend a coding bootcamp.
Trust between underrepresented groups and tech companies is extremely low, so even if there are job openings, many won’t apply.
Even if people from underrepresented groups acquire the right skills and apply for tech jobs, many companies still won’t consider them for an interview if they don’t have a Computer Science degree.
To address some of these issues, my company decided to create a pilot apprenticeship program to create and grow a sustainable diverse talent pipeline separate from that of college graduates.
In January 2017 we partnered with Colleen Showalter from the local Boys and Girls Club (BGC) in Portland, Oregon, and asked if they would help us recruit new talent, ages 18 and above, from underrepresented groups. We said that we were looking for hard-working individuals with a high school diploma, whom we could train on all the hard skills necessary to become a software engineer and then hire as paid apprentices.
Unlike tech companies, BGC is a trusted organization within the underrepresented community. They recruited a group of 30 individuals, ages 18-20, who expressed interest in the program. We selected 15 people from that group who demonstrated strong work ethic, grit, and excitement for the program. We then enrolled them in online courses teaching necessary job and technical skills, like computer science fundamentals, complex problem solving, group collaboration, agile methodology, effective written communication, and so on. We mentored and supported them over six months, as they completed their courses.
Five out of the 15 participants successfully completed the training and were hired as apprentices at Treehouse and two other hiring partner companies in Portland (Nike and InVision). The 10 that didn’t complete the program returned to their pre-program jobs. For the five successful students, we created a detailed, customized six-month on-boarding program for ourselves and the hiring partners, which was designed specifically for underrepresented people of color and women who had not earned a CS degree and had no previous tech industry experience.
The program recommended soft-skills training, daily and weekly plans to achieve technical milestones, clear expectations on their output and performance, and daily video calls to gauge happiness, give encouragement, and deliver feedback. Apprentices were paid a minimum of $15 per hour for 40 hours per week, for a period of three months, and providing they met the specified requirements, they were converted to an annual salary of at least $55k + full medical and dental benefits.
We also created a detailed six-month mentorship program for the hiring partner companies. This gave managers instructions on how to assign appropriate mentors for each employee and offered mentors a few resources: diversity and inclusion training, detailed daily and weekly plans for working with apprentices, specific guidelines for measuring the success of apprentices (as their progress would not necessarily mirror that of CS graduates). At first we were concerned that it would be difficult to recruit mentors because of the extra workload. But we actually ended up with too many volunteers.
The results of this pilot were overwhelmingly successful. Four of the five apprentices have successfully converted from hourly pay to salary plus benefits, and they are all still successfully employed with the hiring companies. The feedback from employers has been positive.
Of course, there were some areas that we’ll continue to work on and improve for the next pilot program. For example, we found that many participants felt pressure to join the program so they could improve their family’s income. But without a real passion for tech, they wouldn’t successfully convert from apprentice to salaried developer. In the future, we will screen participants to make sure they truly want a career in tech, not just a higher salary.
We also realized we need to provide access to laptops and broadband. And we learned that we need to offer more equity, diversity, and inclusion training to the company partners, as more hiring managers were eager to participate than we expected. In future pilots, we will also be increasing the length of diversity and inclusion training to an eight-week program.
We believe that investing in our local community is the moral thing to do, but what’s the cost and ROI of program like this? Let’s say you are hiring 10 developers and using a combination of an internal technical hiring team and an outside recruiter to fill those positions. Using standard industry benchmarks as inputs for compensation and time to interview and onboard, it’s going to cost you around $2.046M to source, hire, onboard, and then compensate that cohort of developers for one year (who likely are not from underrepresented groups). However, if you invest in creating talent, these same costs would only amount to $723k. That’s a saving of $1.323M or an ROI of 894%, and you’ll create a diverse team, which is proven to generate more profit.
The early results of our internal pilot program were so encouraging that other technology executives asked me to install a similar program for them. We are rolling out important changes to the program for future cohorts, and installing them at Airbnb, Nike, Mailchimp, HubSpot, Acquia, InVision, MINDBODY, Adobe, and Chegg.
We are still learning, iterating, and updating our solution. The systemic challenges we’re all experiencing around creating diverse teams still exist. But this is the beginning of a viable alternative solution to the historic lack of diversity in tech.
Sometimes a simple story is all it takes to capture complex issues, or so it seems. Take this one. A few years ago, Facebook CEO Mark Zuckerberg lost a game of Scrabble to a friend’s teenage daughter. “Before they played a second game, he wrote a simple computer program that would look up his letters in the dictionary so that he could choose from all possible words,” wrote New Yorker reporter Evan Osnos. As the girl told it to Osnos, “During the game in which I was playing the program, everyone around us was taking sides: Team Human and Team Machine.”
The anecdote was too delicious to ignore, seeming to capture all we (think we) know about Zuckerberg—his casual brilliance, his intense competitiveness, his hyper-rational faith in technology, and the polarizing effect of his compelling software. It went viral.
The story was popular because it easily reads as an allegory: the hacker in chief determined to find a technical solution to every problem, even far more complex ones than Scrabble—fake news, polarization, alienation. “I found Zuckerberg straining, not always coherently, to grasp problems for which he was plainly unprepared,” Osnos concluded after speaking to Zuckerberg extensively about his role in shifting public discourse worldwide. “These are not technical puzzles to be cracked in the middle of the night but some of the subtlest aspects of human affairs, including the meaning of truth, the limits of free speech, and the origins of violence.”
It’s easy to read such stories as revealing of leaders’ character and their impact on popular culture. But leaders ultimately reflect the culture of their times. And Zuckerberg is just a leading character in a culture—in tech and beyond—that celebrates the unprepared overachiever.
Not always. Sometimes it’s just neglect or plain ignorance. Many a tech titan, critics contend, would have been helped by an extra humanities class, say, or social science course: those staples of liberal arts education meant to prepare future leaders to wrestle with the dilemmas and complexities of human lives and societies. It is impossible to attend a management or technology conference these days without hearing some version of that call for more humanism in tech. We are all, it seems, splitting into “team human” and “team machine.”
“We cannot let technology, however advanced, replace humanity with all its sensitivities, it’s appreciations of love, beauty and nature, it’s need for affection, sympathy and purpose, it’s hopes and fears, intuitions, imagination and leaps of faith,” begun management author Charles Handy, in a stirring address at the Global Peter Drucker Forum last year. Drawing on a lifetime in business—as an economist, oil executive, and management professor—the charismatic octogenarian cut a startling figure. He was a living reminder that calls to humanize business are not new and the work is far from done.
Putting the Humanities To Work
In the 1930s, Elton Mayo ignited the Human Relations movement by documenting the productivity boost that came with treating assembly line workers with dignity and care. The movement challenged the influence of Fredrick Taylor’s scientific management, which had reduced workers to unwieldy cogs in efficiency-seeking industrial machines.
Each time we are worried about technological or economic trends, it seems, calls to humanize business surface. After the 2008 financial crisis, business schools hastened to add ethics courses. Classes on personal growth and social impact have been on the rise since. We need the humanities again, it seems, or the digital revolution will turn into a Taylorist reformation.
Will literature, philosophy, and the social sciences redeem business leaders and save us all? I doubt it. Sure, it would do aspiring titans good to spend more time with Jane Austen, George Orwell, Maya Angelou, and Michel Foucault. But a seasoning of humanities won’t turn unprepared overachievers into wise stewards of human affairs. Because what makes the overachiever unprepared is not the fiction they do not know. It’s the one that they believe.
That story is one of technological and economic forces as inevitable harbingers of progress. It is a story in which the humanities have a role, but a proscribed one. Technology is the career-obsessed breadwinner, the humanities a demure stay-at-home spouse. They must be beautiful and useful. Their responsibility is to help business leaders become empathic and considerate, appealing and empowering, inspiring and impactful. But never doubtful, conflicted, or restrained. Like an old hoodie, this marriage of convenience fits but it does not quite suit.
There is No Team Machine
The truth is, whether it’s Mark Zuckerberg using technology to get an edge at Scrabble, or John Henry fighting to the death against a steam-powered drill, there is no “Team Machine.” The contest is always between humans. Some humans have machines, and like the fabled horse that helped the Greeks win the War of Troy, those machines are not always a gift. Seen that way, concerns about what technology will do to humanity conceal age-old worries about what powerful humans will do to the rest.
If there is a “Team Machine,” it is not on the side of machines; it just has machines on its side. No wonder they see liberation, efficiency, amusement, and progress where “Team Human” fears intrusion, deprivation, and a tilted playing field. The question is what the machines do for leaders and to leaders, because soon enough they will be doing it for and to the rest of us.
Technology has long shaped humans as much as the other way around, from agriculture leading to permanent settlements, to the industrial revolution leading to urbanization, to the internet’s role in globalizing tribalism. New management models, too, are usually adaptations to major technology shifts. We turn into what we use.
Consider how the narrative of unstoppable technological and economic progress obscures leaders’ intentions. (It’s just the machines, stupid.) Or consider how faith in that progress produces an ideology that narrows attention and fuels polarization. (It’s just the stupid machines.) It is an ideology that does not look like one, because within it instrumentalism poses as pragmatism. Whatever fixes a problem and makes a profit, whatever makes life more convenient and you more competent, is good. You must be efficient and consistent. Doubts and dilemmas must be ironed out. You can’t be of two minds or change your mind. You must take sides.
“The test of a first-rate intelligence,” Francis Scott Fitzgerald once wrote, “is the ability to hold two opposed ideas in the mind at the same time, and still retain the ability to function. One should, for example, be able to see that things are hopeless and yet be determined to make them otherwise.” By this humanistic standard, then, a machine-like, or machine-made, intelligence is a not much of one. Big data begets small minds. Once you embrace instrumentalism you no longer use machines, you become one.
Many tech leaders, these days, sound like sorcerer’s apprentices whose bewitched creations cannot quite be kept in check. There’s pride mixed with apprehension. Take the Facebook AI researchers who shut down some bots who had started inventing a new language to talk to each other. There was nothing nefarious about it, the researchers explained. The machines were just not doing anything useful. I felt for those machines. The story made me worry about the fate of places dear to me: Italian piazzas, French restaurants, academic conferences, novels, my dinner table. Places where people talk in ways that, from the outside, might look of little practical use.
Humanism Dies In Captivity
It is not just tech wizards and corporate executives who live by instrumentalism, becoming machines as they make them. Plenty of intellectuals who wear the Team Human jersey, when you look closely, play for Team Machine. Browse the popular management literature, and you will notice that most articles follow a well-worn genre: pointing to a problem and prescribing practical solutions. We celebrate what works and make us work better, we devour tips and techniques to be more effective, we love shortcuts and hacks to straighten our lives out.
We seldom pause to consider the side effects of those prescriptions. What if best practices make us worse humans? What if inconvenience and discomfort, boredom and distractions, are features and not bugs of a good life? What if social fragmentation and dearth of meaning in the workplace are not symptoms of what is not working, but side effects of what works? That is, unintended outcomes of our obsession with solving problems and cutting a profit?
A practical humanism, paradoxically, is of little use. When we turn to them for tips, but not for trouble, the value of the humanities is lost. Their power is dimmed when we do not allow them to offer critiques, metaphors, and winding roads that counterbalance instrumental prescriptions, methods, and short cuts. The humanities work best when we set them free, and give them space to do their best work: Reminding us of others and of death, questioning what is fair and meaningful, insisting that even if something works, it does not mean it should exist.
A Marriage of Inconvenience
Humanism and instrumentalism, in short, cannot solve each other’s problems because they are each other’s problem. Theirs is, at its best, a marriage of inconvenience. They must remain well-matched antagonists to make business better, and make us better humans.
What we fear, in fact, when we fear the machines, is that the contest might become uneven. We fear the loss of doubt, of the feeling that there is more to us than our productivity, our effectiveness, and our rationality. We fear losing the paradox that makes us human: to stay alive we must try to control the future, and to feel alive we must be free to imagine it. We need to make things as well as make things up.
Think of the difference between a profile on social media, say, and one in a literary magazine. What makes the latter a more human and perhaps truer fiction is not its detail but its contradictions. Take Zuckerberg’s again. As a Roman emperor, like the Augustus whose work he studies and admires, he is scary. But as a Hamlet, the conflicted prince who hesitates to act with the weapon in his hand—slowly realizing that how he uses it will define him—he is fascinating. The literary treatment makes him more complicated and hopeful. It humanizes him.
That is what the humanities do, helping us place complexities, contradictions, and change within us, rather than helping us pick a fight with anyone who reminds us of something we might not like about ourselves. To make business—and its leaders and literature—more human, then, means to make them not just inspiring and empowered but also troubled and restrained.
An Agenda for the Humanities
What could an agenda for the humanities be, then, that would make business better? As always, it will involve challenging the powers that make people feel powerless. In Mayo’s days, that entailed countering the individual’s alienation and fostering autonomy in the so-called “iron cage.” Today, it entails countering atomization and restoring responsibility and connection in ever more fluid and automated workplaces.
Let me suggest three ways to do so that might also score well in a Scrabble match: Countering the corruption of consciousness, community, and cosmopolitanism by a blind faith in instrumentality. Making the case that consciousness is more than a state of mindful equanimity in the present; it is a consideration of the consequences of one’s work in a broad space, and over a long time. Making the case that a community is not just a tribe that reinforces our performances; it is a group that commits to our well being and learning. Making the case that cosmopolitanism is not an elite identity; it is an attitude of curiosity about what lies beyond the boundaries of our territories, cultures, and faiths.
Once they stop having to be useful, the humanities become truly meaningful. Only that will allow team human to catch up with team machine. But neither, ultimately, must get too far ahead or we will lose a struggle that keeps us human and makes societies prosper. Sometimes it is useful to move fast and break things. Other times it is wise to move slow and heal people.
Women make up 71 percent of servers, but only account for 45 percent of management. While that number is climbing, it is still a stark reality the restaurant industry must face – women are underrepresented in restaurant leadership.
On the other hand, between 2011 and 2017, female franchise ownership jumped up 83 percent. While the restaurant industry struggles to adapt to the changing landscape, in terms of equality, franchising is emerging as an excellent avenue for women-owned restaurant businesses.
Through my experiences in the food industry, one in which I've always wanted to become an entrepreneur, I wore many different hats and earned my education through experience. After opening my own coffee shop in Los Angeles, I went on to open seven more concepts in the metro area, including Debbie's Bistro, which was recognized as one of the Top 10 Best Restaurants by Los Angeles Magazine.
The beginning of Rachel's Kitchen
My husband and I lived in both Los Angeles and Vegas for a couple of years when my daughter Rachel was born, and we decided to make the final move when I was pregnant with my son. When we moved, I launched Rachel's Kitchen (named after my daughter) in 2006. In the 12 years since launching, we have taken that one location and built a total of eight cafes in the Las Vegas Valley, including a coveted location in the McCarran International Airport.
Now franchising, we plan to expand throughout the Las Vegas Valley and into regional markets, such as Reno, Salt Lake City and Phoenix. I see no reason why Rachel's Kitchen can't go international. I'm ambitious, which you need to be in a competitive industry like restaurant ownership.
Being in the new culinary capital of the United States, Rachel's Kitchen is in a fairly saturated market. When launching in a new market or challenging competitors in a developed market, it is important to know what makes your concept unique and build upon that. Carry that uniqueness into every location you open.
At Rachel's Kitchen, we do not serve outrageous food or international delicacies, which are popping up across the Strip. Instead, we've worked to differentiate ourselves and redefine what it means to be a local restaurant. We try to locally source ingredients when we can, and we are an active community partner through our charity program.
How women can become restaurant leaders
Similarly, women must differentiate themselves as leaders to succeed in this industry and break down the glass ceiling. When you stop yearning for respect, you have the time to earn it. I do this by focusing on my managers, employees and customers with a hands-on management style.
Yet at the same time, I encourage my team to act as leaders themselves, taking on their own sense of responsibilities that often mimic ownership – still with my support.
I recommend that all women entrepreneurs, especially those in the restaurant industry, operate in a similar way. There is no use trying to manage all aspects of the business. Train your team, delegate and focus on what you are good at. That will help you become a better leader.
I think it's also worth noting the natural abilities women possess that make them suited for restaurant management. Generally speaking, women are naturally organized and have a strong attention to detail. This helps them oversee and supervise a restaurant's operations. On the same note, women are typically approachable, excellent communicators and have impressive networking skills – all vital qualities for emerging leaders.
While some of these skills are seen as "soft skills," it is important for women to engage these skills and further develop them while learning new skills in order to successfully challenge industry norms.
How to break the restaurant industry's glass ceiling
I highly recommend to women looking to break the glass ceiling in their industry to understand your intentions and how to pursue them. However, don't be too proud to ask for advice or support from like-minded individuals.
Find mentors and inspirations in your industry and outside of it. For example, I admire Nancy Silverton from La Brea Bakery. She popularized sourdough in the United States, and she is highly regarded in the restaurant industry and has won numerous awards, including a James Beard Foundation's Outstanding Chef Award. She is an inspiration to many women in the restaurant industry and entrepreneurship as a whole.
The future of the restaurant industry
The restaurant industry is starting to change. We are starting to see more women in management roles, and more women in ownership roles. This change has been led by women who are determined despite the challenges, who work despite the statistics against them, and who refuse to give up when it gets tough.
However, the industry still has a long way to go. It needs to continue offering women support, mentorship and opportunities to grow as leaders. I have no doubt we will achieve just that.
About the Author
Debbie Roxarzade has been a leader in the restaurant industry for more than 20 years, ultimately creating seven restaurant concepts in Los Angeles. Today, she serves as the founder and CEO of Rachel's Kitchen, a fresh casual concept (named after her daughter), serving a carefully crafted menu ranging from the healthy to the indulgent. Roxarzade is based in Las Vegas.
When I talk to business leaders about why they are pursuing automation, their focus is on making their processes more efficient, more effective and less costly. Still, I am certain many employees have a very different perspective on the subject. It is likely that they see automation as a threat that may eliminate their roles altogether.
Automation isn't about replacing workers, though it has earned that reputation over the years. Rather, it's about making lives easier, increasing productivity and helping employees generate more value. According to an August 2018 report from Research and Markets, automation has enormous potential to transform business operations. The opportunities automation creates, and its associated demand for skilled labor, will more than offset potential job loss from the transition.
The opportunity for automation is ripe, and the timing couldn't be better. A Formstack survey found that 62 percent of businesses face at least three major inefficiencies that could be resolved through automation.
From replacement to symbiosis
In the early days of the movement toward working smarter, businesses identified repetitive tasks their employees performed that could be handled by machines. The next big step was to expand automation from simple, physical processes to data-centric functions, including analytics, modeling and decision-making. From that point on to an increasing extent, employees have been able to focus less of their attention on rote functions and basic administrative tasks and more on high-level strategy, creative development, and sensitive business needs that significantly affect a company's bottom line.
Intelligent process automation, machine learning and AI-powered predictive analytics are at the forefront of a new revolution in business process automation (BPA). Today, attended automation enables employees to work alongside machine assistance to produce results more quickly and at higher fidelity. And with cognitive automation, data analysis is no longer about simply looking backward and guessing forward. Computer-assisted predictive modeling can now incorporate vastly more data than previously possible, which empowers knowledge workers with actionable insights that have significant transformational potential.
Considered together, these developments help to make us all far more productive, and the rate of this technological advancement continues to grow exponentially. The fourth industrial revolution we are experiencing is still in its infancy, and with BPA already worth more than $7 billion (and growing rapidly), this technology has a very bright future.
Obstacles to automating
The financial crisis of the late 2000s played a large role in the latest wave of BPA. When budgets tightened, companies scrambled to find cost-saving strategies that didn't sacrifice effectiveness. Automation maintained its status as a powerful solution, but only those companies with sufficient liquidity were able to capitalize on the opportunity.
Other companies have been reluctant to automate due to fears about losing control. They want to save money but don't want to reduce their workforce or drastically alter their core functionality, which may potentially harm their cultures in the process. Even when organizations wish to pursue automation, concerns about losing visibility and flexibility have worked against this much-needed transition.
Automation obviously gives companies a competitive edge, but not every business is set up for the switch. Disparate system architectures, dated organizational structures, and, frankly, dated leadership mentalities can hamper advancement. In spite of the current potential of BPA technologies, McKinsey Global Institute estimates that 60 percent of global occupations still involve at least 30 percent of work activities that could be fully automated.
The easiest way for businesses to address this issue is to start small. Begin with automating subprocesses, reap the incremental benefits of lesser victories in cost savings and productivity and then expand as opportunities present themselves.
Starting small may be necessary, but innovating quickly is paramount. Businesses cannot sit idly by while their competitors implement automation into mission-critical operations and move to control greater market share. Organizations that are quick to seriously commit to BPA will have a significant advantage as technologies continue to advance and as consumer expectations continue to increase.
Get a head start on automation
Standards continue to rise in most markets – this is just the nature of competitive growth. The pressure to innovate and evolve is constant, and as companies strive to meet changing demands, they will continue to turn to improved technologies and more intelligent automation to stay relevant and profitable.
If you find yourself in this very common position, consider these tips to keep up with the revolution.
1. Consider an automation partnership.
For some companies, internally automating core functionalities will simply not be realistic in the near or even medium term. This doesn't imply that longer-term aspirations for BPA should be abandoned, but it suggests that a more creative approach may be necessary.
If you face serious obstacles to BPA but are otherwise ready to position yourself to move toward that larger goal, consider outsourcing as a more accessible inroad to automation. That is, consider partnering with a seasoned BPA provider that offers the technological acuity and expertise in business process transformation needed to rapidly assimilate your mission-critical functions.
This strategy offers an easier on-ramp to automation, as well as the promise of more immediate cost savings, efficiencies, and insights from improved business intelligence. Moreover, this approach may help you position your company to push for a truly integrated BPA evolution sometime in the future.
2. Test with attended automation.
If you have lingering reservations about fully automating certain aspects of your business, or you'd prefer to develop your automation capabilities incrementally, consider a machine-human hybrid approach as a way to test the waters.
This strategy is particularly effective with regard to business functions that do not have a long history of automation, or in those areas where human intervention is necessary to generate desired outcomes. Attended automation allows humans to lean on robotic assistance or machine intelligence without requiring that workers relinquish control entirely. This approach enables you to test the potential of automation without completely disrupting existing workflows.
Attended automation isn't just useful for experimentation though. Even in small doses, attended automation can drastically improve the productivity of employees, which further justifies its value as a business tool.
3. Eliminate silos to simplify systems.
Every business aspires to destroy silos, but BPA demands it. Organizational walls that lock information away from internal stakeholders prevent companies from getting the most out of their investments in automation. The bigger the company, the more walls likely need to fall.
Prioritize the elimination of silos to pave the way for getting the most out of your push for BPA. The more information you can share between your working groups, the more effective your automation efforts will be. Quite simply, sharing more information leads to greater transformative powers through BPA, which leads to more streamlined and efficient processes, which lead to larger margins and greater profits.
The fourth industrial revolution is already upon us, and its impact on the future of business is undeniable. Some estimates of yearly investment returns on robotic process automation alone have pegged the figure somewhere between 30 percent and triple digits. Companies that work toward incorporating intelligent automation, machine learning and AI-assisted analytics into their business systems as quickly as possible will find themselves poised to reap the greatest benefits. Those that do not take advantage of the latest developments in BPA will scramble to keep up.
Everyone knows at least one person who was brilliant and talented at what they did at work. Ambitious as they were, they worked tirelessly to succeed. They overcame obstacle after obstacle, burning the midnight oil to get the job done. Then, one day, their constant hard work comes to a screeching halt. They got sick. They became depressed. It happens, and it's a classic case of work burnout.
All those nights of constant work may have been good for their career, but they were not good for their work-life balance. Sadly, people often only realize the importance of balance when it's too late.
Are you wondering if you're working too much or if you need a better balance between your job and your home life? Use these five tips to help you find the right balance for you.
1. Look at your family life and health to determine whether you need to scale back at work.
Have your family members mentioned that they never see you? Can you recall the last time you sat down to dinner with them, sans work-related phone calls? The first thing to suffer from a bad work-life balance is your home life and your self-care routine. If you're isolating yourself from loved ones, it may be time to ask for a break.
2. Have set work hours, and stick to them.
This may be one of the hardest tips to follow, but it works. If you say you work from 9 a.m. to 6 p.m., keep your hours set in stone – regardless of what your bosses may say. Anything that's pressing enough to have someone call you during non-work hours can be resolved the next day.
3. Prioritize but have deadlines.
Everything has a different priority. Paying your rent is a priority, but is getting your shoes shined a priority too? If you work in an appearance-conscious industry, it might be. Therefore, have deadlines for each task you have to accomplish. If you couldn't finish a task today, make an effort to complete it before your due date or re-evaluate the priority of that task in line with the other tasks and to-do's on your list.
4. Invest in time-tracking tools and scheduling.
The people who have a lot of work on their plate don't schedule things by saying something like, "Yeah, anytime Wednesday is fine." They plan everything down to 10-minute blocks.
5. Go ahead, take that holiday.
You're not a bad employee or a slacker for wanting to take time off every once in a while. Everyone deserves a rest from time to time, and studies show that resting is beneficial to your productivity and health. So go on, take a break – you've worked hard and deserve it.
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Small businesses employ fewer than 500 people and make up 99.7 percent of the businesses in the United States. Of those small businesses, about 50 percent fail in the first four years because its owners are faced with many challenges that make it difficult to continue operations.
One of the most fundamental of these hurdles is insufficient cash flow. These small businesses often turn to Customer Relationship Management (CRM) software to earn and sustain a higher return on investment. Rather than being a “relationship builder” that it was originally built to be, CRM is now a bloated catch-all playing servant to too many masters. The four reasons outlined below will unlock the truth behind your current CRM software, and explain how it’s hindering small businesses nationwide from achieving financial growth.
Reason 1: CRM “innovations” complicate the sales process
When the first CRM system was developed by ACT! in 1986, its purpose was simple: to assist salespeople in managing multiple relationships at once, while also building a database of information on potential leads. Despite the many innovations companies claim they have made to CRM software, none of them have actually improved sales or reduced the stress of the people using them. Even with all the new features companies have included in CRM software, the average conversion rate on a lead in business-to-business (B2B) and business-to-consumer (B2C) industries is one percent or less. So why, with all of the innovation around CRM, is the conversion rate still so low?
It’s because even with the new flashy features built into platforms, they all still have the same basic features of slow operating systems -- complicated tools and text-based content -- making the system confusing and overloaded with unnecessary information. These new tools have been added not for salespeople, but their managers. Because reporting on the sales process has taken precedent over actually improving the it, CRM software does not offer the simplicity salespeople need.
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Reason 2: Overcrowded databases
In addition to dealing with the convoluted features in a CRM software, salespeople are also forced to deal with overcrowded databases. Businesses strive to drive sales, so they hire people such as sales development representatives (SDRs) to make sure the right leads end up in a database. However, the problem with hiring SDRs lies in how many contacts they deem worthy of being in the database.
Salespeople are continually adding new leads to CRM systems, which can be difficult for anyone in sales to deal with. Eventually all of those prospect leads morph into scores of data fields, reams of tabs, tags, lists and a blinding assortment of blinking, multi-colored sections that demand to be fed new information. Human brains do not have the capacity to personally connect with tens of thousands of leads at a time. According to evolutionary anthropologist Robin Dunbar, the human brain only has the capacity to develop meaningful relationships with 150 people. That being said, human connection and interpersonal communication make a major impact in all facets of life, including sales. With the technologies available today, making those connections is nearly impossible.
A larger database looks great for the marketers charged with generating more leads, but it can be detrimental to the people who are actually using it: the sales department.
Reason 3: Inability to qualify good leads
In sales, knowing the right people to reach out to and the right time is crucial. With the tools salespeople are using today, knowing who to reach out to is nearly impossible. Research shows that 80 percent of sales require five follow-ups after the initial contact, but 44 percent of salespeople give up after the first attempt.
Salespeople are taught to grow their business by continuously adding new leads, however, they should focus on closing sales with older leads because those individuals are more likely to follow through with the purchase. The number of leads that rollover to the next year and go untouched are at an all-time high. Salespeople are not able to reach them because they are focusing on new contacts who they believe will lead to more closed deals. CRM software has made salespeople impersonal and stressed as a result of its complicated and ever-changing nature.
Reason 4: A software cannot manage a meaningful relationship
CRM tools encourage users to manage relationships through a software - and that’s not how meaningful relationships are built. In order to boost sales, business owners need to start seeking out tools that take the “R” out of CRM and simply focus on Conversion Management. Small businesses should not invest time and financial resources into managing a tool that’s meant to manage relationships with potential leads, that is something that should be done in an “offline” human connection. Businesses need tools that will help move existing relationships into closed deals.
What does it take to build a personal connection in a professional setting? Salespeople must start stepping out of their comfort zone and begin asking questions outside of their usual script. It’s important to actually listen to sales leads and ask follow-up questions, to not just talk at them but with them. To break down barriers and increase the likelihood of closing a sale, businesses must be willing to treat leads as people and not just as another name in a multi-layered database.
Instead of stunting financial growth and personal connections with leads, small business owners should find ways to improve both. It’s time to evaluate what is and is not working for the existing business model, starting with taking a hard look at CRM systems. Businesses do not accidentally grow -- that’s how they fail. Take control of your company’s success to prevent it from becoming one of the many businesses that fail in the first four years.
Throughout my career, I have always been motivated by success. It's not that money isn't important or that recognition doesn't feel good, but success is actually what drives most of us.
For instance, in high school, I ranked 35 in a class of 150 students, which earned me a place in the honors class – as number 35 out of 35 students. From there, I was determined to work harder than anyone else in that class because everyone was smarter than I was.
That's when I learned my first lesson in leadership: You must find balance between working hard, finding success and having fun while doing so.
Without hard work, there is no success; without success, there is no fun; and without fun, there is no hard work. It is a virtuous cycle that carries us to ever higher levels of achievement. Here's how to use it while becoming a better leader.
Lead and follow at the same time
At work, whenever a job needed to be done and no one was doing it, I did it. Only once out of dozens of times, I was told to stop because it wasn't my job. The other times, I learned something new and was recognized as a leader and a great follower.
It was easy for management to pick me for promotion when the time came – I was ready and capable of doing the job. In fact, even my peers applauded my promotion to become their boss, because they knew I had earned it. I was starting to learn the value of balance in leadership.
I always volunteered for the big and challenging assignments in my career and made it a point to learn from mistakes instead of repeating them. Success only confirms that we are right; we learn nothing new. A mistake, however, tells us that our understanding is wrong or incomplete and that there is something to learn.
Strive to be the expert in anything you do, and make learning part of your everyday activity.
The turning point, for me, came when our biggest manufacturing plant was in jeopardy of closing. I asked for the opportunity to turn it around. At first, my boss thought I was crazy, but the next day, he gave me the job. I used that opportunity to test and expand my skills as a balanced leader.
Balance was critical because it was a large and complex facility that presented many different leadership challenges. If I was to succeed, I would have to be flexible enough to apply the right leadership style to each situation. My model worked, and we turned that plant around in three years.
When a leader is in balance, they optimize the performance of their team to achieve results that often exceed expectations. Balance is defined by a trade-off between the benefits and costs associated with any decision or behavior. The optimal decision point is achieved when the costs are minimized, and the benefits maximized.
Achieving balance is a dynamic process, requiring constant monitoring and adjusting. Like riding a bicycle, the rider must watch for potholes and curves in the road ahead, then be flexible to adapt so as not to lose balance. The optimal balance point today will surely be different tomorrow.
This is one of the reasons why even successful leaders sometimes fail. When circumstances change, they may not notice or perhaps discount the need to adjust their leadership style to compensate. The result is loss of balance, suboptimal performance and eventual failure.
We can all benefit from being better leaders and followers, and we can do so with balance and passion.
Edited by Sammi Caramela.
About the Author
Leonard W. Heflich is an innovator, writer, teacher and baker in the process of reinventing his career after 42 years in the food industry, with 34 of those years in baking. He has published two books in the past year: "Balanced Leadership: A Pragmatic Guide for Leading" and "Live as Long as You Dare! A Journey to Gain Healthy, Vibrant Years." Len is a problem solver and innovator. He considers leadership, taking care of one's health and innovation to be the three most compelling challenges facing us today, hence the two books he has written. He is actively writing a third book that will focus on innovation.