1. ChatGPT is here to stay.  

2. ChatGPT will change the way people write and research software related development, architecture and implementation. 

3. ChatGPT will infringe on other companies’ and individuals’ IP. 

4. Companies will overreact to the infringement (real and potential) and life will become harder for the industry as a whole! 

I posit that all of the above is true.  

As company leaders, engineers and consumers we all have to be aware of the ramifications of using AI in the workplace. I’ll direct you to some recent headlines that touch upon this theme, each with their own distinct angle.  

• JPMorgan restricts employee use of ChatGPT – CNN, February 22, 2023 

• Companies are struggling to keep corporate secrets out of ChatGPT – Axios, March 10, 2023 

• Some companies are already replacing workers with ChatGPT, despite warnings it shouldn’t be relied on for ‘anything important’ – Fortune, February 25, 2023 

• Samsung accidentally leaked its secrets to ChatGPT — three times! – Toms Guide, April 5, 2023 

• ChatGPT Could Face Defamation Lawsuits for Making Up Facts About People – PC Magazine, April 6, 2023 

We have a couple of themes here that will surely underlie many of the future issues that come up as AI adoption increases. 

What Goes In Must Come Out 

The first theme, and likely the most important driver in this conversation, is the leaking of a company’s intellectual property into ChatGPT. The old adage of “What Goes In Must Come Out” applies here.  

Not everybody is aware of how these language models operate. To put it simply, these chatbots are trained on data and that data must come from somewhere. According to Tech Radar ChatGPT 3.5 was trained on 570GB of text data from the internet. That is a ton of data! 

However it doesn’t stop there. Anything a person enters into ChatGPT is likely to end up as a data point in some form or another. In fact the mindset should be that anything that is entered in will eventually come out. It is not a one way street. 

If It’s On The Internet, It Must Be True 

Sure it is. But here’s the rub. AI language models like ChatGPT have a couple of learning phases. The first one is the “generative pre-training” phase. What this means depends on which model is being used. I believe ChatGPT is currently at the GPT3.5 model. In this context the training is done in an unsupervised manner. Thus, data quality is bound to vary. Which is why OpenAI CEO Sam Altman said that it is a mistake to be relying on it for anything important right now.  

The data these chatbots are training themselves on comes in part from the internet because they are neural networks tied to the internet. That much isn’t rocket science. Thus, if we can all agree that the internet is chocked full of information that isn’t true then we can all agree that some of the data that becomes part of the AI’s knowledge base must also be viewed in that same vein.  

As ChatGPT continually adds to its knowledge base through “self learning” we must expect that it acquires knowledge that A) it doesn’t own, nor should have acquired and B.) it’s not all factual. 

I actually asked ChatGPT how it increases its knowledge garnered after the generative pre-training phase. The following image is a snapshot of the answer. 

Feedback from ChatGPT on an a Chatbots Leaning Techniques

The takeaway here is that there is a certain aspect of data acquisition that is culled by “self-leaning” techniques. This makes perfect sense. However, as the name implies, unsupervised is the key here. AI models such as ChatGPT don’t have the ability to independently discern fact from fiction. They aren’t sentient.  

Tie “What Goes In Must Come Out” with “If It’s On The Internet, It Must Be True” and you get “ChatGPT Could Face Defamation Lawsuits for Making Up Facts About People“. Making up facts is a misnomer here, it’s not making them up, at least not maliciously, or minimally not on its own. The data behind ChatGPT is most likely inherently flawed because the sourcing has not necessarily been vetted in the capacity to tag it as false. Anybody that works in data science knows the problems that arise with poor data quality. ChatGPT isn’t alone here.  

We are in uncharted legal territory here. It will need to shake out before companies jump in head first and I would not want to be the canary in the coal mine on that front.  

It’s My IP 

In doing research I had ChatGPT generate some code to see how well it could do the task. It turns out that it did quite well. When asking ChatGPT where the code came from it said that the code was open source licensed under Apache.  

This my friends is an issue. I would wager top dollar that a ton of code generated from ChatGPT is already finding its way into many companies’ applications. It is only a matter of time before the lawsuits start flying. Somebody needs to be the guard at the gate. 

Worse, the ramifications due to what I see as the potential over reaction to the above is that it won’t stop at ChatGPT. Information repositories such as Google, Stack Overflow, Medium and eventually chatbots like ChatGPT are the go to for software engineers. This doesn’t mean that engineers are copying all of the source code they find on the internet and putting it into the applications they write. But it sure as hell implies that people learn from others and that is the basis for much of the software written today. It will only become worse when companies start using AI’s to generate code.  

What Happens Next? 

All of the above begs the question, “what will happen to productivity when companies overreact and prevent their engineers from using the internet to learn how others came up with solutions for commonly occurring problems?”  

There are a ton of people that mentor others through blogs and tech articles, all of which end up on the internet. Much intentionally and free to use. How will this impact a company’s future initiatives If they have to crack down on what is considered acceptable use today? 

As company leaders we need to be prepared to take this on and answer these questions. The plan needs to be in place yesterday. Everybody needs to train their people on these issues as it is yet another in the continuing effort to protect intellectual property and prevent other’s IP from becoming part of someone else’s code base. Those that are ahead of the game will be well prepared to step into the vacuum that will be created by others that don’t. 

Written By Terry Trippany

The post AI, Chatbots and the Ramifications of Using them in the Workplace  appeared first on Creospan.

Imagine this: you ask a generative AI tool to “summarize last quarter’s procurement activity for compliance reporting.” Within seconds, it produces a well-structured summary, complete with headings and bullet points. So far, so good. Next, you instruct it to email the report to the compliance officer, attach the raw data for audit purposes, and log the interaction in your internal documentation system. Here’s where the system begins to falter. It doesn’t remember which procurement dataset it used in the first step. It requires you to re-specify the compliance officer’s details, the file format, the logging protocol, and the context all over again. 

Despite multiple well-crafted prompts, the AI behaves as though each request is a brand-new interaction. It lacks continuity, cannot maintain task state, and cannot autonomously sequence steps or handle exceptions without explicit direction. This is the fundamental limitation of prompt-based AI: it can produce high-quality responses to isolated queries, but it cannot reliably execute multi-step, goal-oriented workflows across systems or time. When this kind of failure is repeated across hundreds of workflows and multiple teams, it goes beyond isolated user frustration. It signals a broader structural weakness that undermines operational integrity and slows down the entire enterprise. 

Enterprise AI project abandonment rates have surged from 17% to 42% in just one year, with companies scrapping billions of dollars’ worth of AI initiatives, according to S&P Global Market Intelligence¹. What makes this trend particularly concerning is that many of these projects succeeded brilliantly in proof-of-concept phases but failed catastrophically when deployed at enterprise scale. While data quality and system maturity are frequently cited as primary reasons for failure, a more foundational yet often overlooked issue lies in how we approach AI. We continue to treat it as a high-powered autocomplete tool that responds to prompts and generates outputs. However, enterprise environments demand more than reactive prompt response behavior; they require intelligent systems that can maintain context, adapt over time, and pursue objectives with continuity, oversight, and alignment to business intent. 

Most AI deployments today operate on a simple prompts-based request-response model. You submit a query, receive an output, and the system essentially starts over. This approach has proven adequate for discrete tasks like content generation or data analysis. However, enterprise needs increasingly extend beyond such isolated use cases. Businesses require AI systems that can operate continuously, execute complex workflows, respond to evolving inputs, and contribute meaningfully to multi-step processes. These demands expose the inherent limitations of prompt-based interactions, no matter how meticulously engineered the prompts may be. 

Prompt engineering is the practice of writing clear and effective instructions to guide an AI model’s response. Over the last few months, prompts have evolved from simple question-and-answer based interactions to sophisticated frameworks incorporating clear instructions and contextual examples, defining model’s role, and using formats like JSON for structured output. Numerous studies have shown that well-crafted prompts can improve the accuracy of the model, reduce hallucinations, and generate outputs that closely align with user expectations. Consequently, prompt engineering has been hailed as a new-age skill; even the World Economic Forum dubbed it the number one “job of the future².^” 

However, as much as prompt tuning helps, it is not a silver bullet for accuracy or complexity. Prompt engineering operates under the assumption that the right words can encode all necessary context, objectives, and constraints. This assumption fails when dealing with dynamic environments where goals may shift, new information may emerge, or unexpected scenarios require adaptive responses. For example, even a perfectly crafted prompt for handling customer complaints cannot anticipate the specific context of a product recall, regulatory change, or competitive threat that might fundamentally alter the appropriate response strategy. Why is that? One reason could be that a large language model (LLM), however sophisticated, is a next-word prediction engine. Even though LLMs can produce text that looks rational, they lack true understanding, planning, or reasoning abilities³.  

While we can instruct an LLM what to do, it has no inherent mechanism to carry out multi-step procedures or remember past interactions beyond what you explicitly include in each prompt. All of this means prompt engineering, by design, was a stopgap to wring more mileage from a static, single-turn AI interaction. It cannot, on its own, give an AI model a persistent purpose or the ability to adapt decisions over time. The next leap lies in moving beyond prompting tricks to architecting AI systems that are goal-driven by design. 

From Chatbots to Agents

An agent is a system that can perceive its environment, make decisions, and take actions to achieve specific goals. In AI, an agent typically uses inputs (like data or user commands), processes them intelligently, and outputs actions or responses to move closer to its objective. In agent-based systems, we don’t micromanage the AI models with one prompt at a time. Instead, we give it an objective, and the system determines its own workflow of actions to fulfill that objective. To achieve this, an LLM-powered agent needs to have certain capabilities:  

• It should maintain its state (i.e., it should have a persistent memory of what has happened so far) 

• It should be able to engage in goal-oriented planning (i.e., figuring out intermediate steps to reach the outcome) 

• It should operate in autonomous loops (i.e., iterating decisions and actions without needing new human prompts at each step). 

What does this look like in practice? Imagine an AI “digital worker” handling compliance reporting. Instead of following a stateless, request-response model that forgets prior actions, it maintains context throughout the task. It remembers which procurement data was summarized, knows who the compliance officer is, applies the correct file formats, attaches the raw data for audit, and logs the interaction in the proper system. The result is a seamless, end-to-end compliance workflow without repeated inputs or excessive manual oversight. 

How Does Purpose-Driven AI Go Beyond the Prompts

The table below outlines these core components of AI agents and how they overcome the limitations of a prompt-only approach: 

According to a Gartner report⁴, over 40% of agentic AI projects will be cancelled by the end of 2027 due to escalating costs, unclear business values, or inadequate risk controls. This prediction underscores the importance of approaching agentic AI implementation with realistic expectations and robust governance frameworks. Success requires moving beyond the mindset that better prompts alone can solve complex automation challenges. Organizations preparing for this transition should focus on developing the infrastructure, skills, and governance frameworks necessary to support agentic AI systems. This includes investing in robust data architectures that can support persistent memory and learning, developing formal goal specification frameworks that align with business objectives, and creating monitoring and control systems that can ensure safe autonomous operation. 

From Vision to Value: Infrastructure That Delivers Results with Agentic AI 

To realize the transformative value of agentic AI, organizations must shift from experimentation to enablement. This requires investment in several critical areas: 

• Robust Data Architectures: Support for persistent memory, retrieval-augmented generation (RAG), and real-time learning loops is essential to empower agents with long-term context and dynamic adaptability. 

• Formal Goal Specification Frameworks: Agentic systems need structured ways to understand business objectives, constraints, and evolving KPIs—beyond hardcoded instructions. Techniques such as natural language goal parsing, reward shaping, and semantic control graphs are gaining traction in this domain. 

• Monitoring and Control Systems: Autonomous systems require clear safety boundaries. Enterprises should develop policy-compliant guardrails, continuous feedback loops, auditability layers, and human-in-the-loop overrides to ensure secure and trustworthy AI behavior. 

• Cross-functional Skills & Teams: IT, data science, operations, compliance, and domain experts must collaborate in designing, training, validating, and governing agent behavior. This calls for upskilling and new operating models. 

As enterprises move forward, those who treat agentic AI as a core strategic capability rather than merely a tool, will unlock disproportionate value. The future belongs to organizations that can architect for autonomy, govern for trust, and scale with purpose. 

Conclusion: Aligning Prompts with Purpose

The evolution from prompt-driven LLM bots to purpose-driven AI agents is underway, and it’s redefining how we build AI solutions. For enterprise leaders and AI product owners, the takeaway is clear: a prompt is not a purpose. If you want AI to drive real outcomes by reliably executing tasks, you must invest in the broader engineering around the AI. This means augmenting large language models with memory layers, planning logic, tool integrations, and guardrail mechanisms. It’s about designing systems where the AI’s objective remains front-and-center throughout its operation, and where the AI has the necessary context and abilities to achieve that objective in a safe, efficient manner. None of this implies that prompt engineering is now irrelevant. On the contrary, writing good prompts is still a crucial skill. It’s how we communicate tasks and constraints to the AI agent within this larger system. In short, prompting is just the starting point. True impact comes from architecting AI systems with purpose at their core. Purpose-driven agents require more than clever instructions; they demand an ecosystem of components that support autonomy, reliability, and alignment with business goals. By shifting focus from isolated prompts to integrated agent architectures, organizations can begin designing AI solutions that are not only intelligent, but also accountable, goal-oriented, and resilient. 

This shift doesn’t happen all at once. As your organization experiments with autonomous AI, start small and sandboxed. Use those experiments to identify where the agent might stray and what additional training or rules it needs. Ensure that for every new power you give the AI (be it a broader context window, an API key, or the ability to loop on its own output), you also add a way to monitor and constrain it. The path to goal-directed AI is incremental: as models improve and our techniques mature, agents will handle more complex work reliably. In the meantime, maintaining a human in the loop for oversight is often wise, especially in high-stakes applications. Ultimately, the promise of agentic AI is tremendous – from reducing mundane workloads to uncovering insights and opportunities autonomously. Realizing that promise requires marrying the creativity of prompt design with the rigor of engineering discipline. By doing so, we can move from simply prompting AIs with questions to trusting them with true purpose, confident that they have the structure and guidance to achieve it. 

References

• Generative AI experiences rapid adoption, but with mixed outcomes – Highlights from VotE: AI & Machine Learning 
• 3 new and emerging jobs you can get hired for this year 
• Where large language models can fail in business and how to avoid common traps 
• AI Prompt Engineering Isn’t the Future 

Article Written By Vishal Shrivastava

The post Prompt ≠ Purpose: Why Goal-Directed Behavior in Agentic AI Demands More Than Just Good Prompts appeared first on Creospan.