Scenario Planning and Traditional Forecasting

Organizations rarely have the option to wait for certainty before making decisions. Although both traditional forecasting and scenario planning can assist leaders in preparing, the two methods address uncertainty in different ways. Forecasting usually involves making an estimate of what is most likely to occur on the basis of current evidence, whereas scenario planning considers what several possible futures might look like if key conditions were to change. In my view, one of these methods is not inherently superior to the other; rather, their usefulness is mostly determined by the level of stability in the environment and the amount of uncertainty associated with the decision.

Scenario Planning

Scenario planning consists of coming up with a number of possible futures and then examining how an organization might respond to each of them. Organizations use scenarios in order to identify risks, assess uncertainty, test their strategies, consider the various options open to them, and improve their strategic flexibility (Mortlock & Osiyevskyy, 2023). The method is useful in cases where leaders know that major forces may change but are unable to confidently predict how those changes will unfold.

The advantage is flexibility since, instead of relying on a single expected outcome, leaders can assess whether a decision would still make sense in different situations. The disadvantage is that the scenarios do, to some extent, depend on judgment; qualitative methods can be susceptible to bias, are more difficult to standardize, and may become too speculative if they are not grounded in solid evidence (Ene et al., 2026).

Forecasting

Traditional forecasting places a greater emphasis on historical data, measurable trends, and mathematical models. For instance, time-series methods make use of patterns that have been observed over time in order to estimate what might occur in the future. They are objective and repeatable, and they perform especially well in cases where the environment is relatively stable and sufficiently reliable historical data are available (Ene et al., 2026).

The weakness becomes more obvious if there is a sudden change in the circumstances. It is taken for granted, on the basis of history, that many of the forces which have shaped the past will act in a similar way in the future. Yet disruptive events can break this assumption and result in the actual outcome significantly differing from the forecast (Alikhani et al., 2025).

Compare & Contrast

Both methods help leaders plan under uncertainty, and they also require good information as well as assumptions that must be reviewed when the circumstances change. The main differences are that forecasting relies more on historical data to project a likely path, while scenario planning uses more judgment to examine several plausible futures.

The most useful thing about these methods is that they do not have to compete with one another; a quantitative forecast can set a baseline and then be modified to take into account different driving forces in order to produce alternative scenarios (Alikhani et al., 2025). This is something I can easily relate to in the context of military planning. The requirements for routine readiness or training could usually be predicted on the basis of past patterns, but contingency planning involved thinking about what we would do if staffing, priorities, or operating conditions changed.

Summary

When historical patterns are still useful, forecasting can be more reliable, and when uncertainty is high, scenario planning offers flexibility. Combining the two allows leaders to have a sensible baseline as well as a means of preparing if reality fails to match the expected course.

References

Alikhani, A., Hosseini Golkar, M., Sharifi, H., Najafi, F., & Haghdoost, A. A. (2025). Study protocol for applying trend impact analysis in health futures studies: A methodological approach illustrated by HIV/AIDS forecasting in Iran. Health Science Reports, 8(4), e70670. https://doi.org/10.1002/hsr2.70670

Ene, E., Unguroiu, M.-M., & Ghiculescu, L.-D. (2026). Strategic forecasting methods for micro and nanotechnologies. Bulletin of the Polytechnic Institute of Iași. Machine Constructions Section, 72(2), 29–42. https://doi.org/10.2478/bipcm-2026-0012

Mortlock, L., & Osiyevskyy, O. (2023). Strategic scenario planning in practice: Eight critical applications and associated benefits. Strategy & Leadership, 51(6), 22–29. https://doi.org/10.1108/SL-08-2023-0090

 

Forecasting and Moore’s Law

Forecasting generally uses existing evidence and trends to estimate where technology may be heading, while a prediction states a more specific expectation about what will occur. Both can influence innovation when organizations use those expectations to decide where to invest, what capabilities to develop, or when to prepare for change. Forecasting matters in innovation because organizations have to make decisions before they know exactly how a technology will develop. Technology forecasting can support technology strategy and roadmap development, or planning a path for future technology goals and decisions, by identifying changes and promising areas early enough to act on them (Kwon et al., 2022). I see the practical value there. A forecast is far more useful when it shapes a decision than when it simply describes what might happen. Forecasting has also become harder as new technologies accelerate the development of the next generation. The time between major advances can feel increasingly compressed, which makes long-term predictions about computing especially interesting to examine.

Moore’s Law is a good example. In 1965, Gordon Moore observed that the number of components in leading computer chips had been doubling about every year. At the time, Fairchild's most advanced chip had about 50 transistors, yet Moore projected that leading chips could reach about 65,000 components by 1975 (Lécuyer, 2022). The prediction became infamous in the sense used for this assignment because it was bold, widely debated, and eventually treated as an industry benchmark. Some engineers initially argued that Moore had too little evidence to support such a long-term prediction (Lécuyer, 2022). Yet the Fairchild and Intel data broadly followed the long-term pattern Moore anticipated (Burg & Ausubel, 2021).

Force One: Manufacturing Innovation

The first force was continued manufacturing innovation. Increasing transistor density required engineers to keep finding ways to produce smaller features as existing manufacturing methods approached technical limits. Burg and Ausubel (2021) found repeated waves of transistor-density growth associated with changes in processor manufacturing. That progression has continued. Basu et al. (2025) explain that extreme ultraviolet lithography, a chipmaking process that uses very short-wavelength light to create microscopic patterns on a chip, can produce structures smaller than 10 nanometers, or 10 billionths of a meter, allowing manufacturers to continue making smaller chip components. Those repeated technical advances help explain how manufacturers were able to fit more components onto chips over time.

Force Two: Industry Roadmapping and Competition

The second force was industry roadmapping and competition. Lécuyer (2022) shows that Moore’s projection became a planning target at Fairchild and Intel, influencing research, product development, equipment needs, and resource decisions. Semiconductor roadmaps, shared plans for the chip industry, eventually spread these expectations across companies, universities, government programs, and suppliers. Once competitors knew the expected pace of development, they had an incentive to meet or beat it. What surprised me was how much the forecast itself became part of the system that helped shape the outcome. The industry was not simply watching Moore’s Law happen. Organizations were investing and coordinating around it.

Summary

Moore’s Law illustrates both the worth and the limitations of technological forecasting; although the general trend was remarkably persistent, technological progress is often uneven and demands continuous technical and organizational effort. This makes sense to me based on my own experience in military planning and leadership; for example, once a projected readiness or training requirement became a common target, schedules and resources were often adjusted around meeting it. Advances in manufacturing continue to make smaller chip components possible, and the use of roadmaps together with competition can help turn a prediction into an industry-wide objective.

References

Basu, P., Verma, J., Abhinav, V., Ratnesh, R. K., Singla, Y. K., & Kumar, V. (2025). Advancements in lithography techniques and emerging molecular strategies for nanostructure fabrication. International Journal of Molecular Sciences, 26(7), 3027. https://doi.org/10.3390/ijms26073027

Burg, D., & Ausubel, J. H. (2021). Moore’s law revisited through Intel chip density. PLOS ONE, 16(8), e0256245. https://doi.org/10.1371/journal.pone.0256245

Kwon, K., Jun, S., Lee, Y.-J., Choi, S., & Lee, C. (2022). Logistics technology forecasting framework using patent analysis for technology roadmap. Sustainability, 14(9), 5430. https://doi.org/10.3390/su14095430

Lécuyer, C. (2022). Driving semiconductor innovation: Moore’s law at Fairchild and Intel. Enterprise & Society, 23(1), 133–163. https://doi.org/10.1017/eso.2020.38

 

From Error to Innovation

Innovation is usually associated with planning and with a deliberate effort to solve a problem, but there are also cases where the starting point is an error or an unexpected outcome. What matters is what happens afterward; merely having an accident does not result in a useful innovation since it is necessary for someone to recognize its value and to build on it so that others can make use of it. Research into materials discovery shows that some major breakthroughs have arisen in this way, although major discoveries that happen by chance are rare (Cheetham et al., 2022).

Teflon resulted from an unexpected chemical finding during research into refrigerants, chemicals used in cooling systems, while Wilson Greatbatch’s work on an implantable pacemaker, a small device placed inside the body to help control the heartbeat, progressed after he installed the wrong electronic component and realized that the resulting signal could stimulate the heart. In each case, the accident created an opportunity, but knowledge, practical usefulness, and continued development turned that opportunity into an innovation.

Teflon: An Unexpected Material Discovery

Polytetrafluoroethylene (PTFE), which later became known by the name Teflon, was discovered in 1938 as a result of research into refrigerants carried out by DuPont (Okamoto et al., 2020). Roy Plunkett had stored a gas called tetrafluoroethylene in a metal cylinder for future use. When it came time to use the cylinder, the pressure had disappeared even though its weight had not altered. Plunkett and a colleague looked into the matter and found a white, waxy solid inside. The gas had changed into a solid material through a chemical process called polymerization. The new material would not dissolve in many common liquids and resisted a wide range of chemicals (Cheetham et al., 2022).

What was important was not simply discovering an unexpected solid. Plunkett realized that the material had properties worth investigating. These properties, including the ability to withstand heat, resist chemicals, and prevent electricity from passing through easily, later supported its use in a number of technical applications (Okamoto et al., 2020). To put it simply, an unexpected result during refrigerant research became useful because it was examined rather than thrown away.

Forces Supporting Teflon

The research environment surrounding the discovery was an important technological and scientific force because it gave Plunkett the knowledge and resources needed to investigate what had happened. He was able to examine the material and recognize that its unusual properties might have value. This reinforces the broader point about chance discoveries: an unexpected result becomes important when someone has enough knowledge to recognize its value (Cheetham et al., 2022).

A second supportive force was economic value. PTFE was later used as plumber’s tape, and one trade account describes a plumbing shop that initially treated the tape as a costly item (Yates, 2012). After comparing the cost of the tape with the labor being lost through older sealing methods, the shop increased its use (Yates, 2012). I believe that this small example is useful because a product working well does not automatically mean people will adopt it; they still need a practical reason to change an established procedure.

The Implantable Cardiac Pacemaker

The case of the pacemaker is different because cardiac pacing, the use of electrical pulses to help control the heartbeat, already existed before Greatbatch’s accident. External pacemakers had already been developed, so Greatbatch’s contribution was not the invention of cardiac pacing itself. His error instead contributed to an electronic design that could be developed into a practical implantable device (Adam, 1995).

Before that, Greatbatch had learned about heart block, a condition in which the heart’s electrical signals do not travel normally from its upper chambers to its lower chambers. He believed electronics might be used to help keep the heart beating at a normal rate. Around 1956, while constructing a different circuit, he intended to use a 10-kΩ resistor, a component that helps control electrical current, but mistakenly selected a 1-MΩ resistor, which had a much higher resistance. Because of the wrong component, the circuit produced a short electrical pulse followed by a pause of about one second. Greatbatch realized that the pattern was similar to that required to stimulate a human heart (Adam, 1995).

The error was merely the starting point; afterward, Greatbatch collaborated with surgeons William Chardack and Andrew Gage, tested the devices on animals, sorted out the failures that occurred when body fluids came into contact with the electronics, and increased the reliability of the device before it could be used by patients; his team began human implantations in 1960 (Adam, 1995). At the same time, other researchers were also working on implantable pacing, and the first complete implantation of a pacemaker in a human took place in Sweden in 1958 using a device designed by Rune Elmqvist and implanted by Åke Senning (Pujol-Lopez et al., 2026).

Forces Supporting Pacemaker Development

One of the main driving forces was the medical need. Greatbatch realized that heart block could interfere with the electrical signals that control the heart, and because of his work with doctors, he had access to the clinical knowledge and setting necessary for testing and improving the device (Adam, 1995).

The technological force was just as important. Smaller transistor-based electronics, which replaced much larger earlier electronic components, made it more practical to place the device inside the body. The group still had to improve how the electronics were sealed inside the body, the wires, called leads, that carried electrical pulses to the heart, the reliability of the device, and its power source. Battery limitations later pushed Greatbatch and others toward longer-lasting lithium batteries (Adam, 1995). That technological development continues today. Modern pacemakers now include leadless systems, which place the pacemaker directly in the heart without the traditional wires connecting it to a separate device (Stark et al., 2025). Researchers are also looking at ways for pacemakers to process heart signals while using less battery power (Nagakumararaj & Baskar, 2025).

Organizational and commercial support was just as important; the relationships that Greatbatch had with doctors and engineers helped to get the project off the ground for clinical testing, and licensing the design to Medtronic gave the company the right to manufacture it and provided a path toward larger-scale production and use (Adam, 1995). More than six decades later, the development of cardiac devices has continued through the use of leadless pacing and other specialized ways of treating abnormal heart rhythms (Pujol-Lopez et al., 2026).

What These Cases Show About Innovation

The key lesson I have drawn from these cases is that an accident offers an opportunity rather than a complete innovation. Plunkett had to recognize that the unexpected material found in the cylinder was worth investigating, and Greatbatch had to realize that a circuit that was behaving incorrectly for one purpose could be useful for another; it was their existing knowledge that made the accidents useful.

Chance mattered, but preparation mattered too; in both instances, it is evident that the factors which promote innovation can change over time; scientific knowledge helped researchers understand why Teflon was useful, while its economic usefulness helped support its later adoption. In a similar way, the pacemaker depended on medical need and on the electronics available at the time, as well as requiring clinical collaboration, organizational support, and continuous engineering improvements.

Even if a new tool or technique proves effective, that does not mean it will automatically take the place of the one currently in use; someone still has to demonstrate that it has value and give people a practical reason to change the way they are currently working. Although innovation can at times begin suddenly, making it useful is usually a far more careful process.

Conclusion

The cases of Teflon and Greatbatch’s implantable pacemaker demonstrate that errors and unexpected outcomes can become the starting points for significant innovations. In both cases, technical knowledge helped someone recognize the value of an unexpected result, while continued development and practical usefulness turned that result into something others could use. The accident is the memorable part of each story, but the work that followed is what made each innovation game-changing.

References

Adam, J. A. (1995). Wilson Greatbatch. IEEE Spectrum, 32(3), 56–61. https://doi.org/10.1109/6.367974

Cheetham, A. K., Seshadri, R., & Wudl, F. (2022). Chemical synthesis and materials discovery. Nature Synthesis, 1(7), 514–520. https://doi.org/10.1038/s44160-022-00096-3

Nagakumararaj, S., & Baskar, S. (2025). Dynamic energy consumption using multiobjective genetic algorithm based FFT for implantable cardiac pacemakers. Analog Integrated Circuits and Signal Processing, 122(3), Article 40. https://doi.org/10.1007/s10470-025-02342-y

Okamoto, Y., Chiang, H.-C., Fang, M., Galizia, M., Merkel, T., Yavari, M., Nguyen, H., & Lin, H. (2020). Perfluorodioxolane polymers for gas separation membrane applications. Membranes, 10(12), Article 394. https://doi.org/10.3390/membranes10120394

Pujol-Lopez, M., Tung, R., & Mont, L. (2026). Innovations in cardiac device therapy in the era of advanced rhythm management: Implantable defibrillators and conduction system pacing. Heart. Advance online publication. https://doi.org/10.1136/heartjnl-2025-325834

Stark, C., Bhat, P., Rytkin, E., & Efimov, I. R. (2025). Temporary pacing for electric cardiac stimulation and neuromodulatory cardiovascular therapy. Cardiovascular Engineering and Technology, 16(3), 363–375. https://doi.org/10.1007/s13239-025-00780-3

Yates, D. (2012). Plumber’s tape and Dr. Roy Plunkett. Contractor, 59(8), 26.

 Group Decision-Making Methods for Innovation

Innovation almost never happens on its own; even if a single individual comes up with the first idea, it is usually group decisions that decide whether or not that idea is improved upon, approved of, and finally put into action. The Delphi technique and the Nominal Group Technique (NGT) are useful since both provide a method for gathering expert input while at the same time avoiding some of the problems that may arise in normal group discussions.

Delphi Technique

The Delphi method uses several rounds of questionnaires. Participants respond individually, receive a summary of the group’s feedback, and then have an opportunity to reconsider their views in later rounds. In the CII-CARE study, three rounds of the Delphi method came after a meeting of an NGT group in order to validate a competency framework, the anonymous nature of the feedback enabling the experts to adjust their opinions without facing direct pressure from their peers (Nachtergaele et al., 2026). A similar three-round approach was used to develop expert rules for a digital twin model of acute stroke care. Statements that did not reach consensus were revised and returned to the panel for further consideration (Dang et al., 2023).

Nominal Group Technique

NGT results in greater interaction, although this takes place in a structured order. The procedure involves a phase of silent idea generation, followed by round-robin sharing, clarification, and then individual ranking or scoring (Riley-Bennett et al., 2024). When adapted for a virtual environment, NGT was facilitated using videoconferencing, shared documents, and online scoring, and the participants generally said that they were at ease in contributing and felt their opinions were being heard. The method can also be completed in a shorter period of time and places greater emphasis on idea generation than Delphi (Riley-Bennett et al., 2024).

Comparing the Methods

Both methods organize participation and decrease the chances of a single person taking control of the decision-making process, although they do so in different ways (Nachtergaele et al., 2026). In the Delphi method, participants respond independently and anonymously, whereas in NGT they are allowed to hear and build upon each other’s ideas with the facilitator managing the order of discussion (Nachtergaele et al., 2026). The CII-CARE study combined both approaches by having structured discussions during the NGT phase and then carrying out anonymous Delphi rounds for subsequent evaluation and refinement (Nachtergaele et al., 2026).

Personality and Tools

A person who likes to spend time thinking on their own, or who is less at ease in challenging others in real time, might find it more effective to take part in a Delphi process. On the other hand, a participant who develops ideas through discussion may get more out of NGT. Yet NGT is not just a method for those who are very social since the phases of silent idea generation and round-robin sharing provide quieter group members with structured opportunities to think and to contribute (Riley-Bennett et al., 2024).

Conclusion

One method is not automatically superior; Delphi is more appropriate when less direct contact is advantageous, whereas NGT is better suited when it is necessary for a group to develop and discuss ideas in a shorter period of time. My choice between the two would depend on the people taking part, the complexity of the decision, the time at hand, and whether or not direct discussion is more likely to improve the ideas or cause the outcome to be distorted.

References

Dang, J., Lal, A., Montgomery, A., Flurin, L., Litell, J., Gajic, O., & Rabinstein, A. (2023). Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit. BMC Neurology, 23, Article 161. https://doi.org/10.1186/s12883-023-03192-9

Nachtergaele, S., De Roo, N., Stevens, T., & Embo, M. (2026). The CII-care framework: A contextual adaptation and validation of EntreComp for intrapreneurial and innovative competencies in healthcare- a mixed-methods study in Flanders. BMC Medical Education, 26, Article 812. https://doi.org/10.1186/s12909-026-09025-w

Riley-Bennett, F., Russell, L., & Fisher, R. (2024). An example of the adaptation of the Nominal Group Technique (NGT) to a virtual format (vNGT) within healthcare research. BMC Medical Research Methodology, 24, Article 240. https://doi.org/10.1186/s12874-024-02362-8

AI Tools and the Future of Inclusive Learning

AI Tools and the Future of Inclusive Learning

Introduction
Universities are currently trying to meet changing student needs while figuring out how to use artificial intelligence in teaching and academic support. The 2025 EDUCAUSE Horizon Report highlights AI tools for teaching and learning as a key technology or practice and continued interest in designing inclusive learning environments as a key social trend (Robert et al., 2025). I chose these two areas because they are closely linked. AI might help schools offer more personal, easier-to-reach support, but it will only improve inclusion if used carefully.

Technology
I do not see AI tools for teaching and learning as one single product. I see them as different kinds of student support. A student working after regular office hours may use an AI tutor to review a concept. Another student may need captions, translation, text-to-speech, or writing help just to access the material in a useful way. Khushalani (2025) gives one example from Shri Vishnu Engineering College for Women, where AI is used for early warnings, learning advice, writing support, and chatbots.

Technological advancement is making this easier than it used to be. AI systems can now handle large amounts of student data, find patterns, and give advice faster than older systems. From my experience with training programs, spotting problems early is important. A student who gets help only after failing a test may already feel discouraged, while support given at the first signs of trouble can stop bigger problems.
The ethical side is where I would slow down a bit. AI advice can be wrong, unfair, or based on incomplete information. Student data may include grades, attendance, learning activity, and personal situations, so privacy cannot be ignored. People still need to check the results because an automated alert should not become a final judgment about a student’s ability, effort, or motivation.

Trend
The push for inclusive learning environments makes sense because colleges are not serving just one type of student. A student returning to school as an adult may need something different from a first-generation student, a neurodiverse student, or a student trying to manage work and family at the same time. A more inclusive environment should make learning easier to access, more flexible, and better matched to what students actually need.

Technology can support that effort in practical ways. Captions may help a student who cannot fully access audio content or who benefits from written support. Multilingual resources may help another student understand material more clearly. Accessible digital materials and personalized recommendations can also reduce barriers, but only when they are designed with student needs in mind. The Horizon Report explains that these tools may expand access and help colleges move away from a one-size-fits-all model (Robert et al., 2025). I agree with that point, but I would be careful about assuming that adding AI automatically makes a course more welcoming.

Ethical use matters just as much as access. If the system is trained on biased data, the support may not be fair. If the tool requires an expensive subscription, some students may be left out. If one group of students receives automated help while another receives human support, the school may create a new kind of uneven experience. Khushalani (2025) points to the need for transparency, consent, algorithm audits, and human involvement.

Summary

For me, the main point is that AI can help with inclusion, but it cannot carry the whole responsibility. The tool may make support more personal, accessible, and available when students need it. Schools still need privacy protections, human judgment, and clear responsibility for how AI-based guidance is used (Seo et al., 2021). 

 

Figure 1. AI-driven collaboration and inclusive learning. Image generated by the author using an AI image-generation tool, Midjourney.

References

Khushalani, B. (2025, May 22). Empowering student success through AI-driven collaboration. EDUCAUSE Review. https://er.educause.edu/articles/2025/5/empowering-student-success-through-ai-driven-collaboration

Robert, J., Muscanell, N., McCormack, M., Pelletier, K., Arnold, K., Arbino, N., Young, K., & Reeves, J. (2025). 2025 EDUCAUSE Horizon report: Teaching and learning edition. EDUCAUSE. https://library.educause.edu/resources/2025/5/2025-educause-horizon-report-teaching-and-learning-edition

Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner-instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18, Article 54. https://doi.org/10.1186/s41239-021-00292-9

Welcome to Practical Futures

Technology often gets discussed in terms of what it can do, but I am usually more interested in what happens when people have to use it in real settings. A new system may be technically impressive and still fail because it is difficult to understand, does not fit the existing workflow, or creates problems that were not considered during development.

I recently retired after 28 years in the U.S. Navy, with much of my experience centered on training, education, advising, curriculum, and organizational leadership. Those roles taught me that innovation is rarely only a technology issue. People need to understand why a change matters, how it affects their work, and whether the benefits are worth the effort required to adopt it. That practical perspective will shape how I approach this blog.

I created Practical Futures as part of my Futuring and Innovation course in the Doctor of Computer Science program at Colorado Technical University. The course examines how innovations develop, the forces that support or restrict change, and how organizations can prepare for emerging possibilities rather than simply reacting after those changes arrive.

This blog will explore developing technologies and future trends through a practical lens. Topics may include artificial intelligence, digital twins, smart homes, education, accessibility, autonomous systems, and the ways technology changes how people work and make decisions. I expect some ideas to be close to implementation, while others may still be several years away. Either way, I plan to consider what makes them useful, what could prevent adoption, and what their effects may be on the people expected to use them.

The goal is not to predict the future with certainty. It is to think more carefully about what may be coming and what responsible implementation could look like.