In today’s competitive economy, nearly every organization aspires to be “data-driven,” but turning that ambition into measurable business outcomes remains inconsistent. Companies widely recognize the value of data and artificial intelligence, yet many are still struggling to operationalize these capabilities at scale.

When data foundations are weak, the effects extend beyond internal operations. Fragmented or unreliable data makes timely, well-informed decisions harder to reach, and increases the likelihood of gaps in areas like security and compliance. Ultimately, those gaps don’t stay internal. They affect the quality and consistency of customer experiences, and the confidence organizations can have in how they’re managing and protecting data responsibly.

Based on new global research from Cloudera, including a study conducted with Harvard Business Review Analytic Services, the gap is stark. Only 7% of enterprises say their data is fully ready for AI, while 27% report their data is not very ready or not at all ready. At the same time, expectations for transformation continue to accelerate, with organizations planning to embed AI across core business functions.

While companies are preparing for large-scale AI-driven transformation, most lack the underlying data infrastructure and maturity required to support it. Until that foundation is in place, the promise of AI remains difficult to fully realize.

Why Data Readiness Is So Difficult

Despite growing investment, data readiness has plateaued.

Enterprise data exists, but it can be hard to find or access because it is fragmented across systems. Over a third (34%) of respondents from the Data Readiness Index survey reported that siloed data was a major issue that prevented them from working together effectively to share, manage, and use data. These silos often stay in place because data isn’t well integrated across systems.

Most respondents said their data sources were somewhat integrated across various environments, but significant gaps remain. Only 30% of IT leaders reported that their data sources were fully integrated, while 52% said they were mostly integrated. While this shows some progress, it also highlights that many organizations are not yet fully prepared to support large-scale AI projects.   

Other barriers compound the problem. IT leaders also identified complicated access (47%), limited data visibility (44%), lack of training (41%), and cultural resistance (34%) as key obstacles.  Each issue slows progress, and together, they create systemic drag. At the same time, regulatory and security pressures are increasing. Data privacy and sovereignty requirements demand tighter control over how and where data is managed. In fragmented environments, meeting those requirements becomes more resource-intensive and more risky.

What “Data Readiness” Means In Practice

Data readiness ultimately comes down to trust and control. Organizations need confidence that their data is accurate, accessible, secure, and governed consistently, regardless of where it resides.

Governance is central to this goal. Findings from the Taming the Complexity of AI Data Readiness survey report show that organizations rank protecting sensitive data and privacy (59%), data quality (46%), and data governance (41%) as the most critical components of their data strategies. These priorities reflect a growing recognition that without strong governance, data cannot be trusted or effectively scaled across the enterprise.

At the same time, the Data Readiness Index reveals persistent structural challenges. Nearly a quarter of organizations (24%) report they cannot access all of their data across environments at any time, and 16% lack complete visibility into where their data resides. These gaps undermine governance at scale, making consistent policy enforcement unreliable and weakening an organization’s ability to manage risk.

Without trust and control, data can’t deliver value. Poor readiness delays insights and decisions as teams struggle to find and trust data. Disconnected environments harm customer experiences by blocking a unified view. Low-quality or poorly governed data leads to missed opportunities and higher risks.

When data is governed and secure, teams move faster and confidently, reducing validation time and increasing value. In the end, organizations must either operationalize data as a strategic asset or absorb the cost of its dysfunction.

A Widening Gap, And A Clear Opportunity

Data readiness is crucial for unlocking AI’s full potential, but readiness goes beyond simply collecting large amounts of data. Organizations also need systems that make trustworthy data connected and usable across the business. That includes improving data quality, establishing clearer governance and access controls, and creating visibility into where data comes from and how it moves through different systems.

These foundational efforts may happen behind the scenes, but they ultimately shape how effectively organizations can apply AI in the real world. In practice, the companies most likely to pull ahead may not be the ones adopting AI the fastest, but the ones building systems capable of delivering reliable, scalable outcomes over time.

This story was produced by Cloudera and reviewed and distributed by Stacker.

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Artificial intelligence is doing something economists rarely see at once: shrinking employment in affected industries while pushing wages higher. New research from the Federal Reserve Bank of Dallas offers a possible explanation — and it has specific implications for anyone early in their career.

Scott Davis, an assistant vice president in the Dallas Fed's Research Department, analyzed employment and wage data across more than 200 occupations since ChatGPT's release in late 2022. The findings, published February 24, suggest AI is simultaneously replacing entry-level workers and making experienced workers more valuable.

The employment picture

Total U.S. employment has grown about 2.5 percent since fall 2022. Employment in AI-exposed sectors has not kept pace. The computer systems design sector — one of the most AI-exposed in the economy — has shed 5 percent of its workforce. Across the top 10 percent of AI-exposed industries broadly, employment is down 1 percent over the same period.

That decline is landing hardest on young workers. Research from Stanford University's Erik Brynjolfsson and colleagues finds the employment drop in AI-exposed sectors is concentrated among workers under 25. Employment totals for older workers have not declined. According to Dallas Fed economist Tyler Atkinson, the issue isn't layoffs — it's that young workers aren't finding jobs in the first place. The entry-level market in AI-exposed fields is getting much harder to break into, a trend that tracks with AI accounting for 25 percent of U.S. layoffs in March according to Challenger, Gray & Christmas.

The wage picture

Here's where it gets unusual. Despite the employment decline, wages in AI-exposed sectors are rising faster than the national average. Nominal average weekly wages across the economy grew 7.5 percent since fall 2022. In computer systems design, they grew 16.7 percent. Across the top 10 percent of AI-exposed industries, wage growth was 8.5 percent.

Davis found no meaningful relationship between AI exposure and wage growth across 205 occupations — until he added one variable: the experience premium.

The codified vs. tacit knowledge divide

Davis draws on a distinction between codified knowledge — the kind you learn from textbooks — and tacit knowledge, the kind you accumulate through years of practice. His hypothesis: AI can replicate codified knowledge but not tacit knowledge. That means AI substitutes for workers whose primary value is book learning, and complements workers whose value comes from hard-won experience.

Using Bureau of Labor Statistics wage data that separates entry-level and experienced worker pay, Davis calculated an experience premium for each of the 205 occupations. He then tested how AI exposure affected wages differently depending on that premium.

The results were clear. For occupations with a low experience premium — jobs where experienced workers don't earn much more than entry-level workers, like fast-food cooks or ticket agents — increased AI exposure was associated with lower wage growth. AI is substituting for everyone in those roles. For occupations with high experience premiums — lawyers, insurance underwriters, credit analysts, marketing specialists — increased AI exposure was associated with higher wage growth. AI is doing the entry-level work while making expert-level judgment more valuable.

What this means for newsrooms and media teams

The implications run directly through white-collar knowledge work, including journalism and media. The traditional career path — take an entry-level job, do the codifiable tasks, slowly build tacit knowledge — is precisely what Davis says firms are finding cost-ineffective to maintain. That dynamic is already visible in the 2026 journalism layoff wave, which has fallen disproportionately on junior and mid-level roles.

For experienced journalists, editors, and media professionals with deep domain knowledge, the data offer some reassurance. Their tacit knowledge — source relationships, news judgment, contextual understanding — is not easily replicated. For new graduates hoping to learn the craft on the job, the environment is harder. The entry-level work AI can do most easily is often the same work that used to teach people the fundamentals.

Davis doesn't suggest this is permanent. "Leaving new employees off the job ladder is not sustainable in the long run," he writes. AI adoption will require rethinking how entry-level employees develop on the job — but that rethinking hasn't happened yet.

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Canva is making its biggest bet since it moved design from desktop software into the browser. On Thursday, the company unveiled Canva AI 2.0 at its annual Canva Create event in Los Angeles — a sweeping overhaul that turns the platform into an agentic, conversational system for getting work done.

The centerpiece is a new architecture layer built on what Canva calls its “frontier AI lab” and years of investment in foundation models for design. Instead of generating a single static output, Canva AI 2.0 maintains context throughout a project, helping users brainstorm, refine, and iterate in a continuous conversation.

“Today’s announcement marks the beginning of the next era of creation,” the company said in its release.

The new system goes live as a research preview on April 16, rolling out to the first one million users who find it on the Canva homepage, with broader access to follow.

What’s new

Canva AI 2.0 introduces four core capabilities:

Conversational Design generates fully editable designs from natural language prompts or dictation. Users describe an idea or goal — no blank page, no template hunting — and Canva AI produces a structured, branded layout. The system stays engaged through the process rather than stopping after the first output.

Agentic Orchestration lets users describe a goal and have Canva AI coordinate the full suite of Canva tools to deliver it. The company’s example: ask for “a multi-channel campaign plan to launch our latest summer products,” and the system generates everything — ready to refine or publish.

Object-Based Intelligence enables precise, targeted edits without disturbing the rest of a design. Swap an image, change a headline, adjust a font — only that element changes, and everything remains layered and fully editable.

Living Memory builds a persistent profile of how a user or team works. The system learns preferences, keeps designs on brand automatically, and gets more tailored with use. Users can also seed it with existing designs to create a custom memory library.

Workflows and integrations

Beyond design generation, Canva AI 2.0 introduces several workflow tools aimed at replacing the patchwork of apps most teams currently use:

Connectors link Canva AI to Slack, Gmail, Google Drive, Notion, Zoom, and Google Calendar. The system can pull from those data sources to generate meeting summaries from Zoom transcripts, turn customer emails into sales pitches, or build newsletters from Slack activity.

Scheduling lets users set recurring tasks — generate a week’s worth of social content every Friday, translate it into ten languages, have briefing docs ready at login — and Canva AI runs them automatically in the background.

Web Research brings research directly into designs. Users can run on-demand searches or schedule background research, and Canva AI delivers structured, editable content into the design — no copy-paste required.

Brand Intelligence enforces brand standards automatically across every new design, applying fonts, colors, and style without manual intervention. It can also reapply updated brand guidelines to existing work in a single step.

Canva Code 2.0 now supports HTML importing. Users can bring any HTML file or AI-generated experience into Canva and edit it visually — no code rebuilds needed. The resulting interactive content can include forms that feed into Canva Sheets, or be published to a custom domain with SSO protection.

Sheets AI generates fully structured, data-populated spreadsheets from a description. Budget trackers, project timelines, content calendars, research tables — delivered already formatted.

Why it matters for media teams

The combination of connectors, scheduling, and agentic orchestration makes Canva AI 2.0 a significant tool for editorial and communications teams. The ability to pull from Slack, Gmail, and calendar data and generate campaign materials, briefings, or social content automatically — on a schedule, in the background — is a meaningful reduction in manual production work.

The persistent memory and brand intelligence features address a consistent pain point: keeping output on-brand without manual QA on every piece. For teams managing high-volume content across channels, that’s not a minor efficiency gain.

Canva serves more than 250 million monthly active users across more than 190 countries. The research preview launches April 16.

The launch comes a day after Adobe announced its own agentic creative assistant, Firefly AI Assistant, which similarly orchestrates multi-step workflows across Creative Cloud apps from a conversational interface.

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Journalism students are more skeptical of AI than their professors expect — and a classroom experiment at Northeastern University is surfacing exactly why that matters for how journalism schools teach the technology.

Dan Kennedy, who teaches a graduate ethics seminar at Northeastern, recently devoted a class to hands-on AI use, asking students to run interview transcripts through Claude and evaluate the results. What he didn’t anticipate: students pushed back harder than he did. “I was surprised to learn that they are as skeptical of AI as I am — maybe more so,” Kennedy wrote in Poynter, noting that he himself regularly uses Claude for source research and brainstorming.

The exercise gave two teams the same transcript — an interview with Tracy Baim of the LGBTQ+ Media Mapping Project — and asked them to generate bullet points, a 600-word summary, a news story, a headline, and a social media post. Students then evaluated each output for accuracy, utility, and ethical disclosure requirements. The bullet points came back too long; the news story was serviceable but flat; the headline Claude auto-generated was judged weaker than the one students explicitly requested.

The discussion questions Kennedy designed cut to the core tensions in AI-assisted journalism: Is it accurate? Is it better than what a human would produce? Is it worth the time saved? And what does disclosure actually require?

One question that generated the most friction: a policy at Cleveland.com and The Plain Dealer, where editor Chris Quinn has reporters submit notes to AI, which then drafts the story for human review before publication. Kennedy asked students whether that practice is ethical if disclosed. The answers, he wrote, were “thoughtful, nuanced” — which is another way of saying the students didn’t let him off easy.

The experiment points to something journalism educators are grappling with across the country: the gap between teaching students about AI and teaching them to use it critically. Kennedy’s approach — put the tool in students’ hands, make them evaluate outputs against specific ethical criteria, then discuss — is closer to the latter. It also surfaces a real tension: students entering the field now are skeptical of AI in ways that may conflict with newsroom practices they’ll encounter on day one.

What Kennedy’s class doesn’t yet account for, by his own admission, is the coming cohort of students who grew up with generative AI as a baseline assumption. How they’ll engage with these same questions — and whether their skepticism will look different — remains an open experiment.

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Wikipedia's English-language edition voted on March 20 to ban all AI-generated text from its 7.1 million articles, drawing one of the clearest lines yet between human-authored knowledge and machine-generated content.

The vote wasn't close. Volunteer editors approved the ban 44–2 after a Request for Comment process driven by mounting frustration over what editors called the "asymmetry of effort": it takes seconds to generate hallucinated, citation-free AI text and hours for human editors to fact-check and remove it. "One person can generate AI text in five seconds and post it on Wikipedia," said Ilyas Lebleu, a French AI research student who drafted the winning proposal under the username Chaotic Enby. "We can spend an hour or longer verifying everything."

The ban covers new and existing articles. Editors may still use AI tools for basic copyediting — provided the AI introduces no new content and a human reviews the changes — and for translating articles from other language editions, so long as the editor can verify accuracy in the source language. Everything else is off.

The decision formalizes what Wikipedia's volunteer editors had been fighting to enforce informally since ChatGPT's 2022 launch. The signs of AI infiltration were not subtle: articles with placeholder prompts still embedded in the text, fabricated citations, and the telltale phrase "rich cultural heritage." A WikiProject called "AI Cleanup" emerged to catalog detection techniques. Editors developed a public guide to spotting AI writing so precise that contributors can sometimes identify which model version generated a passage based on its training cutoff.

One complication: false positives. Some editors raised concerns that autistic contributors or non-native English speakers could be wrongly flagged for writing in an "AI-like" style. Lebleu addressed this in the final policy, explicitly prohibiting sanctions based on writing style alone.

German Wikipedia implemented similar restrictions in February 2026. English Wikipedia's adoption — the largest and most-linked edition — gives the policy global weight.

What Wikipedia has established is a principle other platforms have so far avoided: the source of the text matters, not just its accuracy. Whether Google, news publishers, or social platforms follow is an open question. But Wikipedia has at least made the position legible.

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