Field notes on practical AI.

Cisco surveyed 2,500 CEOs across 23 countries for its 2026 AI Readiness Index. Among organizations they classify as Pacesetters - the top performers across six readiness pillars - 99% have a well-defined AI strategy. Across all organizations surveyed, that number is 58%.

The 41-point gap is not a tools gap. Pacesetters are not running different models. They are not spending more per seat. The separating variable is whether the organization made a deliberate decision about where AI should operate, what it should produce, and who is accountable for the output.

This is the part of the conversation most AI vendors skip. Strategy does not come from the platform. It does not come from the implementation partner either. It comes from leadership deciding what they want AI to do and holding the organization to that answer.

The organizations that are furthest ahead on AI did not get there by moving fast. They got there by making fewer, more deliberate decisions - and measuring those decisions against outcomes, not activity.

Buying better tools into a strategy gap does not close the strategy gap.

Dell's AI Factory with NVIDIA crossed 5,000 enterprise customers this quarter. This week at Dell Technologies World, the company introduced Dell Deskside Agentic AI - on-premises agentic AI that runs without cloud dependency.

Key specifics: handles models from 30 billion to 1 trillion parameters at the workstation. Powered by the NVIDIA NemoClaw stack and OpenShell runtime, which provides a sandboxed environment for building, governing, and running agents across the full stack from desktop to data center. Break-even versus public cloud API costs in as little as three months. New partnerships bring Google Gemini, OpenAI Codex, and Palantir Foundry on-premises through the Dell ecosystem.

For regulated industries - healthcare, financial services, government - cloud-only agentic deployments have hit friction at three points: cost predictability, data sovereignty, and security enforcement. This addresses all three in one architecture.

If your teams are running agents on workflows that touch sensitive data, the on-premises economics are worth modeling now. Visit allemnii.com to talk through what this means for your environment.

In 2012, Dropbox nearly killed a Fortune 500 company's IT security posture. Not through a breach. Through convenience.

Employees started storing sensitive files in personal Dropbox accounts because the internal file system was slower, harder to access from mobile, and never designed for how people actually worked. The term "shadow IT" was coined for exactly this pattern: employees solving real workflow problems with tools the organization cannot see, govern, or secure.

The same thing is happening right now with AI agents. REDWOOD's 2026 enterprise automation research found that teams are routinely deploying AI tools and agents outside enterprise guardrails - moving fast in isolation, creating fragmentation, and introducing security exposure from tools never designed for mission-critical use.

The word "shadow" is doing a lot of work here. These are not rogue employees. These are people solving real problems with the best tools available. The failure is organizational, not individual: governance that lives only in a policy document cannot keep pace with deployment that happens in a Slack thread.

Organizations that embedded governance directly into their automation infrastructure are outperforming those trying to retrofit controls after the fact. The architecture decision is the governance decision. They are not sequential.

If your teams are deploying AI tools without a governance framework that moves at the same speed, that is the readiness gap that matters most right now.

Most organizations do not have an AI roadmap problem. They have a starting point problem.

MIT research from 2026 puts it plainly: 95% of generative AI pilots fail to reach production. S&P Global found that 42% of companies abandoned most of their AI initiatives in 2025, up from 17% the year before.

That is not a technology problem. That is a planning problem.

The roadmap conversation stalls in the same place every time. Leadership agrees AI is a priority. Someone gets tasked with building a plan. The full landscape comes into view - tools, governance, vendors, training, infrastructure - and the plan becomes a research project that never ships.

The organizations that move start differently.

They pick one workflow. One team. One problem with a measurable output. They deploy something small enough to learn from and specific enough to evaluate. That first deployment is not the strategy. It is the evidence base the strategy actually needs to exist.

Without it, every roadmap is assumptions dressed as a plan.

The difference between organizations with real AI momentum and those still in planning mode almost always comes down to this: one group ran something.

If your leadership team is ready to move from planning to execution, that is exactly where we start with clients. Reach out or book a call at allemnii.com.

The two most powerful AI labs in the world spent the same day this week telling the market they are no longer just selling software.

On May 4, Anthropic announced a new AI-native enterprise services firm alongside Blackstone, Goldman Sachs, Hellman & Friedman, Apollo, General Atlantic, Sequoia, and GIC. Hours earlier, Bloomberg reported OpenAI had finalized its own version -- The Deployment Company -- raising more than $4 billion from 19 investors including TPG, Brookfield, Advent, and Bain Capital. Both ventures are built on the same structural logic: embed engineers directly inside client organizations, sit alongside their teams, and build AI into the workflows that actually run the business.

This is not a software product launch. This is Anthropic and OpenAI standing up forward-deployed engineering operations at a scale no technology company has attempted before. The closest precedent anyone reaches for is Palantir. Both labs are now executing that model simultaneously, backed by firms that collectively control trillions in assets and thousands of portfolio companies.

The reason this is happening now is not that the technology suddenly got better. It is that both labs have concluded the same thing: the bottleneck to enterprise AI value is almost never the model. It is the organizational side -- the readiness, the workflow design, the governance, the change management. They have decided it is faster and more valuable to own that side themselves than to wait for the market to develop it.

For every organization watching from the outside, the real question this week's news raises is not which AI vendor to choose. It is what happens to your competitive position when the operations of businesses around you are being rebuilt from the inside by teams funded with billions, while yours are not.

The window to build internal AI capability on your own terms is open. The question is how long it stays that way.

The most important AI skill in 2026 is knowing what not to give to AI.

Everyone is focused on what models can do. That framing produces a specific failure mode: teams hand off tasks that look automatable but require judgment that was never codified. Output degrades. Trust drops. The tool gets blamed.

The organizations that are actually ahead are not the ones moving fastest. They are the ones being precise about where AI belongs, and deliberate about where it does not.

Speed is easy to copy. That kind of precision is not.

59 percent of enterprise leaders say their organization has an AI skills gap in 2026, even though most are already investing in some form of AI training.

That number from the 2026 State of Data and AI Literacy Report tells you something important. The problem is not a budget problem. Most organizations have already allocated spend toward AI. The problem is what they are spending it on.

The report found that organizations with mature, workforce-wide AI literacy programs are nearly twice as likely to report significant AI return on investment compared to those with scattered training. The difference is not the tool stack. It is structured capability building versus content delivery.

Content delivery is a video. A workshop. A one-hour lunch-and-learn that generates nothing measurable. Capability building is a curriculum designed around specific roles, tested against real workflows, and reinforced through practice. Most organizations are doing the former and expecting the results of the latter.

The gap between AI investment and AI ROI is almost always a people problem, not a technology problem. The organizations closing it are the ones treating training as a designed system, not a calendar event.

This is exactly what we build with organizations across healthcare, engineering, financial services, and education. If your team has the tools and still is not seeing results, we would be glad to show you what a structured program looks like in practice. Reach out at allemnii.com.

Most executives are not behind on AI because they lack ambition.

They are behind because no one has shown them what it actually looks like on a Tuesday morning.

Here is what practical AI adoption looks like for a senior leader, no transformation program required.

Before your first meeting, AI has already read the briefing materials, surfaced the three points that need your attention, and flagged the risk buried on page eleven. You walk in informed, not just present.

During the work that drains your best hours, drafting responses, structuring proposals, reviewing documents before they go out, AI removes the blank page and the first draft. You spend twenty minutes instead of two hours. Your judgment stays in the room. The busywork does not.

Between decisions, AI helps you process faster with better inputs and less noise. Not because it decides for you. Because it gives you the context to decide well.

At the end of the week, it surfaces what you committed to, what needs follow-up, and what patterns are emerging before they become problems.

The leaders getting real value from AI right now are not running the biggest pilots.

They identified two or three friction points in their actual week and removed them. Quietly. Practically.

That is where the advantage is accumulating.

Your peers are not waiting for a perfect strategy to adopt AI. They are already using it to reclaim their afternoons, walk into rooms better prepared, and make faster decisions with cleaner information.

The gap between them and you is not technology. It is one conversation.

Book a 30-minute AI Executive Briefing with Allemnii. We will show you exactly where AI fits into your week, what it changes, and what it does not. No slides. No pitch. Just clarity.

Visit allemnii.com or send us a direct message to get started.

Everyone is starting to use AI agents.

They write emails, route requests, summarize information, and make small decisions all day.

Individually, it works. You save time. Things move faster. Less manual work. Nothing looks risky.

The issue shows up when these decisions start stacking.

An agent sends something slightly off. Another one routes a request incorrectly. Something gets delayed because the context was incomplete.

Each one is small, but they don't stay isolated.

We've noticed something while testing this in workflows. The tool is rarely the problem. It's that no one defined what the agent is allowed to decide, what needs validation, and what should never be automated. So everything "kind of works" until something actually matters.

That's where AI policy comes in. Not as a document for compliance, but as a way to answer simple questions. Who is responsible for what the agent does. Where human review steps in. What happens when the output is wrong.

Without that, you don't really have a system. You have a set of tools making decisions independently.

At a small scale, you don't feel it. At a team level, it starts creating friction. People don't fully trust the output. They double-check everything. Or worse, they assume it's correct.

The shift is not "should we use AI or not". It's "where do we allow it to act, and where do we keep control".

Most teams will figure this out after something goes wrong. Some will define it before. That's usually the difference between something that helps and something that creates new problems.

If you want to define it before, that's exactly what we help organizations do. Send us a message or visit allemnii.com.

Most organizations are not behind on AI investment. They are behind on what holds the investment together.

WRITER's 2026 enterprise AI adoption survey of 2,400 executives and employees found that 79% of organizations face significant challenges adopting AI, up by double digits from the year before. That increase is not happening despite higher budgets. It is happening alongside them. Companies spending over a million dollars annually on AI are among the ones reporting the most structural friction.

The pattern is consistent. AI is getting deployed faster than governance is getting built. 67% of executives believe their organization has already experienced a data breach because an employee used an unapproved tool. 55% describe AI use inside their company as a situation without central coordination. Only 29% report meaningful ROI from generative AI, while individual users report productivity gains of five times or more.

That gap between individual output and organizational return is the real story. When every team member's AI behavior is ungoverned, the productivity of a few cannot compound into business value for the whole. It fragments instead.

Is your company in that 79%? At Allemnii, we help organizations close exactly this gap through AI Readiness Assessments, Governance Programs, and Strategy Roadmaps built around where your organization actually stands today. If you are not sure where to start, that is precisely what we are here for. Visit allemnii.com or send us a message.

Every major AI lab moved this week. Here is what shipped and what it signals.

OpenAI released GPT-5.5, Workspace Agents, and ChatGPT Images 2.0. OpenAI is no longer building a chat product. They are building the operating layer for knowledge work.

Anthropic released Claude Opus 4.7 and previewed Claude Design. The bet is clear: the most trusted model wins the enterprise, and trust now includes what it refuses to do.

Google dropped the Gemini Enterprise Agent Platform, Workspace Intelligence, a Workspace MCP Server, 8th-gen TPUs, an Agentic Defense suite, and committed $750M to partner deployment at Cloud Next '26.

Google is not racing on models. They are racing to own the full enterprise stack before anyone else does.

One theme across all three: AI is no longer returning answers. It is executing work.

The organizations ahead in 18 months will not be the ones who read about this.

They will be the ones who decided what to do with it.

Most organizations will spend the next 30 days watching. The ones we work with will spend them building. Send us a message or visit allemnii.com.

Your team's AI agents can now keep working after everyone logs off.

OpenAI just launched Workspace Agents in ChatGPT. Teams build a shared agent once. It runs in the cloud, connects to Slack, Salesforce, Google Drive, and Notion, follows a schedule, and keeps going when no one is watching.

This is a different category from the custom GPTs that came before. Those were individual tools. Workspace Agents are organizational ones. The workflow belongs to the team, not the person who built it.

A financial services firm can put this to work immediately. One agent pulls end-of-week data, generates the charts, drafts the narrative, and drops the report into the right channel. No one touches it. It runs every Friday.

That is one less recurring task that depends on someone remembering to do it.

The infrastructure is already inside your ChatGPT subscription. It is free through May 6 before moving to credit-based pricing. That is a two-week window to build, test, and get a real workflow running at no extra cost.

We design and deliver AI training programs for organizations across financial services, healthcare, engineering, and more. If your teams are not yet using tools like this in their daily work, that is the gap worth closing first.

Send us a message or visit allemnii.com.

Most executive teams are discussing AI, but very few can tie it to a single business outcome.

Across multiple conversations on Lenny's Podcast, a consistent pattern shows up.

Leaders are paying attention to AI. Many have tested tools. Some teams are already using them in small ways.

But when the conversation turns to decisions, the clarity drops.

Where does AI reduce cost in a measurable way?

Where does it increase output without increasing headcount?

Where does it introduce risk that needs oversight?

In most organizations, these answers are still vague.

AI stays in experimentation mode not because the tools are immature, but because the connection to operations is weak.

Executive fluency is not about using AI directly. It is about being able to map it to how the business runs.

Which workflows are repeatable?

Where is data already structured and usable?

Which decisions are slow, manual, or inconsistent today?

These are the entry points that turn interest into allocation.

The shift happens when leadership can describe one clear use case, one workflow, and one measurable outcome.

That is when AI stops being a topic and starts becoming a line item.

If your leadership team cannot clearly define one AI use case tied to a measurable outcome, that is the gap to close next.

We work with teams on exactly this.

DM us or email to start the conversation.

The gap between knowing about AI and being good at AI comes down to one thing: whether you do anything with what you learn.

Jeff Su calls his system the Learning Loop. It has three steps.

Consume: one reliable source daily, skimmed in about 10 minutes.

Act: 30 minutes once a week to test one thing you read about on a real task, not a toy example.

Share: tell one colleague what you tried and what happened.

That last step is optional but powerful. Explaining something to another person is how you find the gaps in your own understanding.

The whole system takes about 30 minutes a week. Most professionals spend more than that watching tool demos they never act on.

This matters for how organizations approach AI training. A one-time workshop does not build a Learning Loop. A self-paced license does not build a Learning Loop. What builds it is structured, repeated practice tied to real work, with enough accountability that the behavior actually becomes default.

The organizations seeing the clearest gains from AI are not the ones with the most tools deployed. They are the ones where their people have built a system for turning what they learn into something they use.

If you asked your team today what AI capability they tried this week, and what happened, how many could answer?

What a week! Five releases. One shift. The AI landscape just changed again.

AI Friday Recap: The Week Everything Got Cheaper & Bigger

Opus 4.7 (Anthropic): Software engineering focus. Better vision, sharper instruction-following. Available now.

Gemini 3.1 Ultra (Google): 2M tokens. Text + images + audio + video, all at once. The context game just changed.

Gemini 3.1 Flash-Lite (Google): Built for scale, 45% faster, $0.25/M tokens. This is the pricing signal everyone should pay attention to.

Gemma 4 (Open source, Apache 2.0): Frontier-class reasoning in an open model. You can run this on your own infrastructure.

Happy Oyster (Alibaba): 3D world generation for gaming & film. If you're building video-at-scale, this matters.

The game isn't "Can I get the smartest model?" anymore.

It's: "Can I get the right model, deployed faster, at 1/10th the cost, without vendor lock-in? "

If you're still debating which frontier model to use, you're asking the wrong question.

The real question: How do I architect my solution so the model choice becomes interchangeable?

That's defensible. That scales. That's what separates builders from AI tourists.

Time to stop thinking about models. Time to start thinking about systems.

Most organizations are measuring AI ROI wrong.

Not because they're bad at measurement. Because they're asking the wrong question.

"How many hours did this save?" is clean. It fits in a slide. And it almost always undersells what AI is actually capable of.

Here's the problem with that lens.

Hours saved treats AI like a cost-reduction tool. The same framework you'd use to evaluate a cheaper vendor or a new hire.

The organizations extracting real value aren't doing the same work faster. They're doing things that weren't possible before.

SaaStr went from 8-9 salespeople to 1.2 humans plus 20 AI agents. Equivalent output. That's not an efficient story. That's a new operating model.

MIT research found a 95% failure rate for enterprise AI projects. Not because the tools don't work. Because most teams are measuring labor efficiency instead of capability expansion.

The shift is simple.

Stop asking: How many hours did this save?

Start asking: What can we now do that we couldn't do before?

That second question is where the compounding starts.

What metric is your organization using to measure AI value?

There's a reason executives in 2026 are more confused about AI than they were in 2023.

More capability. More tools. More options. And no clearer picture of what to actually build.

Dan Martell put a name on it: the AI Anxiety Loop. " Every week a new tool to master. Another automation promising to 10x the business. You don't know which direction to go. So you freeze."

The AI industry has no incentive to solve this.

Their incentive is to sell the next tool, not to consolidate the 12 you already have into one working system.

The cost of AI execution is dropping 40x per year. Capability is going up. The organizations that win aren't the ones with the most tools. They're the ones that are committed to building something real with what they already have.

The gap between awareness and operation is where most organizations are stuck right now.

What breaks the loop is not more research. It's clarity on where to start, which tools actually fit your workflows, and a team that can see the path forward.

That's where most organizations are stuck. And it's exactly where we work.

What's keeping your team from making the call?

Anthropic launched Managed Agents this week. Production-grade AI agents deployed in weeks instead of months. The access problem is essentially solved.

The implementation gap is not.

Lenny Rachitsky said it plainly: as AI makes building trivial, the hard problem becomes distribution, getting deeply embedded, compounding the advantage over time.

Here's what that looks like inside an organization.

Two companies buy the same AI stack. One runs a pilot, adds it to the deck, moves on. The other builds AI fluency into every team, every workflow, every new hire's onboarding.

Three years later they are not in the same place.

The C-suite leaders we work with are past "should we use AI." They're stuck at "how do we make it actually work at scale."

That is a training and strategy problem. Not a technology problem.

The firms that solve it now will own the next decade.

What's the real adoption gap inside your organization?

Big news for the builder community: Anthropic has officially launched its first technical certification: Claude Certified Architect.

As someone focused on moving AI from "cool demo" to "production reality," this certification hits the mark. It focuses on the core technologies that actually matter for developers today:

Claude Code & Agent SDK: Building autonomous, reliable agents.

MCP Integration: Mastering the Model Context Protocol for backend connectivity.

Advanced Context Management: Efficiently handling 200k+ token windows and prompt caching.

This is a proctored, scenario-based exam designed for those of us in the trenches of AI implementation.

Want to join the first wave of Certified Architects? You can request access through the link below. https://lnkd.in/ewdvEk-m

Could AI be the unexpected engine powering the US economy through uncertain times?

In a recent statement, Palantir CEO Alex Karp highlighted a fascinating dynamic: the rapid expansion of AI-driven data centers is currently a significant factor staving off a potential US recession.

According to Karp, this surge in infrastructure development-fueled by the insatiable demand for AI capabilities-is injecting vital momentum into the economy.

However, he warns that any slowdown or pause in this growth could have serious repercussions, potentially tipping the economic balance toward contraction.

This perspective underscores the increasingly pivotal role that AI infrastructure plays beyond technological innovation-it is now a critical economic driver. The data center boom reflects not only investment in hardware but also signals broader confidence in AI's transformative potential across industries.

As organizations accelerate AI adoption, demand for compute power and data storage escalates, creating jobs, stimulating supply chains, and fostering ancillary tech developments.

Yet, this also raises questions about sustainability and resilience: Can the economy maintain this pace without overheating or facing supply constraints? Moreover, how can policymakers and business leaders work together to ensure that AI's economic contributions are balanced with long-term stability?

Looking ahead, it's clear that AI's role in economic growth is not just a trend but a structural shift.

As we witness this interplay between technology and macroeconomics, one critical challenge emerges: How do we harness AI-driven expansion responsibly, avoiding sudden shocks while maximizing its benefits for the broader economy?

The dialogue around AI must now include economic strategy as much as innovation roadmaps.

What strategies do you think are essential to sustaining AI's economic momentum without risking overdependence or volatility?

In the rapidly evolving landscape of AI agent platforms, a new strategic play is unfolding that could reshape market dynamics significantly.

Nvidia, renowned for its hardware innovation, is making a bold move by leveraging an open-source approach to challenge OpenClaw's early lead in AI agent dominance.

This development raises a critical question: Can Nvidia's hardware-backed open source strategy successfully commoditize the AI agent platform layer before OpenClaw secures strong, defensible network effects?

OpenClaw has been gaining traction by establishing early network effects, which are vital in platform-driven markets.

These effects create high switching costs and user lock-in, often making it difficult for late entrants to disrupt the ecosystem.

Nvidia's strategy, however, is unique in that it combines its unparalleled hardware ecosystem with an open-source software model, potentially lowering barriers to entry and accelerating adoption.

By doing so, Nvidia is not just competing on software innovation but is also integrating its powerful GPUs and AI accelerators as a foundational layer, which could drive performance and scalability advantages for developers and enterprises alike.

From an industry perspective, this move underscores a growing trend where hardware and software synergies are becoming critical differentiators in AI infrastructure.

Nvidia's gambit reflects a broader shift toward open innovation, where collaboration and community-driven development can coexist with proprietary hardware advantages.

If successful, this strategy may democratize access to advanced AI agents, fostering a more vibrant ecosystem and sparking new waves of innovation.

However, the challenge remains: can Nvidia move quickly enough to build a compelling, open platform that attracts a thriving developer community before OpenClaw's network effects become insurmountable?

As we watch this unfolding competition, a pivotal question emerges for AI leaders and developers alike: In a world where hardware and open-source software intersect, what will be the most critical factors that determine which platform ultimately shapes the future of AI agents?