The Intelligent Enterprise 2.0 – Integrating Artificial Intelligence

“If only I could find an article that focused on AI”… said no one, any time recently.

In a perfect world, I don’t want “AI” anything, I want to be able to be more efficient, effective, and competitive.  I want all of my capabilities to be seamlessly folded into the way people work so they become part of the fabric of the future environment.  That is why having an enterprise-level blueprint for the future is so critically important.  Things should fit together seamlessly and they often don’t, especially when we don’t design with integration in mind from the start.  That friction slows us down, costs us more, and makes us less productive than we should be.

This is the third post in a series focused on where I believe technology is heading, with an eye towards a more harmonious integration and synthesis of applications, AI, and data… what I previously referred to in March of 2022 as “The Intelligent Enterprise”.  The sooner we begin to operate off a unified view of how to align, integrate, and leverage these oftentimes disjointed capabilities today, the faster an organization will leapfrog others in their ability to drive sustainable productivity, profitability, and competitive advantage.

Design Dimensions

In line with the blueprint above, articles 2-5 highlight key dimensions of the model in the interest of clarifying various aspects of the conceptual design.  I am not planning to delve into specific packages or technologies that can be used to implement these concepts as the best way to do something always evolves in technology, while design patterns tend to last.  The highlighted areas and associated numbers on the diagram correspond to the dimensions described below.

Natural Language First (1)

I don’t own an Alexa device, but I have certainly had the experience of talking to someone who does, and heard them say “Alexa, do this…”, then repeat themself, then repeat themself again, adjusting their word choice slightly, or slowing down what they said, with increasing levels of frustration, until eventually the original thing happens. 

These experiences of voice-to-text and natural language processing have been anything but frictionless: quite the opposite, in fact.  With the advent of large language models (LLMs), it’s likely that these kinds of interactions will become considerably easier and more accurate, along with the integration of written and spoken input being a means to initiate one or more actions from an end user standpoint.

Is there a benefit?  Certainly.  Take the case of a medical care provider directing calls to a centralized number for post-operative and case management follow ups.  A large volume of calls needs to be processed and there are qualified medical personnel available to handle them on a prioritized basis.  The technology can play the role of a silent listener, both recording key points of the conversation and recommended actions (saving time in documenting the calls), and also making contextual observations integrated with the healthcare worker’s application (providing insights) to potentially help address any needs that arise mid-discussion.  The net impact could be a higher volume of calls processed due to the reduction in time documenting calls and improved quality of care from the additional insights provided to the healthcare professional.  Is this artificial intelligent replacing workers?  No, it is helping them be more productive and effective, by integrating into the work they are already doing, reducing the lower value add activities and allowing them to focus more on patient care.

If natural language processing can be integrated such that comprehension is highly accurate, I can foresee where a large amount of end user input could be provided this way in the future.  That being said, the mechanics of a process and the associated experience still need to be evaluated so that it doesn’t become as cumbersome as some voice response mechanisms in place today can be, asking you to “say or enter” a response, then confirming what you said back to you, then asking for you to confirm that, only to repeat this kind of process multiple times.  No doubt, there is a spreadsheet somewhere to indicate savings for organizations in using this kind of technology by comparison with having someone answer a phone call.  The problem is that there is a very tedious and unpleasant customer experience on the other side of those savings, and that shouldn’t be the way we design our future environments.

Orchestration is King (2)

Where artificial intelligence becomes powerful is when it pivots from understanding to execution.

Submitting a natural language request, “I would like to…” or “Do the following on my behalf…”, having the underlying engine convert that request to a sequence of actions, and then ultimately executing those requests is where the power of orchestration comes in.

Back to my earlier article on The Future of IT from March of 2024, I believe we will pivot from organizations needing to create, own, and manage a large percentage of their technology footprint to largely becoming consumers of technologies produced by others, that they configure to enable their business rules and constraints and that they orchestrate to align with their business processes.

Orchestration will exist on four levels in the future:

• That which is done on behalf of the end user to enable and support their work (e.g., review messages, notifications, and calendar to identify priorities for my workday)
• That which is done within a given domain to coordinate transaction processing and optimize leverage of various components within a given ecosystem (e.g., new hire onboarding within an HR ecosystem or supplier onboarding within the procurement domain)
• That which is done across domains to coordinate activity that spans multiple domains (e.g., optimizing production plans coming from an ERP systems to align with MES and EAM systems in Manufacturing given execution and maintenance needs)
• Finally, that which is done within the data and analytics environment to minimize data movement and compute while leveraging the right services to generate a desired outcome (e.g., optimizing cost and minimizing the data footprint by comparison with more monolithic approaches)

Beyond the above, we will also see agents taking action on behalf of other, higher-level agents, where there is more of a heirarchical relationship where a process is decomposed into subtasks executed (ideally in parallel) to serve an overall need.

Each of these approaches refer back to the concept of leveraging defined ecosystems and standard integration as discussed in the previous article on the overarching framework.

What is critical is to think about this as a journey towards maturing and exposing organizational capabilities.  If we assume an end user wants to initiate a set of transactions through a verbal command, that then is turned in a process to be orchestrated on their behalf, we need to be able to expose the services that are required to ultimately enable that request, whether that involves applications, intelligence, data, or some combination of the three.  If we establish the underlying framework to enable this kind of orchestration, however it is initiated, through an application, an agent, or some other mechanism, we could theoretically plug new capabilities into that framework to expand our enterprise-level technology capabilities more and more over time, creating exponential opportunity to make more of our technology investments.  The goal is to break down all the silos and make every capability we have accessible to be orchestrated on behalf of an end user or the organization.

I met with a business partner not that long ago who was a strong advocate for “liberating our data”.  My argument would be that the future of an intelligent enterprise should be to “liberate all of our capabilities”.

Insights, Agents, and Experts (3)

Having focused on orchestration, which is a key capability within agentic solutions, I did want to come back to three roles that I believe AI can fulfill in an enterprise ecosystem of the future, they are:

• Insights – observations or recommendations meant to inform a user to make them more productive, effective, or safer
• Agents – applications that orchestrate one or more activities on behalf of or in concert with an end user
• Experts – applications that act as a reference for learning and development and to serve as a representation of the “ideal” state either within a given domain (e.g., a Procurement “Expert” may have accumulated knowledge of both best practices, market data, and internal KPIs and goals that allow end users and applications to interact with it as an interactive knowledge base meant to help optimize performance) or across domains (i.e., extending the role of a domain-based expert to be broader to focus on enterprise-level objectives and to help calibrate the goals of individual domains to help achieve those overall outcomes more effectively)

I’m not aware of the “Expert” type capabilities existing for the most part today, but I do believe having more of an autonomous entity that can provide support, guidance, and benchmarking to help optimize performance of individuals and systems could be a compelling way to leverage AI in the future.

AI as a Service (4)

I will address how AI should be integrated into an application portfolio in the next article, but I felt it was important to clarify that I believe that, while AI is being discussed as an objective, a product, and an outcome in many cases today, it is important to think of it as a service that lives and is developed as part of a data and analytics capability.  This feels like the right logical association because the insights and capabilities associated with AI are largely data-centric and heavily model dependent, and that should live separate from applications meant to express those insights and capabilities to an end user.

Where the complicating factor could arise from my experience is in how the work is approached and the capabilities of the leaders charged with AI implementation, something I will address in the seventh article in this series on organizational consideration.

Suffice is to say that I see AI as an application-oriented capability, even though it is heavily dependent on data and your underlying model.  To the extent that a number of data leaders can come from a background focused on storage, optimization, and performance of traditional or even advanced analytics/data science capabilities, they may not be ideal candidates to establish the vision for AI, given it benefits from more of an outside-in (consumer-driven) mindset than an inside-out (data-focused) approach.

Summing Up

With all the attention being given to AI, the main purpose of breaking it down in the manner I have above it to try and think about how we integrate and leverage it within and across an enterprise, and most importantly: not to treat it as a silo or a one-off.  That is not the right way to approach AI moving forward.  It will absolutely become part of the way people work, but it is a capability like many other in technology, and it is critically important that we continue to start with the consumers of technology and how we are making them more productive, effective, safe, and so on.

The next two articles will focus on how we integrate AI into the application and data environments.

Up Next: Evolving Applications

I hope the ideas were worth considering.  Thanks for spending the time to read them.  Feedback is welcome as always.

-CJG 07/28/2025