Architected Intelligence

January 19, 2026

Last week, I took a brief detour from writing about AI tools in the context of business to look at the AI-based tools I use every day. Now I want to return to looking at how AI can be used in your business, and specifically the role of (relatively) simple automation tools.

Automation tools are platforms that let you build workflows to automate tasks. They typically provide a graphical drag-and-drop interface where you can visually build the workflow to connect different apps (via built-in "connectors"), data sources, and decision logic.

What makes modern automation tools different isn't automation itself (that's been around for a long time) but the way AI allows these workflows to work with unstructured data, make probabilistic decisions, and operate in situations where rigid rules would previously break.

Before going further, it's worth setting expectations. Simple refers to the tooling, not the outcome. If a manual task has many steps or judgment calls, automating it will still involve complexity; the platform just makes that complexity easier to manage.

If this still feels abstract, here are a few concrete examples of workflows that can be automated with these tools:

• Watch your email for incoming receipts → decide what category the receipt belongs to → save a PDF copy of the receipt into a folder → update a spreadsheet with the receipt details
• Watch for new voice memos recorded on your phone → transcribe the voice memo into text and save it for future reference → generate a to-do list based on the conversation → add to-do list to Google Tasks
• Receive contact information from multiple sources → search contact's website and other publicly available information to build database of their business → generate report summarizing fit between their company and your products or services → generate highly customized outreach email messages for validated contacts

I describe these tools as "relatively simple" because, in practice, they can quickly become complex. While many workflows can be built without writing code, it's common to need light scripting for data transformation, validation, or more complex logic.

The AI-assisted automation space is evolving rapidly, with new platforms appearing frequently. To give you a sense of the current landscape, here are some of the most commonly used tools today.

Zapier
Zapier is the long-established player in this space, first launched in 2012, with AI capabilities layered in more recently. It's a no-code platform focused on ease of use and speed, making it accessible to non-technical users. Its maturity shows in the sheer number of SaaS integrations available. Zapier is an excellent choice for straightforward, event-driven workflows, though costs and limitations can appear as complexity grows.

n8n
n8n is an open-source workflow automation platform aimed at technically inclined teams that want deep control over integrations, logic, and data flow. It can be self-hosted or used as a managed cloud service. n8n supports complex branching logic and long-running workflows, but that power comes with a steeper learning curve and a greater need for technical expertise.

Microsoft Power Automate
Power Automate is Microsoft's automation platform, tightly integrated with Microsoft 365, Azure, and the broader Power Platform. It's a natural fit for organizations already standardized on Microsoft tools. That said, its user experience can feel clunky, its non-Microsoft integrations are more limited than competitors', and its AI capabilities tend to lag behind best-in-class platforms.

Relay.app
Relay.app takes a different approach from the tools above. It's designed specifically around human-in-the-loop workflows, blending automation with approvals, handoffs, and collaboration rather than trying to eliminate people from the process. It works particularly well for workflows that require judgment and review, but it's not intended for highly technical or data-heavy automation.

Next week, I'll look at where these tools make sense—and where they don't. Automation can be incredibly powerful, but it also has clear limits. Understanding those limits is essential before relying on these tools for critical business processes.

This post is part of a series on the current state of AI, focused on how it can be applied in practical ways to deliver measurable improvements in productivity, cost savings, and response times. If you'd like to explore more, all previous posts are available here; please read them and then reach out with any questions or comments you have. I'm also available for consulting engagements—feel free to reach out using the contact link here if you'd like to talk further.

January 12, 2026

In previous posts, I gave a high-level overview of personal and team-based AI tools. These are off-the-shelf tools that are easy to adopt, often free or inexpensive, and can deliver immediate productivity gains. However, they share one common barrier to delivering company-wide dramatic productivity gains: They are standalone tools that aren't directly integrated into your workflows or business.

This week, I'm going to take a brief detour into the way I use personal AI tools. I spend a great deal of money evaluating AI tools every month so that my clients don't need to, and I wanted to share my current AI tool stack with you as well.

Chatbots
I use chatbots every day, primarily ChatGPT but also Perplexity and Claude. They've largely replaced Google for my searches unless I need to search for something current (the models have a lag in how fast they ingest and process data).

A game-changer for me was organizing my usage by chat. All the chatbots will save your past queries under something like "Your Chats" or "Library". Instead of creating a new chat every time, I take a related previous chat and put my latest query in there. That provides the chatbot with context on your previous searches for this subject matter and greatly improves the quality of the responses.

The other key thing I do is prompt engineering. This topic could be a whole series of posts itself, but the take-away is that your prompts to chatbots are probably way too short and undetailed. The next time you're asking a chatbot for some information, leverage the chatbot to help you build an effective prompt. Start with a prompt like this: "I'm going to be looking for a vacation based on preferences that I will share. Craft a prompt to accomplish this search. Include a persona that will make you act as an expert in this field. This persona must ask me for my preferences and ask follow-up questions to clarify my intentions." I think you'll be surprised by the prompt that the chatbot gives you to use—and by the results!

A cautionary note: By default, all chatbots will take your data and ingest into their systems to use how they wish. I will not use a model that doesn't let me turn that off, so the first thing I do with any chatbot is look for the "Allow your content to be used to train our models..." and turn that setting off.

Automating the Web
Oftentimes I need do some tasks or research involving several different websites. Agentic browsers are a relatively recent development that let the AI system take over your browser and act as you on the web. That sounds dangerous, and it is, so I recommend supervising them if you're asking it to actually do things on your behalf.

I use these agents with prompts like "Check my Gmail and LinkedIn accounts for recent messages; give me a summary and let me know if there are any I need to follow up on ASAP" or even tasks like "Find the 5 highest ranked pizza shops in Hamilton based on 3rd party reviews. Compare their menus for a meat-lover's pizza; identify the best value, then create an order on their website that I can complete with my information." The agent will then work away (you can watch it or just let it do its thing in the background) and complete the task for you.

Claude has a browser plug-in that gives you an agent right from within your browser, while Comet (from Perplexity) is a standalone browser. I use the Claude plug-in the most simply because it is right there in my default browser. However, because this technology is still young and can provide uneven results, it isn't unusual for me to give the same task to both Claude and Comet to compare the results.

Data Organization and Retrieval
I typically have a lot of different projects going on at any one time, and it's always a challenge to stay on top of them. There are several tools that let you organize documents and then connect them together, acting as an AI research analyst that sits on top of your private material. I use NotebookLM (from Google) for this.

Within NotebookLM, you create notebooks and then upload your documents or link to related websites. I create one notebook per project and upload all my project-related documents into it.

NotebookLM has tons of features, including mind maps, audio and video overviews, and (particularly useful for students), flashcards and quizzes. However, I use the query functionality the most. That's where I ask a question and it provides an answer based on the full corpus of knowledge in that notebook. The free version of NotebookLM is good enough to get started with (100 notebooks, 50 documents per notebook), but if you have a lot of material to organize, you'll find that you need a paid plan.

As a free plug, if you're looking for an enterprise-class AI expert for your business's policies, procedures, and other documents, I have an app that does this on an enterprise scale. It acts as the go-to expert for the corporate knowledge you want to make available, providing fully cited responses to queries along with full analytics, access controls, unlimited documents, audit logs, and more. Feel free to ask for more information!

Coding and other IT tasks
For coding and other IT-related tasks, there are a couple of major categories. The first category is app builders (often referred to as "Vibe Coding Platforms") that can generate full apps from prompts. Some of the leaders in this space include Lovable and Replit AI. I want a lot more control over the process than they offer, so I use products from the second category, AI coding assistants.

AI coding assistants are typically command-line based tools that can generate code for large multi-file projects in all major coding languages. I use Claude Code the most, although for some specific use cases I will use Codex and/or Cursor.

I want to highlight that these AI coding assistants are not just useful for writing code. If you spend a lot of time at a command prompt, you should be using an AI coding assistant. They are invaluable for all the things you do at the command line including scripting, administrating servers, and managing Internet domains.

Images
A picture speaks a thousand words, and sometimes you just need a picture. AI image generators have come a long way recently and are able to generate useable images from just a simple prompt. At this point in time, I think there's only one choice for image generation—the interestingly named "Nano Banana", accessed by going to the Gemini chatbot and typing in a prompt such as "Generate a picture of someone using an impressionist style. The person should be at a computer, hunched over typing. Add some kind of element to the picture to make it AI relevant."

Conclusion
The state of the art in AI is changing at a terrific pace, certainly the fastest I've ever seen any technology change in my 30+ years in the business. It's going to be interesting to review this post in a month and a year and five years to see how my tool set has changed.

Next week I'll return to looking at how you can use AI in your business as part of a toolkit to deliver productivity gains and cost savings, starting with simple automation tools that offer a practical way to implement real process change.

January 5, 2026

In earlier posts, I focused on personal AI tools such as chatbots, meeting transcribers, research assistants, and writing aids that help individuals work faster and more clearly. These tools are easy to adopt, often free or inexpensive, and can deliver immediate productivity gains.

They also share an important limitation: they optimize individual effort, not collective execution. They don't fundamentally change how work is coordinated, shared, or sustained across a group.

This post marks the next step in the progression: AI tools designed for teams.

What Team AI Tools Enable That Wasn't Possible Before
Team-based software itself is not new. Organizations have used project management tools, shared documents, and collaboration platforms for decades. What is new is how AI reduces the amount of manual coordination work required to keep teams aligned.

Modern team AI tools can automatically summarize shared work, synthesize unstructured input, surface patterns across team activity, and generate usable artifacts without requiring someone to do that work by hand.

This is the real shift. AI doesn't simply help teams do the same work faster; it changes how much coordination work needs to be done at all.

How Team AI Tools Differ From Personal Tools
The most important distinction is this: Personal AI tools help individuals think and produce; team AI tools help groups align and execute.

Personal tools live in private contexts—your inbox, your notes, your prompts. Team tools live in shared environments, where visibility, permissions, consistency, and trust matter.

That difference has consequences. Team AI tools introduce cost, require standardization, create switching friction, and expose workflow weaknesses more quickly. Once AI moves into a shared workspace, it stops being a personal productivity aid and becomes a leadership decision.

It's also worth noting that the line between personal and team tools is often blurry. Meeting transcription is a good example. It frequently starts with one person using a tool to summarize meetings, then evolves into a shared reference point for the entire team. The value doesn't come from the AI doing something novel, but comes from the output being shared quickly, accurately, and automatically.

Key Categories of Team AI Tools
To make this concrete, here are several important categories of team AI tools, along with representative leaders in each space.

Collaborative Thinking & Whiteboarding
Tools like Miro support brainstorming, planning, and visual collaboration. AI changes this category by clustering ideas, summarizing sessions, and turning messy, unstructured input into organized outputs that teams can actually act on. The whiteboard has been around for a long time, but this category of tools extends it to help create and extract meaning from collective thinking.

Project & Work Management
Platforms such as Hive use AI to analyze project data, surface risks, predict delays, and reduce manual status tracking. Instead of acting as passive systems of record, these tools increasingly function as coordination aids that help teams anticipate problems rather than react to them.

All-in-One Workspaces
Tools like Notion combine documentation, planning, and knowledge sharing in a single environment, with AI layered across everything. The value here comes from AI operating across content, reducing duplicated documentation and creating a living knowledge base.

Shared Meeting Intelligence
Tools such as Fireflies or Otter extend beyond transcription to create shared summaries, searchable histories, and consistent follow-through. Meetings stop being ephemeral and start becoming durable team assets with actionable outcomes.

These categories existed before. What's changed is that AI now reduces the effort required to keep teams aligned.

What Team AI Tools Actually Help (and Don't Help) With
Used well, team AI tools tend to help in three practical areas.

First, they reduce coordination overhead. Less time is spent summarizing, reminding, chasing updates, and reconciling different versions of reality. Second, they improve shared understanding. Teams spend less time aligning on facts and more time discussing decisions and trade-offs. Third, they make work visible by default. Progress, ownership, and blockers are easier to see without additional manual effort.

However, team AI tools are far less effective at fixing poorly defined roles, broken processes, misaligned incentives, or cultural issues. In practice, AI often exposes these problems faster by removing the friction that previously masked them.

While AI certainly can add value, leadership, judgment, and process design still matter far more than technology.

Common Mistakes and Risks
As teams adopt AI tools, a few risks show up repeatedly.

One is defaulting to bundled AI simply because it's convenient. Built-in assistants (particularly those embedded in large productivity suites, for instance, MS Copilot) often lag behind best-in-class tools, offer limited configurability, and depend heavily on the quality and structure of underlying data. They may be useful, but they are rarely the best choice.

Another is buying into aspirational promises rather than concrete capabilities. AI tools are frequently described as "transformative" or "end-to-end." In practice, most solve a narrow set of problems, and even then, only under the right conditions.

Finally, there is the hidden cost of tool churn. Changing tools disrupts habits, shared understanding, and trust in systems. Productivity almost always dips before it improves, and repeated changes train teams not to invest deeply in any system. Knowing when to stop experimenting and commit is one of the most underrated leadership decisions in this space.

Conclusion
At the team level, AI doesn't fix how work gets done but rather enhances the existing workflows. As a result, well-defined workflows become easier to manage and coordinate while poorly defined ones become harder to ignore. That's why introducing AI into team environments is less about the technology itself and more about judgment: choosing the right tools, setting realistic expectations, and rolling them out deliberately.

Team AI tools are an important step forward. They improve visibility, reduce coordination overhead, and help teams work more effectively together. But they are still an intermediate step. They don't fundamentally change how work moves between systems or across the organization.

That next shift (automation) is where AI begins to reshape workflows themselves. In my next post, I'll look at simple automation tools that offer a practical way to move from better coordination to real process change.

December 29, 2025

In the last couple of posts, I focused on chatbots as the easiest entry point into AI, and then briefly highlighted the risks they present that leaders need to understand.

Before moving into team-based workflows and organization-wide AI use, there's one more important stop: personal AI tools. These are tools individuals use to reduce friction in their own work. They don't change processes, but they can dramatically improve productivity, clarity, and focus. Here are some of the most common and useful categories I see today.

Online meeting tools
Tools that automatically transcribe meetings, generate summaries, and extract action items are among the highest-value AI tools available. They reduce notetaking, improve follow-through, and create searchable records of conversations. If you spend a lot of time on calls, this alone can save hours each week.

Writing assistance
AI-assisted writing is now built into almost everything. Used well, this helps people communicate more clearly, adjust tone, and reduce friction. The biggest gains come from using these tools as an editor and thought partner, not as a wholesale replacement for judgment.

Deep research and synthesis tools
All major AI providers offer tools (through paid plans) that will gather information from many sources and synthesize it into detailed, professional research papers. For leaders, this can dramatically reduce the time spent researching unfamiliar topics or preparing for decisions. As always, the key is treating the output as a starting point, not a final authority.

Copilots inside existing platforms
For organizations already standardized on tools like Microsoft 365, AI copilots can be a natural extension of personal productivity. They work best when expectations are realistic and the underlying data is well-organized.

Browser-based automation agents
New tools can navigate websites, gather information, fill forms, and perform repetitive digital tasks. These can be powerful for specific roles, but they're still in their infancy. For now, they tend to shine in targeted use cases rather than broad adoption.

Image and video generation
AI-generated images and video can be impressive and extremely valuable in creative or marketing contexts. For most roles though, they're less impactful than tools that reduce everyday cognitive and administrative load.

Across all these tools, a pattern emerges: they make individuals faster and more effective. They don't redesign how work gets done, but they can (and do!) make employees more productive when performing rote tasks.

In the upcoming posts, I'll shift from personal productivity to team workflows. This is where AI starts supporting (or even reshaping) shared processes, coordination, and decision-making. In my opinion, this is where the real organizational payoff begins and where informed leadership involvement becomes absolutely essential.

December 22, 2025

In my last post, I talked about chatbots as the easiest place to start with AI. They're accessible, easy to adopt, and can deliver immediate productivity gains when used well. However, those same qualities are also what make chatbots risky when they're used carelessly or scaled across an organization without clear expectations.

This isn't an argument against chatbots, but rather is about understanding where employee's use of chatbots can expose the organization to risk.

Here are some of the most common pitfalls I see:

Data privacy and confidentiality
Employees routinely paste highly sensitive information into public tools without realizing where that data may go or how it might be used. Without guidance, people make their own judgment calls, and those don't always align with organizational risk tolerance.

Confidently wrong answers
Chatbots are designed to sound authoritative. They can (and do!) fabricate details, provide outdated information, or give answers that are correct in one jurisdiction but wrong in another. Treating AI output as trusted instead of as a starting point is a common mistake and can lead to compliance issues or reputational damage.

Bias
AI models reflect the data they're trained on. In areas like HR, policy language, or customer communication, even subtle bias or framing can create real problems if left unchecked.

Loss of authenticity
An over-reliance on AI-generated text leads to a communication style that is readily identifiable. When AI-generated text is used without editing or judgment, it can erode trust in your messaging. There's also a longer-term risk: over-reliance on AI can quietly erode critical thinking and writing skills if it replaces judgment instead of supporting it.

Shadow AI
Let's be realistic: Employees will use AI tools whether leadership approves it or not and ignoring that risk doesn't make it go away. AI tools are increasingly capable, but they are definitely not accountable.

The real issue isn't whether chatbots can help. It's about where they make sense, how they should be used, and what guardrails need to be in place. Without that clarity, organizations risk exposing sensitive information, amplifying bias, introducing compliance issues, or quietly degrading the quality and authenticity of their work.

None of these risks are reasons to avoid chatbots, but they are reasons to be deliberate. Chatbots are powerful tools, and like any powerful tool, they require judgment, oversight, and clear expectations. That responsibility ultimately sits with leadership, not technology teams or individual employees.

In my next post, I'll move beyond chatbots and look at other personal AI tools before shifting into how AI can support team workflows and shared processes.

December 15, 2025

Over the past couple of posts, I've shared my perspective on AI: it's powerful and genuinely transformative, but it isn't a magic fix you can simply drop into an organization and expect results. AI is part of the toolkit, not the toolkit itself.

Over the next several posts, I want to get much more practical. I'll walk through how organizations can start using AI in meaningful ways, beginning with the quick wins: low effort, low risk, and immediate value. From there, we'll move into more integrated uses of AI that require deeper changes to processes and workflows but deliver far greater long-term payoff.

There's a lot to cover, and not every approach will make sense for every organization. My goal is to provide a clear, realistic path for leaders who want to move beyond AI hype and toward real, measurable impact. Today, I'll start with the simplest place to begin: AI chatbots.

When most people think about "using AI," what they're really thinking about is chatbots. They're the easiest place to start, require minimal technical setup, and can deliver immediate value. There are many options available, and all offer free tiers alongside paid plans. From a practical standpoint, the differences matter less than many people think; the real value comes from how you use them.

Used well, chatbots function like a tireless assistant. High-value use cases include drafting emails, reports, policies, and presentations; summarizing documents, meetings, and email threads; organizing and prioritizing work; explaining complex topics; and critiquing ideas or proposals.

These are low-risk, low-effort ways to save time immediately. Depending on your workflow, you may be able to reclaim hours each week simply by using one or more chatbots effectively.

There are also simple techniques for getting better results. One of my favorites is using two chatbots to critique and refine the other's output. This often produces stronger results and helps surface blind spots. Don't overlook conversation history either; keeping related work in the same chat thread preserves context.

Beyond text, there are also powerful tools for image generation (e.g., Stable Diffusion), video generation (e.g., Sora), and music creation (e.g., Suno), though those are topics for another day.

AI tools like chatbots are undeniably powerful. Used thoughtfully, they can save time, improve clarity, and support better decision-making. But over-reliance (or a lack of awareness of the risks they introduce!) can just as easily create new problems.

The real challenge isn't whether AI can help, but where it makes sense, how it should be applied, and what guardrails need to be in place. That's where many organizations struggle.

In my next post, I'll focus on the other side of chatbots: the common pitfalls, risks, and mistakes leaders need to understand before scaling their use across a business.

December 8, 2025

Last week I wrote about how too many vendors position AI as the fix for every organizational problem. To them, AI becomes the hammer, and suddenly everything looks like a nail.

(If you haven't seen that post, it might be worthwhile scrolling back and reading it first.)

Some may have taken that message to mean I'm AI-agnostic, or even anti-AI. Nothing could be further from the truth. I'm genuinely excited about AI; its usefulness, its accessibility, and its potential to create real business value. After 30+ years in technology, I've seen waves of innovation come and go, but I've never seen anything move this quickly or with this much transformative potential.

In 2017, Google researchers published Attention Is All You Need, the breakthrough that made modern large-language models possible. Fast forward to November 2022, when ChatGPT introduced the first broadly accessible LLM. Today (just three short years later) the landscape is crowded with capable models from dozens of providers, with each release bringing new capabilities that until recently felt like science fiction.

Modern AI can engage in interactive text and voice conversations, answer questions based on your private knowledge bases, generate high-quality video and music, and even assist in advanced areas such as drug and materials discovery. The recent emergence of AI agents has pushed things further, enabling systems to orchestrate complex, multi-step work such as writing and debugging code, handling customer service workflows, or managing sales leads. Entire industries and processes are already being reshaped, and we're still just at the beginning.

Next week, I'll share where AI can be introduced into your business in practical, meaningful ways. There are quick wins available, but the real value often comes from taking a thoughtful look at your processes to see where AI can integrate into and streamline (or even completely replace) existing workflows. Part of that discussion will also cover the caveats: AI can deliver tremendous upside, but it also brings risks and pitfalls that leaders must understand.

And finally, a brief disclaimer. Yes, I'm writing these posts because I'm passionate about the potential of AI to transform businesses, especially at a time when productivity, quality, and cost management are more critical than ever. But I'm also writing to raise awareness of the advisory and knowledge-transfer services I provide to businesses who don't have the time or resources to develop in-house expertise.

If you're interested in exploring how AI could improve your business, I'd be happy to have that conversation with you.

December 1, 2025

After 30+ years in IT management (and later in HR management as well) I've seen a familiar pattern repeat itself.

Vendors show up with a shiny new tool, convinced it's the answer to every organizational problem. Their solution becomes the hammer, and suddenly everything in your business looks like a nail.

Today, that hammer is Artificial Intelligence.

And to be fair, AI is powerful. It can write better emails, summarize meetings, support customer service, and streamline countless small tasks. But none of that automatically solves the real challenges organizations face.

Most companies have complex workflows, homegrown processes, layers of culture and subcultures, and years of operational habits wrapped around them. Some are helpful. Some aren't. But they're all deeply intertwined, and you can't simply drop AI on top of that and expect transformation.

That's why I get skeptical when I hear, "You just need to roll out Tool X across the organization."

Ten years ago, Tool X was CRM or a new ERP. Today, Tool X is AI.

AI absolutely belongs in the toolkit—but it isn't the toolkit itself.

Real improvement starts with understanding your core issues, rethinking processes, managing change well, and shaping culture. Only after that do you decide where AI fits. And when it's added in the right places, it can amplify everything else. But when AI is treated as a magic fix, expectations get inflated, trust erodes, and the real problems remain unsolved.

I believe AI is transformative, but only as part of a larger, thoughtful strategy.

What do you think? Is AI being oversold, or are organizations underestimating what it can do?