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? 

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. 

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

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

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?

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.

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

Future of AI

Fear of AI 

AI Inside Out  

AI Tributaries

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. 

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?

Operationalize AI Effectively with Processes

AI and processes go together very well, and the risk of trying AI in processes is pretty low, as process resources can all benefit from being more competent and reacting faster to changing conditions. AI is typically aimed at traditional/everyday processes. Still, organizations are also looking at more game-changing impacts related to new products where innovative business models can be trialed with processes and data. AI also aims to address emerging business changes that show new opportunities and threats. It’s not just pure automation and optimization, though there continue to be benefit pools there. Processes are often the basis of organizational actions that cross internal and external boundaries. These processes often employ resources that could benefit from AI’s assistance, especially where knowledge, decision-making, and agile optimization based on changing or emerging goals are required. I was asked to write a white paper on operationalizing AI in and around processes. This paper is available for free from a processes vendor called Agilepoint. 

CLICK HERE FOR THE FREE WHITE PAPER

There are a number of blog posts on AI here, as well as processes. here are some exciting posts on AI

AI Posts

Definition of AI

Future of AI

Fear of AI

Top 5 Technology Trends for 2024

Last week, I published the Top 5 Business Trends for 2024 (click here), and this week, I narrowed down several technology trends to my top 5 that organizations need to start responding to intensely in 2024.

Harnessing Usable AI

Most organizations will probably have some form of the many types of AI in progress. Progress could range from experimentation to production-enabled and active in several business and technology domains. Since organizations do not fear another AI Winter because of broad-based data-driven successes, they are looking to take advantage of various kinds of AI (click here for AI Tributaries and Types for 2024). Significant efforts in and around Natural Language Processing (NLP) will allow for human understanding and appropriate responses like generating human-like interfaces in chatbots and language translation services, for example. There will be more virtual assistants that will supercharge customers and employees to be more effective even beyond their inherent knowledge and skill levels. It will expand AI to voice, image, and video analysis to create a more inclusive context for decisions and actions for carbon-based participants and robotic assistants. There will be an emphasis on emotion recognition to deal with the human factors of doing business. This new capability and power will need to be protected, so intelligent cybersecurity will get a boost to detect and prevent threats leveraging AI. Expect organizations to use AI until governance issues become the focus.

Leveraging Intelligent Customer Experiences and Processes/Applications

Organizations will likely start switching from flow-directed approaches to goal-directed ones where the flow is based on the changing goals of a customer journey or process. Savvy organizations will include their goals with the goals of customers, partners, and employees in the goal-directed approaches and balance seemingly conflicting objectives in a balanced approach. Personalization will now consider goals and measure feedback through real-time observation and analysis. Of course, better user experiences and omni-channel experiences will continue as table stakes, but more will be demanded. User-centered design employing more gamification components will play a role as AI and algorithms will expand their reach to customers, employees, and partners to advance Customer Relationship Management (CRM). Human/ tech collaboration will get a fresh look, including new forms of augmented reality over time. Continuous improvement and aggressive automation will continue in times of stability; however, changing conditions may unhinge current optimization patterns. Intelligence will be used to adapt processes and user experiences more acceleratedly. Organizations will leverage predictive methods and more aggressive scenario management and monitoring. It will be a necessity with supply chain shifts and optimization particularly.

Moving to Convergent Business and Technology Platforms

While individual technology stacks bring benefits, costs, and challenges, organizations will eagerly watch for the convergence of focused functionality into platforms that more easily integrate technology functions to enable faster and cheaper business results. Desire will force broader technology options at a more affordable cost and potential mergers and buyouts. Convergence will create aggregated specialty platforms and generalized digital business platforms. The effect is fewer vendors to manage for organizations and more integrated business/technical functionality. Examples include generalized Digital Business Platforms (DBP), Business Application/Package Platforms, Sales/Customer Platforms, Process Platforms, Collaboration Platforms, Data Science/Analytic Platforms, Automation Platforms, Lowcode Platforms, Cloud Platforms, Data Mesh Platforms, and Security Platforms. For a quick overview of the players, click here. I expect AI platforms to emerge as success is experienced and integration becomes necessary.

Building on Intelligent Infrastructure

As all business-driven intelligence and agility become a competitive weapon, the need for intelligent infrastructure will emerge quickly. It means that the infrastructure players that leverage AI and analytics in either a reactive or proactive manner will flourish. It will create a race to intelligence under the covers of processes, systems, and applications. Edge computing and IoT integration are perfect examples of where putting intelligence at the edge or even outside of a business process will be necessary. First, it will be monitored soon after there will be recognition of the need for decisions close to the edge and intelligent actions to deal with the changing conditions. Eventually, AI-driven intelligent bots or agents will be brokering response patterns at the edge. Examples of success today would include Smart Cities infrastructure. Digital twins will flourish in intelligent infrastructure, leveraging clever hybrid and multi-cloud along with smart data meshes. All of this will require smart security that is blockchain-enabled. In the future, quantum computing exploration will keep a watchful eye on the swarms of agile AI bots responding to infrastructure and business needs.

Living with Governed Leverage with Sustainability

Like it or not, organizations will have to balance their business results with the trail of impact their business activities create. There will be the emergence of renewable energy integration where it makes sense. Recycling or recreation will be more emphasized in 2024, along with eco-friendly packaging solutions. Smart buildings that leverage AI for energy efficiency optimize energy consumption in many aspects of an organization's activities. Remote work will play a role in the delicate balance of progress and preservation. Technology will be essential in an organization's ability to measure, monitor, and reduce its carbon footprint over its complete operation as and its supply chain. Water management is becoming a vital resource to monitor and optimize, leveraging tech and advanced waste management technology and techniques.

Top 5 Business Trends for 2024

Adapting to Dynamic Business Conditions

The first trend is around businesses staying dynamically adaptive to change. Organizations must go beyond scenario planning exercises that sit on the shelf. While the planning exercises are proactive and sound, they also allow businesses to develop strategies for the various likely and unlikely scenarios. Savvy organizations will test the most likely scenarios, making an organization better prepared to adapt when change occurs. This means organizations must cultivate a culture of innovation that builds on leveraging agile methodologies and employee training/development. Cross-functional teams with individuals who embrace technologies by staying abreast of technological advancements will often develop solutions that support executive goals within alternative scenarios. Strategic partners will also help craft solutional alternatives, including backup partnerships. Remember that organizations are moving to real-time data/event-driven decisions that will adjust processes and applications. Careful planning will help legacy platforms and packages remain in a changing business environment. Managing change is essential in a world that is speeding up to new rates of change. This is not a "one-and-done" exercise, as adaptability is an ongoing process that needs regular reassessment.

 
Augmenting Your Customers

While most organizations think they are creating and refining user-friendly interfaces, the reality from the customer side is another thing. Customers find most interfaces are designed from the inside out, leveraging existing software and support teams. While multi-channel engagement and responsive customer service go a long way to helping the customer reach your organization's goals, the reality is that the customer journey is rarely just about your transactional efficiency when working with them. Think about how organizations had outsourced the keystrokes to the customer and the dumb chatbots they must deal with daily. To add insult to injury, the surveys are designed to get managers their rewards, not the customers' perspective. It's time to think "outside-in" from the customer's goals through the interaction with your organization and its legacy systems. This will give organizations new insights to personalize the experience, including smarter chatbots that recognize feedback in voice or visual cues to include sentiment, both positive and negative responses, in real-time with transparency. Let's assist our customers in their journeys, not just optimize costs for organizational goals based only on transactions your organization controls. Let's shift from reactive cost containment to proactive customer satisfaction and loyalty.

Augmenting Your Employees and Partners

Often, employees become the shock absorbers between your organization and other constituents. This means that they deal with the lack of integration across internal stovepipes that live with conflicting internal and external goals. What employees want is better job satisfaction, recognition/rewards, flexible work arrangements, and mostly career growth opportunities. They are driven by the need to keep up with ever-rising costs in their life situations. While some of these needs can be managed with a change in management tactics, employees need help. They need a better collaborative work environment with effective communication that helps them develop and learn to augment their career growth. Why not remove their "dirty work" with automation and let them become more knowledgeable workers through AI augmentation and reward-driven employee empowerment? Giving employees more autonomy and decision-making authority within their roles or across stovepipes with collaboration with others and AI bots is desirable. The best suggestions for continuous improvement will come from happy employees. All organizations need the table stakes of wellness programs, diversity, and workplace perks, but augmentation for advancement will be a key theme as we advance.

Managing Elusive and Shifting Costs

While the traditional methods of cost analysis, budgeting, expense monitoring, and control will still deliver savings for organizations, there are additional issues to consider. Negotiating with suppliers and partners to seek better deals, discounts, and more favorable payment terms is a great place to find incremental savings. Some big numbers in process optimization employ cost-reduction technologies that are now smarter than those of previous generations. Process mining and AI monitoring are good places to find nuggets of opportunity. Organizations may have to invest in technologies that streamline processes, automate repetitive tasks, and improve efficiency. It can lead to long-term cost savings and increased productivity. Cross-training and workforce optimization can help leverage existing employees or bots to handle various types of tasks and responsibilities. It creates excellent leverage and flexibility when experiencing peaks that typically require hiring additional staff. Telecommuting and remote work is a great cost-cutting trend for office costs. Ensure cost cutting is not arbitrary to meet numbers only to hurt long-term trends. Often, overzealous cost-cutting on minutia sometimes backfires with unexpected behaviors.

Providing Secure Digital Commerce

Ensuring secure digital commerce is crucial to protecting your business and customers from potential security threats. While customer education on best security practices, adherence to payment standards, and continuous fraud detection in real-time to mitigate suspicious activity leveraging machine learning are table stakes, there are additional efforts to take for organizations. Two-factor authentication is a must for user accounts. It adds a layer of security by requiring users to verify their identity. Regular backups of critical data combined with comprehensive data plans are essential in the event of a security incident. Encryption of all stored data protects data in case of a breach. Organizations are encouraged to choose secure hosting providers and cloud services that prioritize data security. Hosting environments should have robust security measures, including firewalls, intrusion, and detections. All your infrastructure and business software needs to be updated by patching and updating your systems. Regular software updates can close vulnerabilities and protect against known security threats. Above all, conduct regular security audits and vulnerability assessments of your e-commerce platform. Identify and address potential weaknesses in your systems to stay ahead of emerging threats. Remember that this is a war with bad actors.