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All enterprises, even the most innovative, strive at some level to become efficient creatures of habit. At some level the aim is to transform the business into a money-making machine. Switch it on and it knows what to do and how to do it. In fact the objective of the first 100 days is to move the business to a money machine status. This is the mountain that the four steps of the first 100 days are designed to climb.

Step One: Set the goal

It’s the executive leadership team that convenes to set this target which is expressed in dollars, margin, and a measure of time. For example, reach 2.5 billion in revenue, have high teen margins, and 300 million in ebaada by this day, five years from today. That goal is the what. The harder part is to figure out, the how. Fortunately if you dig into your wish list to build a money-making machine there’s a set of four how’s that give you a head start on reaching the goal you’ve set. The first is how to grow. This, in turn, implies another how – you earn the right to grow. And how do you do that? Simplify your business. This of course is another how that implies yet another. You simplify your business by applying the 80/20 process. 

Step Two: Frame your strategy

Your executive leadership team or as we call it, the ELT, or whatever you may choose to call it must build the strategy to achieve the goal. Framing the strategy requires assessing the current state of the business and then separating what’s working from what’s not. The picture that fits inside the strategic frame is a portrait of what works. Left outside of the frame are the outtakes – elements of the picture that do not work. Some of these may be capable of repair, redesign, and rehabilitation. You’ll worry about that later, for now just set them aside so that you can focus on what is within the frame. This is the basis of your strategy – it’s your first pass at simplification.

Step Three: Build that structure

Now that you have framed what works, organize your business into segments well defined by performance so that you can be sure to focus on the customers and the products most likely to propel you to your goal. 

Step Four: Launch the action plan

Here you need to define the how’s of the how. The tactics and the initiatives required to execute the plan to transform the business into action. In that first 100 days you strive to do something now that you have reason to believe will work at least more or less to how you move towards your goal. You need to act now so that you cannot even try to imagine perfection. If you really want to make that money machine, the sooner you act the sooner you are likely to build it. So don’t wait for perfection. You will never achieve perfection, you might get close enough to call what you do perfection but that’s all and thats pretty good. You will however never get anywhere at all without action – action produces results that can be measured. What is measured can be improved. Now you can’t imagine a great deal of doing much without anything but you cannot know anything without acting and evaluating the results. Agree to act, agree to observe, to assess, and to make sound informed decisions based on measurement and evaluation of your performance. You make these decisions, not in the quiet of your closed door office, but right out in front where the rubber hits the road. Where the results can be seen, measured, and queried and improved continuously.

In the first 100 days you will build the airplane as you fly it. It’s going to be bumpy at times but by monitoring and evaluating performance, you will know what works and what does not work. Experience will teach you, at first your flight will be rough and you will be uncomfortable. The discomfort is your ally because it will motivate you to make the plane perform better and better. If you talk yourself into being satisfied too soon with too little, you will crash and burn or run out of fuel and just crash without burning.

Remember: Together, We Will Fly.

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Stop Blaming the Election for Layoffs: Know the Truth and Opportunities Behind Job Cuts  first appeared on Bill Canady by Bill Canady.

It’s a familiar narrative: every election year, businesses brace for economic and political uncertainty, with layoffs often seen as an inevitable consequence. However, while elections may cause turbulence in political offices (such as one party wanting to fire the other) layoffs in the business world are driven by something else; strategy, or the lack of it. 

  

The reality is that business leaders deal with uncertainty every day, not just during election cycles. The fourth quarter is infamous for layoffs, but not because of who’s running for office. It coincides with the end of the fiscal year, the season of performance accountability, when executives who’ve been making non-strategic choices wake up to poor results and scramble to salvage their financials. The culprit isn’t the election, it’s a failure in data-driven decision-making and poor planning throughout the year. 

  

Take CVS, which is preparing to eliminate thousands of jobs in an effort to reduce costs and streamline operations, affecting a significant portion of its workforce. Similarly, Verizon recently announced plans to cut 5,000 positions by March, reflecting a broader push to enhance operational efficiency. While competitive markets remain volatile, these cuts highlight the importance of strategic planning over reactionary measures. Elections are predictable events, happening every four years without fail. What’s unpredictable, however, is the marketplace, which is why leaders must prioritize year-round, data-driven decision-making to maintain resilience and growth. 

  

THE COVID-19 EFFECT 

Admittedly, the past few years have been uniquely challenging. The pandemic brought a business environment unlike any other.  Initially, it triggered fear, but then demand surged, margins grew, and companies ramped up spending. Businesses got government aid, and homebound consumers with extra time and money shopped like never before. Prices went up, inventories swelled, and it seemed like demand would just go on booming. 

  

Now, the party’s over. 

As people continue to return to in-person work, the focus has shifted back to productivity in shrinking markets. The post-pandemic reality is forcing companies to return to basics, with the Pareto Principle (80% of revenue come from 20% of resources) reasserting its dominance. Leaders are now shedding unproductive segments and doubling down on what works. 

  

While layoffs can be painful, they’re not infrequently a strategic necessity. The lowest-performing quartile in a business often drains resources, and downsizing can free up capital and personnel for top-performing areas. But leaders must trade their chainsaws for scalpels, turning layoffs into opportunities for strategic growth. The goal isn’t just survival, it’s laying the foundation for future profitability. 

No question about it. The post-pandemic era has forced companies back to fundamentals. The 80/20 rule, which dictates that roughly 80% of profits come from roughly 20% of resources, is making a comeback. This is a development not to be mourned but applauded, loudly. And while we’re at it, let’s not put all the blame (or bestow all the credit) on the post-Covid hangover. Automation has been a driver of layoffs since the industrial revolution. Today’s dominant mode of automation, AI, threatens (or promises) even more profound workforce disruption. 

  

AI OPENING DOORS 

We see AI proliferating where automation has always proliferated, replacing human labor with machines in jobs and processes that are dangerous, dirty, or repetitive. More interesting but less predictable is its impact on what William F. Heitman (The Knowledge Work Factory) calls “knowledge workers.” For instance, from customer support (CS) to software coding, AI is making deep inroads. Online, human . As for software coding, Nvidia CEO Jensen Huang told his audience at the World Government Summit in Dubai: “Over the last 10-15 years, almost everybody who sits on a stage like this would tell that it is vital that your children learn computer science, everybody should learn how to program, In fact, it is almost exactly the opposite.” 

  

Critically, there is uncertainty in this megatrend. AI customer support cannot adequately resolve complex customer issues. The most successful CS operations deploy hybrid solutions, in which AI fields incoming calls but exercises its artificial intelligence to hand over complex calls to a human agent. As for AI software coding, experts believe that generative AI will “take over the jobs of low skilled coders, but experts will likely become even more important, providing architectural vision and direction.” 

  

The only certainty is that the future keeps coming. At present, however, an Upwork survey found that 77% of workers who use generative AI complain that “it has added to their workload and is hampering their productivity.” 

  

PLANNING IS THE ANSWER 

The key takeaway? Stop blaming uncertainties wrought by elections, pandemics, and AI. Layoffs can, and should, be part of a proactive strategy to streamline operations, refocus resources, and position your company for sustainable growth. As the pandemic taught us, reactive decisions won’t protect you from uncertainty, but a strategy grounded in data-driven insights will. Leaders must embrace year-round planning, making tough decisions early and often, not just when fiscal deadlines loom. 

  

To thrive in a volatile market, businesses need to prioritize long-term resilience over short-term reactions. The time for strategic, proactive leadership is now—don’t wait for external forces to dictate your next move. 

As seen on Human Capital Innovations.

The post Stop Blaming the Election for Layoffs: Know the Truth and Opportunities Behind Job Cuts  first appeared on Bill Canady by Bill Canady .

The Butterfly Effect Means Your Business Plan Will Never Be Perfect. Deal with It. first appeared on Bill Canady by Bill Canady.

Edward Lorenz was an MIT professor and meteorologist who, in 1972, gave a presentation at a meeting of the American Association for the Advancement of Science he called “Predictability: Does the Flap of a Butterfly’s Wings in Brazil Set Off a Tornado in Texas?” 

In the days long before the advent of AI, Lorenz was developing a digital algorithm to analyze the effect of atmospheric phenomena on weather so that he could better predict weather conditions in given place at a given time. He tested the first iteration of his program by applying it to historical weather data, so that he could evaluate the accuracy of the predictions the program produced, comparing prediction to known outcome. His software predicted sunshine in a certain location on a certain day. Great! But then he looked at what had actually happened in that place and on that day.

It was rain, lots and lots of rain.

As a scientist, Lorenz understood that failure teaches as much as success. He dug back through his work in search of a wrong turn. But as far as he could make out, the software had done everything right. Confirmation is always comforting, but a scientist looks even harder for exception, which is always more useful. After much review, he finally recognized a possibly significant variable. Back in 1972, computing power and digital memory were trivial in comparison to what we take for granted today. This being the case, Lorenz sought to save bytes and processing time by performing his calculations only to the thousandth decimal place, which seemed to him both economical and more than sufficient.

But, he asked himself, What if it wasn’t? What if the calculations were simply too coarse too reveal the truth?

So, he tried calculating the same data to the ten thousandth decimal place and got the same erroneous result. Undeterred, he reran the algorithm to the hundred thousandth decimal place. With this adjustment, the forecast changed to rain.

A humble weather scientist, Lorenz suddenly found himself the pioneer of a new field, Chaos Theory. Having developed a mathematical model to track how air moved around in the global atmosphere, he discovered that the most minute differences in the data he put into his algorithm—the difference between rounding to the thousandth decimal place versus the hundred thousandth—yielded enormously different weather forecasts. This prompted the conclusion that within the apparent randomness of large dynamic systems such as global atmospheric conditions, minute differences in the initial inputs can trigger major unexpected changes later in time and even at downstream locations quite remote from those inputs. He called this phenomenon a “sensitive dependence on initial conditions,” but it has come to be known as the “Butterfly Effect,” which is dramatic shorthand for the fact that some seemingly insignificant input—for instance, the flapping of a butterfly’s wings in Brazil—can affect the trajectory of something as momentous, say, as a tornado in Texas.

Lorenz discovered that his forecasting calculations were insufficiently granular, so gross, in fact, that they missed the most critical cause-and-effect relationships altogether. For business leaders, the First Hundred Days of an effort to earn the right to grow will yield an abundance of data that can be used to guide initial initiatives of segmentation and simplification. But, given the urgency of a company in need of a turnaround and the limitations of short-term data, the numbers will likely be too gross to create optimal predictive guidance. Only repeated iterations of the four-phase cycle of Segment, Simplify, Zero-Up, and Grow throughout the first full year of the deployment of a new Business Plan will enable executives to tune the focus on the critical few in the business more precisely and productively. As the iterations are repeated throughout the full span of the Business Plan—typically three to five years—the focus will become sharper and sharper, the predictions more and more accurate, and the adjustments more and more effective in driving profitable growth. Will you eventually find perfection? No. But you will be getting warmer all the time.

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