As market participants scramble to win the “AI Race”, the concept of Artificial Intelligence propagates solutions more as a buzzword than as a technology. Thus raises the questions: can we trust that we are getting AI technology, and is the market as advanced as we would like to believe? 

To distinguish the facts from the ‘science fiction’, the market and its participants first need to adopt a certain literacy and perspective around what qualifies an AI solution. Since the hype of ChatGPT disrupted the market in 2022, AI proponents became quick to position themselves as ‘mature to sell’. What this led to, however, was a liberal application of the term to plaster upon some technology solutions to generate a “next-gen” appeal. While AI technology may be employed by these solutions, it is up to the user to examine the nuance of what type of AI capability they are receiving. 

Waves of Technological Adoption

Technology can be characterized as seeping into the market in ‘waves’ – while the terminology differs, essentially, Artificial Intelligence can evolve from Assisted intelligence, to Augmented, and Autonomous thereafter. These terms, coined by Thomas H. Davenport in his book The AI Advantage and generalized in this article, help delineate the different types of AI capabilities experienced by the market today. First, Assisted intelligence is leveraged as an algorithmic approach to decision making, and serves as an “assistant” to its human counterpart. In the middle-adoption era of Augmented intelligence, machine learning and other techniques are superimposed upon information systems to build and augment analytics. Finally, Autonomous intelligence occurs when processes are digitized and automated, such that machines and systems can act upon information with minimal human intervention.   

Where Are We with Artificial Intelligence?

While many would use the term ‘AI’ to insinuate that their solutions or offerings are at an augmented or autonomous level of maturity, the fact remains that the market itself is not ready to accommodate such claims. Irrespective of the technology itself and individual users, enterprise organizations struggle to operationalize AI. Thus, what the market really sees are consulting firms or incumbent software providers attempting to whitelabel their services or repackage algorithms as ‘AI-driven’. 

The AI solutions that do exist in the market mislabel the type of technology used, in order to advertise themselves as being in an Augmented or Autonomous state of intelligence, which is not yet the case. Gaining AI literacy means being able to disambiguate between the different buzzwords that pervade the market. These ‘buzzwords’ are: Agents, Copilots, Chatbots, and Large Language Models (LLMs). Most often, the confusion arises from any AI technology being generalized as the idealistic version of an AI product: an AI Agent.

Defining AI Agents

So, what is an AI Agent? An AI Agent is a packaged unit of Artificial Intelligence technology–Model-backed prompts, queries, and logic—endowed with the ‘agency’ to exact upon a certain purpose or ‘reason of being’. What this means is, an Agent can be directed to fulfill a process without human intervention. To use a common analogy, an AI Agent can be considered a ‘self-driving car’. 

Defining Copilots & Chatbots

A Copilot, however, is merely a companion–meant to complement the processes of a human counterpart. Copilots are most commonly employed as an Assistant to enable and navigate a user to better use a system or software. While an Agent can act as a Copilot, the same cannot be said vice versa. Many products listed as “Agents” in the market are really Copilots (or a set of them). A Chatbot Assistant, without any functionality other than knowledge retrieval, is one of them. While Agents can have these “Chatbot” interfaces, the defining feature of an Agent is that it can perform actions on the behalf of a user, those which extend beyond a query. To refer to our previous analogy, a Copilot is akin to the ‘smart navigation system’ of a human-driven car.

Defining Large Language Models (LLMs)

Finally, an AI Model is the foundational building block of an AI solution. A model alone does not constitute a solution, unless there is an interface or vehicle (Copilot or Agent) to pour logic into. Consider the model to be the computer system of the ‘smart’ car. LLMs, or Large Language Models, are a common type of model that uses machine learning to comprehend human language inputs and therefore provide outputs in a humanistic fashion. Chatbots are often ‘chat interfaces’ that embody LLMs, just as ChatGPT is a chatbot atop OpenAI’s GPT-4o LLM. 

How to Identify Your Solution

One of the best courses of action towards becoming a keen AI adopter is to scrutinize the market and its climate, what gaps there are to fill, and where your demand for or supply of AI is. Performing the following will enable you to better discern the music from the noise: 

• Understand for what purpose you are employing AI; what your use case(s) are.

• Decide whether you’re complementing or modelling human processes.

• Become smart on how you’re doing it (what AI solution works best for you).

• Ready your data and your processes. Adjusting for AI requires change management, and your AI agents and models will only be as good as your data.

• Document where the success is, and where the gaps in your organization are for adoption.

• Create benchmarks, key performance indicators, and metrics aligned to your technical and business imperatives to follow the efficacy of AI usage and adoption

Above all, question how the solution you elect qualifies as “AI” within its different capabilities: Agents, Copilots, and Chatbots. 

We Specialize in AI Agents

If what you are looking for are AI Agents, Arhasi Inc. provides one of the most mature platforms for Enterprise AI in the market. Built on the proprietary R.A.P.I.D.™ (Ready AI Provisioning and Integrity Defense) platform, these AI Agents are foundational but extensible in nature, covering a variety of functions and enabling a multi-Agent ecosystem for use in endless applications and industries. These Agents are truly such – they are entities programmed and modeled with AI to serve a specific but innate purpose. For example, our Hexasphere Agent is a model prompted to independently discern location data from datasets and apply geospatial intelligence to automatically build dynamic maps to draw insights for use cases. The applications of this agent are boundless–we have leveraged Hexasphere to solve for Last Mile, mining, agriculture, and transportation & logistics use cases. With a specific prompt such as agriculture, for example, the Hexasphere Agent  would be able to locate key farming sites by scrutinizing geographical, atmospheric, and biochemical data. This example is presented as an exercise to you: albeit with this limited description, does this solution sound like an AI Agent? 

Some may argue the importance of making a distinction between what type of AI capability a solution embodies. We at Arhasi, AI with Integrity™, take the representation of Artificial Intelligence, how it is secured and governed, with the utmost sincerity. We encourage the market, its users and suppliers, to reflect this distinction in kind–lest we unintentionally encumber its growth.

©2025, Melody Roth for Arhasi Inc.