Informed i’s Weekly Business Insights

Engineering has become a source of pride and power in China. The country is an “engineering state”, according to Dan Wang, author of “Breakneck”, a lucid new book about the country. Whatever labels the country attracts, its true commitment is to infrastructure and industry, bridges and widgets, building and making—as well as some dystopian exercises in social engineering, such as the former one-child policy and the zero-covid regime.

China’s predilection for engineering now has fresh urgency. China wants to master “chokehold” technologies, such as advanced chipmaking equipment, that it can no longer count on importing from America and its allies. Students are responding. Among regular undergraduates, 36% sign up for the discipline. The share has been increasing in recent years, even as university enrolments swell. Chinese commentators argue that the country’s advances in technology represent an “engineering dividend” to replace the demographic dividend it reaped in generations past.

But there are some economic forces which even the engineering state cannot bend to its will. A leadership and student body stuffed full of engineers has not prevented manufacturing and construction declining as a share of GDP, the result of deep-seated trends in productivity and demand.

Economists have long argued that industrialisation is “hump-shaped”. As workers move from farms to factories, manufacturing grows as a share of the economy. But as people grow richer, they tend to switch their spending to services, and manufacturing recedes. These trends can be amplified by price changes. Manufactured goods often become relatively cheap, thanks to rapid gains in productivity that are not matched in other parts of the economy.

It seems plausible that engineering’s appeal is also hump-shaped, rising as manufacturing gains in importance, then falling as a country deindustrialises. The discipline does seem most popular in upper-middle-income countries like Malaysia. It also looms large in countries with a communist legacy, like the former Soviet republics. China is not an outlier in the international data, which also include vocational education. And it, too, seemed to have crossed a hump in this century’s early years: the share of regular undergraduates enrolling in engineering fell from 36% in 2001 to under 32% from 2004 to 2011.

China’s leadership once seemed at peace with this pattern. Its 13th five-year plan, covering the years from 2016-20, set a goal of increasing the share of services in GDP from 50.5% in 2015 to 56% in 2020. Even the leaders themselves seemed to embody this evolution. The number of engineers in the highest ranks of the party declined. They gave way to students of management, social scientists and even lawyers. In 2013 Cheng Li, then of the Brookings Institution, an American think-tank, wrote about the “rapid rise” of the lawyers up China’s political ranks. “This ongoing elite transformation…will likely shape the leadership’s socioeconomic and political policies,” Mr Li wrote.

But it did not. By the end of the decade, China’s leaders became newly determined to resist the turn away from manufacturing. In Donald Trump’s first presidency, export controls almost crippled some of China’s most prominent technology firms, including ZTE and Huawei.

In response Mr Xi insisted that the country must build a “complete” industrial system that would make it less dependent on others and others more reliant on it. The five-year plan approved in early 2021 dropped the commitment to increase services’ share of GDP and promised instead to keep the share of manufacturing stable. The percentage of students enrolling in engineering was already rising again. The number of engineers among full members of the Central Committee also rose..

None of this turn in policy, however, seems to have arrested industrial woes. The prodigious output of China’s manufacturing and construction industries is struggling to find buyers. Newly built homes are sitting on developers’ books unsold. Factory-gate prices for industrial products have been falling for almost three years. And although students are happy to enroll in engineering degrees, that does not mean they are equally keen to get their hands dirty. According to a survey last year by Zhaopin, a recruitment agency, only 8% of students want to enter manufacturing. (Over a quarter want instead to go into IT, betraying a preference for bits over bolts.) Even among those who studied science or engineering, only 37% pursue engineering-related careers.

Stanford University holds down the top spot among full-time MBA programs at US schools, as it has for the past six rankings. At first glance, it might seem little has changed for the schools on this year’s ranking—students again have weighted the career prospects available to them, post-degree, as a top interest. Consulting, technology and finance account for the biggest share of those starting new jobs within three months of graduation. But shifts are underway globally that could result in long-term, fundamental changes to these programs in the years ahead, as schools outside the US start to assert greater dominance. All this poses an intriguing question: Is the future of business education starting to move beyond USA, even if slowly?

New this year to the list are two schools: TAP Management Institute, in India; and Abu Dhabi University College of Business, founded more than 20 years ago in the United Arab Emirates. The regions this year have been updated, to make way for the latter addition as part of Europe & the Middle East, along with US, Canada and Asia-Pacific.

For most graduate business students, getting an MBA is largely a path to getting a job you want, one with more responsibility and greater earnings potential. Consulting, finance and technology are the top industries for pulling in B-school graduates, but opportunities await in several of the other fields we track, notably health care. Artificial Intelligence is creating some instability across the board, redefining (or in some instances eliminating) roles typically filled by B-school graduates.

Business school is pricier and less profitable for students today than it was in 2024, according to Bloomberg analysis. The latest update to the Bloomberg ROI calculator finds that return on investment fell at 4 out of 5 US schools.

AI is bringing the largest organizational paradigm shift since the industrial and digital revolutions. This new paradigm unites humans and AI agents—both virtual and physical—to work side by side at scale at near-zero marginal cost. We call it the agentic organization.

McKinsey’s experience working with early adopters indicates that AI agents can unlock significant value. Organizations are beginning to deploy virtual AI agents along a spectrum of increasing complexity: from simple tools that augment existing activities to end-to-end workflow automation to entire “AI-first” agentic systems. In parallel, physical AI agents are emerging. Companies are making strides in developing “bodies” for AI, such as smart devices, drones, self-driving vehicles, and early attempts at humanoid robots. These machines allow AI to interface with the physical world.

The agentic organization will be built around five pillars of the enterprise: 

1. Business model.  In the agentic era, companies will gain a competitive advantage by getting closer to customers via AI channels to offer real-time hyperpersonalization, streamlining processes to become AI-first, and building a walled garden of proprietary data as their superpower. AI-native start-ups and agentic companies can potentially disrupt industries, with a fundamentally different level of productivity (revenue per employee), cost decoupled from growth, and greater speed to market and innovation.
2. Operating model.  In the agentic era, how organizations are built and operate will evolve as much as the products or services they deliver. Work and workflows will be reimagined as AI-first, and operating models will evolve to flat networks of empowered, outcome-aligned agentic teams.
3. Governance.  In the agentic organization, governance cannot remain a periodic, paper-heavy exercise. As agents operate continuously, governance must become real time, data driven, and embedded—with humans holding final accountability.
4. Workforce, people, and culture.  In the agentic organization, humans will move from executing activities to owning and steering end-to-end outcomes. That shift demands new profiles with different skills and a culture that provides cohesion and purpose.
5. Technology and data.  In the agentic organization, technology and data will get democratized, supported by an agentic AI mesh. Agent-to-agent protocols will make integration across systems, machines, and humans easier and cheaper. Successful scalers will balance build-versus-buy decisions based on sources of distinctiveness and competitive advantage, avoiding technology or vendor lock-in so they can adapt quickly to a fast-evolving offering landscape.

The most frequent question the authors heard in their discussions with executives was, “How do I start?”  According to the authors they believe that companies that want to secure a competitive advantage in developing an agentic organization should think boldly, move fast, and go deep.  The authors encourage leaders to think through three radical shifts to make a step change in how to transform for the agentic era:

1. From linear to exponential: While technology develops exponentially, organizations and operating models typically evolve linearly, which can limit how much value an organization can ultimately capture. Don’t let this happen. Leadership teams will need to take bold stances in adapting operating models toward the agentic organization—replacing functional silos with cross-functional autonomous agentic teams, redesigning incentives and support processes to enable the change, and investing in required capabilities.
2. From technology-forward to future-back: Delegating the agentic transformation to your technology leader, as you would with a software deployment, will not suffice. Leaders need to envision the organization of the future, its full value potential with AI-first processes and a hybrid human–agent organization—and then work backward to identify the places to begin. You can only learn by doing, not by reading books or talking about it on the golf course. Bringing this to life by boldly reimagining one end-to-end domain will go a long way in building the organization’s learning muscle. And in parallel, leaders should start planning for and building the scaling enablers beyond their first lighthouse.
3. From threat to opportunity: Leaders may feel apprehension about agentic AI’s impact on day-to-day operations. It is critical for executives to continuously engage with employees about the new possibilities that this technology can unlock, not just for the organization’s growth and purpose, but also for them as professionals. Overinvesting in upskilling beyond basic literacy—as well as change management, incentives, budget, communications, and performance management to support the transition—will help pave the way.

Concretely, leadership teams can start by taking these steps: making agentic AI a prominent part of the top team agenda; outlining the CEO’s vision for creating an agentic organization; ramping up an AI center of excellence; upskilling people; and rewiring one or two lighthouse domains1 to launch agentic processes quickly and “learn live.”

It certainly wasn’t the savior’s welcome Lip-Bu Tan may have expected. It was March 2025, and he had just been named Intel’s CEO, lured back after resigning from the company’s board of directors seven months earlier. Intel was drifting, overseen temporarily by two executives after the previous CEO abruptly retired. Now, for the first time, Tan was addressing online all Intel employees globally. They were looking for straight talk—and if they felt they weren’t getting it, the Intel culture would let them say so.

Tan “was immediately asked, ‘Why did you quit [when he resigned from the board]—and now you think you’re going to come back and save us?’” Tan’s answer that he was dealing with personal things did not assuage the crowd. The veteran employee says side chats immediately lit up with criticism. “They were very frustrated with his answer.”

If Tan was sweating then, the heat cranked up considerably in August, when Arkansas Sen. Tom Cotton alleged that Tan controlled Chinese companies and had a stake in hundreds of Chinese technology firms, some of which reportedly had ties to the Chinese army. President Trump quickly posted on Truth Social that Tan “is highly CONFLICTED and must resign.”

Yet after Tan met with Trump four days later, the winds started to shift for Intel. The two seemed to hit it off, and not long thereafter they struck a nearly unprecedented deal: Intel would send 9.9% of its stock to the federal government, and the U.S. would convey $8.9 billion to Intel. Then, in late September, the world’s most valuable company, Nvidia, agreed to invest $5 billion in the chipmaker. Under their agreement, Intel will produce a broad range of new chips combining technology from both companies, with Nvidia buying some of the chips.

Craig Barrett, a former Intel CEO, told Fortune recently that this model—customers putting new capital into the company—could save Intel, which desperately needs cash. “The only place the cash can come from is the customers,” he says. “They are all cash-rich, and if eight of them were willing to invest $5 billion each, then Intel would have a chance.” In addition to Nvidia, those companies would likely include Apple, Broadcom, Google, Qualcomm, and a few others that might want a second source of high-value chips that are otherwise available only from TSMC, the Taiwan-based chipmaker that is the world’s largest.

Intel, a once-great company, seems to have found itself a plausible shot at redemption. Depending on how its leaders execute, it will go down in history as a turnaround for the ages or a case study in mismanagement and government overreach. Either way, it will also be much more—because without anyone intending it, saving Intel isn’t just vital for the company’s stakeholders. Its survival will have a profound effect on America’s national security.

How America’s leading maker of chips, a pioneer of Silicon Valley, got to this dark place was by missing not just the next big thing, but the next big things. After its glamour years furnishing chips for personal computers in the 1990s, Steve Jobs wanted Intel to make chips for Apple’s iPhone when that was just a concept, but the company declined. The company never created a successful graphics processing unit, a type of chip then used for games but now adapted and used for Nvidia’s world-changing AI chips, which are manufactured entirely by TSMC. In the 2010s Intel declined steadily under a series of wrong CEOs. Revenue peaked in 2021 and has fallen sharply since. Wall Street analysts project this year’s revenue will decline again.

Today seemingly no one at Intel denies the company is a rescue project. “Intel has a lot of work to do in catching up and participating in the AI transformation,” says Sachin Katti, Intel’s chief technology officer and AI leader, a Stanford professor who came to Intel three years ago. One of his top priorities is “reinvigorating the engineering and technical culture that made Intel really successful. Intel needs to rebuild the momentum it seems to have lost over the past few years.”

The problem is that Intel must transform a downward spiral into an upward spiral, which is rarely easy. For most of the company’s life it attracted some of the world’s most brilliant engineers and scientists. But as the company declined, it became less alluring to the world’s brainiest 1%. Now Intel is turning a bug into a feature. The pitch: If you come to Intel now, “you’re not joining the company at its highest,” Katti says. “You’re joining it to build it back to its glory days.

Another ditch Intel must climb out of is financial. S&P Global in December downgraded Intel’s credit to the lowest investment-grade rating, BBB, with stable outlook; more recently, Fitch in August downgraded it to BBB with negative outlook. Institutional Shareholder Services’ financial assessment business, ISS EVA, which covers 28,000 businesses globally, puts Intel’s overall financial quality in the fifth percentile.

It’s all preparing the ground for production of leading-edge chips once again. But to do that, even after Tan’s much needed belt tightening, virtually everyone inside and outside the company agrees on one thing: Intel will need a ton of money from outside sources.

“It all hinges on whether they can execute the process,” says Stacy Rasgon, an analyst at Bernstein. “Process” is the industry’s term for all the steps in making a chip. “What customer is going to bet their future on Intel if they’re not 100% confident that Intel can deliver?” Specifically, Intel must prove it can deliver leading-edge chips in high volume, on time, at spec, at acceptable cost. “That’s table stakes,” says Rasgon. Intel must also clear an extra hurdle: its own reputation for poor performance. As Rasgon notes, “Betting on their failure has been a pretty good bet for the last 10-plus years.”

A confusing contradiction is unfolding in companies embracing generative AI tools: while workers are largely following mandates to embrace the technology, few are seeing it create real value. Consider, for instance, that the number of companies with fully AI-led processes nearly doubled last year, while AI use has likewise doubled at work since 2023. Yet a recent report from the MIT Media Lab found that 95% of organizations see no measurable return on their investment in these technologies. So much activity, so much enthusiasm, so little return. Why?

In collaboration with Stanford Social Media Lab, the authros research team at BetterUp Labs has identified one possible reason: Employees are using AI tools to create low-effort, passable looking work that ends up creating more work for their coworkers. On social media, which is increasingly clogged with low-quality AI-generated posts, this content is often referred to as “AI slop.” In the context of work, the authros refer to this phenomenon as “workslop”, defined as workslop as AI generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task.

Here’s how this happens. As AI tools become more accessible, workers are increasingly able to quickly produce polished output: well-formatted slides, long, structured reports, seemingly articulate summaries of academic papers by non-experts, and usable code. But while some employees are using this ability to polish good work, others use it to create content that is actually unhelpful, incomplete, or missing crucial context about the project at hand. The insidious effect of workslop is that it shifts the burden of the work downstream, requiring the receiver to interpret, correct, or redo the work. In other words, it transfers the effort from creator to receiver.

If you have ever experienced this, you might recall the feeling of confusion after opening such a document, followed by frustration—Wait, what is this exactly?—before you begin to wonder if the sender simply used AI to generate large blocks of text instead of thinking it through. If this sounds familiar, you have been workslopped.

Here’s what leaders need to know about workslop—and how they can stop it from gumming up the works at their company.

The Workslop Tax.  Cognitive offloading to machines is not a novel concept, nor are anxieties about technology hijacking cognitive capacity.  Unlike this mental outsourcing to a machine, however, workslop uniquely uses machines to offload cognitive work to another human being. When coworkers receive workslop, they are often required to take on the burden of decoding the content, inferring missed or false context. A cascade of effortful and complex decision-making processes may follow, including rework and uncomfortable exchanges with colleagues.  Based on participants’ estimates of time spent, as well as on their self-reported salary, we find that these workslop incidents carry an invisible tax of $186 per month. For an organization of 10,000 workers, given the estimated prevalence of workslop (41%), this yields over $9 million per year in lost productivity.  Respondents also reported social and emotional costs of workslop, including the problem of navigating how to diplomatically respond to receiving it, particularly in hierarchical relationships.

The most alarming cost may be interpersonal. Low effort, unhelpful AI generated work is having a significant impact on collaboration at work. Approximately half of the people we surveyed viewed colleagues who sent workslop as less creative, capable, and reliable than they did before receiving the output.  Over time, this interpersonal workslop tax threatens to erode critical elements of collaboration that are essential for successful workplace AI adoption efforts and change management.

What Leaders Can Do.  If AI is everyone’s job, it is also—and foremost—the job of organizational leaders to develop guidance for employees to help them use this new technology in ways that best align to the organization’s strategy, values, and vision.

Within our exponentially volatile world, organizational growth is a constant challenge and, in turn, leading change is a critical leadership competency to answer this challenge. However, too many change efforts still fail.

Why is leading change so hard at times of growth? Many leaders bemoan employee resistance and hidden stakeholder agendas as key factors. While these can be significant factors, according to the author he doesn’t think blame can be pointed only at the employees and stakeholders. The truth is actually quite the opposite. In his experience working with companies trying to scale, the reason most change efforts fail is that leaders suffer from the hero complex – defined as a toxic mix of seeking overinflated credit (for the change) and experiencing extreme psychological ownership (of the change).

The complicating factor: Many aspects of leading change tend to bring out the hero complex — especially at times of company growth. Change puts leaders “onstage” to be lauded (or loathed) by those above (investors, senior management, partner organizations) as well as those below (employees, stakeholders). Being at the center of that stage also increases feelings of being the change’s star and, thus, owner. That can make leaders less likely to listen to others’ ideas for improvements to the change solution and/or more likely take any criticism of the change as a personal insult.

So how can you steer clear of these traps? Consider these three pieces of advice, based on real-world companies’ experiences, that will help you fight the hero complex as you scale change for growth.

1. Don’t go solo with a solution: Build a coalition of experts.  In his book Leading Change, organizational change expert John Kotter promotes the value of building a “powerful coalition” early on to lead a change initiative. However, many leaders wait to build a coalition until after they have decided on the change solution. This can feed into the hero complex because the leaders are likely to pick coalition members who understand — and/or already support — the solution rather than people who understand the actual problem that the change needs to solve.  The solution: Assemble a powerful coalition based on each person’s understanding and expertise regarding the problem the change will address — rather than their perspective on the proposed solution.  Four change coalition roles to be helpful: Technologists, Evangelists, Analysts, and Advocates or Sponsors.
2. Don’t just sell a change vision: Tell the problem’s origin story.  Many change leaders focus so much on selling a change vision (and solution) that they lose sight of the problem they are trying to solve.  Ask some basic questions as you write the problem’s origin story: What is the problem that we are trying to solve? How did we discover this problem? What pain points need to be resolved? What is the opportunity that we are trying to capitalize on? How did we come across this opportunity? More than likely, you have a plethora of internal or external customer stories that poignantly explain the problem’s pain points.
3. Don’t assume that the culture must change: Ask how the culture supports the change.  Many change leaders, again with aspirations of being the hero, assume that every change requires a transformation in organizational culture. However, this is more myth than reality.  Why do so many leaders see this as reality? These leaders focus too much on implementing a change solution rather than solving the change problem.  Once a minimally viable solution has been designed, instead of first asking “What about the organizational culture needs to transform to implement the solution successfully?” ask these three questions:  What about the solution supports the current organizational culture?  What about the current organizational culture supports the solution?  What about the solution may need to be revised to best align with the current organizational culture while still solving the problem?

In the skilled trades, career growth often comes down to one crucial decision: depth or width. Should you specialize in a single discipline—becoming the master of one—or spread your knowledge across multiple, related areas to gain versatility? This choice defines more than just a career path; it shapes how professionals are valued, how they lead, and how they adapt as industries evolve.

Specialization is the backbone of technical excellence. Specialists command respect because they solve problems others can’t. Their mastery often leads to premium rates, consulting opportunities, and demand in niche markets.  Employers and contractors alike know the cost of getting it wrong, which makes deep expertise a form of job security.  The path from blue-collar roles to corporate leadership is gaining sharper focus. Companies that once drew a hard line between ‘frontline’ and ‘executive’ are rethinking how they build talent pipelines.” That same evolution underscores why technical mastery still matters—it’s the foundation of credibility and leadership readiness.

Those who chase “width” often see the bigger picture. A generalist who understands HVAC systems, energy auditing, and building performance can manage broader scopes of work or pivot into leadership and project management.

Many seasoned tradespeople face a third path: moving from “hands-on” to “heads-up.” As supervisors, foremen, or COOs, they draw from both depth and width. Specialists make strategic leaders because they understand the precision required in the field. Generalists make strong executives because they grasp interconnectivity.

A 2024 McKinsey report highlighted that many skilled trade leaders began as technicians. Their success wasn’t accidental—it stemmed from knowing when to pivot from learning more to leading more.  

The truth is, every skilled trade needs both types of professionals.  The key is intentionality. Ask yourself:  Do I want to be the person everyone calls for one specific issue—or the one who oversees the entire operation?  Do I enjoy perfecting a craft or solving bigger systems problems?  Each answer leads to a different type of excellence. One path isn’t superior to the other. It’s about alignment between skill, ambition, and lifestyle.  Depth creates mastery. Width creates opportunity. The best professionals learn when to use both.

Universities have been helping students become entrepreneurs since before the word “startup” existed, but it wasn’t until the dot-com boom that they started taking the commercial value of campus innovation seriously. In the mid-1990s, many technology licensing offices and campus venture funds began popping up. And by the 2010s, more universities were realizing that throwing real resources behind nascent companies could stimulate local economies, bring academic work into the public sphere, and leverage powerful alumni networks.

Today, top universities are moving past lectures and pitch nights to play a more direct role in company creation. That can mean anything from providing lab space and the light-touch mentorship of an incubator all the way to running full accelerator programs that offer funding in exchange for equity. In contrast to traditional accelerators such as Y Combinator and Techstars, which sit outside the campus ecosystem, university accelerators can tap cutting-edge research, student talent, and alumni to be engines of commercialization within centers of learning.

At the University of Michigan, that effort takes the form of the Innovation Partnerships program, which functions like an incubator with industrial-grade infrastructure.  The program targets students and faculty who want to commercialize their innovations and entrepreneurs who want to make use of UM intellectual property. In the university’s 2025 fiscal year, its innovators registered 673 inventions and launched 31 startups, a new record that made its commercialization success among the best in the nation.  A key differentiator of the University of Michigan’s approach is its openness to international entrepreneurs.  

Stanford’s StartX flips the model by focusing on the team, not the intellectual property. To join, at least one team member must be a Stanford student, professor, or alum, but the underlying tech can come from anywhere, and the program doesn’t limit itself by stage or industry.  That ethos has grown into a formidable network. More than 2,700 entrepreneurs have come through StartX since its founding 16 years ago, building 1,300 companies in fields such as AI, fintech, and medicine. Alumni include the founders of three decacorns, more than 20 unicorns, and 144 other companies valued at nine figures. StartX runs three 10-week programs per year that include up to 150 classes and events. And program alumni stay connected through an active email network, regional chapters, and access to co-working and lab space in Stanford Research Park.

Dozens of school programs fall between Michigan’s IP-first model and Stanford’s founder-first community. MIT has its Martin Trust Center for Entrepreneurship, which blends coursework with advisory programs. Emory runs the Hatchery, supporting student entrepreneurs with mentorship, instruction, and makerspace access. Notre Dame’s IDEA Center offers coaching and resources for student entrepreneurs. And the University of Texas at Austin’s Technology Incubator has helped raise over $1.7 billion and notch 10 IPOs since launching in 1989. During a time when the value of degrees is attracting more skepticism than ever, each program demonstrates how universities are moving past instilling entrepreneurial spirit to becoming full-fledged players in the startup ecosystem, redefining the college experience.

One of the hardest parts of freelancing isn’t the work itself. It’s managing clients.  Clients are the lifeblood of your business, but without the right systems in place, they can also become the biggest source of stress. Over the past 13 years, the author has worked with hundreds of clients — and she has learned (sometimes the hard way) how important it is to set boundaries, onboard effectively and guide clients through a structured process.  Here are her best strategies for building client relationships that are healthy, professional and sustainable.

1. The boundary problem.  The number one mistake freelancers make is failing to set boundaries early.  In the beginning, you’re so excited to land projects that you’ll accept almost anything. That might mean letting clients pay too little, pushing your deadlines or piling on requests. The issue is that once you start working this way, it’s incredibly hard to unlearn.  The reality? Clients aren’t necessarily being unreasonable; they’re just working with the information you gave them. If your proposal says “consulting for a month,” they might assume that means 30 to 40 hours of access to you. Unless you define it clearly, their assumption isn’t wrong.  Boundaries aren’t about being rigid. They’re about clarity. A solid scope of work, a kickoff call and documented agreements protect both you and the client.
2. Lessons from difficult clients.  Early in her career, she didn’t set limits on revisions. That led to projects dragging on for weeks, endless edits and clients watering down strong drafts with overthinking.  Now she always:  Define the number of revision rounds in the contract (usually two).  Give clients instructions on how to provide feedback for each round.  Teach them tools like Suggestion Mode in Google Docs.  Another issue she ran into was vague feedback like, “I don’t like the style.” To fix this, she added an addendum where clients initial that they’ve reviewed my writing samples and agree that the project will reflect a similar style and tone.  Small changes like this prevent frustration — and they make the client experience smoother too.
3. Why freelancers struggle with boundaries.  Let’s be honest: Boundaries are hard when you’re new.  You don’t yet know what red flags to watch for. You’re excited about opportunities. And clients themselves often don’t know how to work with freelancers — which means it’s your job to guide them.  The author’s advice: Treat every project as a lesson. At the end, ask yourself:  What caused friction?  What could I add to my contract or onboarding to prevent this?  Where did I need to set clearer expectations?  Over time, these micro-adjustments build a system that protects your time and sanity.
4. Onboarding as boundary insurance.  Onboarding isn’t busywork. It’s insurance for the client relationship.  A strong onboarding process usually includes:  A kickoff call to recap deliverables, reporting cadence and timelines.  A written onboarding document to centralize details like logins, assets and deadlines.  Clear reminders about payment schedules and review periods.
5. Kickoff calls and scope creep.  Even with a contract in place, kickoff calls are essential. Why?  They translate the contract into plain language. They also give you a written record if you use an agenda and notes. Later, if scope creep pops up, you can confidently point back to what you discussed.  A few must-ask questions in every kickoff call:  Do you have any questions before we start?  What’s your preferred communication method?  How would you like to provide feedback?  Kickoff calls don’t just prevent misunderstandings. They set the tone that you are a professional running a business, not a freelancer scrambling to “make it work.”
6. Ongoing client management.  Once the project is underway, communication style matters.  Some clients want details. Others only want a high-level summary. My approach is to adapt:  Use recap emails or reports with bullet points for big-picture clients.  Record quick video updates for those who prefer visual explanations.  Hold weekly or biweekly calls when ongoing input is critical.  If a client goes quiet, give them some grace. Life happens. But keep nudging gently — and if needed, explain how delays will affect the timeline.
7. The golden rule.  After more than a decade freelancing, here’s my golden rule: The best client management strategy is to say no to the wrong clients before you ever start.  Your initial sales call tells you almost everything you need to know. Do your personalities align? Are their expectations realistic? Have they burned through other freelancers before? Those are signs to proceed with caution — or not at all.  Screening for fit saves more headaches than any contract clause or process ever will.