Barry AI

Artificial intelligence is evolving faster than any technology wave most organizations have experienced.

New tools appear every week, capabilities change every month, and most leaders quickly find themselves asking the same question: What tools should we actually use?

At Artificial Organizations, we work with executives, founders, and leadership teams who are redesigning how their organizations operate in the age of AI.

Through our accelerator programs, coaching engagements, and research, we see hundreds of AI workflows being tested inside real organizations.

Some tools create real leverage, others create noise.

This page captures the tools we consistently see delivering the most value for leaders and teams today.

Think of it as a living AI stack for building Artificial Organizations. We update it regularly as the ecosystem evolves.

 

How to Think About Your AI Stack

One of the biggest mistakes organizations make is starting with tools. In Artificial Organizations, we introduce the 3T model, a simple sequence for adopting AI effectively:

Traits → Tasks → Tools

First, understand how you and your team think and work best. Then identify the high-leverage tasks where better thinking and decision-making create disproportionate value. Only then should you select the tools that support those workflows.

The tools below are organized around the core capabilities leaders need to build AI-augmented operating systems, and build better judgment, speed and results with human AND machine intelligence.

Interested in a specific section — jump to it

 

The Artificial Organizations Recommended AI Stack

 

_AI stack for leaders - Recommended AI Tools and Agents

 

1. Personal Assistants + Writing AI

These tools act as thinking partners for leaders, helping synthesize ideas, draft communication, and pressure-test decisions.

 

ChatGPT

ChatGPT Logo - AI stack for leaders

One of the most versatile AI assistants available. ChatGPT is widely used for writing, idea generation, synthesis, and reasoning support.

Pros

Cons

 

Claude AI

 

Claude AI - AI stack for leaders

Claude is known for its thoughtful reasoning and long-context capabilities, making it particularly useful for working with long documents.

Pros

Cons

 

Google Gemini

Google Gemini

Gemini integrates deeply with Google’s ecosystem and is particularly useful for teams using Google Workspace.

Pros

Cons

 

Perplexity AI

Perplexity AI

Perplexity is a powerful AI search and research tool designed to provide real-time information with citations.

Pros

Cons

 

2. Work Systems

AI becomes dramatically more powerful when embedded inside a work system where knowledge accumulates.

These platforms allow teams to capture thinking, organize work, and maintain institutional memory.

 

Notion

Notion

A flexible workspace combining documentation, databases, and AI-assisted knowledge management.

Pros

Cons

 

Microsoft Copilot

Microsoft Copilot

Microsoft’s AI layer embedded across Office, Teams, and enterprise workflows.

Pros

Cons

 

Superhuman

Superhuman - AI stack for leaders

An AI-enhanced email platform designed to help professionals manage high communication volume.

Pros

Cons

 

Google Workspace

AI Stack for leaders - Google Workspace

A widely used productivity ecosystem now integrating AI across its products.

Pros

Cons

 

3. Meeting AI + Organizational Memory

Meetings are where most organizational decisions begin.

AI meeting tools capture conversations and convert them into structured knowledge.

This capability forms the foundation of the Capture → Transcribe → Synthesize → Act workflow described in Artificial Organizations.

 

Otter.ai

AI stack for leaders - Otter.ai

A widely used meeting transcription platform for capturing conversations.

Pros

Cons

 

Fathom

AI Stack for leaders - Fathom

A meeting assistant that automatically captures key moments and summaries.

Pros

Cons

 

Fireflies.ai

Fireflies.ai

 

A meeting intelligence platform designed for team collaboration.

Pros

Cons

 

Granola

Granola

A newer tool focused on turning meeting notes into structured insights.

Pros

Cons

 

4. Content Creation Tools

AI dramatically accelerates the creation of visual, written, and multimedia content.

These tools help leaders communicate ideas quickly and clearly.

 

Canva

Canva

 

A powerful design platform with integrated AI for visual creation.

Pros

Cons

 

Midjourney

Midjourney

A leading AI image generation tool known for high-quality visuals.

Pros

Cons

 

Descript

Descript

A platform for editing audio and video through text-based workflows.

Pros

Cons

 

5. Automation + Workflow AI

Automation tools allow organizations to connect systems and create AI-powered workflows.

Zapier

Zapier

 

One of the most widely used automation platforms.

Pros

Cons

 

Make

Make

A powerful visual automation platform for complex workflows.

Pros

Cons

 

N8nn8n

An open-source automation platform increasingly used by AI builders.

Pros

Cons

 

OpenAI Assistants

ChatGPT Logo

Tools for building custom AI agents and workflows.

Pros

Cons

 

6. Insights + Research

These tools help leaders quickly explore research, insights, and complex information.

Perplexity AI

Perplexity AI

One of the fastest ways to explore real-time information.

Pros

Cons

 

ElicitElicit

An AI research assistant designed for analyzing academic papers.

Pros

Cons

 

GleanGlean

An enterprise knowledge search tool for internal information.

Pros

Cons

 

Scite.aiScite.ai

A research tool that analyzes how academic papers cite one another.

Pros

Cons

 

AI Agent Builders

These tools allow organizations to build AI-native workflows and autonomous agents.

These platforms enable teams to design multi-agent AI systems that can execute complex tasks.

They are typically used by:

 

Barry O’Reilly’s Personal AI Stack

While the ecosystem evolves quickly, Barry’s personal stack focuses on a small number of tools used deeply.

 

_AI stack for leaders - My Personal AI Stack

 

Personal Assistants

Productivity

Meeting AI

Content Creation

Automation

Research

AI Builders

The goal is not to use more tools. It is to build workflows where AI amplifies thinking and decision-making.

 

The Stack Test

Many organizations accumulate AI tools without gaining real advantage. If you want to evaluate your own stack, ask three questions:

1. Does this tool accelerate decision-making?

If it doesn’t improve how quickly and clearly your team makes decisions, it’s probably noise.

2. Does it capture knowledge that would otherwise disappear?

The most valuable AI tools create organizational memory.

3. Does it compound over time?

Great AI workflows improve continuously. Weak ones stay static.If your AI stack passes these three tests, you’re likely building the foundations of an Artificial Organization.

Remember, experiments entertain curiosity. Stacks boost performance.

 

Build Your Artificial Organization

The tools on this page are only the beginning. Real advantage comes from how they are used together to create systems where:

That is the core idea behind Artificial Organizations, and it is the work we help leaders build every day.

 

FAQ

Q1. What is an AI stack for leaders?

An AI stack for leaders is a set of tools and workflows that improve how leaders think, make decisions, and manage work using AI.

Q2. How should leaders choose AI tools?

Leaders should start with how they think and work, then identify high-leverage tasks, and only then choose tools that support those workflows.

Q3. What is the biggest mistake when building an AI stack?

The biggest mistake is starting with tools. Without clear workflows and decision needs, tools create noise instead of real performance gains.

Q4. What tools are essential in an AI stack?

Most effective stacks include AI assistants, meeting capture tools, work systems, research tools, and automation platforms that support decision-making.

Q5. How does an AI stack improve decision-making?

An AI stack improves decision-making by capturing information, synthesizing insights, and reducing the time needed to prepare and act on decisions.

Q6. What makes an AI stack effective?

An effective AI stack accelerates decisions, captures knowledge that would otherwise be lost, and improves continuously over time.

Q7. Do leaders need many AI tools to build an effective stack?

No. The best leaders use a small number of tools deeply. The goal is not more tools, but better workflows that improve thinking and results.

References

Why the companies that redesign how they think with AI will outperform those that simply adopt tools

Most companies think the AI race is about tools. It isn’t. The real race is about how leaders and organizations think.

Every major technological shift forces us to reinvent how we operate.

The industrial era created the hierarchical organization. The software era created Agile organizations. The AI era will create something different again. Not companies that simply use AI tools, but companies that think, learn, and operate with AI as part of their core system.

These are Artificial Organizations, companies that deliberately combine human judgment with machine intelligence to redesign how decisions are made, how knowledge flows, and how work improves over time.

The result is an organization that moves faster, decides more clearly, and compounds advantage continuously.

 

The Problem Most Organizations Face

Today, leaders everywhere are experimenting with AI. New copilots, new assistants and new dashboards appear almost weekly.

Too many organizations approach AI the same way they approached previous technologies. They try to bolt it onto existing ways of working. This rarely produces real transformation.

Instead it creates:

The result is activity without transformation.

Research reflects the same pattern. In one major MIT study, roughly 95% of enterprise GenAI initiatives fail to deliver measurable business value, with only about 5% scaling into production impact.

The issue isn’t the technology. It’s the system leaders are trying to plug it into.

Organizations designed for the pre-AI era struggle to absorb AI in meaningful ways. They layer tools on top of outdated workflows instead of redesigning how work actually happens. The organizations that succeed will do the opposite.

 

The Real Shift Is Not Technology

The real shift happening with AI is leadership behavior. AI changes the economics of knowledge work.

For the first time:

When this capability becomes embedded into how leaders and their organizations operate, something powerful happens. Work begins to compound.

Small improvements in thinking, communication, and decision-making accumulate into structural advantage. The companies that understand this shift are not treating AI as software. They are redesigning how judgment works inside the organization.

As I wrote in Artificial Organizations, the real opportunity is not automation. It is judgment infrastructure—systems that allow leaders to sense, think, decide, and act faster without sacrificing clarity.

Organizations that build this capability develop something incredibly powerful: decision velocity and decision advantage.

 

From Tool Adoption to System Design

Most organizations today focus on AI tool adoption. Artificial Organizations focus on AI system design.

Instead of asking: “What tools should we use?”

They ask: “How should the organization work when intelligence is abundant?”

This leads to a different approach.

Leaders redesign:

AI becomes the infrastructure that enables these systems. It moves from being an experiment at the edge of the business to becoming a core capability that accelerates how organizations think and learn.

 

The Era of the Artificial Organization

An Artificial Organization is a company that deliberately designs compounding loops between people and AI. These loops continuously improve how work gets done.

For example:

Each cycle strengthens the next.

Over time the organization begins to learn faster than its competitors. That is the true advantage of AI.

Not automation. Organizational intelligence.

Human and machine intelligence compounding inside the organization.

The Architecture of an Artificial Organizations

 

The Leadership Shift

Artificial Organizations require a new type of leadership. In the past, leaders focused primarily on:

In the AI era, leaders must also design how you and your organization think.

This means redesigning the workflows where judgment, communication, and knowledge creation happen.

It begins with you, as an individual leader. If leaders do not redesign how they personally work with AI, attempts to scale AI across teams will fail.

Technology adoption alone does not transform organizations. Leadership behavior does. When leaders redesign their workflows, teams follow. When teams redesign workflows, organizations evolve.

 

How to Lead an Artificial Organization

In Artificial Organizations I introduce the 3T Model for leading in the age of AI:

Traits → Tasks → Tools

Artificial Organizations are not built with tools first. They are built by redesigning leadership workflows.

Most leaders start with tools. That is the mistake. Tools are the last step, not the first, and the order matters.

The 3T Model

 

Traits

Start with how you naturally think and work.

Your traits determine how you create, process, and use information.

Some leaders think best when speaking. Others when writing, mapping ideas, or reviewing structured analysis. When AI aligns with these natural thinking patterns, thinking accelerates.

Tasks

Next identify the tasks where your judgment creates the most leverage.

These include:

AI should automate low-leverage administrative work so leaders can focus on high-leverage judgment.

Preparation, summarization, documentation and follow-up can increasingly be handled by AI. That frees leaders to focus on the work that matters most.

Tools

Only then should you choose tools.

When tools align with traits and tasks, they become multipliers rather than distractions.

As the book puts it: “Start with traits. Follow with tasks. Then choose tools.”

Over time these workflows become systems.

And those systems become the foundation of an Artificial Organization.

 

The Compounding Advantage

Leaders that adopt this model gain something powerful: decision velocity and decision advantage.

They:

The result is faster decision cycles, better informed choices and stronger execution.

Leaders using our AI-augmented judgment systems have reported 30–50% faster decision cycles and significant reductions in preparation, follow-up and implementation time. Also captured in case studies with American Airlines, Skyscanner and Slack to name a few in the book. 

Small improvements compound, and the organization becomes progressively more intelligent.

 

The New Competitive Frontier

In the coming decade, companies will not compete primarily on:

Those advantages are becoming increasingly democratized. Instead they will compete on how effectively their organizations think and learn with AI.

The companies that figure this out first will not just move faster. They will operate with fundamentally different economics. Decisions will be prepared faster. Knowledge will accumulate automatically. Leaders will spend more time exercising judgment and less time gathering information.

Over time this creates something every executive understands instinctively: a widening decision advantage.

Many companies already recognize the urgency. McKinsey reports that 88% of organizations now consider AI transformation a top priority.

But adoption alone does not create advantage. Leadership behavior does. Some companies will remain traditional organizations that use AI tools. Others will become Artificial Organizations.

The gap between the two will widen steadily—and then suddenly.

 

The Movement

Artificial Organizations is not just a framework. It is a movement to help leaders redesign how their companies work in the age of AI.

The book introduces the model.

The accelerator helps leaders implement it.

And our community becomes a place where the next generation of organizations is being built.

 

The Opportunity

The leaders who understand this shift early will not simply adopt AI faster. They will design organizations that become progressively smarter over time.

Organizations where knowledge compounds, decisions accelerate and judgment improves.

Some companies will remain traditional organizations that use AI tools. Others will become Artificial Organizations.

The difference between the two will define the next generation of market leaders—and we are only just beginning.

The real question for leaders now is simple: are you experimenting with AI tools, or redesigning how you and your organization thinks?

 

FAQ

Q1. What is an Artificial Organization?

An Artificial Organization combines human judgment and AI systems to redesign how decisions are made, how knowledge flows, and how work improves over time.

Q2. How are Artificial Organizations different from companies using AI tools?

Most companies add AI tools to existing workflows. Artificial Organizations redesign how work happens, using AI as a core system for thinking, decision-making, and learning.

Q3. Why do most AI initiatives fail in organizations?

Most fail because companies layer AI onto outdated systems. Without redesigning workflows and decision processes, AI creates activity but not real transformation.

Q4. What is decision velocity in an Artificial Organization?

Decision velocity is the speed at which organizations move from signal to judgment. Artificial Organizations increase this by capturing, synthesizing, and acting on information faster.

Q5. How can leaders start building an Artificial Organization?

Leaders start by redesigning how they personally work with AI. They align their thinking (traits), focus on high-leverage tasks, and then choose tools that support those workflows.

 

References

Most leaders think AI will automate work. The real change is more uncomfortable. AI is beginning to expose how leaders actually think.

For the past two years, a question has dominated conversations about artificial intelligence: Will AI replace leaders?

The short answer is no.

AI will not replace leadership but it will replace leaders who refuse to evolve how they work.

Leadership has never been about executing tasks. It has always been about exercising judgment under uncertainty. Leaders interpret signals, evaluate trade-offs, and make decisions when the answer is not obvious.

AI cannot replace that but it is fundamentally changing how that work happens.

The leaders who learn to integrate AI into their thinking will gain a structural advantage over those who do not.

 

The Hidden Bottleneck in Leadership

Leadership has always been a thinking profession.

Executives are not paid to produce documents or process information. They are paid to exercise judgment. Yet most leaders spend the majority of their week doing work that surrounds decisions rather than making them.

Harvard Business Review reports that senior executives spend more than 23 hours per week in meetings, with additional hours spent preparing for them. In our Artificial Organizations AI Executive Study (2025), we asked leaders to map how they actually spend their time and a striking pattern emerged. Roughly 80% of leadership time is consumed by:

Yet 80% of leadership value comes from:

This isn’t a time management problem. It’s a decision allocation problem.

Until leaders redesign how judgment flows through their organization, no AI tool will fix it.

AI replacing leaders

This misallocation creates the hidden friction of modern leadership. Leaders are drowning in context reconstruction instead of focusing on the work that matters most: thinking clearly about the future. AI changes this equation.

 

The Market Is Not Waiting

The shift is already underway.

Between October 2025 and February 2026, Amazon eliminated 30,000 middle-management roles as part of a broader effort to flatten decision layers and accelerate execution.

At the same time, companies are aggressively competing for AI-capable talent. In 2025, Meta reportedly spent over $300 million recruiting top AI engineers.

The economic signals are becoming impossible to ignore.

By early 2026, workers with AI-related skills commanded a 23% wage premium — higher than the premium associated with most advanced degrees. The labor market is reorganizing rapidly.

Research from the Burning Glass Institute analyzing millions of job postings after the release of ChatGPT revealed something important:

But the deeper insight lies in how those trends overlap.

Across 759 occupations, automation exposure and augmentation exposure were strongly positively correlated (r = 0.87). That means the same roles being automated are also being augmented. AI is not eliminating knowledge work. It is redesigning it from within.

The project manager whose scheduling tasks are automated is the same project manager whose strategic responsibilities expand. The financial analyst who no longer builds models from scratch is the same analyst who now interprets and challenges AI-generated output.

The unit of change is not the job. It is the judgment required for the job, and this is where many leaders misunderstand what is happening.

AI isn’t replacing leaders. It’s exposing them.

Leaders who rely on manual preparation and fragmented information will increasingly be outpaced by leaders who augment their thinking with AI.

The difference may not be obvious at first. But over time, it compounds. One leader arrives in the room having spent hours reconstructing context. Another arrives having pressure-tested the issue, explored multiple perspectives, and clarified decision paths before the discussion even begins. The advantage becomes obvious quickly.

As NVIDIA CEO Jensen Huang put it: “AI won’t take your job. But someone who knows how to use it will.”

 

From Tool to Thinking Partner

Most people treat AI like a productivity tool. They ask it to write emails, summarize documents, or generate presentations. Those uses are helpful but they barely scratch the surface. The real leadership advantage appears when AI becomes a thinking partner.

Instead of generating output, AI helps leaders interrogate their reasoning. Leaders can ask questions such as:

This shifts AI from producing answers to improving thinking.

When leaders embed AI into their daily workflow, something powerful begins to happen—we call it the CTSA Loop.

The CTSA Loop Capture → Transcribe → Synthesize → Act

 

Leaders begin by capturing conversations and decisions rather than letting them disappear across fragmented tools and inboxes.

AI then transcribes those conversations into searchable information.

It synthesizes themes, risks, and unresolved issues, allowing leaders to interrogate the material quickly.

Finally, leaders act on those insights, making decisions with clearer context. Those decisions generate new conversations and new information, feeding the loop again.

Over time the organization develops something powerful: institutional memory and faster judgment. 

The result is not just productivity. It is compounding decision advantage.

 

A Case Study: Starting With Yourself at Skyscanner

Andrew Phillips, CTO of Skyscanner, operates inside one of the most complex digital systems in travel.

The platform serves more than 160 million monthly users and processes billions of flight price checks every day.

Yet when Andrew began exploring AI, he didn’t start with a transformation program.

He started with a personal question: “Is this actually making my day better?”

Like most executives, his schedule was packed with leadership meetings, architectural reviews, and strategy discussions. The challenge wasn’t lack of information. It was holding context across dozens of decisions. Instead of launching a company-wide initiative, Andrew began experimenting inside his own workflow.

Some experiments worked. Others didn’t. He shared both openly with his teams. That behavior created something powerful: permission to experiment.

Over time those experiments spread across the organization. AI stopped being a technology initiative. It became a learning process embedded in leadership behavior.

Organizational change began not with technology, but with leadership experimentation.

 

Two Types of Leaders Are Emerging

Across organizations, two distinct leadership patterns are beginning to appear.

The difference is not intelligence or experience.

It is how leaders choose to work with AI.

AI replacing leaders

The gap between these approaches will widen.

And eventually it will define which leaders — and which organizations — move fastest.

 

The Decision Advantage

Organizations rarely fail because they lack information. They fail because they make decisions too slowly.

Research from Bain & Company found organizations that excel at decision-making outperform peers financially by up to 95 percent.

AI accelerates decision-making by shortening the distance between signal and judgment.

When information is captured automatically, synthesized instantly, and explored interactively, leaders can focus on the work that matters most: making better decisions faster.

This is where decision velocity increases and decision advantage begins to compound.

 

The Leadership Question

AI will not replace leaders but it will change how leadership works.

Some leaders will continue preparing the way they always have, manually gathering information, reconstructing context, and synthesizing insights alone.

Others will redesign how they work. They will integrate AI into their thinking. They will pressure-test ideas before presenting them. They will arrive in the room with clarity others struggle to match.

And over time, that difference will compound.

Because the leaders who build AI into their thinking processes will not simply work faster. They will make better decisions faster than everyone else in the room.

And in a world defined by uncertainty and accelerating change, that may become the most important leadership advantage of all.

 

FAQ:

Q1. Will AI replace leaders?

AI will not replace leaders. It will replace leaders who don’t adapt. Those who integrate AI into their thinking will make faster, better decisions and outperform others.

Q2. Why is AI changing leadership now?

AI reduces time spent gathering and processing information, shifting leadership toward judgment. Decision speed and quality now matter more than managing tasks or workflows.

Q3. What does it mean to use AI as a thinking partner?

Using AI as a thinking partner means testing assumptions, exploring alternatives, and challenging decisions. It helps leaders improve how they think, not just what they produce.

Q4. What is the biggest mistake leaders make with AI?

The biggest mistake is treating AI as a productivity tool. Leaders who only use it for output miss its real value in improving decision-making and judgment.

Q5. How does AI improve decision-making for leaders?

AI captures and synthesizes information quickly, helping leaders see patterns, evaluate trade-offs, and make decisions with clearer context and greater confidence.

 

References: