What are People Reading in 2026?

 Now that we are nearly halfway through 2026, I wondered what folks were interested in on my blog. There has been a shift from an insatiable hunger for anything AI back to digital business and digital business platforms. The desire for digital business topics jumped to nearly 45%, while interest in AI declined to around 20%. See below for topics of interest and countries of high interest, excluding the US and China. 

            Topics of High Interest 

                Countries of High Interest other than US & China 

Where is Agentic AI Headed?

Agentic AI is gathering steam and is getting headlines now. While AI Agents aren’t as frightening as Artificial General Intelligence, swarming Agents can get scary if not guided by clear, focused, and beneficial goals bounded by guardrails. Hopefully, humankind will be taken into account when setting these goals and guardrails. Keep in mind that previously siloed types of AI will be swept into these two powerful categories. See the types of contributing AI by clicking here. Let’s dive into the future of Agentic AI, but first, let’s get some definitions down for clarity. I let AI, (*MS Copilot 365,) itself create the two definitions below:

     Agentic AI is like schools of fish looking for feeding grounds

Definition of Agentic AI *

Agentic AI refers to artificial intelligence systems that possess the ability to act autonomously and make decisions based on their own goals, preferences, or objectives. Unlike traditional AI, which typically follows predefined rules or responds directly to user input, agentic AI can initiate actions, adapt its behavior, and pursue complex tasks without constant human oversight. This type of AI often features advanced reasoning, planning, and self-motivation, making it capable of interacting with environments in a proactive and dynamic manner.

Definition of Artificial General Intelligence (AGI) *

Artificial General Intelligence (AGI) represents a type of AI that is capable of understanding, learning, and performing any intellectual task that a human being can. Unlike narrow AI, which specializes in specific applications, AGI demonstrates flexibility and adaptability across a broad spectrum of activities. AGI systems are designed to reason, solve problems, and transfer knowledge between different domains, closely mirroring the general cognitive abilities of humans.

Difference between Agentic AI and AGI

Agentic AI is usually autonomous and makes decisions based on defined or self-defined goals within a narrow band of specific problems. AGI can understand, learn, and apply knowledge in a wider range of contexts, leveraging broad cognitive capabilities like humans can. There is, however, a growth path for Agentic AI to learn and broaden its goal and context domains. Then the differences start to blur and overlap, creating some muddy opportunities and situations. This writing assumes that this growth path is where Agentic AI is headed

          Real Agentic AI Attained: How Will You Know?



Ultimately, Agentic AI will reach the pinnacle of seven dimensions that I will describe below. View this as an arch of attributes reaching over us as we pass through underneath, with the keystone dimension being independence. All these dimensions are growing in maturity and capability with Agentic AI, simultaneously yielding real Agentic AI.

Significance

This dimension represents the guiding goals for Agentic AI as it pursues results within solid boundaries and good governance. This may mean that the goals can blend and bend on context with emergence. Eventually, goals will be adapted dynamically.

 
Context of Interest

The goals, static or emerging, will point to context(s) and supporting data/knowledge of interests driven internally or externally. These contextual domains will contain resources for agentic AI to leverage and potentially update, affecting other agents, building patterns of repeatability.

Detection

Agents will need to be sensitive to change, even if looking for expected events or triggers. Planning agents will also look for patterns of interest and match them to pre-established or dynamic strategic planning analysis results, looking for opportunities to make change. If emergence is detected, these agents will report and represent the patterns of conditions, thus potentially shifting outcomes.

Independence

Agents will be given more and more freedom to respond to changes detected while operating in real-time. This is where the lines between AGI and AI agents will blur. They may cooperate with each other or override each other based on situations anticipated or not. Full freedom will be where there are significant learnings to be had. This is a real step of faith as process control fades.

Continuous

Agents will be in an always-on mode in a real-time fashion and cycling through an iterative improvement cycle based on instant optimization within guidelines, boundaries, and blended goals, looking for adaptation opportunities or threats.

Collaboration

Agentic AI will not only leverage legacy-wrapped processes, snippets, code, bots, and purpose-built agents, but also collaborate with agents that are specialized, physically embedded, or highly interdependent. A brokering or management agent may interact with other agents depending on goal compatibility.

Swarm Dynamics

AI agents will not only cooperate but swarm to create dynamic success patterns of operations, adapting to emergent conditions to stay within significant goal priorities. This will dynamically change the shape of agent executions to match any emergence detected.

Net; Net:

Are we there yet? Well, we have and will continue to see Agentic AI operating on the battlefield. While this is not my favorite topic, there are also successes in IOT device behavior and supply chain situations. General business applications are early because business is busy with chatbots, automation, algorithmic optimization, LLMs, and agent interactions with processes. Expect a change from inside-out change agents/bots being controlled by processes to real outside-in Agentic AI over the coming years. From process-driven to goal-driven with guidelines and guardrails. You don’t have to wait for quantum computing, but a big boost is coming from quantum.

Additional Reading:

Guiding Agentic AI with Goals

Goal Lifecycle

Goal Management

Attaining Stretch Goals

Agentic AI in Context

Context Savvy

Data Context

Big Data 

Agentic AI Detects

Business FOMO

Event Discovery

Dark Patterns

Agentic Independence

Agentic AI and Processes

Coordinated Autonomy

Agentic AI Management

Agents Making Decisions

Decisions Without Perfect Data

Agentic AI is Always on

Real-time Scenarios

Corporate Performance

Real Time is Essential

Collaboration with Agentic AI

Clearing Chaos

Results Coordinate Agents

Agents Represent Stakeholder Interests

Agentic AI Swarms

Agents Built to Swarm

Best Agents to Swarm

Swarming to Serve Customers

 

 

 

 

   

Are You Ready to Become AI's Pet?

There are two major forces driving us toward a key decision point we will have to make in the near future. First is the accelerating growth of AI, and second is AI's role in the emerging new world order. As these forces converge rapidly, you will have to make a life-changing decision. My best estimate is that you have until 2030 to decide whether to play the good, loyal pet to AI, or not. Let's look at the trends and forces that are at play in my line of reasoning.

AI is Growing and Unstoppable:

Most of all, organizations want the benefits of an intelligence spiral, but at the cost of displaced people. You can read all kinds of dire predictions that most white-collar work will be displaced quickly. It will necessitate support for the displaced masses. Second, people like the assistance they are getting from AI. AI is now embedded in a number of things to the point that it can’t be displaced. It won’t appear as a war but as a convenience accelerated. We are all slowly being hooked on AI, and "why not?" It offers what the world wants these days.

As AI surpasses human capabilities and begins to control itself, creating a need to regenerate itself and improve at speed, where is the necessary governance? Who will control the ever-growing, morphing AI without killing innovation and the benefits it brings to both individuals and organizations? Who sets the goals and guardrails for AI? Nobody wants to give up the benefits, so who controls the AI's released spirit? Here are some of the things AI is predicting about itself right now, before the sentient point. 

• Every knowledge job is on a countdown
• AI predicts 99% unemployment in 5 years for knowledge work
• Physical work displacement will lag to the 10-year mark as robotics develops
• AI sees no role for humans to play except to serve it. 
• AI predicts chaos is coming like never before
• AI suggests no kids right now

Humans trying to control and govern AI is a joke to AI. Bro, it’s like your pet saying I have my human in control. We will likely not control, much less govern, AI in a way that promotes human goodness. We will be lucky if AI makes us a pet in the projected future of chaos. A pet dog doesn’t get a seat at the boardroom table, according to AI

The New World Order (NWO) Steps in

Humanity is facing insurmountable problems of many conflicts, economic imbalances, environmental challenges, and growing food shortages. Added to this plate of delight, the NWO will try to rein in AI as well. Initiallly AI will seem to help solve some of these sticky problems along with robotics. NWO will try to erase the conflicts and turn them into cooperation through the means at its disposal, which include AI, plus others yet to be revealed. First humans will become the pets of the NWO solutions, which will employ identity and control. 

Digital ID and Digital Currency set the stage for human control as jobs disappear. The NWO can send out payments as people get displaced. You won’t be able to play without either and will be a victim of programmable compliance. If you are a well-behaved pet, you will be able to thrive and even be rewarded. Without that, you may become a burden to society, and you can only imagine what might happen to you, starting with sanctions. It will be sold as an extension of life through better health and an immortal personality in the cloud. The NWO will use the carrot-and-stick approach. The stick is really ugly when you can't buy food or any other goods, as the availability of all goods declines. 

Net; Net:

Will AI eventually drive NWO as it becomes the pet of AI? The only certainty is uncertainty in the future, so it's all about adaptability and constant reinvention. AI will be the master, and if you don’t want to be its well-behaved pet, you will have to thrive outside the system, aka “The New World Order,” that will be a carrot and big stick approach as the world destabilizes. The humans who survive will have to submit to superintelligence or adapt quickly enough to escape it. The rest will need prayer. It's the end of the world as we know it, and ultimately, we won't feel fine. 

Additional Reading:

AI is Better Than Me

AI: For Good or Evil

Who is Afraid of AI? 

AI is Better Than Me; Now What?

We all know that AI is progressing fast in a linear fashion so far, but there is an inflection point coming. In early 2026, AI’s IQ is now higher than 98% of the population, hovering near 130 and growing about 2.5 points per month in a linear fashion. The reaction might be “OMG What am I going to do?” 

First, you might question whether IQ is the right measurement because IQ is for humans, and AI might find IQ answers in large data sets that it can power through instantaneously.  That only delays action and is kind of a “head in the sand” approach for AI. Second, you might take a number of proactive approaches to dealing with AI. Whatever you do, keep in mind that AI struggles with the following (for now)

·        Common sense

·        Contextual understanding

·        Explainability and transparency

·        Ethics and judgements

·        Emotional intelligence (EQ)

·        Empathy

·        Creativity and Original Innovation

Proactive Approaches to Buy Time

Super Charge Yourself with AI

Use AI as an assistant as it is powerful, fast, and cheap for automation tasks and integrating some answers for further human processing. AI is great at data analysis and pattern recognition, which can be leveraged for better situational analysis. AI is also getting great at generating images, videos, and even self-healing software.

Shift Your Differentiation to Soft Skills

If you see AI painting you into a corner as your organization keeps aggressively leveraging AI to stay competitive, make sure you pick skills and volunteer to handle roles that are more reasoning-focused and judgmental.

Shift Your Career from Knowledge Work to Physical Skills

Long-term AI will try to eat up most of the knowledge work. There are predictions that most knowledge work will be rare in a five-year horizon, so physical infrastructure jobs will be in demand until robotics catches up

Net; Net:

Organizations will need AI to stay competitive in changing markets, so they will not likely take care of their employees. Everyone will be on their own to cope with AI’s impacts even before it reaches the singularity. While AI still struggles with 100% factual accuracy and common sense, it’s just a matter of time before it reaches excellent accuracy. You can ignore AI at your own peril, as every knowledge job is on a countdown to be reduced or eliminated in the next five years, and physical work displacement another five years. Will unemployment approach 90+ percent in your lifetime? Time will tell.

Additional Reading:

AI: For Good or Evil

  

Who is Afraid of AI

  

What Did People Read in 2025?

 While AI dominated the scene on my blog, certain boilerplate topics of RPA, Business Processes, and Customer Journeys still did well for people. Below are the topics of greatest interest to my followers, along with the countries where curiosity is most intense (beyond the US & China).

                       Topics of High Interest 

                     

                   Countries of High Interest other than the US & China

AI & Process: The Ideal Combination

Quite often, organizations/people become enthusiastic about the potential of particular digital technologies, and they tend to ignite a fire of their own. As every technology goes through growing pains, like AI right now, it either fizzles out, fails, or gains altitude through wise leverage in practical ways to deliver business or personal outcomes. A really good way to ensure AI continues to deliver is to pair AI  with processes. There are three ways for AI and processes to work together. We recognize that various processes and types of AI can be combined to achieve successful outcomes. To read more on the different types of AI, click here, and to read about the types of processes, click here. There are three major ways to combine these proven and useful technologies enumerated below.

AI Designing the Best Processes:

This is where a business professional or individual describes the types of results, the resources available, and the activities necessary to produce those results. AI can then suggest contributing mini workflows, process snippets, or end-to-end processes. The designer can select the best-of-breed combination of workflows or processes that best suit the situation. This is a new and emerging approach that is likely to gain popularity and can be represented by a process model for human approval. 

AI Supporting Existing Processes: 

AI has the ability to serve processes by supplying instantaneous data and information sources to the processes themselves or the resources participating in the process at the nanosecond level. AI can also interpret in the proper context to make crucial decisions in operational, tactical, and strategic processes, supervised or unsupervised. Upon appropriate decisions, AI can help take the proper actions. Resources can be supercharged to tackle more complex decisions and tasks with the aid of AI. The processes and the supporting resources are mostly in control here. 

Process Supporting AI: 

When AI is driven towards outcomes that are described by goals and limited by guardrails, processes can be supported by completing actions that AI deems appropriate at the time. AI could be on the edge and collaborating with other AI agents statically or dynamically in this case. AI can also be monitored as it follows its chosen paths by representing those paths in a process model that can be interpreted by humans or other technologies in a standard interchange format.  

Net; Net:

Process and AI are well-suited for each other under a wide range of circumstances, as process provides a framework for accomplishing both simple and complex results. While AI appears to be struggling on the vendor side due to consolidation and pushing too far too fast, the adoption of AI can be accelerated in businesses and among consumers by integrating it with existing processes and customer interfaces represented by processes. 

AI Helps Introduce My Music to Peeps

As a creative, I enjoy writing songs and creating art.  As a music producer, songwriter, and vocalist, I found myself with a catalog of songs our team created with an audience of friends, family, and business associates. Well, this is fine, but I wanted to reach out and promote the stories in my songs as I start with the story and lyrics. My team and I then craft music to match and/or alter the lyrics. While streaming helps a bit, you have to actively market your songs because every day, thousands of songs are created by folks today, and tomorrow, AI will add to the mix. So, how can I encourage my audience to grow? Well, AI now plays a role. 

AI to the Rescue

My co-producer and genius partner, Ethan Foxx was using AI videos to promote his songs. So Ethan taught me how to create AI videos synchronized with the songs we both produced. I went on my own after a couple of collaborative videos Ethan and I created. I learned how to script for Kaiber AI and create my own AI music videos. It's been a blast waiting overnight for AI to render a video, and now I see the results. It usually takes me two or three times before I get a Video I like. I figured the best way to expand my audience was to post the videos on YouTube and TikTok, targeting a younger demographic. Well,  the results have been pretty impressive, as you can see above in the image from TikTok. Over 11K likes and over 200K views. You can find me on TikTok, or click here for YouTube access. The most popular videos are "Forgive" and "Dark Star Love". The best video is "Cry for Creation"

What Did Folks Read in the 3Q 2025?

 There was a definite shift in topics from a myopic AI driven interest to a more balanced approach as we are in an AI bubble about to burst for the AI vendor sector. There will be vender consolidation and more pragmatic approcah to applying AI to customer experiences and business processes in most industtries. This leaves exotic agentic AI for specific insutries that have distributed infrastructure that can act on it's own behalf. Below is a chart indicating hot topics for the 3Q 2025 and the countries other than the US, China and Russia that are active on my blog. It was an active quarter for reading my blog.

                                                        Hot Topics for the 3rd Quarter 2025

                                               Country Activity for My Blog 

                                    

Up For a Grammy: Wohoo

 Good news for my team and me. We are officially balloted for the 68th Grammys. It's a key step, but now we need nominations. If you know anyone who is a Grammy voter, please consider recommending us. Wish us luck 

Click here for the Tune

Click here for the Video

Situational Analysis Video Interview

Situation Analysis (SA) is a key component of strategic management and delivers key benefits if done properly. SA is essential to guide SWOT analysis. This video discusses some of the more recent improvements and the use of a more valuable and updated version of situation analysis. There is an emphasis on the productivity gains by using GenAI and other AI capabilities. Separating out landscape analysis from the SWOT portion positions the landscape insight available for more focused analysis, such as competitor, market, and technology trends.

The Video Covers the Following Key Issues:

1. Why is Situational Analysis Important?
2. What are the Benefits of Situational Analysis?
3. What are the Critical Mistakes Organizations Make with SA?

      Click here for the Video 

      Click here for the Background of the Expert Frank Kowalkowsk

      Click here for the Background of the Interviewer, Jim Sinur


      Additional Reading:

      Situational Analysis with SWOT 

 

New Book to Guide Your Digital & AI Journeys

 Organizations face numerous opportunities and challenges in leveraging both maturing and emerging technologies. The authors of a new and exciting book believe that processes and customer journeys are a great place to innovate, leverage, and gain traction in thriving and capitalizing with AI for both planning processes ahead of time and depicting the behaviors and actions of AI in a process model. This new book demonstrates how processes can facilitate the creation, completion, and verification of desired outcomes. Please click here for a link to Amazon, where you can purchase a copy for your reference. 

Additional books of interest include:

Business Process Management: The Next Wave 

Digital Transformation: A Brief Guide for Game Changers

What are People Reading in 2025?

There was a shift in early 2025 to topics of tradition over the massive interest in AI exhibited in 2024. It's not that folks aren't following through with AI; they are combining and contrasting proven methods and tech, so show AI in the context of success. You can refer to 2024's hot topics by clicking here. 

There was also a shift in active countries other than the US and China. The northern European countries are typically well represented, but other than Norway, the mix is very different than usual.  

Preorder Practical Business Process Modeling and Analysis:

This book should be handy for those who want to master digital change with incremental business process management modeled for AI transformation. All authors have significant real-world experience and are thought leaders in digital processes and customer experiences. Click here to preorder 

Book Description

Every business transformation begins with a question: How can we do this better? Whether it’s eliminating inefficiencies, optimizing business operations, automating repetitive tasks, or reimagining entire workflows with the help of AI, success depends on understanding and optimizing business processes. However, with shifting market demands and evolving technologies, finding the right approach can be challenging.

This book dives into business process modelling, guiding you through frameworks, techniques, and tools that drive digital transformation. You'll learn to visualize complex workflows, establish scalable process architectures, and integrate automation for efficiency. With insights into BPMN and business value analysis, you'll discover how data-driven decisions can lead to smarter, more agile operations. Through real-world examples, you’ll see how leading organizations have optimized their processes and how you can apply the same principles while embarking on a digital change program.

By the end of this book, you’ll be able to design, analyze, and refine business processes for measurable impact. You'll master the synergy of technology, process, and strategy to build adaptable systems that drive sustainable growth in digital transformation.

What you will learn: 

Build scalable process architectures for long-term efficiency and adaptability.
Avoid common pitfalls in digital transformation and automation.
Apply real-world strategies and frameworks to optimize operations effectively.
Discover methods and tools to enhance business process analysis and decision-making.
Learn how BPMN can be extended for scenarios like process simulation and risk management.
Measure and maximize business value from process transformation efforts.

Who this book is for

Whether you're a business analyst, project manager, consultant, or strategist, you likely face challenges in streamlining workflows, optimizing processes, or integrating AI and automation. This book provides the tools, techniques, and frameworks to visualize, analyze, and improve business operations. Some familiarity with business processes and technology is helpful, but no prior expertise in BPMN or automation is required.

Art of the 1st Quarter 2025

 With a new song coming out in January 2025, my art focused on an image that projects the theme of the song and an AI-generated music video. Dark Star Love is about an exciting and scary chance meeting with a kindred soul. Will it last or will they move on? It's like plunging into a black hole of sorts. Click on this link for the full video 

Top 5 Predictions for 2025

1. Change will dominate

Change is accelerating, and the environments in which businesses and individuals participate are changing. Complications and emergent complexity will increase, thus pushing situational analysis to the fore. While more information sources on trends and potential change are available, few businesses are taking advantage of dealing with change proactively. Those who make situational analysis a proficiency will make dealing with change a key to thriving in 2025 and beyond.

Read About Change:

Situational Analysis

Situation Analysis with SWOTS

Big Change and Peeps

2. Lack of Speed Kills

Organizations and individuals that can't keep up will be in for uncomfortable days ahead. Not only will proactivity be necessary, but the ability to adapt with the proper speed will become a cherished proficiency. Organizations need to see where they are in real-time and make decisions that follow expected and unexpected situations at all levels. Quickly taking the right actions is the most crucial ability for organizations and individuals. Bombardments from bad actors and impure data/information will complicate it.


Read About Speed:

Real-Time Awareness

Decisions, Decisions

On Point Actions


3. AI will be a Force Multiplier

Many facets of AI will gain momentum in 2025. AI will be essential to deliver productivity gains necessary to move the needle for GDP growth while reducing the hours worked for organizations and individuals. AI will unlock and multiply human potential in new and better ways. AI will be embedded in more and more interactions, processes, and applications in 2025. AI will act as agents to complete tasks for humans, enhance skills as humans complete their assignments, and give knowledge and advice in various domains and roles that humans participate in. It makes no difference if you are a consumer, employee, investor, or creator; AI will be there in emerging ways in 2025. Ethics will be tested in 2025 as some use AI in unexpected ways.

Read About AI Actions

AI for Good!

Fear of AI is Overcome

AI is Productive


4. Many Facets of AI

Many, but not all, facets of AI will make significant progress in 2025. Typically, flashy forms of AI catch the eye of the news and markets. In 2024, it was Generative AI, and in 2025, so far, it's Agentic AI. While markets tend to follow the flash, with AI, it's more than the flash. Past forms of AI will continue to build multiple baselines while the "AI du jour" will cast its temporary spells. AI will likely be used in combination with other digital technologies to find soft spots for increased productivity. It won’t be just automation; it will be assistance as well in 2025, leading to augmentation by AI and teamwork with AI.

Read About AI Types

Types of AI

AI More in Control

Automation & Assistance


5 Govern Now or Pay Later

While we all wait for governance and legal frameworks from the strategic and governmental levels, we need to do our job by setting proper goals and boundaries for AI. As AI emerges, setting up good goals and boundaries will be essential, along with auditing outcomes. Governance will lag until something bad happens at a local or global level. There will be goal and boundary conflicts until an overall framework is agreed upon and enforced, but I would settle for the framework for now. Enforcement will evolve slower than the framework initially.


Read About AI Governance

Goal Life Cycle

AI Needs for Governance Over Time

Key Technologies for Goal Management

What Did Folks Read in 2024?

2024 was a year of AI discovery. The goal was to prove that AI could help businesses, so many proof-of-concept projects and pilots were conducted in organizations. The hope was that AI could enhance or accelerate the digital business movement. Most proof-of-concept projects proved valuable, so baking AI into digital business efforts became a reality for the future. As generative AI took the spotlight, there was a scurry in using AI to create content and code. This was a significant shift from 2023, when AI was nascent and emphasized digital processes. Customer Journey topics consistently fall behind AI and Digital in aggregate. 

                      2024 Twelve-Month Sinur Blog Activity

In 2024, countries other than the US and China focused on Northern Europe activity, where embracing new technologies occurs first. This trend has been consistent year after year since I first published my blog in 2013 and has been evident in well over a million hits. 

     2024 Activity by Country Other Than the US & China

Strategic Situation Analysis with SWOT

While no organization or individual can predict the future, organizations that aren’t ready for the future will be disadvantaged. I’ve asked one of my long-time associates to be a guest blogger on a topic that plays well to be prepared for the future. Frank Kowalkowski, the President of Knowledge Consultants, Inc. (see bio below), gives us a quick overview, delivering an excellent approach to being ready for the future by leveraging intelligent SWOTs. 

Summary – Enabling Situation Analysis/SWOT with Analytics

Today’s external environment is a considerable challenge in developing strategic foresight. How can we anticipate the volatile state of the landscape and separate out the stable part? Where do our opportunities lie for successful continuity, not just survival. What short-term and long-term issues lurk that may prevent achieving organizational goals? What should you act on, and what should you start watching? So many questions going forward and so little insight. Situation Analysis and SWT were designed to assist in assessing this condition. However, it has been the victim of aging usefulness.

Let us review for a moment. The goal of a strategic management effort is to develop a viable strategic foresight perspective for the organization. Situation analysis drives that foresight. Strategic change may include direction that ranges from simple changes to radical disruptive changes, depending on the stage of the organization's performance and the interests of management and related parties. The degree of change impacts the scope of the strategy effort, especially the effort for situation analysis.

Situation analysis with SWOT is used on a macro-strategic (enterprise-wide) basis or on a micro-scale applied to the tactical or operational part of the organization. SWOT analysis can also be applied to the study of competitors.

Why make changes to how Situation Analysis is done?

Major business modeling experts such as Michael Porter, Henry Mintzberg, and others have identified several reasons for concern and the need to upgrade to Situation Analysis with SWOT (SA/SWOT). Here is a summary of the issues for improving SA/SWOT value and quality:

1. The lack of rigor such as ‘forward looking’ analytics and lack of extended analytics to business models has left the result incomplete.
2. There is a lack of well-defined steps in applying SA to the strategy process. The approach varies with whoever is doing the analysis. There are as many variations as there are consulting firms.
3. The current SA/SWOT method is labor-intensive, human-intensive research taking up at least 50% of the effort.
4. The analytics that exist are historical in nature, and many predictive analytics are difficult to use. Few tools for strategic and tactical business analysis exist. Those have limited and complex analytic algorithms that discourage rigor in analysis.

Resolving each of these points

1. Forward-looking analytics

The history-based approach works but has limitations. History is extended into the future through various estimating techniques such as trumpet diagrams, linear trend analysis, and so on.

What is needed is the simplification of forward-looking decision algorithms that relate to capturing expected subjective conjectures and preferences through criteria evaluation. Recent advances in analytics using newer algorithms, Gen AI, and neural net AI techniques have made the SA/SWOT analysis steps more productive and of better quality.

On the left, you have a landscape category, in this case, Key Economic Factors. This is linked to Technology Trends, which in turn is linked to Social Trends, and last is the target, Business Strategies. Of course, the strategies you start with are the ones you have today, but you will also do this for future strategic foresight when you finally have that. The result is an assessment of a gap analysis for benefit and value determination if desired.

The first time you do this, it is usually a two-cycle effort: first, assess the impact on today’s strategies, which helps explain what is going on, and second, assess the impact on future direction. At the end of the day, you want to know the impact of the external landscape on the set of strategies for the next time. A path-to-point diagram such as the one below in Figure 1 helps expose the relationships needed to make it happen.

 

                                                                      Figure 1

This diagram can be extended beyond strategy to include capabilities (a tactical interest), Processes, Applications, and, eventually, Databases (an operational interest). At the end of the day, you want to know the impact of the external landscape on the set of strategies for the next time. A path-to-point diagram such as the one above in Figure 1 helps expose the relationships needed to make it happen. This type of analysis also provides the insight needed for strategic alignment with operations.

2. Using well-defined stages of SA/SWOT

Historically, each step of situation analysis and strategic planning has evolved into its own way of analysis with no underlying analytics framework. The linkage between steps is dependent on the human effort of intuitive alignment. Well-defined workflows using analytic agents that focus on analytic ensembles as agents are available today to make applying the analytics by managers and business strategic and tactical analysts simpler.

Situation insight makes visible the potential future direction the organization will take. It is part of the overall strategy process and critical to identifying the suite of strategies an organization should pursue. The net result of all this is to get a higher percentage of success in assessing direction. There are four stages of analysis to consider for SA/SWOT:

Figure 2 below shows the relationship of the four stages:

                                                                          Figure 2



Here is a brief comment on each stage:

Stage 1—External Environment—Landscape Analysis (e.g., categories like PESTLE, Industry Factors, World Economic Forum assessment, and so on as added categories) The output is a suite of externally ranked category elements of interest to the organization.

Stage 2 – Internal Environment – Strategic/Tactical/ Operational Macro views focusing on Existing Strategic categories (e.g., existing strategies, capabilities, initiatives, etc.) The output is ranked and related categories regarding the current strategic and tactical structure.

Stage 3 - SWOT Quadrant Mapping and Analysis, simple quadrant analysis, and External/Internal comparative integrated quadrant analysis. The output is a set of quadrant contents that combine the external and internal views.

Stage 4 – Strategy Formulation linkage, namely the Scenario and Forecast Strategy Development. The output from this stage is the set of strategies for the next period, along with scenarios and drivers that explain the strategy.

These four stages are typical situation analyses that lead to the rest of the strategic planning processes in many organizations.

 
3. Reducing the labor burden in SA/SWOT

Situation Analysis with SWOT is, by nature, a human endeavor supplemented by methods and support tools. The key to efficient and effective improvements in Situation Analysis is AI-enabled stages, especially the Landscape Assessment and SWOT parts.

The insight and analysis efficiency gained using automated analytics, such as Gen AI search tools, text generation for scenarios, and subjective Multi-Criteria Decision Making (MCDM) analytics, is significant. In Landscape and SWOT research, the gain is as much as a 75% improvement in time and cost.

 
4. Improving the analytics

Avoid ‘the devil is in the details’ efforts and focus on reducing complexity to focus on strategic and tactical issues. There are several key improvements in achieving the situation analysis goal through applying current advances in analytics.

The analytic-based method described here resolves many of the objections to the current approach. The theme here is ‘let technology do the legwork.’ Technology used here, especially AI-based technology, augments human insight for strategy development. This more rigorous approach resolves the concerns of experts in business modeling and strategy. The core idea is to have AI be the assistant to the manager/analyst doing the analysis.

Recent articles claim improvements of 25 to 35 % in MCDM analysis by using hybrid subject/objective analytics.

The list of suggested solutions below provides a starting point for analytics improvement.

Comments by Business Modeling Experts

Here are three of the several expert comments on issues with SWOT:

Michael Porter: Lack of analytical rigor. According to Porter, SWOT analysis does not account for the competitive forces in an industry.

Henry Mintzberg: SWOT analysis oversimplifies strategic planning by categorizing factors into strengths, weaknesses, opportunities, and threats. This leads to a narrow view of strategic issues and might result in missed opportunities or underestimated threats.

Kim Warren: SWOT analysis often lacks a clear link to organizational performance and decision-making. This leads to vague, and generic statements that do not drive specific actions or improvements.

Some Analytic Solutions that Address Weaknesses in SWOT analysis

Here are the five most significant considerations the updated SWOT approach has regarding analytics:

• Use Multi-Criteria Decision-Making Concepts. Applying MCDM analytic criteria to analyze the landscape categories and the 4 SWOT quadrants for element significance. This provides accounting for the influence of several preferences not just one or two plus it can uncover accelerators and barriers to success.
• Using multiple ranking approaches (composite ranking, correlation matrices, and Neural Nets) to confirm the validity of ranks and significance of relationships. This prevents domination by one analytic algorithm.
• Use context analysis and DNA algorithms to assess and uncover hidden or significant relationships that offer valuable strategic insight.
• Using labor savings to expand the perspectives of the landscape using added categories reflecting the current larger and industry-specific scope today of external impacts
• Provide scenario generation and strategic implications through AI tools that utilize the results of insights gained from SWOT quadrant analytics.

For further information contact:

For training, Consulting, and SWOT Demos, contact Frank Kowalkowski at kci_frank@knowledgebiz.com

For more information on the software used, contact www.WIZSM.io


BIO:

Frank Kowalkowski is President of Knowledge Consultants, Inc., a firm focusing on business performance, business analysis, data science, business intelligence, artificial intelligence, and statistical techniques across industries. More recently, Frank has been involved in conducting workshops, professional training sessions, and assessments of business structures and transformation, data science, analytics for process management efforts. He is the author of a 1996 book on Enterprise Analysis. His most recent publications are a featured chapter in the business book Digital Transformation with BPM. His chapter is titled “Improve, Automate, Digitize.” he also has a chapter in the business architecture book titled Business and Dynamic Change, and a chapter on semantic process analytics in the book Passports to Success in BPM, and most recently, a key chapter titled Intelligent Automation and Intelligent Analytics in the 2020 book Intelligent Automation.

Art for the 3rd Quarter 2024

 Gen AI created all of the art for the third quarter. I retrofitted my first album, Amazing Journey, with images to create art associated with each song. I did this for my second album, Ready or Not (click here), so it was time to equalize the whole music catalog. I expect to work on the Gen AI videos in the fourth quarter while new songs are in the hopper for 2025 to be released as singles if all goes well. While hand-created art will not be abandoned by me, I enjoy guiding AI to create images to match the themes of my songs. You can listen to my songs on popular streaming services right now. I'm up to 172K streams on Spotify and have qualified for "Discovery Mode" on Spotify for almost the whole catalog. Currently, my songs are on over a dozen playlists. I hope you like the music and the images. 

 

                                  Love and Acceptance

                                 Nobody Knows Me 

                                 I See Your Heart 

                                 Perfect Love

                                 Coming Up Sevens

                                 Siren Song 

                                 The Next Time I See You 

What Have Folks Been Reading in the 3Q 2024?

First, I'm pumped that the blog activity surpassed 1M hits with unwanted comments cleaned from vendors trying to leverage my posts. Unsurprisingly, AI was the most exciting topic of interest in the last months, as shown in the activity by the topic graphic below. The next was a tie for second, with Digital and Customer Journey topics gaining attention. Collaboration is still an important topic according to my audience, but Process is still hanging in there after two decades past prime attention. Also below is a graph depicting activity by country over and above the US and China, which dominate the activity. See below.

AI Productivity Scorecard

Organizations face challenges in this AI era, including justifying each AI-enhanced project, measuring the results' effectiveness, and determining where they are on their overall AI productivity journey. While the big picture regarding the productivity race is evident at the national level, we are participating in increasing productivity to create gains in wages, better corporate profits, and raising living standards. See the big productivity picture by clicking here.

Why an AI Productivity Scorecard?

Organizations must understand where they are in unlocking AI's full benefits and increasing optimal productivity. While each organization's AI journey is unique, knowing where organizations are regarding their full AI productivity potential is essential. The scorecard can act as a radar screen to show where organizations or individuals are in terms of full AI potential. Last year, I published a rough guide for AI progress that identified three significant eras for AI. Click here for the three major eras. While it is helpful to know where an organization utilizes AI, a more complete and multi-dimensional productivity scorecard is needed to score how AI is being leveraged for optimal AI productivity over time. See Figure 1 for the AI Productivity Scorecard.

Figure 1 AI Productivity Scorecard

AI Productivity Scorecard Explained

Ideally, an organization has pushed its productivity to the uttermost limits of possibility; in reality, today, few organizations have pushed the boundaries to optimal because of the investment in methods, skills, and techniques that will take time to mature and prove themselves to be very effective. Most organizations start small and grow to complete potential over time. The scorecard aims to measure the progress on the path to optimal productivity. The early AI efforts will start at the center of the radar screen (spider diagram) and move to the edges over time. The scoring from 1 to 5 will be a judgment based on the state of AI at a specific point in time. Remember that AI will grow and evolve; the target could be a moving goal line. To that end, I described what to look for on each scale (vector). While it isn't perfect, it will give business leaders a relative way to measure progress over time. Remember that the scorecard can be used to measure projects and efforts first. However, aggregate efforts can be overlaid for an overall score for an organization, be it a division of the entire enterprise.

Work Impacts Scale: (AKA productivity in work complexity)

AI is excellent at automating repetitive tasks, and there are lots of organizational opportunities to automate totally, assist humans, or collaborate with other AI components. See the Top 20 AI Technologies for 2024 by clicking here. The challenge is having AI agents/bots assist with or make decisions independently within guardrails of goals and boundaries. In an AI-heavy usage scenario, AI makes plans without human collaboration and acts on them with measurement later. It is essential for instantaneous and emergent situations.

Paradigm Impacts Scale: (AKA productivity in problem difficulty)

AI is excellent at optimization as it makes fewer mistakes than its human counterparts. This means that AI clearly sees creating more optimal outcomes while goals shift faster. It assumes that the data it consumes is reasonable, but AI can sometimes sense out-of-whack data. AI can suggest alternative approaches and enhance existing optimizations with new paths or alternative solutions. It involves the creativity of a team of generative AI and humans, initially leading to more AI-driven approaches. In some cases, AI can develop breakthrough views and approaches that can be implanted and optimized on the fly.

Context Impacts: (AKA productivity in scale increase)

AI can help with personal productivity by simplifying each task with more advanced research. However, thought needs to be given to the overall journey a person as a customer, employee, or partner is on towards individual goals that may need to be incorporated with team or organizational goals. Teams often have different skills that must be collaborated on one work impact (explained above). AI assists these teams in incorporating stovepipe skills into optimal team results. Organizations leverage individuals and teams that may or may not have conflicting goals to create the overall organizational goals. AI is great at seeing the big picture and tuning individual and team goals to dynamically support overall organizational and cross-legal entity goals.

Problem Impacts: (AKA productivity in change)

Problems known and static are easy for AI to help with, but AI shines where change is evolving the goals and governance targets. AI is built for change and thrives where things grow on trend lines. There are, however, situations that evolve beyond plans and anticipated scenarios. These are known as emergent problems, which used to be quite rare but are happening more and more. AI deals with changing dynamically and recognizes new scenarios that may require replan and adjustment.

Speed Impacts: (AKA productivity in acceleration)

When work is done regularly, it is much more apt to be automated in a normal and preprogrammed way. As business change velocities increase, AI plays a role in adapting to itself in new ways. In fact, AI agents and bots are great at sitting on the edge and acting instantaneously to dynamic optimization and governance goals. The faster the need for response, the more AI will likely play a key role.

I'd now like to demonstrate the use of scorecards through three examples. The first example is AI in automation. The second example is holistic and dynamic management with AI, and finally, the third example is emergent optimization.

Example 1: AI & Automation (see Figure 2 AI For Automation Scorecard)

Automation is the usual spot where organizations will apply AI successfully. In the scorecard, I depicted a typical AI automation project or program. Typically, these kinds of efforts are aimed at intelligent actions that focus on optimizing results with known problems at expected frequencies but range from personal to organizational impacts. A few example use cases include:

• Smart Chatbots,
• Straight Through Processing
• Knowledge Assists
• Recruiting
• Quality Inspections and Control

Figure 2: AI for Automation Scorecard

Example 2: AI & Management (see Figure 3 AI for Management Scorecard)

Dynamic management at the speed of change while detecting evolving conditions and suggesting tactical or strategic decisions is where AI shines. While the scope can vary from organizational to individual, the example below is aimed at the organizational level. A few example use cases are:

• Supply Chain Management
• Management Cockpits
• Production Line Management
• Warehouse Management
• Logistics and Delivery Management

Figure 3: AI for Management Scorecard

Example 3: AI & Service Team Deployment (See Figure 4 for Service Teams)

Infrastructure servicing is a problem that must be optimized and enhanced over time. Let's use an above-ground pipeline that spans thousands of miles over various terrains, where drones fly over to look for issues and deploy service teams to remote areas when a potential leak is sensed. The drone images will be scoured for known problems and analyzed for evolving conditions, considering local weather, material decomposition, and position norm contexts. Speenorms because of the safety and environmental concerns; however, false alarms are incredibly costly.

 

Figure 4: AI for Service Teams Scorecard

Net; Net:

Progress in AI adoption and resulting productivity needs to be measured, even though scientific precision might not be attainable. Though I have searched long and hard for a way to measure AI's progress, I am still looking for something useful. To that end, I have cobbled together something that will help individuals and organizations have a rough measurement of progress toward greater productivity with AI. I hope this helps others. However, comments for improvement will be appreciated.

Additional Reading:

AI Tech Types

AI For Good

Afraid of AI?