Linthicum Research

• Founded and lead Linthicum Research, specializing in custom research for major analyst organizations and media groups.  
• Developed and executed courses, webinars, and articles, enhancing knowledge sharing in AI and cloud computing.  
• Designed and implemented optimized cloud and GenAI architectures, driving efficiency and innovation across projects.

Founder/Lead Researcher

LinkedIn

Over 70 on-line video courses on the important topics within the cloud computing space.   This includes architecture, migration, and DevOps, as well as the basics such as core concepts, storage, and networking.

Author

YouTube

• Spearheaded the production and deployment of the "Cloud Computing Insider" YouTube channel and podcast, focusing on enterprise cloud computing and AI.  
• Collaborated to create engaging content that educates and informs industry professionals.  
• Analyzed audience feedback to refine content strategy, resulting in a 30% increase in viewer engagement over six months.

Creator-Cloud Computing Insider Channel/Podcast

Pearson

Author of over a 17 books on Enterprise Computing topics.

InfoWorld

Cloud computing blogger for InfoWorld, providing news and commentary within the cloud computing space.   The InfoWorld cloud computing blog is frequently the most read blog in the cloud computing space.

Blogger

Louisiana State University

Creating course content, as well as providing remote instruction.   Courses include cloud computing, DevOps, and cloud computing architecture.

Adjunct Instructor

Deloitte Consulting

Focused on cloud technology strategy and architecture practice. Wearing many hats including thought leadership, demand creation, public outreach, service offering innovation, and project leadership.   Created several inovative programs including Cloud Complexity Management, and Responsive Data Architecture.   Oversaw several larger cloud computing deals, as well as delivery on those deals.

Chief Cloud Strategy Officer (Retired)

GigaOm

Provide analysis of key cloud computing technology and trends, including creation of blogs, major reports, news updates, and advisory. Speaks at most GigaOM conferences, including Structure Data and Structure. Curator, Analyst, and contributor for GigaOM Pro.

Technology Futurist

Cloud Technology Partners, a Hewlett Packard Enterprise company

SVP at Cloud Technology Partners providing core leadership in support of client engagements including cloud strategy, architecture, and implementation.   Member of the executive team, providing strategic guidance for the company.  Additionally, providing key thought leadership activities including keynote presentations at most major cloud computing conferences, commentary to the press including "Forbes" and "Business Week," Webinars, and publication of over 200 blogs and articles.

SVP [Exit]

Blue Mountain Labs

CTO and Founder of the Blue Mountain Labs focusing on emerging technology spaces including cloud computing, and the use of this technology within enterprises and government agencies.   Started Blue Mountain Labs with the vision that emerging technology has value when proper planning and strategy creation occurs, and has provided guidance for dozens of Global 2000 enterprises around the ability to leverage cloud computing and SOA for business value.

Founder and CTO (Former CEO)  [Exit]

Internationally Known AI and Cloud Computing Thought Leader and Influencer, Enterprise Technology Innovator, Educator, 5x Best Selling Author, Speaker, YouTube/Podcast Personality, Over the Hill Mountain Biker.

*** Please follow me, don’t request a connection, I ran out of allowed connections years ago. ***

David Linthicum is a globally recognized thought leader, innovator, and influencer in artificial intelligence (AI), cloud computing, and cybersecurity. Until early 2024, he served as the Managing Director and Chief Cloud Strategy Officer at Deloitte Consulting LLP, where he brought over 30 years of extensive experience in enterprise technology, championing innovative AI-driven solutions that enabled corporations and start-ups to disrupt their markets and enhance operational efficiency.

David's extensive expertise in AI is well-regarded, with key contributions to developing advanced AI strategies and architectures for global clients across various industries. During his tenure at Deloitte, he spearheaded the integration of AI technologies into cloud infrastructures, creating intelligent systems that drive business transformation and competitive advantage. His thought leadership in the AI domain has been featured extensively in prominent publications and platforms.

David frequently appears on most lists of top cloud computing and AI thought leaders and influencers. He has authored over 17 best-selling books, thousands of articles, and over 70 courses on LinkedIn Learning, many of which focus on the practical applications and strategic benefits of AI in business. His influence extends to mentoring the next generation of technology professionals through various AI talent development programs.

Currently, David continues to provide key thought leadership, architecture, and technology leadership, serving Global 2000 companies, new innovative companies, and government agencies. He provides essential guidance for navigating the emerging world of enterprise technologies. David Linthicum remains a pivotal figure in advancing AI and cloud computing technologies, making him a frequent keynote speaker, podcast contributor, and media commentator on AI, digital transformation, cloud architecture, and security topics.

AI’s Dirty Little Secret: You Can’t Automate Your Way Out of a Broken Business

Lately, I’m hearing a lot of double-speak in the tech world:

“AI makes us more productive.”
“We need fewer people because of AI.”
“We’re innovating faster than ever!”

And yet—Salesforce just announced its worst revenue growth ever while letting go of 4,000 employees, all in the name of “AI productivity.” I hear the same narrative from other tech companies struggling to get their AI acts together. The story is always the same: We’re spending millions on AI, but our numbers aren’t moving the way we expected.

Here's the truth:
AI doesn’t fix stupid. It magnifies it.

Case in point: I spoke to a $200M healthcare CEO who couldn’t figure out why $3M on AI tools didn’t move the needle—because they were automating a billing process that was already broken. You can’t bolt a Ferrari engine onto a car with no wheels and expect it to go anywhere.

I see it over and over in the enterprises I advise:

Company A spends $5M on chatbots—customer satisfaction drops 40%.
Company B uses AI to help their best people find leads—revenue jumps 35%.
What’s the difference?
Company B empowered their winners. Company A tried to replace their losers.

Out of 10 companies I’ve worked with, 8 failed spectacularly at “AI transformation”—burning hundreds of millions—because they were trying to automate chaos. Only 2 succeeded, and only after fixing broken processes first, then adding AI where it could actually help.

Here’s the brutal reality:

Fix your broken stuff manually before you try to automate.
Use AI to make your best teams better—not to salvage the bottom 20%.
If customers hate your AI, kill it. That’s not innovation, that’s self-inflicted damage.
This isn’t just a Salesforce problem—it’s an industry problem. Too many leaders believe AI is a strategy when it’s just a tool.

The truth:
Firing thousands won’t fix your business. AI won’t save broken processes. Leadership means calling out the hard truths, not hiding behind buzzwords.

Let’s stop the double speak and get real about how to drive value with AI.

#AI #DigitalTransformation #Leadership #TechTruths

David Linthicum

Top 10 Global Cloud & AI Influencer | Enterprise Tech Innovator | Strategic Board & Advisory Member | Trusted Technology Strategy Advisor | 5x Bestselling Author, Educator & Speaker

Open article: Salesforce CEO confirms 4,000 job cuts 'because I need less heads' with AI by nbcbayarea.com

Salesforce CEO confirms 4,000 job cuts 'because I need less heads' with AI

The “Agentic AI” Scam: Stop Buying Platforms Looking for a Problem
I’m going to say the quiet part out loud:

Most “agentic AI frameworks” being pushed by big consulting firms right now are solutions in search of a problem.

Shiny brand. Slick logo. Buzzwords like “multi‑agent orchestration,” “digital workers,” “agentic operating model.”

Same playbook every time:
   Firm walks in → leads with its branded framework → retrofits YOUR problems to THEIR platform → sells a massive “transformation.”
18 months later?
   You’ve spent 10x what a focused, problem‑driven solution would cost
   You’re getting maybe 20% of the promised value
   You’re stuck with an over‑engineered “agent platform” no one really needs
   That’s not innovation. That’s theater.
   Agentic AI isn’t the problem. The way it’s sold is.
   Agentic AI can be powerful for complex, exception‑heavy, cross‑domain work.

But right now it’s being shoved into:
   Processes that need basic automation, not multi‑agent orchestration
   Functions with no clear ownership, KPIs, or change plan
   Architectures optimized for slideware, not for your teams and constraints
   Why? Because firms have sunk millions into branding their frameworks and must make them look indispensable.
   If you’re an enterprise leader, draw a line:
   When a firm leads with “Here’s our agentic AI framework,” push back.

Make them answer:
What are our top 5 problems?
   How would you solve them if your framework didn’t exist?
   Where does agentic AI clearly beat simpler options in $$, risk, and time?
   What’s the smallest thing we can do first to prove real value?
   If they can’t do that, they’re not advising you. They’re selling you inventory.
   To the consulting firms: you’re better than this.

You know the right order:
1️⃣ Understand the client’s reality
2️⃣ Prioritize the right problems
3️⃣ Design the simplest effective solution
4️⃣ THEN decide if agents belong

Your framework is a tool, not the product. Act like it.

Who Are "Agent Bros?"  

Agent Bros are the guys who heard “AI agents can do tasks for you” and immediately tried to outsource their entire personality to a workflow.
They don’t send emails anymore; they “spin up an agent.” They don’t book flights; they “instantiate a travel stack.” Their Notion is 90% diagrams for systems that don’t work and 10% screenshots of those systems when they briefly did.

They genuinely believe that if they chain enough agents together, they’ll never have to open a Google Doc again. They describe normal human actions like “replying to a Slack” as “low-leverage manual ops” that will soon be “fully delegated.” They talk about “my agent team” with the same pride people used to reserve for having actual employees.

An Agent Bro will confidently demo a “fully autonomous system” that:
Reads your email
Gets confused
Asks you what to do
Does the wrong thing anyway
And then they’ll say, “Yeah, it’s still early, but you can see the potential.”

Their life motto is:
“If I can do it in 2 minutes, I should definitely spend 2 weeks wiring up agents to do it badly and expensively.”

Stop Aiming for Big Consulting as Your “Dream Job”

I’m increasingly telling people—especially students and early-career professionals—not to start their careers at big consulting firms.
Not because consulting is bad. Not because there aren’t great people there. But because the game has changed, and nobody is really telling you that before you sign the offer.

Here’s the uncomfortable version I’m now sharing with the hundreds of people I mentor, formally and informally:

-  AI is coming straight for the work you’ll be doing in your first 2 years.
Decks, research summaries, basic analysis, boilerplate proposals, and status reports. This is exactly the class of work generative AI is already automating. I’m not an alarmist about “AI replacing all consultants,” but I am realistic that it will absolutely replace a lot of junior consulting work. Why build your early career on quicksand?

-  Merit doesn’t drive careers in many of these firms anymore, politics does.
The people who rise are often not the best at cloud, AI, or architecture—they’re the best at navigating internal power structures. Making the right friends and aligning with the right partners frequently beats actually delivering innovative, high-quality work. Being creative and pushing better ideas can get you labeled as “disruptive” and quietly pushed to the margins.

-  The AI services boom is a bubble, and you probably won’t like the hangover.
Right now, there’s a feeding frenzy for AI projects. But I’m already seeing the same patterns that preceded past crashes: overpromising, underdelivering, and massive, overhyped programs that will not meet expectations. When that corrects, big consulting—with its fragile pyramid model—will feel it hard.

Of course, this does not apply to every firm. You may work at (or be joining) a consulting org that truly rewards merit, supports innovation, and uses AI as a force multiplier, not a crutch. That’s great. But pointing to exceptions doesn’t invalidate the broader pattern—or the outcomes I’m seeing in failed projects, talent drain, and declining trust.

I just wrote a longer piece on why I’m advising people entering the workforce to think twice before jumping into big consulting right now: AI risk to entry-level roles, the decay of meritocracy, and a very uncertain long-term outlook once the AI hype cools off.

I’d love to hear from you:
If you’re in a big consulting firm, does this resonate or is your experience totally different?
If you’ve left, what finally pushed you out?
If you’re just starting your career, what are you seeing and being told?
Let’s have an honest conversation about what consulting is—and isn’t—anymore.

#consulting #careers #AI #cloudcomputing #managementconsulting #futureofwork #digitaltransformation #techcareers #genai #leadership #big4 #strategy

Making AI work for businesses (not the other way around) | CEO @ Particula Tech

$47K a month.

That's what a healthcare company was paying AWS to run their diagnostics AI.

Last week, we moved it off the cloud entirely. Back to their own servers. The thing everyone says you should never do anymore.

They'd been running patient diagnostics for 18 months. Analyzing scans, flagging abnormalities, routing to specialists. Critical stuff that couldn't go down.

But here's what changed. They hit scale. Processing 200K scans a month now. At that volume, cloud costs weren't just expensive - they were exponential.

We did the math. $6M over three years on cloud versus $340K for their own hardware. Same performance. Better latency, actually. (Patients in the same city as the servers.)

And here's the part nobody mentions. Local models mean patient data never leaves their building. No uploads. No API calls sending scans to some data center. No wondering which jurisdiction your data is sitting in.

Their compliance team was relieved. HIPAA audits got simpler. Security team stopped worrying about third-party breaches.

Not saying cloud is wrong. For most companies starting out, it's perfect. You need flexibility. You're testing. You don't know your usage patterns yet.

But once you hit predictable, high-volume workloads with sensitive data? The math flips. And the risk drops.

Nobody talks about this because "cloud-first" became gospel. And for early stage? It is. But at scale, sometimes the old way makes sense again.

Worth running your own numbers before renewing that next contract.

Today, on Veterans Day, I want to honor the memory of my father, Maj Ronald G. Linthicum, US Army. My dad served our country with incredible dedication for over 25 years, including two tours in Vietnam—where he was wounded in combat during his second deployment.

He was more than an outstanding officer and an inspiring warrior; he was, above all, an extraordinary father and devoted husband. His legacy is built not just on his military achievements and courage under fire, but on the strength of his character, his unwavering sense of duty, and the boundless love and support he gave our family.

Though he’s been gone for some time now, there isn’t a day that goes by that we don’t miss him deeply. His lessons and his love continue to shape who I am and guide me in everything I do.

On this Veterans Day, I remember you with pride and gratitude, Dad. Thank you for your service, your sacrifice, and—for me—the gift of being your son. 

#VeteransDay #Family #Honor #Gratitude

Here we go again!   I'm unable to complete my work this morning because another cloud service is currently down.    I'm sure I'll be made whole by the offending provider...oh wait.    

It's called resilience planning... look into it.

https://lnkd.in/eNNe5m3h

Excited to Connect at AWS re:Invent 2025—Let’s Make It Count!

I'm thrilled to announce that I’ll be at AWS re:Invent this year in just a few weeks! It’s always an incredible opportunity to connect with some of the brightest minds in cloud and technology.

Interested in meeting up? I only have a handful of meeting slots available, but I’d love to hear from you! Send me a message here on LinkedIn—let me know why you’d like to meet, and what you think would make the conversation interesting for both of us.

Please don’t be offended if I’m not able to meet everyone; slots really are limited. Looking forward to some great discussions and new connections. See you in Vegas!

Dave

#AWSreInvent #CloudComputing #Networking #LasVegas #TechEvents #AWS #Innovation #LetsConnect

Agentic AI Is Overhyped – And AWS AgentCore Proves It

Agentic AI is important. It’s not that important.

We’re in a moment where “agentic platforms” are being sold as the new foundation of enterprise AI. Amazon’s AgentCore is a prime example: powerful, ambitious, impressive on paper—but for most organizations, adopting it now is more risk than reward.

Here’s why I wouldn’t use AgentCore today in most cases:

You’re buying complexity you don’t need
AgentCore adds a full platform layer—runtime, gateway, identity, memory, observability—on top of your models and frameworks. That’s useful for large, multi-agent, multi-tool environments. Most enterprises just need a few solid LLM-powered workflows. “LLM + APIs + Lambda/containers” is simpler, cheaper, and easier to debug.

It deepens your AWS gravity
It’s “framework-agnostic,” but everything around it is pure AWS: IAM, CloudWatch, VPC, PrivateLink, CloudFormation. You’re pushing core business logic into a proprietary runtime. If you care about multi-cloud, portability, or negotiation leverage, that’s a problem waiting to happen.

Governance gets harder, not easier
Identity-aware agents, on-behalf-of patterns, OAuth token vaults, MCP tools, fine-grained IAM… all powerful, all complex. Most organizations are still figuring out basic AI governance. Adding an agent platform layer increases your risk surface and cognitive load.

It’s a platform tax on simple use cases
For customer support triage, internal knowledge assistants, dev copilots, and similar workloads, AgentCore is often overkill. You’re paying for long-lived sessions, observability, and a control plane that many use cases simply don’t require.

It’s early, and you pay to be early
AgentCore is new. Patterns, ecosystem support, and hardening are still maturing. Unless you have a strong platform team and high tolerance for bleeding-edge complexity, you’ll be learning some lessons the hard way.

Here’s the uncomfortable truth:

Agentic AI is being oversold as “the new operating system” of the enterprise. It isn’t.

Most organizations don’t need a full-blown agent platform to get real value from AI. They need:

Solid data foundations
A few well-designed, high-impact AI workflows
Good observability, testing, and governance
You can achieve that with far simpler architectures than AgentCore.

Will platforms like AgentCore matter? Yes—for a narrow set of large-scale, long-running, multi-agent, highly regulated scenarios. But that’s not the default, and it’s not where most enterprises are today.


Agentic AI is important. It’s just not as important—or as universally necessary—as it’s being depicted. And yes, I’m sticking to that story.

#AgenticAI #AWS #AgentCore #CloudComputing #EnterpriseAI #AIArchitecture #MultiCloud #AIGovernance #DavidLinthicum #CloudStrategy

What an incredible day out on the gravel bike in Loudoun County VA! The weather was perfect—one of those days that reminds you just how good it feels to be out there, soaking up the scenery and fresh air.

Of course, gravel biking in my 60s comes with its own special highlights:

-My GPS might just be my most enthusiastic riding partner, constantly “recalculating” after every detour I can’t resist.
-I like to think the local wildlife are my biggest fans, especially when they witness those less-than-graceful moments on loose gravel.
-And let’s be honest: the true reward is justifying that second pastry after the ride—all in the name of proper recovery!

Sometimes you just need a great ride to recharge before diving back into work, videos, and new projects. Here’s to many more days like this!

🚀 Exciting Milestone Alert! 🚀

I am thrilled to announce that “Agentic AI Fundamentals: Architectures, Frameworks, and Applications” is about to cross 150,000 learners, officially making it the most popular agentic AI course in the world!

This achievement isn’t just a number—it’s a testament to the curiosity, drive, and trust of every single learner who joined me on this journey. I could not have reached this milestone without the incredible community who believed in this topic and trusted me to help guide your learning.

Thank you for your engagement, feedback, and passion for exploring the future of intelligent agents together. Here’s to breaking new ground in AI and continuing to learn and innovate as a global community!

If you haven’t joined us yet, now’s the perfect time: let’s shape the future of agentic AI together! 🌎🤖

#AgenticAI #ArtificialIntelligence #AI #LearningCommunity #Milestone #ThankYou #FutureOfWork 

https://lnkd.in/eaKxixXP

Technology, Policy, and Strategy | Middle East & Africa Policy and Business Development

As talk of an #AI #bubble grows louder, few are looking at the financial plumbing driving it. Beneath the hype, two circular systems are forming, each powering, and inflating, the other.

In one loop, OpenAI–NVIDIA–Oracle:
#Nvidia invests in OpenAI ➡️ #OpenAI rents compute from Oracle ➡️ #Oracle buys Nvidia’s chips ➡️ Nvidia records record-breaking sales.

In the rival loop, Anthropic–Google:
#Google invests in Anthropic ➡️ #Anthropic trains on Google’s TPUs and Cloud ➡️ Google records record-breaking sales.

Each side’s acceleration pushes the other to spend more. Two circular economies, two narratives of dominance, and one increasingly inflated AI market.

The High Cost of Building on Vibes: Why “Vibe Coding is Stupid” (Even With AI)

You’ve seen the meme—“Vibe Coding is Stupid.” But behind the laughs, there’s a painful reality. I’ve watched top-notch teams torch huge budgets chasing the next big thing, letting AI fill in the blanks while they code off the cuff. Instead of strategy and standards, they lean on “feeling it out.” The result? Bloated, unmanageable systems and expensive rewrites that kill momentum.

AI is powerful, but if you’re just going with the flow, you’re not innovating—you’re setting up for a collapse. Cutting documentation and skipping structure doesn’t make you agile; it makes you the next case study in what NOT to do.
I just dropped an article about how vibe coding wrecked a well-funded startup, and why getting serious about standards is the only way to win long-term. If you want to stop burning money and actually build sustainable systems, do yourself a favor and read this piece.

Because “move fast and break things” only works if you know how to fix them. If you’re tired of shortcuts that lead to dead ends, check out the story.

#VibeCodingIsStupid #Cloud #AI #SoftwareEngineering #TechDebt #Innovation #DevFail

Look, I don’t like to say “I told you so”…

Actually, that’s a lie.
I love to say I told you so. And this new Amazon–Google multicloud partnership is basically my Super Bowl.

For years I’ve been on stages, in podcasts, and in articles saying some version of:
“You’re going to run on more than one cloud.
Not because it’s trendy, but because reality is messy.”

Every time, some people nodded thoughtfully. Others said things like:

“We are an AWS‑only shop. Period.”
“Standardization > flexibility.”
“We don’t want the complexity of multicloud.”

Those same organizations now quietly have:

GCP projects
Azure pilots
A random SaaS/data platform or two
And a network diagram that looks like a crime investigation board

Now we have AWS and Google Cloud — direct competitors — teaming up to launch a co‑engineered multicloud interconnect service.

That’s not just multicloud.
That’s official, jointly‑announced, legal‑approved, productized multicloud.

Am I saying I saw this coming?

Let’s just say my career has been built on a few core beliefs:

You won’t stay single‑cloud forever.
   M&A, new business units, regional requirements, partner ecosystems — they all conspire to drag you into multicloud whether you have a slide for it or not.

The network is the real boss.
   Everyone loves to talk Kubernetes, AI, and serverless.
   But when your app in AWS can’t reliably talk to your data in GCP, the only thing anyone cares about is: “Why is everything on fire?”

Hyperscalers would eventually have to make cross‑cloud sane.
   You can only ask enterprises to duct‑tape IPSec tunnels and third‑party appliances together for so long before someone says:
   “What if… we didn’t make this awful?”

And here we are. A first‑class, jointly supported AWS–GCP multicloud connectivity service.

Somewhere, a thousand handmade VPN meshes just felt a disturbance in the force.

If you’re a CIO, architect, or platform lead, this announcement is:

Validation that multicloud is no longer an edge case
A sign that co‑opetition is the new normal (compete on services, collaborate on plumbing)
A big hint that your network and data architecture need to be designed for cross‑cloud as a first‑class requirement

Also, let’s be honest:
It’s fantastic for my “I’ve been saying this for a decade” street cred.

Key takeaway:
Multicloud isn’t a fad, it’s where the industry ended up when strategy met reality.
Now even the hyperscalers are building products around that reality.

I’ll happily take the “I told you so” on this one.
And if you want a serious breakdown (minus the gloating) of what this means for your architecture, governance, and cost models — I’m always up for that conversation.

#cloud #multicloud #aws #googlecloud #cloudarchitecture #networking #enterpriseIT


Amazon, Google team up to unveil multicloud service for interconnectivity https://lnkd.in/eMzCGd67

It's time for a reality check on the "agentic AI" gold rush.

Everywhere I look, companies are rebranding old tech and wasting significant capital building complex, high-cost "agentic" systems for use cases that simply don't need them. This is not innovation; it's a triumph of hype over sound architectural principles.

In my latest article, I pull back the curtain on the three core mistakes the industry is making:
The Lie: Systems that are "agentic in name only," using monolithic mechanisms behind a new marketing label.
The Misguided: Calling loosely coupled components "agents" when they lack true autonomy and are bound to central processes.
The Waste: Building genuinely agentic, high-cost systems for problems that could be solved 10x cheaper with existing, conventional automation.

We need technical rigor, not just buzzwords. It’s time to reevaluate, apply discipline, and build with purpose. The future of AI depends on delivering value, not chasing a hype train.

Read my full breakdown of why most agentic AI systems being built should not be built using agentic AI. 

#AgenticAI #AI #EnterpriseArchitecture #CloudComputing #Innovation #TechTrends #RealityCheck #BusinessValue

Here is what I feel like this week at Re: Invent.   😆

💣 Agentic AI Is Gaslighting the Enterprise

I’m seeing something with agentic AI that I’ve never seen at this scale in 30+ years of enterprise tech:

Two completely different realities are being sold simultaneously.

On one side:
  “2025 is the year of the AI agent.”
  “Most enterprises are already using agents.”
  “Autonomous digital workers are transforming operations.”

On the other:
  “Agentic AI is niche, early, fragile, and mostly stuck in pilots.”
  “Half of what’s called an ‘agent’ is just a scripted workflow with an LLM glued on.”

Both can’t be right.  Someone is lying!  

What’s actually happening is predictable but ugly:
  -Vendors, cloud providers, and big consultancies stand to make a fortune if you believe agents are already mainstream.
  -Sponsored surveys and ‘thought leadership’ are written to support that story.
  -Meanwhile, independent analysis and real‑world experience show a technology that’s promising but nowhere near the ubiquity and impact being claimed.

We’ve entered the era of agent washing:
  -Old RPA and orchestration rebranded as “agents.”
Slideware sold as reality.
  -And no standards or reference architectures to call BS on any of it.
Result?
  -CIOs are confused.
  -Boards are confused.
  -Teams on the ground know the hype doesn’t match what they’re wrestling with in production.

If we keep “faking it” at this level, there’s going to be a trust crash:
in vendors, in cloud providers, and in the big consulting firms that are marching in lock‑step with the hype.

I just wrote a piece digging into this conflict and why the agentic AI story is so disconnected from what’s actually happening inside enterprises — and why that should worry you more than the tech itself.

If you’re tired of being told “everyone is doing it” while your real projects tell a very different story, this is for you.

Curious:
Are you seeing real agentic AI in production, or mostly rebranded automation and PowerPoint?

Private cloud is now a strategic priority.

Public cloud services have long been perceived as the primary choice for hosting cloud workloads, as highlighted in VMware’s Private Cloud Outlook 2025 report. However, the data gathered from participants in the study suggests a contrasting reality. Their comprehensive research uncovers that a significant 69% of enterprises are actively migrating workloads back from public cloud environments. This trend is primarily driven by heightened concerns over security vulnerabilities and the unpredictability of associated costs.

Enterprises are increasingly cautious about the potential risks of data breaches and unauthorized access that can occur in public cloud settings. These security apprehensions are prompting businesses to reassess their cloud strategies and explore alternative options. Additionally, the fluctuating nature of public cloud expenses, which can vary based on data usage and other factors, is causing enterprises to reevaluate the economic viability of such solutions.

To delve deeper into these issues, we invite you to join our live event tomorrow featuring me. I will lead an insightful discussion on the often-overlooked expenses of hybrid cloud models and provide guidance on strategic planning for 2026. This event promises to equip you with the knowledge needed to recalibrate your organization's cloud strategy, ensuring it is robust and cost-effective in the face of evolving technological landscapes.

https://lnkd.in/e9Y9KZVT

Resilio Adam Kranitz
-------------------------

Just stopped by to catch a demo at the N2W booth at #AWSreinvent.   Crazy value in storage, make sure to check them out if you’re on the expo floor this week.

Are We Living in Two Different AI Worlds?

Recently, I’ve read two widely circulated reports on enterprise AI adoption: one from MIT Technology Review Insights with Cisco, and another from Channel Futures summarizing findings from Google Cloud’s AI ROI study.

What caught my attention? They don’t seem to add up.

On one hand, the Google Cloud study paints a picture of rapid ROI, business growth, and successful AI deployment—especially for so-called early adopters. Lots of success, lots of enthusiasm, and increasingly larger budgets for AI. On the other, the MITTR/Cisco report takes a more sobering view: only a tiny minority of organizations are truly “ready” for AI at scale; most are grappling with foundational challenges around data, talent, culture, and long-term enablement.  See the links to both reports below:

https://lnkd.in/eru3NbsT 

https://lnkd.in/eivxq3Kz 

So who’s right? Is AI working or not?

If it is working, where exactly is it working—and under what conditions? If it’s not, what’s holding most organizations back? Are we looking at a tale of two realities: one for the leading edge, and another for everyone else?

I’d love to hear what the community is experiencing on the ground. Are these reports describing your reality? Is AI something your organization gets tangible value from today—or is broader, enterprise-wide impact still just out of reach?

Let’s keep the conversation honest, specific, and actionable.

#AI #DigitalTransformation #Cloud #EnterpriseIT #Leadership #Strategy

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David Linthicum's Best Posts (last 30 days)

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David Linthicum

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}David Linthicum's LinkedIn Performance

What is David talking about?

David Linthicum's Best Posts .css-1emoo84{font-size:var(--chakra-fontSizes-md);font-weight:400;color:var(--chakra-colors-gray-600);}(last 30 days)

Ready to grow your LinkedIn presence?

David Linthicum's Linkedin Analytics

Performance ScoreDavid Linthicum's LinkedIn Performance

David Linthicum's Best Posts (last 30 days)