How U.S. Cities Are Using AI to Solve Common Problems

by Mathis Bitton, Elizabeth Haas and Peter Hirshberg

Summary. AI has the potential to revolutionize local government operations, but American cities face significant challenges in adopting these technologies. In interviews with more than 150 local leaders across dozens of U.S. cities, respondents consistently reported three problems: sclerotic and siloed bureaucracies, burdensome regulations, and risk aversion. AI, however, could be a turning point. Cities have successfully deployed AI to: automate tasks, make better decisions with data, and better engage with the community. The key factors in the cities that have done this successfully are: clear vision and strategy, systematic de-bottlenecking, public-private partnerships, and adherence to governance principles.


America leads the world in innovation. The United States has the highest-valued startups, the most prestigious universities, the most prolific researchers, the best AI companies, and the most venture capital funding. American cities, however, are the exception. In the latest ranking of smart cities by the World Competitiveness Centre, no American city made the top 30 — and only New York, Boston, and Washington, D.C. made the top 50.

There are a number of reasons why government — and local government in particular — operates differently than businesses. Three years ago, we started to work with the United States Conference of Mayors to understand which of these reasons matter most. In particular, we tried to find the main obstacles standing in the way of AI adoption at the local level.

In interviews with more than 150 local leaders across dozens of U.S. cities, respondents consistently reported three problems:

  • Sclerotic and siloed bureaucracies (83%), or the way in which local departments tend to work in isolation from one another, with few incentives to reform themselves.
  • Burdensome regulations (44%), or the web of rules that prevent cities from acting boldly, including purchasing requirements, building codes, and other regulations that make it practically impossible for cities to work with startups.
  • Risk aversion (31%), or the sheer fear that often animates local leaders when it comes to new technologies — significantly more than in business leadership. Mayors know that technology can help their cities, but they remain afraid of experimenting with tools about which they know relatively little.

Combined, these three roadblocks have made local governments remarkably stagnant in an otherwise dynamic and entrepreneurial country.

The explosion of AI capabilities, however, could be a turning point. In this article, we look at the ways in which cities can use AI and provide a framework for local leaders looking to transform their cities.

How Cities Can Use AI

Broadly speaking, cities are pursuing AI projects in three broad categories: automating tasks (mentioned by 76% of respondents), making better decisions with data (41%), and engaging the community (23%). In what follows, we explore some of the main use cases in each category and explain how these capabilities have the potential to make cities both more efficient and more responsive to their residents’ needs.

Automating tasks

Today in Los Angeles, a small construction company needs an average of 14 procedures, 105 days, and $85,841 to obtain construction permits. In San Francisco, it needs an average of 19 procedures, 184 days, and $108,063 to obtain the same permits. Entrepreneurs looking to open a restaurant, salon, or shop in San Francisco must navigate over 25 different requirements spanning building codes, fire codes, zoning ordinances, ADA rules, and more. Just applying for these permits costs on average thousands of dollars and takes more than six months before they can break ground.

Tomorrow, AI could automate most of these processes away — and hundreds more. Every permit application could be filled or reviewed in a matter of minutes. Every Excel spreadsheet could be analyzed and updated in real time. Data could be shared from one agency to another without human supervision. In the cities we’ve studied, bureaucratic automation was by far the most common use of AI. And for good reasons: Of all capabilities, it remains the least expensive and easiest to implement.

For example, in Honolulu, Hawaii, the Department of Planning and Permitting has cut the time to complete residential permits by 70% with AI. The head of the department is now expanding the initiative into a new platform “not unlike TurboTax” that will “ensure permit applications meet all necessary requirements before being transferred for [automatic] completion.” If successful, the project could automate hundreds of tasks, save thousands of hours, and save millions of dollars in the long run. The initial project cost $200,000 — about one-tenth of the benefits expected in the first five years of implementation.

Some might fear that cities will replace their employees with algorithms, but in practice we’ve seen that AI makes day-to-day operations more efficient without removing the need for human input. Instead of filling out forms or spreadsheets, civil servants can dedicate their time to bigger-picture and larger-impact projects for their cities. As Reno’s mayor Hillary Shieve told us, “We’re not using AI to replace our people but to make them focus on what matters.”

Making better decisions with data

Cities already collect enormous amounts of data on the urban environment; with sensors and cameras, they monitor air quality, noise, utility consumption, traffic density, parking violations, construction activities, and environmental conditions. The problem is, they seldom take advantage of this wealth of information. The vast majority of our interview respondents (over 87%) admitted that their departments are sitting on data that they lack the skills or resources to process, let alone leverage in real time.

That’s what AI brings to the table. It can handle data on a much larger scale, at a much lower cost, than human beings. It can integrate diverse streams of data — cameras, sensors, surveys, language, etc. — and analyze, predict, simulate, and forecast trends in real time. It can also get better with time, detecting hidden patterns and adapting to changing circumstances. In short, it can help cities make smarter decisions about and provide enhanced services in every domain of urban life. These include:

Transportation

  • Make traffic signals more responsive
  • Implement dynamic pricing on roads and parking
  • Re-route transit services in real time
  • Communicate predicted bottlenecks to residents

Infrastructure

  • Predict maintenance or upgrades
  • Optimize resource allocation to extend asset lifetimes
  • Forecast costs, timelines, and impacts of capital projects
  • Optimize rollout plans to minimize disruptions

Crisis response

  • Forecast potential crisis scenarios to take preemptive measures
  • Model impact, timing, and scale to guide the allocation of supplies
  • Automate communication strategies with at-risk populations

Social services

  • Identify intervention points
  • Target the provision of preemptive measures
  • Simulate resource constraints against evolving demand
  • Adjust eligibility requirements in real time

Urban planning

  • Model decade-long scenarios
  • Simulate the impact of potential policies and investments
  • Evaluate alternative paths of development

Innovative cities are already launching pilot programs in each of these areas. In transportation, Seattle has partnered with Google Research’s Green Light initiative, which uses AI to manage signal timing to improve the flow of traffic. In a few months, the city achieved $10,000 of delay savings in eight locations. In infrastructure, Deloitte has found that AI-driven maintenance reduces infrastructure repair costs by 25% in more than a dozen cities. In crisis response, California has successfully used AI to monitor over 1,000 cameras to detect wildfires. Within the first four months alone, AI detected 77 wildfires and proved so successful that TIME magazine recognized the program as one of its “best inventions of the year” in 2023. In social services, the city of Allentown, Pennsylvania has saved an estimated $1 million in taxpayer dollars by streamlining incident investigations across 21 departments with AI. And in urban planning, researchers at Tsinghua University in China have just developed the first AI planning system capable of outperforming human architects.

In all of these examples, the city in question was already collecting the right data — and already had systems in place to do so — but could not process it. AI just gave them the opportunity to harness masses of data that they would otherwise leave untouched. And this kind of intervention has the potential to reinvent urban decision-making for the better.

Engaging the community

The final capability that AI brings to cities has to do with the local government’s relationship to residents. In recent years, the “smart city” paradigm has — rightly — been criticized for its technocratic bent. Advocates of smart cities often push for top-down innovation, neglecting the populations that new technologies are here to serve. Fortunately, AI can help make cities more, not less, democratic by simplifying access to both information and services. For example:

  • Answering questions: In Raleigh, North Carolina, AI chatbots are able to manage 90% of calls to administrative agencies, which frees up time for operators to answer more complicated or time-sensitive inquiries.
  • Filling out documents: In more than a dozen American cities, AI chatbots are helping residents fill out hundreds of documents. The bot asks simple questions and fills the form.
  • Translation: AI translation companies like Unbabel can translate emails and web pages into more than 20 languages. With a team of human editors to verify for accuracy, they complete this service at $0.02 per chat, a much cheaper rate than traditional translation services.
  • Interactive modeling: The city of New Rochelle, New York, has built a platform that combines visual computing and AI to model changes to the built environment. Citizens can evaluate proposed changes, make suggestions of their own, and see what their ideas would look like in practice.
  • Dynamic services: The MIT Media Lab has worked on a platform that adapts zoning laws to the real-time preferences and needs of residents. The platform surveys locals on their preferences, collects data on living costs and other relevant variables, and updates zoning practices to evolving circumstances. The Lab has worked with Hamburg on an experimental project with this platform, which accelerated the construction of more than a thousand houses by more than a year.

The last two use cases are more experimental, but they have the potential to alter the way in which cities interact with their residents. In the not-so-distant future, we could imagine residents collaborating on all sorts of projects, with AI aggregating their contributions into coherent recommendations for the city. We could also envision dynamic public services — not just zoning laws, but also social programs, policing practices, or building codes — adapting to the real-time preferences and evolving needs of residents. If generalized, these capabilities would make cities altogether more responsive to popular input, providing a democratic counterpoint to the technocratic excesses of the “smart city” paradigm.

Key Success Factors

The question then becomes: How should cities think about integrating AI into their operations? Across our case studies, four success factors stand out:

Vision and strategy

First, a successful AI strategy fits into a broader vision for the city, with a clear set of priorities. In a lot of our interviews (about 70%), local leaders admitted that they experimented with AI without a clear goal. They launched pilot projects here and there, hoping that some would prove successful and scale. This approach costs more and delivers less.

Successful cities begin by identifying their most pressing needs and focus their use of new technologies accordingly. In other words, AI should not be implemented for its own sake — it is not a shiny object but a toolkit to solve specific problems. The question is not “How can we use AI?” but “What problem are we trying to solve, why, and how could AI help?” If AI is not the best means to tackle a given challenge, its implementation should not be forced. Conversely, if AI does prove useful, the development process should follow four simple steps:

  1. Align on vision: Identify challenges and design a strategy with clear priorities.
  2. Pilot: Test value and feasibility with limited users in a controlled environment.
  3. Refine: Expand use to additional users, optimizing the technology along the way.
  4. Scale: Roll out the technology to capture all the value.

The first step is often the most important. In every case, local leaders must begin by understanding their own context. What are the city’s needs and challenges? What will the city look like in 10 years? In New York City, for instance, former Sidewalk Labs CEO Dan Doctoroff and Robin Hood CEO Richard R. Buery, Jr. have advised the city to 1) automate citizen requests, 2) democratize access to information with chatbots, and 3) optimize traffic to tackle congestion because these were three consistent sources of frustration in citizen surveys, standing in the way of future-focused growth. In Las Vegas, the city created an “innovation district” for experimentation with autonomous vehicles to cement its position as a rising hub for transportation technology. Other cities that face a housing shortage might similarly prioritize AI-powered planning to accelerate construction and/or zoning.

Overall, developing an AI strategy is not about having a document called “an AI strategy.” It’s about establishing clear goals and priorities for the city, and then seeing how AI can help turn that picture into a reality.

Systematic de-bottlenecking

AI can improve city operations across every domain of urban life. But burdensome regulations and perverse incentives are often standing in the way. In our interviews, we consistently found that local bureaucracies resist transformation, either because current processes do not allow radical change or because the people in place have every reason to fight change. Cities must transform themselves before technology can transform them in turn.

In practice, this means:

  • Including an innovation team in every city agency
  • Creating incentives for bureaucracies to reform themselves
  • Building an Office of Civic Innovation that rewards bold ideas with hackathons, prizes, and pilot programs
  • Changing procurement processes to let startups compete for public contracts

In short, it means ensuring that the spirit of innovation thrives at every level of government.

For instance, until a few years ago, New York City could not partner with startups because its procurement process favored older, larger companies. Two years ago, the city changed purchasing requirements to encourage pilot programs, and the results speak for themselves: In 2023, over 600 companies applied to pilot products and over 50 pilots were deployed — 10 times more than in prior years. This is but one example, but it illustrates the kind of change that cities can bring to their operations. Across the board, local leaders should identify and remove the obstacles standing in the way of innovation.

Public-private partnerships (PPPs)

PPPs foster collaboration between city governments, private companies, academic institutions, and nonprofits. These partnerships facilitate the co-development of urban solutions, enable knowledge and resource sharing, and encourage experimentation with private initiative and public backing. For AI, a technology that researchers and startups understand a lot better than local governments, PPPs are crucial to success, because each side brings complementary qualities to the table.

In our case studies, we noticed that the most successful cities formed long-term partnerships rather than project-specific, short-term contracts, formalizing their arrangements in new institutions such as “advisory boards” or “smart city initiatives.” For instance, the city of Columbus, Ohio, has established the “Smart Columbus Initiative,” which involves the city, technology companies, universities, and community organizations. In five years, the initiative has developed AI-powered mobility solutions, logistics systems, and data-driven services.

Cities can also partner with venture capitalists to secure funding and support for their ventures. Along these lines, Toronto, Canada, has established the Toronto Innovation Acceleration Partners program, which connects startups with public-sector partners and university researchers to accelerate the development of civic-minded innovations. In the last few years, the program has not only turned Toronto into Canada’s new startup hub, but also led to dozens of AI projects in local government. Here as in most of our cases, all parties benefit from each other’s involvement.

Governance principles

Lastly, every step of the way, cities must enforce ethical AI principles.

First, municipalities should establish AI oversight boards to audit data sources and algorithms for accuracy and bias. Every agency will need clear processes to make sure that the datasets used to train AI are representative and don’t reinforce historical inequities related to demographics like race, gender, age, or ability status.

Second, every AI system interfacing with residents must be tested to ensure accessibility and inclusiveness across languages, dialects, and cultures. And any AI decision-support system must remain under human oversight for local leaders to retain full responsibility for their decisions.

Third, cities will need to obtain consent and protect the privacy of their citizens. Privacy protection should be engineered from the ground up rather than tacked on. Citizens deserve transparency into what data is collected, how it is secured, and how it is used.

Fourth, cities need to get citizen input along the way. At a minimum, they should organize grassroots campaigns to demystify AI, foster technological literacy, and understand popular concerns. At best, cities will also bring citizens into the development and deployment process – drawing on their feedback to ensure that the technology serves the community on its own terms.

With all this in place, cities can build sustainable models that inspire confidence rather than fear or skepticism. Only by putting AI ethics at the center of their approach can cities prove that they can be “smart” and humane at once.

A Call to Action

AI has the potential to reduce the size of bureaucracies and reinvent the way in which local governments make decisions, deliver services, and serve their citizens. But only with the right strategy can this potential translate into real solutions.

In some cities, the change will be evolutionary: Local leaders will seek incremental improvements, building AI on top of existing structures to deliver value sooner rather than later. In other cities, the change will be revolutionary: Local leaders will reimagine existing structures as if they started from scratch. Either way, AI can inject a culture of innovation within city hall that spills over far beyond city hall. If American cities heed this call, they will make their way back to the top of the smart cities rankings.

Ultimately, only with the right kind of leadership can cities harness the capabilities of AI. If technical acumen can open new doors, only local leadership can ensure that these doors lead to a better place. With more imagination, and less fear of innovation, cities can act as the architects of a new social contract between citizens and their communities.

Presidential Debates On Twitch Highlight New Generation Of Politics

by Elizabeth Haas Edersheim and Lee Igel

Twitch viewers of the debates between United States President Donald Trump and former Vice President Joe Biden were more engaged in the substance of the matter than those following along through CNN, FOX News, and the New York Times. Despite the headlines in traditional news sources, the big news is that fans of esports and gaming are engaging in the 2020 election in a new way and at a new level. If elected officials are truly serious about focusing on the next generation and its impact on the future, why aren’t they paying attention to where that group is now?

More than 73 million people tuned in to television broadcasts of the first presidential debate between Trump and Biden. More than 63 million tuned in for the second round. While many observers were weighing in on the television numbers and viewer reactions, they missed out on the over one-million viewers on live-streaming platforms such as Twitch.

Research conducted for the Mayors ESports Network highlighted a fundamental difference in the conversations and exchanges taking place during the debate among audiences across CNN, FOX News, the New York Times, and Twitch. The research, led by teams from New York University and Shenandoah University, analyzed 1,000 comments posted on each of the outlets’ sites. The differences between them–and, especially, the “mainstream” CNN, FOX News, and the New York Times and the “newstream” Twitch–are clear.

The first thing has to do with what viewers were interested in about the debate. On CNN, FOX News, and the New York Times, the discussion was about the quality, or lack thereof, of the debate and who was perceived to be winning. On Twitch, viewers were concerned about how engaging the debate was–that is, what the candidates were saying. In both debates, Twitch viewers expressed an openness to listening in a markedly different way than the viewers on any of the traditional stations.

The next thing is that a quarter of the users across all channels and platforms were disappointed in the first debate. More of the viewers on CNN, FOX News, and The New York Times showed levels of anger in the first debate and were supportive of the candidate in the second debate.  Twitch viewers, however, weren’t expressing anger or a similar emotion. They were surprised by what was being said by the candidates and supportive of what the candidates are proposing to do should they win election to the White House.  It resembled an audience that cares about tomorrow and wants facts.

There was also a real difference in the content and tone of support. On the CNN, FOX News, and New York Times channels, supportive comments were primarily for and about a preferred candidate. Meanwhile, on Twitch, support comments were primarily for and about primarily the idea being discussed on the debate stage.

For example, when healthcare alternatives were being discussed, the bulk of comments on CNN and The New York Times were supportive of Biden, while comments of FOX News were supportive of Trump. On Twitch, the comments were about the realities and prospects for healthcare plans, such as what kinds of options would be available and who would be covered.

The final thing is to take notice of: the reference points used. Commentators on CNN, FOX News, and The New York Times were trying to prove a point about a candidate or policy matter. On Twitch, commenters were posting links and screenshots of studies from reputable medical journals in an effort to learn with each other. As one member of the Conference of Mayors research team noted about what was happening on Twitch channels, “the sheer amount of information that was put out was more substantial than any other platform.”

As the debates went on, the Twitch audiences were fact-checking at a rate almost two times more, used negative references and name-calling much less, and referenced memes in conversation much more than audiences on CNN, FOX News, or The New York Times. To be sure, the language being used in comments on Twitch was decidedly more vulgar. But it showed up in a way that is a norm on the platform, in a manner that doesn’t take away from–and, to an extent, enhances–the content.

Twitch is often thought of by most people as an island where esports and gaming fanatics find entertainment. But it is actually a much larger community that seeks out engagement. Interestingly, on Twitch, the politics-related conversation is about us, while on traditional channels the conversation is about us versus them.

As the 2020 campaigns make the stretch run to Election Day, there is increasing excitement about efforts aimed at getting Millennials and Gen Z to turn out for the vote. Campaigns interested in attracting voters would do well to pay attention to where what they call “the future” are now and the ways they are engaging.

Elizabeth Haas Edersheim is an adjunct professor and Lee Igel is a professor at New York University’s Tisch Institute for Global Sport. They lead an NYU initiative with the United States Conference of Mayors Professional Sports Alliance that produces new knowledge on sports in cities.

January 1, 2018

This morning, Frances Hesselbein asked me, as she regularly does, the Peter F. Drucker question:

When you look out the window, what do you see that is not yet visible?

As I looked out the window (literally), my attention was drawn to something very visible: a curled brown leaf dancing over a snow-covered field. That brought to mind an Einstein quote:

“There are only two ways to live your life. One is as though nothing is a miracle. The other is as though everything is a miracle.”

If you don’t know Frances: You should. She rose from a volunteer troop leader to CEO of the Girl Scouts, which she turned around when it was on the brink of failure. She’s a leadership expert , who has taught at West Point and was the first woman on the cover of Business Week.

Anyway. I laughed and told Frances it feels like we are living a “back to the future” moment, like Americans did in the waning horse-and-buggy days. Frances asked, how are you seeing this in organizations? I responded: What is visible and not yet fully noticed is “Charlotte’s web” and “Marvin ear.”

Charlotte’s Web: That’s the power and promise of the connections that defines us now — artificial intelligence meshes; DNA databases, currency platforms, the innovative combinations that follow. These connections are changing our trust compasses, expanding what is possible.

An example: WhileI was staying at a hotel land tossedmy towel on the floor in the morning, I realized if I were staying at an Air BnB, I would never do this. I have an identity in an AirBnB. Charlotte would write “sloppy” in the web

Unilever CEO Paul Polman, who thinks and talks more about sustainability than all his predecessors combined, called on a network of CEOs from institutions growing beans to serving cofee. Together, they ]modified processes to save enough water for a million people every year. When he did the same thing with CEOs in Switzerland, they reduced the collective carbon footprint by a third.

And this: I recently read about the network for collective learning and scaling ideas that Teach for All is operating.

Sometimes I get frightened. Like when I read Scott Galloway’s book, The Four, about Uber paying its top executives almost $1 million an hour and its drivers $7.25 an hour. Or the prediction that Amazon will be sending me my needs every week, with a return box inside, getting smaller and smaller, as Amazon learns my needs better than I know them myself.

To anwer Frances: Looking out the window, I see a force that is changing the game, a force that can be incredibly good. And yet I see the downside: Ideas built for a time when we believed in freedom are upended. No business, no government agency is truly wired for this age. Frances, we are at a crossroads: We can harness the forces out there for humanity, or we can undermine democracy.

Marvin ear: is the cognition associated with everyone in an organization having a voice, creating tomorrow. Marvin Bower, McKinsey & Co’s founder believed that the Great Depression arose in part because employees in organizations failed to tell CEOs what customers were telling them, creating a gap in organization intelligence. Today, too, front-line associates know more about the day-to-day challenges than their bosses The truly responive organizations are embracing this.

For example Ultimate Guitar, based in Moscow, gives every employee two mentors — one in their area, such as software development, and a pitch coach, to work with on translating their ideas into experiments that are tried and from which the organization learns. Ultimate Guitar has pitch meetings once a week; every employee has to pitch at least one idea every six weeks.

Say what you want about Google’s innovations, but here’s what I like: , Employees spend 20% of their time dealing with the next challenge, not today’s routine work; the organizaiton has changed how they hire and develop eployees with an absolute emphasis on building employee voice and teams; Google has rules to help women self-promote and so forth.

Of course, Frances knew how to put all this in perspective: “Jim Collins calls those edgy companies.”

I turned the question to Frances, who was born before World War I. What is visible but not seen? “Millenals are the greatest generation of leaders since the Crucible Generation,” she said, referring to those associated with WWII.” I thought of you. Happy 2018.

Reviving Management: Don’t Reinvent Management, Apply Lessons From History To Shape Tomorrow

From corporate boardrooms to small-business incubators, from academic conferences to MBA classrooms, discussions center on re-inventing management. McKinsey and Gary Hamel are offering generous prizes for reinvention ideas.  But why re-invent something that’s been invented and practiced for centuries – from building pyramids to organizing classrooms?

As a discipline, managements can be traced to the mid-nineteenth century. Management has made possible a world in which organizations are so woven into the fabric of our lives that we take them for granted, from art museums and advertising agencies to zipper manufacturers and funeral homes.  One might even say that today a civilization is the summary of the organizations that exist. And yet so often we complain about “bad management,” “tone-deaf management” – whether we’re talking about an airline that has surly employees or a cruise ship that sinks while the captain appears to be cavorting.  Why does management constantly frustrate us?

Management has a history of creating idea after idea, a permanent revolution – Theory X versus Theory Y; lean manufacturing; the balanced score-card; the learning-organizaiton, 6-sigma and total quality control; Porter’s five-forces; re-engineering, empowering, change-management, clustering, integrative design…Today, over three quarter of a million people are studying management at the graduate level and it is the most popular undergraduate degree in the world.  Yet universities are teaching the theories of the day, not the time-tested lessons of practice.

Too often, we throw away what we know in a misguided attempt to create a new theory. For example, when Tom Peters and Bob Waterman wrote In Search of Excellence, they documented timeless principles about great management. Unfortunately, years later when several firms they featured failed to deliver, the lessons were pushed aside.   When the Dana Corp. went bankrupt in 2008, for example, the media stopped reciting lessons from visionary CEO Rene McPherson.

If the Hostess Co. had learned from Rene McPherson’s lessons, 18,500 people would still be employed and making a difference. Consider this line from McPherson: “The way you stay fresh – you never stop traveling, you never stop listening, you never stop asking people what they think.” The maker of Twinkies and Ring Dings died when management stopped challenging practices and assumptions, and the old way of doing business was assumed to be the right way – the only way.   Hostess died when it stopped respecting customers and when  new ideas were not encouraged.

We do not need to reinvent management.  We need to synthesize what we’ve learned over the decades, make it practical, and individually and collectively reapply the lessons to the challenges of tomorrow.   The biggest failure of management is the habit of taking a snapshot of a moment in time and try to reinvent theories, rather than to recognize that management is a living practice. In that way, it’s like medicine. Even the experts have a great deal to learn from past practices  and thousands of decisions, adapted to today’s context.

A great idea in management is a great idea, whether in the era of industrialization or connected-commerce. When Jim Collins wrote Built to Last, he jokingly told his publisher, “We should just call it, ‘Drucker had it right.’”  His co-author Jerry Porrassaid, “How about Waterman and Peters had it right.”

Waterman and Peters credit Chester Barnard, the pioneering AT&T executive who said corporations will collapse unless they emphasize effectiveness of people.  Chester Barnard was the Steve Jobs of the 1920s and 1930s.

Rather than embracing the next new idea, management needs access to the notions and experiments that have been tested, the practice that’s made a difference.  That’s why we have drafted an interactive tool to help managers access the best ideas.  We named it TheME, which stands for “The Elements of Management Effectiveness.”  It is available on the Web at NYCP.com or at the App store for the iPad.

Its integrated framework provides users easy access to quotes, video clips, anecdotes, and exercises for solving problems, envisioning a new venture, starting a conversation, or simply learning about management, past and present.

As Peter Drucker phrased it, from the outside, business can look like “a seemingly mindless game of chance at which any donkey could win provided only that he be ruthless.  But that is of course how any human activity looks to the outsider unless it can be shown to be purposeful, organized, systematic; that is unless it can be presented as the generalized knowledge of a discipline.” Our challenge: present management as a generalized knowledge. Management must reapply its lessons; it must revive to survive.  Take your hand to ThEME.   Let us know the lessons that are missing, as well as those lessons that you find useful. Tell us how you are using history to create tomorrow.