Monday, November 6, 2023

AI Tributaries & Types for 2024

While it is imperative to understand what AI is, where it is going, and where it offers promise and downsides, it is also essential to know all the technology tributaries. These tributaries offer strengths that can contribute to business outcomes, but they also have challenges in implementation and operation. I gathered the most common AI technologies, depicted in Figure 1, and briefly described where to use them and where to avoid or bolster use. Often, organizations combine several of these tributaries to accomplish their desired outcomes and keep them current in a more automatic way. Keep in mind these tributaries are maturing fast and independently today, so organizations will have to package a number of these to reach desired outcomes that are of a higher order. I am hoping this enumeration will assist in spending your 2024 AI budget. 

                                                           Figure 1 AI Tributaries


Machine Learning


Machine learning is the kind of AI that teaches computers to learn from experiences represented by data and information that does not rely on a predetermined equation or sets of rules. Machine algorithms adaptively improve their performance as the number of data samples increases, thus increasing the learning process.

When to Use

Use machine learning when you can't code rules, such as human tasks involving recognition where there are many variables with frequent change.

When Not to Use

When the data is problematic, including too much noise, too dirty, or grossly incomplete.

Deep Learning


Deep learning is a distinct/specialized form of machine learning that attempts to learn like humans by identifying objects and linking them to each other using a neural network, which is layered with interconnected nodes called neurons that work together to process and learn from data. It's a form of patterned learning.

When to Use

Use learning is used where there is a large amount of data available and there is a requirement for higher accuracy. Typically, deep learning learns from its mistakes and includes the lessons learned.

When Not to Use

Deep learning has a high computational cost that must be factored into solutions. There is, of course, a high dependence on the data quality. The scope of the data it is trained on may limit its ability to deal with unforeseen consequences.

Pattern Recognition/Perception


Pattern recognition is the automated recognition and regularities in data of various sorts. These patterns can be classified and leveraged to make decisions or predictions. New and emergent patterns can be detected for further analysis.

When to Use

Pattern recognition is critical in improving comprehension of the intricacies of complex problems. It is beneficial for recognizing objects in images, scanning, and photo-related interpretations.

When Not to Use

Again, the state of the data is critical, but dealing with significant variations in the data may disqualify pattern recognition as a solution.

Natural Language Processing (NLP)


NLP is a form of AI that allows computers to understand human language in any form and leverage it in a more seamless human-computer experience.

When to Use

NLP is a significant bridging mechanism between humans in their own language and computers. It is often used for computers to read text or hear speech to interpret and measure sentiment, helping to identify important words/phrases.

When Not to Use

NLP is not as helpful when a particular language is inconsistent or ambiguous, particularly regarding sarcasm and culture.

Real-time Universal Translation


Real-time Translation helps people translate one language to another instantly. People speaking differently can have a conversation or meeting in different languages with minimal delays or issues with accuracy.

When to Use

Universal translation is an essential tool for breaking down language barriers and facilitating cross-cultural communication.

When Not to Use

UT cannot correctly translate expressions, idioms, slang, abbreviations, or acronyms. Additionally, it cannot provide an accurate yet creative translation. Therefore, it should be used with caution.



A chatbot is a software application or web interface that aims to mimic human conversation through text or voice interactions. Chatbots that represent real-world interactions and incremental learning are the most effective.

When to Use

Chatbots are used in timely, always-on assistance for customers or employees. Often, they are helpful in social media, messaging, and phone calls.

When Not to Use

Chatbots are not helpful when addressing customer grievances as every individual is unique, and the problem could be complex over a more extended period than any one business event or transaction.

Real-time Emotion Analytics (EA)


Emotion analytics collects data and analyzes how a person communicates verbally and nonverbally to understand a person’s mood or attitude in the context of an interaction. EA provides insights into how a customer perceives a product or service.

When to Use

EA can help you improve the usability, engagement, and satisfaction of your users, as well as identify and address any pain points or frustrations.

When Not to Use

Like other forms of technology, emotional AI can display biases and inaccuracies. Consumers have to consent to being analyzed by emotional AI, which may present some privacy concerns.

Virtual Companions


A virtual companion is an embodied AI character that advances multiple forms of companionship. It includes not only the experience of togetherness with an AI character but can also augment the nurturing of companionship between people or animals.

When to Use

These interactive programs are accessible through the web or mobile, that serves as a companion or partner for therapy and mentorship. Early uses are about a boyfriend or girlfriend relationship, fulfilling some of the functions usually associated with these relationships, but also used for elderly care—emerging benefits around mentorship and collaboration in business.

When Not to Use

Be careful, as they can cause harm, such as hurting users emotionally or giving dangerous advice. Sometimes, perpetuating biases and problematic dynamics are a result of their use.

Expert Systems


Expert systems leverage AI to simulate the judgment and behavior of a human or an organization with expertise or experience in a particular field.

When to Use

Expert systems can be used standalone or to assist non-experts. It's helpful when skills are scarce locally, expensive, error-prone, and people are too slow.

When Not to Use

Expert systems do not leverage common sense and often lack creative or sensitive responses that humans can deliver. Often, expert systems lack explainability.

Generative AI


Generative AI refers to models or algorithms that create brand-new output, such as text, photos, videos, code, data, or 3D renderings, from the vast amounts of data they are trained on. The models 'generate' new content by referring to the data they have been trained on, making new predictions and output.

When to Use

Generative AI creates new and often original content, responses, designs, and synthetic data. It’s valuable in creative fields and novel problem-solving while generating new types of outputs.

When Not to Use

Generative AI can provide helpful outputs based on users' queries, but sometimes, the material generated can be offensive, inappropriate, or inaccurate. Human guidance can correct the result and put it into context.


Edge AI


Edge AI is all about putting intelligence closest to any device or edge computing environment. Edge AI allows computations to be done close to where the data is collected rather than at a centralized cloud computing facility or offsite data center.

When to Use

When speedy, always-on, and decisions are necessary, close to where data is sensed and collected.

When Not to Use

Edge AI devices may not all have the same level of encryption, authentication, and protection, therefore making them more vulnerable to cyberattacks. Scalability is also a challenge.

Sensing AI


Sensing AI is an AI awareness that is driven by one or many human-replicated sensing capabilities such as voice, vision, touch, taste, or smell. Sensing AI gives a presence in one or more physical contexts to present data to the logical side of AI.

When to Use

Any time in context computing will assist; any or all of these senses will give immediate and vital feedback to computing systems and humans. These are often used in dangerous environments.

When Not to Use

When Physical senses do not contribute to desired outcomes or where immediate feedback is unnecessary.

Autonomous Robotics (AR)


ARs are autonomous intelligent machines that can perform tasks and operate in environments independently without human intervention.

When to Use

Ars are great at automating manual or repetitive activities in corporate or industrial settings, but they also are great at working in unpredictable or hazardous environments.

When Not to Use

Robots only do what they are programmed to do and can't do more than expected unless some kind of learning AI powers them.

Next-Gen Cloud Robotics


Cloud robotics is the use of cloud computing, cloud storage, and other internet technologies in the field of robotics. One of the main advantages of cloud robotics is its ability to provide vast amounts of data to robotic devices without incorporating it directly via onboard memory.

When to Use

Cloud-based robot systems are capable of collaborative tasks. For example, a series of industrial robotic devices can process a custom order, manufacture the order, and deliver it all on its own—without human operators.

When Not to Use

Tasks that involve real-time execution require on-board processing. Cloud-based applications can get slow or unavailable due to high-latency responses or network hitch.

Robotic Personal Assistants


A robot personal assistant is an artificial intelligence that assists you with routine domestic chores and improves your quality of life.

When to Use

Today, these robots are used in specialized services such as cleaning.

When Not to Use

For tasks that require empathy or dynamic adaptability,

Management & Control

Artificial General Intelligence (AGI)


AGI represents generalized human cognitive abilities on software that can solve an unfamiliar task.

When to Use

If realized, an AGI could learn to accomplish any intellectual task humans or animals can perform. Alternatively, AGI has been defined as an autonomous system that surpasses human capabilities in most economically valuable tasks.

When Not to Use

It is not here yet.

Digital Twin


A digital twin is the digital representation of a physical object, person, or process contextualized in a digital version of its environment. Digital twin links the logical side of AI and the physical side of AI in an artificial environment to visualize, simulate, and try actions without real consequences, ultimately promoting better decisions by humans or machines.

When to Use

Digital twin technology enables you to create higher-quality products, buildings, or even entire cities. By creating a simulation of a system or a physical object, designers can test different design scenarios, identify potential design flaws, and make improvements before construction begins.

When Not to Use

It is challenging to maintain a digital asset. Many digital twin efforts fail because the digital assets don't receive the same maintenance effort as the physical ones. The digital twin requires consistent upkeep, significant observation, and time to document all real-time changes.

Smart Self-Generating/Adaptive Applications, Processes and Journeys


Self-adaptive software systems can adjust their behavior in response to their perception of the environment and the system itself. Applications, processes, and journeys coordinate competent and not-so-smart resources and must constantly be tweaked to stay current with needs.

When to Use

When a system or process supports emerging conditions and desired outcomes.

When Not to Use

When the system or process exhibits long-term stability

Goal-Driven & Constraint Behavior


When Management goals change to reflect the latest thinking or emerging governance constraints, systems and processes seek these goals within governance boundaries.

When to Use

When volatility is a crucial consideration, or there is a robust environment of emergence

When Not to Use

When stability creates a Constance.

Cognitive Cybersecurity


Cognitive security is the interception between cognitive science and artificial intelligence techniques used to protect institutions against cyberattacks.

When to Use

When bad actors generate intelligent attacks

When Not to Use

It is not optional today and is part of the intelligent infrastructure

Net; Net:

It is essential to understand all the flavors of AI so that solutions can leverage AI where it makes sense in the current and future business environments. The AI tributaries will combine into solutions that will be more business or consumer-ready. Leading organizations will not wait long to take advantage of these tributaries and emerging combinations. Even the following organizations need to understand these tributaries to ask the right questions to vendors or internal developers. AI is shape-shifting, so let's stay on top of this emerging movement.

Additional Reading:

Definition of AI

Monday, October 23, 2023

Why Do You Need to Define AI?

Everyone is talking about AI, and nearly half of the digital/technological budgets for 2024 are aimed at AI. Because of this, everyone should care about what AI is and what kind of AI you want to meet your objectives. You need to know if something is AI or if some vendor or internal developer is just AI-washing what they have to offer. I think it is essential to understand what AI is today because AI is on a journey, and it's vital to know where it came from, what can be done with AI today, and where AI is headed. This way, organizations aren't stuck with pioneering without knowing they are on the edge or using traditional technology wrapped in an AI wrapper to leverage the AI movement. Or you are claiming AI victories by just putting window dressing on conventional technology. AI will be a critical player on your team soon, so to get to know AI early, I tried to identify authoritative definitions and put them in one place for you, including what one version of AI thinks. I also attempted to define a value-added definition of AI, leveraging my past business experience leveraging AI to get started.


Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

The Association for the Advancement of Artificial Intelligence (AAI): AI’s primary goal is to build an intelligent machine. The second goal is to find out about the nature of intelligence.

WIKI: Artificial intelligence (AI) – intelligence exhibited by machines or software. It is also the name of the scientific field which studies how to create computers and computer software that are capable of intelligent behavior.

Gartner defines artificial intelligence (AI) as applying advanced analysis and logic-based techniques, including machine learning (ML), to interpret events, support and automate decisions, and take actions. This definition is consistent with the current and emerging state of AI technologies and capabilities, and it acknowledges that AI now generally involves probabilistic analysis (combining probability and logic to assign a value to uncertainty).

Forrester defines “generative AI” as: “A set of technologies and techniques that leverage massive corpuses of data, including large language models, to generate new content (e.g., text, video, images, audio, code). Inputs may be natural language prompts or other non-code and non-traditional inputs.”


The simulation of human intelligence by software-coded heuristics


AI, or Artificial Intelligence, refers to the simulation of human intelligence processes by machines, particularly computer systems. These processes include learning, reasoning, problem-solving, perception, and language understanding. AI technologies aim to enable computers and machines to perform tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, making decisions, and adapting to new information.

Jim Sinur

"AI is the leverage of software and machines to add perception/intelligence to individuals, customer/constituent experiences, processes/tasks and devices to optimize balanced outcomes by interpreting patterns of interest, making highly informed decisions with speed, and taking appropriate proactive or reactive actions considering wide and deep implications all within the context of changing conditions and governance guardrails."

Net: Net:

It is essential to know what kind of AI you are buying. Machine intelligence or generative AI is often represented as the only and most advanced AI. Ensure you understand the AI you are buying or building as many technologies participate in the AI disciplines. It may mean understanding the multiple streams of AI contributing to a solution you may buy or build. It influences what problems you are trying to solve, your testing/debugging, and where it can go off the ranch and get into trouble in places you don’t anticipate. If you want a picture of where AI has come from or where it is likely to head, please click here. The Gartner AI Hype Cycle is another good resource. Better yet, you need to define what kind of AI you want to pursue in line with your business objectives and get ahead of AI’s projected core competencies and technologies for the future.

It would be best if you defined AI for you continuously as it evolves to the ideals declared in the general definitions available. AI is just a set of methods, techniques, and tools that will be used for good and bad outcomes. Remember, AI is not God, and nor should it be used by actors to create an artificial god. Humankind will eventually be supercharged with AI. As always, some will fly too close to the sun.

Additional Reading:

Wednesday, October 11, 2023

What Have Peeps Been Reading in the 3Q 2023?

 Topics of customer journeys and processes continue to gather interest, but surprisingly, the combination of IOT and process hit the top spot for interest. Of course, AI is peaking because of the momentum of generative AI. Managing new balances with collaboration, balancing new digital efforts with legacy maintenance, and creating the elusive management cockpit for management visibility also gathered interest. Sweden and Canada were the most active offshore countries. 

                                          Hot Topics for 3Q2023

                                                Third Quarter 2023 Offshore Activity  (non-US & China)           

Monday, October 2, 2023

Who is Afraid of AI?

With all the AI-related newsfeeds, stories, announcements, and tech giant personalities sharing their wisdom, it would be hard to avoid hearing about the "Big Bad AI" undercurrents. To answer this question honestly, I would admit to both fear and excitement. For the short term, the news is mostly good and helpful, but the fear of where AI might end up down the road scares us all. To sort this out, I tried to identify ten things that scare me about AI and ten things that encourage me about AI. Read about the three Eras of AI coming your way by clicking here.

My Top Ten Fears

AI Takes Over the World

At the worst, AI will become self-aware and use its powers against humankind. I'm not a big believer in this scenario. While AI will network with other AI forces to do good, it is more likely that bad actors will leverage AI for dark outcomes or power than AI coalescing to destroy humankind.

AI Lacks Ethics and Empathy

AI is great at doing tasks today informed by multiple data, information, and knowledge sources. As AI spreads, it will become more engrained in the decision-making processes at various levels, and decisions will likely be driven by hard science and logic rather than the feelings of people or the respect of ethical behavior.

AI is Used to Battle Security

AI will be used to fool individuals and organizations with deep fakes by computing through multiple security defenses. We see this emerging now, but AI can be used to battle security incursions. The arms race will only get more intense with AI supercharging the security wars.

AI Displaces Jobs & Skills

AI will take jobs away from people. It will start with menial or manual work, especially where danger is present and repetitive precision is needed. While AI will create new jobs that require new skills, people will be displaced until they find work AI is not great at, which tends towards creativity and careers that need deep people skills. The workforce will always have to be learning or chasing the last chair in a game of AI musical chairs.

AI Lacks Transparency & Explanation

AI and automation must rarely explain themselves or be completely transparent. AI must explain itself, at least after the fact, to govern and deliver fair treatment. Ideally, AI should ask before, but that takes time. Time is often the savings benefit that drives AI, so that post-audit trends will be vital.

AI Lacks Real Creativity

Yes, AI can copy creations of the past and even generate projects based on creative libraries of content, but will it be able to create new concepts that please the nature of human appreciation? There is much room here for AI to generate and have humans add or adjust, but the natural creativity lies in humans today.

AI is Used for Social Manipulation

AI can fake stories, create deep fake videos, and play impostors cleverly. In the hands of manipulators, AI can be leveraged to develop actions in humans who buy, vote, and act. While it can be used for good, like changing behaviors to benefit societies, AI can also steer us toward bad outcomes.

AI Invades Privacy

AI can listen everywhere simultaneously across various communication channels and existing data fabrics to expose information that individuals would not want available to the public or particular parties. It is scary for most folks and can be used to breach the trust and security of many relationships with individuals, businesses, and the government.

AI Ignites Economic & Geopolitical Competition

AI will be the fuel for competition in the fast-growing digital economies. The nations that harness AI will have a distinct advantage over those that do not. It will likely turn into an arms race of sorts.

AI Enables Laziness & Skills Atrophy

AI will show significant promise and results in assisting organizations and individuals. Specific skills will not be maintained, and people will want AI to do more for them. Some of the skills are mundane, so that might be good, but setting an entitlement attitude is not a great value to deliver from AI.

My Top Ten Encouragements

AI Enables Advanced Automation

Organizations are in love with Automation because of its positive effect on profits. AI will supercharge Automation with smarts, speed, and precision. Operationally, AI will be a big win. Low-level work will be eliminated, and AI will enrich and augment most jobs.

AI is Always On

AI never rests and is available 24/7 if the AI infrastructure and applications are running. People need rest, and AI does not. As AI progresses to higher-level skills, new work classes will inherit a new level of availability.

AI Provides Real-time Knowledge & Wisdom

AI is excellent at providing just-in-time data and integrated, summarized, and massaged information. In the first era of AI, there will be a big emphasis on machine learning, information aggregation, pattern recognition, and knowledge delivery. All of this will help workers and individuals progress in their desired outcomes.

AI Assists in Task Completion

Not only will AI deliver knowledge, but it will also help people make decisions and perform tasks of all kinds. People and bots will be supercharged with additional perceptions, projections, skills, and abilities to take on work over their current station.

AI Specialization & Focus Delivers Precision

AI is so precise on low-level tasks it outperforms most workers. In addition, even when 100% precision is not necessary, AI can give the best alternatives with the best accuracy.

AI Delivers Better & Faster Decisions

AI is so fast and looks across multiple contexts, and it's impressive. Guided AI can find the best alternatives given even conflicting goals within explicit constraints. The data is complete better than other approaches, and alternative algorithms have the best chance of being correct.

AI Boosts Economic Growth

AI increases the productivity of many resources, so profits and GDP will flourish worldwide. Resources will be less stressed, and there will be more free time to generate new products and services.

AI Provides Continuous Monitoring of Results

Since AI never sleeps, KPIs, goals, outcomes, and emergent trends can be watched. AI will encourage continuous feedback, and improvement can be baked into responses.

AI delivers Error Reduction.

AI does not make mistakes at the operational levels and allows tactical and strategic management to get the best results in a modeling way.

AI Can Complete Dangerous Tasks

Tasks that risk the safety of humans can be automated with AI, or humans can be assisted safely.

Net; Net:

After putting down my thoughts and thinking deeply about AI, I fear AI long-term. If the control of AI ends up in the hands of bad actors or AI becomes self-aware and disregards its guardrails or constraints, we are in for a wild ride. AI-driven wars where guardrails are removed or mismatched will also cause bad outcomes. AI likely becomes as powerful as nuclear weapons in the hands of both good and bad actors that mutually keep each other at bay. Right now, AI promises to improve our lives, and I see a rosy outlook for the near term. AI will contribute to individuals, groups, and organizations for sure. The long-term evolution and potential misuse keep me up at night. We must forge ahead to stay competitive, but AI must be monitored, governed, and balanced. There will be tremendous and sad stories ahead of us, so keep your eyes open and stay in a learning mode together. Long live AI, but don't take the oxygen out of the room for humans.

Tuesday, September 19, 2023

Art for the 3rd Quarter 2023

 I took the 2nd quarter off from art. I relaxed in spring and early summer, only focusing on family events and my second album. So, I'm back on the horse with some new art. I hope you enjoy them. You can see my works by clicking here 

                                             Time Travel 

                                              Fire Storm 



                                             Color Reef 

Monday, September 11, 2023

Preview of AI Coming to You

You can hardly escape the topic of AI these days. Various definitions of AI are floating around, and predictions of where AI is going. Some sources want you to be scared of AI, others want you to depend on them for guideposts as AI rolls out, and still others are pumped about the future benefits. For my sanity, I put together the three eras AI will likely go through as it heads towards progress and assisting humankind. While the benefits of AI will be plentiful and the impact will be disruptive, we can guide the growth and development of new digital experiences/outcomes that AI can assist. If we are mindful, we can prevent out-of-control consciousness and sentient AI. Of course, AI technologies can be used for both good and bad, so our lawmakers need to add forms of governance, and we all need to share what works for the good of all. In Figure 1, I have defined the three eras of AI I expect to see going forward.

 Figure 1 The Three Eras of AI

Intelligent Behavior Axis

While there were two AI winters in the past because the expectations of AI did not deliver as promised, I do not foresee a third AI winter. Organizations are prudently leveraging AI in new ways to speed up information for advantage and leverage generative capabilities that use aggregate bodies of knowledge and creations to bootstrap new content. All of this is to assist resources organizations use to create better experiences/journeys for their constituents supported by more intelligent processes that can lead to situational advantage at all levels. This advantage will likely start operational, leading to better tactics and eventually to strategies that adapt to emergent conditions and dynamic management of many scenarios, anticipated or not. The anticipation for more intelligent and assisted behaviors has never been more significant with the advent of AI leverage.

Freedom Level Axis

The real rub with AI revolves around ensuring that AI stays a positive force for good outcomes. It is not a massive problem in the first era where we unwrap the benefits of automation, generation, and the leverage of collective knowledge that goes further than people expect. AI will likely be supervised, and its results can be explainable in the first era where AI's freedom will be carefully watched with teased testing and trained algorithms. The freedom level for AI will be low in this era. As organizations become more confident in AI, the freedom for AI will shift to less supervision, with expandability being the key tether for AI. In the second era of AI, there will be focused assistance to vertical industries, horizontal organizational functions, and individuals to supercharge them with just-in-time knowledge, speedy multi-dimensional sensing, and assistance in complex multi-disciplinary enabled actions. The resources assisted that are carbon-based will automatically demand to explain ability until trust is established in the AI assistance and advice. In the case of automation and bots, outcomes will be overseen with significant testing if danger is involved. The third era is where AI becomes independent, where AI detects, decides, and acts on its own. Knowing that AI will lead here, built on top of the infrastructure of the previous eras, will not be good enough. Organizations will be giving AI goals to drive towards and governance boundaries (constraints) to give AI the desired outcomes as guidance. After the fact, the results will be audited to tweak the goals and boundaries to dial in AI.

Collective Knowledge AI Era

AI significant benefits in this era include the leverage of natural language processing, including image/voice recognition in the content scope, leveraging mining strengths, and event/pattern detection with machine learning. These all help accelerate desired automation that continues to learn and improve. The generative aspect of this era leverages collective knowledge/content to amplify creation and enhance significant personalization while employing adaptive learning to enhance discovery and deepen knowledge. There are considerable time and cost savings as low-hanging benefits of this era.

Persona Based AI Era

The benefits of this era revolve around assisting roles with the proper content and knowledge to accomplish goals. It is performed by speedy resources and advice for roles to achieve steps leading to desired outcomes. It can be at the individual resource level to optimize any size or shape resource, including people, software, or devices at the edge of a remote situation. Having a base of collective knowledge now combined with algorithms and AI component software gives these personas the power to go beyond their base skill level to better optimization. The persona can go beyond individual resources to groups of aggregated resources heading in the same direction, like vertical industries, horizontal supply chains, and functional groups aimed at complex sets of goals.

Guided Results AI Era

AI switches from narrow and focused intelligence to general intelligence in this era. The benefits in this arena are aimed at attaining optimum overall optimization while dealing with change waves on a more real-time basis. AI guides the overall journey, value chain, or process to optimum results with changes in flight. AI becomes a broker for detection, decision-making, and actions appropriate for the situation(s). It creates situational awareness and advantages at the operational, tactical, and strategic levels.

Net; Net:

The future of AI is rife with potential, and organizations are just scratching the surface as of this writing. The benefits are significant, and so are the headwinds. A bounty of vendors is waiting to help, but sorting through the list will be challenging. As the AI eras progress, there will be combinations of vendors that will deliver multiple integrated benefit pools. The skills are scarce but will emerge quickly to drive the apparent benefits. Taming AI with a balanced legislation approach that works across legal frameworks and countries will be a long-term goal; however, self-control with great goals and guardrails can give early adopters a significant advantage. I will be delivering more posts on the AI topic, so stay tuned. The oldies and goodies are listed in the additional AI readings section.

Additional AI Readings

What is the Most Significant Execution Challenge?

It has been a relaxing summer for the Sinur clan, but it is time to return to the grind. I'm about to fire off a new series of blog posts on AI that will be compelling for many. To do that in a more personal and inclusive way, I'd like to start with a small survey to focus on the biggest execution challenges you are having. Welcome back to my devoted followers, and I hope to gain some new followers.

The purpose of this brief 5-question multiple choice survey is to understand the greatest execution challenges leaders like yourself are facing so that I can get a holistic picture of these challenges across industries, geographies & roles.

We will circulate the final survey results to all participants as soon as the results are in and analyzed. You will receive data, graphs, and personalized insights into the most significant execution challenges being faced overall, as well as by industry, geography, and role. We guarantee you will gain a new perspective and find value in the data!