Showing posts with label AI. Show all posts
Showing posts with label AI. Show all posts

Friday, August 1, 2025

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




Tuesday, May 20, 2025

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.  



Wednesday, April 23, 2025

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.

Tuesday, April 15, 2025

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 




Wednesday, January 29, 2025

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

















Monday, January 13, 2025

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







Wednesday, December 4, 2024

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.



Wednesday, November 6, 2024

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 

Monday, October 21, 2024

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.





Monday, October 7, 2024

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:





Tuesday, September 17, 2024

AI Must Increase Productivity or Else

The theme for the coming years will be a significant increase in productivity. According to my favorite definition from a Google search, “Productivity is a measure of performance that compares the output of a product with the input, or resources, required to produce it. The input may be labor, equipment, or money.”




AI must be a key driver to not only innovation but also a way to increase baseline productivity measures. It is a must at the macro level, by country and industry, and at the micro level, with AI-enabled projects. This is true for all on a personal basis and an organizational basis. It is not a zero-sum game where organizations win, and individuals lose. It balances organizational outcomes gained with personal satisfaction without time synchs forced on individuals inside or outside an organization. The good news is that we have high productivity rates in mature economies. See the GDP Per Hour Worked by Region in Figure 1. The bad news is that the productivity increase rate is not what it could be, averaging only a meager 2.1 percent on average since 1947 and a shallow level of 1.6 percent recently. See Productivity Change Rates by period in Figure 2.



                                          Figure 1 GDP Per Hour Worked by Region


The additional good news is that AI is capable and is growing in its influence and impact by the minute. As long as AI is applied in a goal-driven fashion governed by reasonable boundaries, the possibilities are endless. The individuals who control these goals and constraints will lead the way to greater productivity. We can’t sit still and must apply AI aggressively on a local basis, constantly looking to the global impact incrementally. Every country can benefit from AI and potentially leap up in productivity.





                                      Figure 2 Productivity Change Rates by Time Period

Net; Net:

AI has the potential to decrease the hours worked for all of us. An accurate "more with less" enabled by AI. This is true for those who complete repetitive simple tasks, those who make decisions at the tactical level for optimization of work while taking great care of customer, employee, and partner journeys, and those who set strategy or create response scenarios in an ever-changing set of markets, industries, regions, and legal frameworks. While AI is exciting in its potential for productivity, it carries the fear of change and control. Let’s manage this once-in-a-lifetime opportunity AI gives us. This is the first post in a series on the productivity and application of AI. Watch this space.

Thursday, July 18, 2024

Art for the 2nd Quarter 2024

I continued experiments with Gen AI, leveraging Kaiber to create more music videos. These videos coincided with my new album, "Ready or Not." Please click here for an album summary. For a quick preview of each song, click here. I generated more AI videos for multiple songs. I started with a storyboard to tell the story of each song. Click here for my song videos so far. Please comment or subscribe as I deliver more in the future. I also created a few more fractals this quarter, as shown below. Visit my art website by Clicking here



Decisions


Layers


Vapors





Monday, July 8, 2024

What Have People Been Reading in the 2nd Quarter 2024?

 Again, AI and Customer Journey topics dominated the activity. However, Results-oriented communications /collaboration is rising fast as a topic of interest. Surprisingly, Processes made the leaderboard for the first time in a while. 

                                        Blog hits in the 2nd Quarter of 2024 



                                                Offshore Activity in the 2nd Quarter 2024





Wednesday, May 29, 2024

AI: For Good or Evil?

AI has significant momentum now and contributes positively to businesses and everyday life. Today, industry and government leaders warn of the dangers of unbridled AI. So, let's peel back the onion on AI for Good and AI for Evil. Next, let's see what it takes to steer AI positively with as few side effects as possible. We all know all advancements come with good and bad effects. Look at the automobile, for instance. Autos take us to many places, but driving them unsafely without following the rules of the road leads to injury and even death.



AI Brings Good for Many Industries.

· Healthcare uses AI for preventive medicine, advanced diagnosis, personalized treatment plans, and drug discovery.

· Education uses AI for lifelong learning, adapting to changing career changes, shifting to new opportunities, and personal interests with virtual tutors and dynamic personalization.

· Transportation uses AI to optimize the planning and operation of smart cities, support various levels of autonomous vehicles, and optimize eco outcomes within the need for goal-directed efficiencies.

· Finance uses AI for investment management, wealth management, and fraud detection.

· Customer Service uses AI for hyper-personalization, sentiment analysis, and virtual assistants.

· Agriculture uses AI for sustainable farming by optimizing resource uses, automated or not, reducing waste, developing crops for climate change, and practicing sustainable soil management.

· Environmental Protection uses AI to design and implement effective climate change mitigation strategies, biodiversity monitoring, and resource management.

· Manufacturing uses AI in smart factories for optimization, efficient supply chains, and innovative material discovery.

· Entertainment uses AI for immersive experiences, innovative content creation, and audience engagement.

· Accessibility uses AI for inclusive design, enhanced communication across language barriers, and assistive technologies for the less capable.


AI Brings Good for Businesses

Businesses, in general, are using AI for enhanced decision-making in both a proactive and reactive manner. They are Improving the customer experience with AI while increasing their operational efficiency in a balanced fashion with AI. Marketing and sales are expanding their reach through better targeting and predictive forecasting. Businesses are creating new products and services with AI while better supporting their existing portfolio of products and services. AI helps with human resources with better engagement and automated recruitment. AI helps with fraud detection, compliance, and speedier governance. Businesses leverage AI for better expense management and investment strategies. Organizations can predict customer churn and measure customer sentiment in real time. Speaking of real-time, threat detection and vulnerability management can take advantage of AI. Businesses can increase their productivity, revenue, and costs while leading in sustainability through resource optimization and sustainability. Companies can take great advantage of various types of AI.

AI Brings Good for the Consumer

Consumers are experiencing many benefits of AI today, and AI is leading to even more benefits. Personalization provides tailored recommendations, customized voice/language engagement, and satisfaction with 24/7 availability and quick responses to overall needs, not just transactions. The shopping experience is improving with virtual try-ons and augmented/enhanced reality. Health assistants with links to telemedicine will help with preliminary notice of symptoms and diagnosis. Financial advice will be provided to optimize customer goals, both long and short-term. Recommendations for targets in the tsunami of content emerging to help the viewing/listening experiences. Home management and security will benefit from AI as well. Consumers will benefit from sustainability suggestions as well. Civic engagement, cultural preservation, and mental well-being are also benefits.

It's hard to argue that AI is not used for good and that the expectations for more AI benefits are sky-high. Yet, at the same time, some horror is dribbling from under AI. There are bad actors out there trying to do evil with AI. Without all the rules of the AI road laid out yet, there is an opportunity for these bad actors. Some individuals use AI to gain advantage, leverage, and illegal financial gain. Some use AI to subvert power, weaponizing AI for offensive purposes.

AI Enables Bad Actors

· An early evil use of AI is for cyberattacks that target critical infrastructure, financial systems, or government systems for monetary gain, espionage, or sabotage.

· AI can bring social engineering and manipulation by using social engineering techniques to manipulate individuals, influence public opinions, and spread misinformation for financial or ideological gains.

· AI can perform mass surveillance, tracking individual movements, communications, and activities without their knowledge. AI can infringe on privacy rights and civil liberties.

· AI can power autonomous weapons and other forms of lethal weapon systems without human intervention. Drones or robotic soldiers are examples of weapons that could act alone or swarm to escalate conflicts or undermine international stability and security.

· AI can be used by adversaries, including cybercriminals, state actors, and terrorist organizations, to create a movement against established society.

· AI can inadvertently exhibit unintended behaviors or consequences that manifest as errors, biases, or impacts on individuals, organizations, or society at large.

· AI can be harnessed to perform financial crimes and fraud toward individuals or organizations.

· AI can be used to create deepfakes and misinformation to gain financial advantage or take down the reputations of individuals or organizations.

· AI can affect biases and exacerbate inequalities by targeting individuals or groups to displace people from jobs or make it hard to thrive.

· In the worst-case scenario, AI could pose existential risks to humanity if misused or developed with inadequate safeguards.

AI Enables Power Plays

Geopolitical competition will drive rivalries among nations, pushing them to vie for dominance and try to leverage AI for innovation for economic gain and technological and military advantages. While not all this is bad, it can be taken to extremes, leading to new global pressure points and chess matches. Some of it could lead to arms proliferation and a new arms race. The amount of cyberwarfare and spying is bound to increase. The real risk is the need for clear regulations, guidelines, and treaties. There is likely to be a blurring of AI for military and civilian uses that will also muddy the mix. There is also a real danger of a supervillain or group leveraging AI to extort or unduly influence others.

Net; Net:

The fear of Evil AI will not drown out Good AI for now. The control of autonomous weapons, cyber warfare, aggressive intelligence, and psychological operations should be planned for the future. Establishing international regulations, norms, and treaties is the best way forward. Agreeing on what ethical AI development is and monitoring it with transparency. A move towards more “human-in-the-loop” for lethal actions is needed. Establishing robust oversight and governance and promoting peace and diplomacy with AI can work for us. The proactive measures of establishing robust international regulations, enforcing ethical guidelines, promoting transparency, and fostering a culture of peace and diplomacy can mitigate risks and bad behavior from bad actors. The alternative is mutually assured destruction, as we have with nuclear powers.

Additional Reading:

Definition of AI










Thursday, May 9, 2024

Stepping Up the Music Game

 Most of my friends and family know about our new album, "Ready or Not," but its reach has surprised all of us. The numbers are encouraging, and the comments about the quality really excited us. We reached a worldwide audience and 62K streams on Spotify alone. See the charts below for the last 28 days' worth of activity on Spotify. Just search for Jim Sinur on your favorite streaming service. If you don't stream much, click here for the new album. The most popular songs are Forgive, Mercy Me, and Kinder, according to the numbers (see below). My favorites are Cry for Creation and Restoration. 

The AI Gen videos are also drawing folks into the music. In fact, we have had some fortune with longer songs because of the compelling nature of the videos. Click here for all the videos completed to date. The two newest are Mercy Me and Restoration. My favorites are Cry for Creation and Forgive. The core team of Ethan Foxx, Jimmy Caterine, Pete Crane,  and I hope you enjoy the creativity, core stories, and professional engineering. Primarily, we hope to see you dancing to some of these tunes someday. 





Tuesday, May 7, 2024

When AI Goes Inside Out

AI is progressing well in many industries, assisting people or independently completing tasks. These uses of AI are often operational and can usually be embedded in business processes, software, or devices. We all, except for Luddites, expect the continued success of AI on focused tasks at the operational level. In fact, AI is progressing so well that it is catching up with humans for specific skills and combinations of skills (see Figure 1) from Stamford University below. It all sounds good, but the story could be different as AI ventures out to handle tactical management and executive strategy. AI will break out of processes and devices to combine various types of AI to assist and eventually automatically manage critical adjustments for businesses. It will happen as AI bootstraps success, leaving us with new challenges and driving us into AI fear zones. It is exciting and points to higher benefit levels for AI, but is this a Pandora’s Box?


Sample AI Operational Successes

· Automated Data Analysis: AI algorithms can analyze large volumes of data or content of various types to extract valuable insights and trends, enabling businesses to make data-driven decisions more efficiently.

· Process Automation: AI-powered robotic process automation (Smart RPA) can automate repetitive tasks such as data entry, invoice processing, and customer support inquiries, freeing up employees to focus on higher-value activities.

· Predictive Maintenance: AI can predict equipment failures by analyzing sensor data and historical maintenance records. It enables businesses to schedule maintenance proactively to minimize downtime.

· Dynamic Pricing: AI algorithms: AI algorithms can analyze market conditions, competitor pricing, and customer behavior to optimize pricing dynamically, maximizing revenue and profitability.

· Inventory Management: AI can optimize inventory levels by forecasting demand, identifying slow-moving items, automating replenishment processes, and reducing stockouts and excess inventory costs.

· Customer Service Enhancements: AI-powered chatbots and virtual assistants can handle customer inquiries, provide personalized recommendations, and assist with problem-solving, improving the overall customer experience.

· Fraud Detection: AI algorithms can detect fraudulent activities, such as payment fraud, identity theft, and account takeover, by analyzing patterns and anomalies in transaction data, reducing losses and risk.


We all know that AI is progressing steadily towards an even brighter future for business applicability. AI is picking up skills fast and will equal human capabilities in any individual skill. See Figure 1 for a sample set of skills that AI is progressing.



                                            Figure 1 AI Skill Levels Over Time

This progress is impressive, and when combined with algorithms, goals, and boundaries, AI will go broader, deeper, more complicated, more complex, and more independent. AI will go from task to function while taking on tactics and strategy. Instead of just inside known and established processes, AI will break and challenge coordination and management tasks at the tactical level, eventually working its way into shaping strategy. Thereby putting AI in a position to respond to situations as AI deals well with emergence (complexity); this is an inside-out moment for AI that will start in the coming months and years. I expect the "inside out" trend to begin with processes, as AI can quickly move from tasks to management. The inside-out processes will likely start with monitoring, leading to notification and then to suggestions for action. Eventually, AI will take action with or without permission. 

The transition of AI from inside operational processes to outside processes typically involves the evolution of AI applications from narrow, task-specific implementations to broader, tactical, or strategic capabilities that impact various aspects of the business that cross traditional organizational boundaries. It includes the following:


· Scaling AI Across the Organization

· Integrating with Enterprise Systems

· Cross-Functional Collaboration

· Strategic Alignment and Executive Sponsorship

· Data Governance and Quality Assurance

· Continuous Learning and Improvement

· Partnerships and Ecosystem Collaboration

AI at the Tactical Level

At the tactical level, AI can contribute to essential cross-functional efforts and processes that require constant monitoring and adjustments that are tied to goals (static or emergent). Examples include:

· Customer Relationship Management: Besides the usual inquiry aid, AI can segment customers and proactively predict customer churn.

· Sales and Marketing: AI can drive better lead identification and suggest products/services to those leads. By analyzing activity, AI can target offers individually or with campaigns.

· Supply Chain Management: AI can forecast demand and market trends and tune logistics optimization dynamically while optimizing transportation costs.

· Operations and Manufacturing: AI can optimize production schedules, suggest improvements, and manage energy efficiency.

· Human Resources: AI can streamline recruitment and analyze employee performance for career development.

AI at the Strategic Level

At the strategic level, AI can contribute to the organization's executive level as it monitors the attainment of conflicting goals while maintaining profitability and reputation as a good community member locally and a great place to work while appealing for future investment. It is where emerging conditions must be monitored and intercepted and, where appropriate, changes. AI can start with being a sentinel, but bigger toles may be possible regarding the freedom to act independently. Examples include:

· Market Analysis and Competitive Intelligence: AI algorithms can analyze vast amounts of data from diverse resources to provide insights into market dynamics while identifying opportunities and threats from the competition.

· Forecasting and Planning: AI-powered predictive analytics can forecast future trends, demand patterns, and potential business outcomes, which might mean adjusting capital and resource allocation, inventory management, and production/service planning to optimize efficiency and reduce risk.

· Risk Management and Mitigation: AI can analyze various event patterns and data to identify potential risks and vulnerabilities, such as fraud, cybersecurity threats, and market fluctuations. It allows for proactive risk mitigation and safeguarded assets.

· Strategic Decisions: While AI might not make the decisions initially, AI-powered decision support systems can simulate various scenarios and the likelihood of them happening and have a plan of action. Whether it is a new market or investment, AI can help.

· Product and Service Innovation: AI technologies can be baked into offerings to create new products and services. Examples include computer vision, machine learning, voice-driven sentiment analysis, and intelligent service bots.

Net; Net:

AI will be going inside out and will have more influence on business outcomes at our organizations' operational, tactical, and strategic levels. The question is, what level of freedom will AI be given to act independently, especially if we get into an AI arms race in individual industries or between countries with very different value systems? It is inevitable unless AI has some overall meltdown. I have yet to see AI taking over from humans at the highest level of risk. The question for me is, "Will AI only be used for GOOD once it is given freedom, or Will it also be used for EVIL?" That is a topic for another day. AI will be used successfully as it has proven helpful in many use cases, with more coming. Will the winds of change rip the inside-out umbrella of AI out of our hands?

Monday, April 15, 2024

Art for the 1st Quarter 2024

 All of the art for the first quarter resulted from experiments with Gen AI. I used Kaiber for the videos and Microsoft Image Creator. These art projects coincided with my new album, "Ready or Not." Please click here for a summary. For a quick preview of each song, click here. I generated four videos, as listed below, but I started the videos with the following images to give the AI tool a starting point and a storyboard to tell the story of each song. Overall, I liked the experience as an artist, but it won't keep me from doing art by hand or fractals, which also require computer assistance. Here are the art pieces for each song in the order they are on the album. 


                                                        Are You Ready for Me?


                                                         Spiritual Treasure 


                                                                Forgive 


                                                            Thankful Now 


                                                            Judge Not 


                                                               Kinder 


                                                   Walking the Talk


                                                            Mercy Me

                                                          Cry for Creation


                                                           Restoration 


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