What are 3 big AI Shifts in the midst 2026?

AI is leaving the “wow” phase and entering the “prove it” phase. Many companies are about to realize their strategy was built for demos, not reality. In other words, AI is growing up. It’s time to move on from the hype toward the hard tradeoffs. What are 3 big AI shifts?

• Magic to Money 
• Demos to Deployment 
• Capabilities to Consequences

For the past few years, AI has lived in the realm of spectacle—impressive demos, viral moments, and “did you see what it can do?” reactions. That phase is ending. What’s replacing it is more grounded, more valuable, and a lot less comfortable. 

AI is shifting from magic to money, demos to deployment, and capabilities to consequences. Let’s examine these more fully.

Magic to Money

The novelty is wearing off. Organizations are no longer impressed that AI can generate content, write code, or analyze data. People are asking whether it actually drives revenue, reduces cost, or creates a defensible advantage. This is where many AI initiatives encounter friction: what initially appears magical, struggles when tied to real business metrics, messy data, and existing workflows. A current challenge is proving consistent, measurable ROI, not just isolated wins and talk.

Demos to Deployment

We’ve all seen the polished demos. But deploying AI into production is a different game entirely. Integrations, governance, reliability, edge cases, and user adoption quickly surface. The gap between “it works in a demo” and “it works every day in real life” is where most efforts stall. The winners are no longer the ones with the flashiest models, but rather those who can operationalize them at scale. A current challenge of getting to “real life” is bridging the last mile from prototype to dependable, repeatable execution.

Capabilities to Consequences

As AI capabilities grow, so do concerns about accuracy, bias, job displacement, environmental cost, security, and trust. Leaders are increasingly forced to weigh not just what AI can do, but what it should do and what risks they’re willing to accept. The conversation is shifting from innovation to responsibility, often faster than organizations are prepared for. A current challenge is how to manage risk and accountability without slowing innovation to a crawl.

What shifts are you seeing, and what challenges are you facing in getting to the full usage of AI?

Risk of AI Sameness

Everyone is racing to adopt AI tools.  Most people are using AI to create “speed”.  Faster emails. Faster proposals. Faster code. Faster content. And yes, faster is good.  But here’s the quiet risk no one is talking about: if everyone in your company uses the same AI the same way, you may slowly start sounding exactly alike.

Are you ignoring “AI sameness” risk? Without intention, standard AI tools can homogenize your messaging, flattening distinct perspectives into one generic voice. The danger isn’t bad output. It’s average output at scale. AI isn’t going to replace your team. It’s going to standardize them, but not in a good way.

When everyone uses the same tools, trained on the same data, prompted in the same way, you don’t get divergence, you get convergence. Everyone will sound the same, with the same sentence structure and tone, and with the same “polished but generic” voice. Then there is the further danger that over time, unique thinking gets flattened into safe, average, AI-shaped output. Not because your people aren’t smart, but because the tool defaults to the statistical middle.

AI should amplify your edge, not sand it down. AI should sharpen your thinking, make it more opinionated, and more differentiated. If you’re not intentional about how your teams use it, it can lead to this sameness.  What can you do to avoid this sameness?

• Craft your own perspective before prompting AI.  As it relates to the topic, what do you actually believe? What do most people get wrong about this? What would you argue in a debate?
• Use AI to provide you with a draft, not the deliverable. Within that context, establish one strong opinion. Provide one specific example from your world. Craft your own voice so that sentences sound unmistakably like you.
• Add Friction to the output. AI will often be a people pleaser, so challenge your output. Ask what’s missing. Determine if it feels too safe. Consider if it sounds too predictable? If it reads smoothly but doesn’t make you think, that’s a warning sign.

AI naturally drifts toward the statistical middle. Avoiding sameness requires intentionally looking for differences. It must include injecting strong beliefs, specific context, and human judgment layered on top.  And honestly? The companies that figure this out won’t just use AI faster. They’ll use it to amplify their uniqueness.

AI Coding: Shifting the Developer Role

Coding with AI is producing code at a faster rate than ever and accelerating the release of production increments. The code can be generated in minutes and feels good because of how quickly it is created. This begs the question, what does the software developer do now?  It changes where the developer’s focus goes. 

While AI is generating the code, it doesn’t own the code.  The developer remains accountable for it, which means they must review the code deeply enough to understand how it works, why it works, and where it could fail. Also, they must focus on verification activities surrounding the code. This article is based on some experimentation with AI and ensuring the developer has a good understanding of the code changes. 

Think of AI as a Junior Engineer, and it is your job to raise them up. It can produce a lot of code quickly, and it can be confidently wrong when doing so. It has no sense of risk, context, or consequences.  Think of the verification as the handoff where ownership transfers to a human. It is still your job to ensure a verified and quality outcome. This should take a majority of engineering time or later on, logical gaps leading to failures in services, data, and infrastructure. How does the Developer responsibility shift?

• Review code written for understanding.  Ask the AI tool to explain to you what this code does, line by line. Ensure it's not vague and be sure it aligns with what you are thinking. Ask what the expected outputs and outcomes would be. Then ask what would break the code. Finally, ask AI why this approach was chosen over alternatives. A useful litmus test is if you wouldn’t feel comfortable maintaining this code for the next year, you don’t understand it well enough
• Ensure that the code was version-controlled properly and in the correct branch. This includes checking for potential issues before merging it into the main codebase.
• Step up Code Reviews.  This means to peer-check code for quality and adherence to standards.  The Developer should share coding standards with the AI tool to ensure it aligns with standards. If coding standards are missing, then they must be written and then added to the AI tool’s vector of information.   
• Spend time sharing responsibilities with Testing to ensure all verification activities are completed.  This should include appropriate testing: Unit Testing (e.g., testing individual components or functions in isolation), Integration Testing (e.g., testing how different components work together), System Testing (e.g., testing the system as a whole for speed, scalability, and stability), and more. 

AI reduces typing time. It does not absolve you of the responsibility and judgment for a product well built! AI changes where time is spent, not whether time is spent. While we will spend less time coding, we are still accountable to spend time verifying and understanding the code that has been generated to ensure it meets the needs of the outcomes we are looking for.  

Beware of AI BS (aka, Hallucinations)!

User beware! Did you know that your AI query is subject to hallucinations?  What is an AI hallucination? When AI inadvertently generates false or misleading information that seems plausible but is not rooted in reality. It is trying to give you an answer. These are errors in AI outputs that arise from flawed reasoning or inaccurate training data, typically not from malicious intent. 

For example, a language model like ChatGPT might generate an article with fake references or make up scientific facts because it is just predicting what should come next based on patterns in data. In fact, when I asked “what was the duck wearing when it won the Boston Marathon?”, it said that “the duck was wearing a quacking pair of sneakers and a feather-light singlet when it flapped its way to victory!”

This should not be confused with people deliberately using AI tools to create misinformation, typically to manipulate public opinion or cause harm. AI itself may be used to generate highly realistic but fake content, such as fabricated news articles, doctored images, or videos. For example, AI may be used to create Deepfake videos to manipulate someone's face and voice to make them appear to say something they never did. 

Turning back to actual AI hallucinations, what are the risks where it inadvertently poses several serious dangers? Generally, creating and sharing hallucination misinformation can spread quickly, particularly in news, health, legal, or political contexts.  Users who trust AI outputs may unknowingly share false information, amplifying its reach. What are more specific dangers?

• Generating legal and medical judgments or diagnoses. AI-generated hallucinations in legal documents, medical advice, or financial reports can lead to harmful or even illegal outcomes. This can damage reputations or result in malpractice.
• Misinterpreting security and safety threats. In cybersecurity or military applications, a hallucinated misinterpretation of data in critical systems (e.g., aviation or nuclear control) could trigger wrong decisions with high-stakes consequences.
• Spreading stereotypes and reinforcing bias. Hallucinated outputs might reflect or invent stereotypes or discriminatory patterns that reinforce social biases. This can be especially harmful in generative content involving race, gender, religion, or culture.
• Damaging reputations and polluting research. Fake references or fabricated studies can pollute scientific research, especially if unnoticed in peer review or student submissions. AI hallucinations in education can mislead learners or promote academic dishonesty.

After enough hallucinations are shared and spread, repeated exposure to hallucinated content undermines trust in AI tools and technology in general. Ultimately, if enough misinformation occurs, there will be an erosion of trust and hesitancy to adopt AI. The important thing is be aware that AI tools will inadvertently generate false or misleading information. Don’t accept answers at first blush. Instead, verify the answers, verify the references, fact-check the outputs, and ask AI to double-check its results.

Are there Benefits for adding ChatGPT as a team member?

There is evidence that ChatGPT can be beneficial in helping you do your work. Involving ChatGPT today is already occurring in repetitious, creative, and diagnostic type work. Some say it’s inevitable and you should learn to work with many forms of AI. Current uses have shown that it can improve work efficiency, assist with tedious tasks, help you with creative tasks, and facilitate learning. We are also learning that because ChatGPT is based on a large language model, it can act as your assistant; providing personalized responses based on your inputs, helping you work smarter, and boosting your productivity.

As it can help an individual in their work, how about helping a team?  In this article, I explore how helpful ChatGPT can be for a team. In other words, I suggest making ChatGPT a member of your team. ChatGPT is an artificial intelligence chatbot capable of mimicking human-like conversations so why not be a member of your team? As mentioned, ChatGPT has been recognized to boost productivity so let’s consider the context of a software engineering team who are producing new features and correcting bug fixes to the code base.  To consider this, here are the potential positives, negatives, and limitations of incorporating ChatGPT as an engineering team member. Here are some considerations:

First, let’s start with some Positives:

• Multi-tasking: ChatGPT can handle many questions, inquiries, and tasks simultaneously allowing certain work to be handled more efficiently and scaled to a higher volume of work.  
• Quick feedback: ChatGPT provides quick feedback to questions and inquisitions allowing for more input for potential better options and decision-making.
• Availability: ChatGPT is technically available 24/7 and can work while team members rest allowing for busy work to get completed and tasks to be ready for team review when they are back online.
• Scalability: As an AI, ChatGPT can handle a high volume of inquiries without experiencing fatigue or requiring breaks.
• Database of information: ChatGPT has access to a vast amount of information and can provide accurate and up-to-date answers to team members' queries.
• Human Languages: ChatGPT can speak in multiple languages and can accommodate global teams across multiple boundaries and locations.  
• Programming Language: ChaptGPT has the potential for programming capability across various language platforms.  

Next, let’s move to the Negatives:

• Time from Team Members: Working ChatGPT will take time from some team members. A buddy for ChatGPT will need to be designated to help provide context for ChatGPT, line up tasks, reduce ambiguity of the requests, verify and validate the work done by ChatGPT, and more.
• Lack of emotional intelligence: ChatGPT lacks emotional understanding and empathy, which may limit its ability to provide refined and empathetic output to team members.
• Limited contextual understanding: ChatGPT will struggle to understand the context in which you are working including the complexity of the work, potentially leading to misunderstandings or incorrect responses.
• Bias and completeness fn training data: the database from which ChatGPT pulls has already shown some bias based on patterns and data provided which means it may generate reasonable responses but may be incorrect or biased if not carefully reviewed.
• Lack of creativity: Because ChatGPT pulls from existing data and patterns, this limits its ability to generate genuinely innovative or creative ideas.

Finally, several considerations should be factored in. The first is ethical considerations as ChatGPT may inadvertently generate or reinforce biased or discriminatory responses due to its training data (which includes such biases). Careful monitoring and bias mitigation strategies will be necessary. The second consideration is legal and compliance challenges.  Incorporating ChapGPT into a product team may raise legal and compliance concerns, particularly in regulated industries that require human input, oversight, and/or accountability.

It's essential to consider these factors and strike a balance when integrating ChatGPT or any AI model into a product team. Human supervision, ethical guidelines, and continuous evaluation can help mitigate the limitations and ensure optimal utilization of AI technologies like ChatGPT. Now it is time for you to wrestle with this question: Are there Benefits of adding ChatGPT as a team member? Hopefully the overview, positives, negatives, and considerations can help you with your answer. 

----------

If you are interested in learning more about ChatGPT in relation Agile, Teamwork, or experimentation, consider reading these additional articles:

• A Discussion with ChatGPT about Agile 
• Are there Dangers with ChatGPT for Agile? 
• How to Experiment with ChatGPT 

Are there Dangers with ChatGPT for Agile?

How will ChatGPT impact Agile? This article discusses ChatGPT and its implications to Agile in the industry today. ChatGPT is taking the internet by storm and hard to ignore. Because of this, it cannot be ignored by those in the Agile field. What are the implications of ChatGPT on Agile? Here is a brief summary of what is ChatGPT and a review of what is Agile and its current journey.

What is ChatGPT?

ChatGPT is an artificial intelligence (AI) chatbot-type tool developed by OpenAI. It is adept at producing human text-based output on the input it is given. This model incorporates a large body of text data and can create responses to questions, write articles, and more. The challenges with ChatGPT are that it is only as good as the “large body of text data”, can be used maliciously and with bias, can spread misinformation, and is ethically complex in its application and future application. This applies to any field that people may use it for including Agile.   

What is Agile?

Once upon a time (in 2001) Agile was unveiled based on the Manifesto of Agile Software Development which is comprised of Agile Values and Principles. The objective of articulating the values and principles is to apply them in the form of an Agile transformation to derive better business results. However, the manifesto does not provide guidance on how to apply Agile. 

Soon, a number of processes and methods (e.g., XP, Scrum, Kanban, SAFe, etc.) were established to construct and apply agile ways of working. Agile has also spawn a number of certification programs in an attempt to educate people in Agile ways of working, in some cases aligned with a process or method. During this same time, Agile coaches were educated to help their own companies and Agile consultancies to help other companies apply Agile ways of working. The Agile movement has grown and expanded in a number of fields beyond software development.  After over 20 years, what are the results? The challenges are three-fold.  

• First, the current state of Agile is underwhelming. The most recent State of Agile Report (16th Annual – 2023), tells us the following. Only 18 percent of organizations implemented Agile for all the teams. Around 50 percent of respondents report that less than half of their teams are using agile, and 84 percent acknowledge that their organizations are below a high level of competencies. There is clearly plenty of opportunity for growth.
• Second, some of the Agile savvy (e.g., coaches, consultants, leaders, managers) seemed to lack an understanding of what is agile. In an Agile study where 109 agile professionals answered a survey on Scrum events and Agile principles, 59% could name 3 or more of the five Scrum events, while only 11% knew 3 or more of the twelve Agile principles. This is quite astounding. And they didn’t need the full statement of the principle but got credit for even the key words of the principle. The concluding hypothesis is that the reason there is such a lack of awareness of Agile principles is that there is much less focused on the mindset and culture and maybe too much focus on the mechanics.
• Third, the implementation of an Agile transformation is complex per the definition provided by the Cynefin framework. Agile transformations are neither linear nor predictive. It depends on the readiness of the culture and willingness of its leaders in their ability to move forward. Complexity means that it is not clear on what the best next step is until you act, ergo you need to probe, sense, response your way forward. This is why experimentation helps reveal what is possible each step of the way. You must both meet the company and teams where they are and help them determine what is the next step to further the transformation.  

What this tells us is that there are great opportunities for improvement and that there is no easy way to apply Agile, no one-size fits all, and no clear roadmap. Why? Because every organization is different due to their current culture, size, fields, practices, and more. 

Implication of ChatGPT and Agile

Now that we have an overview of both topics, the question is what are the implications of ChatGPT to Agile (and vica-versa)?  I’ll start by saying “What you put in is what you get out”. ChatGPT is only as good as the “large body of text data” available to pull from. The good news is that today there are reams of text data on Agile. The bad news is that there is no rating system on the quality of most of the Agile related information. With the advent of blog’s, there is a large body of unverified knowledge that enters into the “large body” of available data. What are the implications of this? 

• Arguable Quality of response - The quality of ChatGPT generated articles and answers should be read with a grain of salt. This isn’t a “knock” on ChatGPT, and instead it is due to the quality of the body of text data that ChatGPT draws from. And the reality is there is no one right way of applying Agile.  
• Propensity for Misinformation - There is a danger of misinformation and abuse of those who use ChatGPT to bias their responses. Some may be accidental as the body of text being pulled in isn’t broadly approved or agreed upon. While I don’t expect that most will be intentionally abusive, do keep in mind, there is money to be made in selling agile so bias may be seen.  
• Not doing your own Research - While you may want to occasionally use ChatGPT, it is better to learn from the body of Agile knowledge out there (e.g., books, articles, presentations, seminars, etc.) according to the areas that will benefit your current needs in your Agile transformation or need. In other words, do your own research so you can critically judge the quality of information that gets generated.
• Taking Agile Jobs - Can ChatGPT take jobs away from Agile Coaches and Consultants? This is unlikely as a significant part of an Agile transformation include coaches and consultants who have been on a transformation journey that can help companies navigate the complexity of both the current needs and the anticipation of near-term needs. ChatGPT cannot “read the room” like an Agile Coach. Should a company think that ChatGPT will be “enough”, it highlights that they don’t understand the complexities of a transformation and what it takes to change culture.

Summation

Now that you have some background, let us again turn to the question, “how will ChatGPT impact Agile?” There will be those that use ChatGPT to provide answers for Agile theory and questions. If you want to write an Agile article, it will help provide input and insight, although you have to be aware that the value of the information is only as good as what it pulls from. Think of ChatGPT as another resource to help you think through your ideas on agile topics and how it may help you in your Agile transformation. However, just remember, it is just a tool like other tools.     

It is unlikely that ChatGPT will take over Agile roles and the art of the transformation. A big part of Agile transformation is discovering, observing, and experimenting on what will work and what will make progress. Remember, when defining Agile, it really implies a transformation. This is a combination of doing agile and more importantly being agile. This means transforming mindset and culture. It is currently unlikely that ChatGPT will have this capability as transformations are complex with the real need to experiment (e.g., probe, sense, response) toward progress.  Coaches and consultants are still important to help transform organizations and more importantly to help leaders and teams make the mindset-shift to truly becoming Agile. 

-------

If you are interested in learning more about ChatGPT in relation Agile, Teamwork, or experimentation, consider reading these additional articles:

• A Discussion with ChatGPT about Agile 
• How to Experiment with ChatGPT 
• Are there Benefits for adding ChatGPT as a team member? 

Building an AI and Agile Culture of Learning

Does your AI and Agile education begin and end with barely a touch of training?  A number of colleagues have told me that in their companies, training ranged from 1 hour to 1 day.  With this limited training, they were expected to implement and master the topic.  AI nor Agile isn’t simply a process or skill that can be memorized and applied. It is a culture shift. Will this suffice for a transformation toward AI and Agile?

Education is an investment in your people.  A shift in culture requires an incremental learning approach that spans time.  What works in one company doesn’t work in another. A learning culture should be an intrinsic part of your transformation that includes skills, roles, process, culture and behavior education with room to experience and experiment.

A transformation requires a shift toward a continuous learning culture which will give you wings to soar!  You need a combination of training, mentoring, coaching, experimenting, reflecting, and giving back. These education elements can help you become a learning enterprise.  Let's take a closer look at each:

Training is applied when an enterprise wants to build employee skills, educate employees in their role, or roll out a process. It is often event driven and a one-way transfer of knowledge. What was learned can be undone when you move back into your existing culture.

Coaching helps a team put the knowledge into action and lays the groundwork for transforming the culture. Coaching provides a two-way communication process so that questions can be asked along the way. A coach can help you course-correct and promote right behaviors for the culture you want.

Mentoring focuses on relationships and building confidence and self-awareness. The mentee invests time by proposing topics to be discussed with the mentor in the relationship. In this two-way communication, deep learning can occur.

Experimenting focuses on trying out the new skills, roles, and mindset in a real world setting.  This allows first-hand knowledge of what you’ve learned and allows for a better understanding of Agile.

Reflecting focuses on taking the time to consider what you learned whether it is a skill, process, role, or culture, and determine what you can do better and what else you need on your learning journey. 

Giving back occurs when the employee has gained enough knowledge, skills, experience, to start giving back to their community to make the learning circle complete. Helping others highlight a feeling of ownership to the transformation and the learning journey.

It takes a repertoire of educational elements to achieve a culture shift and becoming a Learning enterprise. When you have people willing to give back is when the learning enterprise has become full circle and your enterprise can soar.

-------------------

For more Agile related Learning and Education articles, consider reading:

• Agile Education Engine - Bringing power to your Agile Transformation - http://cmforagile.blogspot.com/2014/11/agile-education-engine-bringing-power.html
• There is no Success of Fail, only Learn - http://cmforagile.blogspot.com/2015/12/there-is-no-success-or-fail-only-learn.html
• How a Self-organizing Agile Education Vision can help your Agile Transformation - http://cmforagile.blogspot.com/2014/02/how-self-organizing-education-vision.html