How I built an AI operating system to run my publishing company

Date:
Sat, 28 Mar 2026 09:00:00 +0000

Description:
If an AI agent can do the work of ten people, why would you pay for ten software seats?

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now subscribed Your newsletter sign-up was successful Join the club Get full access to premium articles, exclusive features and a growing list of member rewards. Explore An account already exists for this email address, please log in. Subscribe to our newsletter We have all been there. You find a piece of business software that looks like it will solve everything. The demo is polished. The landing page promises the world.

You sign up, pay, get inside - and discover that the feature you actually wanted is locked behind a "Premium" tier. Or an "Enterprise" plan. Or, my personal favorite, a "Contact Sales for Pricing" button that leads to a 45-minute discovery call where someone tries to upsell you on a package you never needed. It is the universal SaaS trap. And in 2026, the market is finally calling it out. Article continues below You may like Testing AI is
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Five AI agent predictions for 2026: The year enterprises stop waiting and start winning AI is no SKUand what that means for the enterprise Wall Street has wiped over $1 trillion in market capitalization from the software-as-a-service sector this year alone. Salesforce , once the
undisputed king of enterprise software, has seen its stock plunge 26% after earnings. Atlassian has cut 10% of its workforce - 1,600 people - while its stock has dropped 84% from its 2021 peak. Forrester calls it the "SaaS-pocalypse." TechCrunch is writing its obituary. Scott Purcell Social Links Navigation

Co-Founder of Man of Many. The reason is simple. If an AI agent can do the work of ten people, why would you pay for ten software seats?

A $350,000 Salesforce contract was reportedly terminated for a custom-built alternative. Retool's 2026 Build vs. Buy Report found that 35% of enterprise teams have already replaced at least one SaaS tool with custom software, and 78% plan to build more this year. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news
and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors By submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over.

I run a media company, not a software company. But when I looked at what we were paying for - and what we were actually getting - I decided to stop
buying and start building. The problem we were solving Man of Many is Australia's largest independently owned men's lifestyle publisher. But we are a small team operating in an industry under existential pressure.

Meanwhile, every SaaS vendor was knocking on the door with point solutions. One tool for SEO . Another for social media scheduling. Another for
analytics. Another for CRM . The average large enterprise now runs over 2,000 applications, with more than 60% not formally approved by IT. That is not a tech stack. That is tool sprawl. What to read next The next big lemming-like rush will be to artificial intelligence: While 1985 was hailed as the year of AI, Bill Gates ignored the hype to focus on softer software How to finally operationalize Agentic AI and realize its full potential 3 risks hindering enterprise-ready AI and how low-code workflows help

So instead of subscribing to more software, I built an AI operating system . What we built Otto OS is an AI Chief Operating Officer that runs our back-office operations through a network of specialized AI agents. It handles editorial workflows, sales intelligence, competitive analysis, financial reporting, content strategy, and business monitoring.

The architecture follows what is called the WAT Framework - Workflows,
Agents, and Tools (hat tip to Nate Herk) - which separates AI reasoning from deterministic execution. This separation matters because when AI tries to handle every step directly, accuracy compounds downward. Five steps at 90% accuracy each give you 59% overall success.

I built it using Claude Code as both the builder and the brain. No
traditional developers were involved. The initial build took roughly a week
of active development. What it actually does Every morning, Otto generates a briefing that pulls live revenue data from Google Ad Manager, checks traffic trends from GA4, scans for SEO anomalies, reviews outstanding invoices in
Xero , and flags anything that needs human attention. That used to require someone logging into five different platforms and manually compiling the information.

The security architecture uses a traffic-light system. Green zone: read-only access. Amber zone: draft-only access requiring human review. Red zone: any action involving money, public posting, or data deletion requires explicit human approval every time. What I learned (and what is transferable) 1. Map your tool sprawl. List every SaaS product you pay for. Next to each one,
write down which 20% of its features you actually use.

2. Start with the boring stuff. Reporting. Data consolidation. Status updates. Invoice chasing. These are the tasks where automation delivers immediate, measurable ROI.

3. Separate thinking from doing . Let AI handle reasoning and orchestration. Let deterministic scripts handle execution.

4. Use plain text as your database. Simple markdown files are the most effective memory system for AI.

5. Security is non-negotiable from day one . Build the guardrails before you build the features.

6. Document everything as you go. The system should write its own SOPs. The bigger picture We are not the only publisher thinking this way. Dow Jones, Business Insider, and Forbes are all investing heavily in internal AI
systems. Reuters Institute's 2026 predictions report found that 97% of publishers now consider back-end AI automation "important."

Bain and Company frames this as a fundamental restructuring of the software industry. Dean Shahar, who manages a three billion euro fund at DTCP, put it bluntly: "The SaaS world is dying. Not software itself, but SaaS as a
business category."

For us at Man of Many, Otto OS is how a small independent team competes against publishers with hundreds of staff. It cost a fraction of what we were spending on SaaS subscriptions that underdelivered.

The tools to do this are available to anyone, right now. The question is not whether your industry will be disrupted by AI. It is whether you will be the one building the system, or the one still waiting on a vendor to ship the feature you needed six months ago. Check out our list of best Large Language Models (LLMs) for coding.



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