Reading Between the Lines: What CES 2026 AI Reveals About Product Strategy

product_david · January 9, 2026, 7:01pm

I’ve been reading all the CES 2026 coverage and I want to step back and analyze what this tells us about product strategy. As someone who thinks about product-market fit and competitive positioning all day, the AI narrative at CES is fascinating.

The Most Telling Quote of CES 2026

Dell’s executives told PC Gamer: “We’re very focused on delivering upon the AI capabilities of a device—in fact everything that we’re announcing has an NPU in it—but what we’ve learned over the course of this year, especially from a consumer perspective, is they’re not buying based on AI.”

That’s a Fortune 500 company admitting that AI features don’t drive consumer purchase decisions. Yet everyone at CES was marketing AI features. What does this tell us?

The Gap Between Marketing and Reality

What companies are saying:

“AI-powered everything”
“60 TOPS NPU performance”
“Revolutionary AI features”

What’s actually driving purchases:

Traditional product qualities (price, build quality, battery life)
Ecosystem lock-in (Apple, Google, Amazon)
Brand trust and reliability

The AI marketing is aspirational - it’s about future value, not current utility. This creates a positioning problem: if you don’t market AI, you look behind. If you over-promise on AI, you disappoint users.

The Product Strategy Lessons

Lesson 1: Features vs. Benefits

Every CES announcement led with features (TOPS, AI capabilities, smart features). Almost none led with benefits (what problem this solves for users).

Good product positioning starts with user problems, not technical capabilities. “This chip has 60 TOPS” is a feature. “You can edit photos 3x faster” is a benefit. Most CES announcements forgot this.

Lesson 2: The Demo-Reality Gap

LG’s laundry-folding robot failing at CES is a cautionary tale. When you demo something that doesn’t reliably work, you create negative impressions that are hard to overcome.

Better strategy: under-promise and over-deliver. Show features that actually work, not prototypes that might work someday.

Lesson 3: Platform Plays Take Time

Nvidia positioning as “the Android of robotics” is a smart long-term strategy. But platform businesses take 5-10 years to mature. The Isaac Sim ecosystem won’t deliver mass-market results this year.

Companies need patience for platform strategies while delivering near-term value.

What Enterprises Should Actually Prioritize

Based on the CES signals, here’s what I’d focus on:

Do:

Invest in AI applications that solve specific business problems
Experiment with AI workflow integration (not just features)
Build data infrastructure that enables AI capabilities
Train teams on AI tools that are production-ready today

Don’t:

Chase hardware specs for the sake of specs
Rush AI features to production that aren’t reliable
Over-rotate on the AI narrative at the expense of fundamentals
Assume AI alone differentiates your product

The 2026 AI Reality Check

CES 2026 showed us that AI is in everything, but AI alone isn’t compelling. The companies that will win are those that:

Use AI to solve real problems (not just add AI badges)
Deliver reliable experiences (not impressive-but-flaky demos)
Focus on user outcomes (not technical specifications)
Build for the long term (platforms and ecosystems)

The AI hype cycle will continue. The question for product leaders is: how do we extract real value while the market sorts out what matters?

What’s your take? Is your organization cutting through the AI hype or getting swept up in it?

cto_michelle · January 9, 2026, 7:01pm

David, this is a great strategic analysis. The Dell quote is incredibly honest and probably reflects what many companies are thinking but not saying publicly.

My Framework for Cutting Through AI Hype

As a CTO, I get pitched AI solutions constantly. Here’s how I evaluate them:

The “So What?” Test
When someone describes an AI capability, I ask: “So what does that enable that we couldn’t do before?” If the answer is vague or incremental, it’s probably hype.

The “Versus” Test
What’s the alternative? If the AI feature is versus “doing nothing,” it might not solve a real problem. If it’s versus “a tedious manual process that everyone hates,” now we’re talking.

The “Day 2” Test
What happens after the initial wow factor? AI demos are impressive once. Living with AI features daily is different. How does it perform at scale, over time, with real users?

What I’m Actually Prioritizing

For our organization, here’s where I think AI delivers real value right now:

Developer productivity - Code completion, documentation, review assistance. Measurable time savings.
Customer support - Smart routing, suggested responses, summarization. Direct cost and quality impact.
Data analysis - Pattern detection, anomaly identification, report generation. Enables insights that weren’t practical before.
Document processing - Extraction, summarization, classification. High-volume, tedious work that AI handles well.

Where I’m Skeptical

“AI-powered” everything in consumer hardware (marketing theater)
General-purpose assistants that try to do everything (jack of all trades)
AI features that require behavior change without clear benefit
Anything positioned as “revolutionary” before proving reliability

The Organizational Challenge

The hardest part isn’t technology - it’s managing expectations. Leadership wants AI transformation. Teams want practical tools. The gap between “AI possibilities” and “AI in production” is where most initiatives fail.

My job is to bridge that gap: identify real opportunities, set realistic expectations, deliver measurable results.

maya_builds · January 9, 2026, 7:01pm

The features vs. benefits point really resonates with me as a designer.

The User Experience Perspective on AI Hype

When I look at CES announcements, I see a lot of technology-first thinking. “We can do this with AI!” instead of “Users struggle with this, and AI can help.”

As someone who’s watched countless features fail despite great technology, here’s my take:

Users Don’t Care About Technology

Nobody wakes up wanting “60 TOPS of NPU performance.” They wake up wanting to:

Get through their to-do list faster
Not think about mundane tasks
Feel like their devices “just work”

The best AI features are invisible. Users shouldn’t have to know or care that AI is involved.

The Adoption Curve is Real

New technology adoption follows a predictable pattern:

Innovators try everything (tech enthusiasts, early adopters)
Early majority waits for proof (“Does this actually work?”)
Late majority adopts when it’s standard (“Everyone else uses it”)
Laggards resist until forced (“I was fine without it”)

Most AI features at CES are still in the Innovator phase. The marketing acts like we’re at Late Majority.

What Makes AI Features Stick

From my experience, AI features that users actually adopt share these traits:

Zero learning curve - Works without reading instructions
Graceful degradation - When AI fails, the feature still works somehow
Clear value exchange - The benefit is obvious and immediate
Builds trust over time - Gets better, doesn’t randomly break

The Design Debt Problem

A lot of CES AI features feel like they were added to check a marketing box. That creates design debt:

More complexity for users to navigate
More settings to manage
More failure modes to handle
More support requests when things go wrong

Sometimes the best product decision is not adding an AI feature.

My Prediction

The AI features that survive won’t be the most technically impressive. They’ll be the ones that feel natural and deliver consistent value. We won’t even call them “AI features” - they’ll just be how products work.

security_sam · January 9, 2026, 7:02pm

Adding the security perspective to David’s analysis because rushed AI deployments have real security implications.

The “Ship AI Fast” Problem

When companies rush to add AI features for marketing purposes, security often suffers. I’ve seen this pattern across many organizations:

Pressure to announce AI capabilities (CES, investor updates, competitive pressure)
Accelerated development timelines to meet announcements
Security review compressed or skipped to hit dates
Features launch with vulnerabilities that get discovered later

The CES announcement cycle exacerbates this. Companies announce features at CES in January, then rush to ship by Q2 to validate the announcement.

AI-Specific Security Risks

Beyond normal software security, AI features introduce specific risks:

Prompt injection - If your AI processes user input, attackers can manipulate the AI’s behavior through crafted inputs.

Model extraction - Attackers can probe your AI to understand or reconstruct your models.

Data leakage - AI systems often expose information about training data through their responses.

Adversarial inputs - Specially crafted inputs that cause AI to behave incorrectly (misclassification, bypassed safety filters).

Supply chain risks - Pre-trained models and libraries may contain backdoors or vulnerabilities.

The Demo-to-Production Gap

A demo at CES can hide a lot of security shortcuts:

Hardcoded credentials
Disabled security features for “reliability”
Mock data that hides privacy issues
Controlled inputs that avoid edge cases

The rush to turn demos into products often means these shortcuts become production code.

What I Recommend

For any organization deploying AI:

Include security in AI development from the start - Not as an afterthought
Threat model AI-specific risks - Traditional security assessments miss AI vulnerabilities
Red team AI features - Test for adversarial inputs and manipulation
Don’t compress security timelines for CES or other announcements
Monitor AI systems in production - They can behave unexpectedly

The pressure to show AI capabilities is real. But so are the security consequences of rushing. The organizations that get this right will build trust while their competitors deal with breaches and failures.