AI is causing cognitive fatigue. Here’s how to work with more haste and less speed


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ZDNET’s key takeaways

  • Staff who use AI can end up with more to do, not less.
  • Think carefully about the tools you’re using and why.
  • Adopt a set of standards and refine your outputs.

The promise of productivity boosts from AI can come with an unwelcome side order of stress. Harvard Business Review found that AI doesn’t reduce work; it intensifies it, leading to cognitive fatigue and unsustainable hours.

While the common perception is that AI can help reduce workloads, allowing employees to focus more on higher-value and more engaging tasks, HBR’s research found that staff using AI worked more quickly and often ended up with more to do, not less.

Also: Forget productivity: Here are 5 strategic shifts that drive real AI value

While we’ve written about how some professionals are finding ways to turn AI’s time-saving magic into a productivity superpower, we’ve also recognized that some employees have started to become tired with the low quality of AI outputs.

Ankur Anand, group CIO at tech recruiter Harvey Nash, said professionals who want to avoid cognitive fatigue must understand how to use AI effectively and its potential risks.

“That focus will help to reduce the noise around the workload that AI creates,” he told ZDNET, suggesting that many people have unrealistic expectations about the productivity boost that AI will provide.

Also: Why I ditched Copilot for Claude in Word, Excel, and PowerPoint – and how you can, too

“Many organizations are telling their people, ‘We want to understand how you’re making an impact with AI,'” he said. “But these professionals are not empowered, which means that using AI adds a lot of pressure, because they need to prove themselves on their own terms.”

If you’re going to make the most of AI at work, then you’re going to have to find an effective balance between completing tasks quickly and producing high-quality work. 

Here’s how the experts believe professionals can ensure they reap the benefits, not the problems, of AI — and they suggest that you’ll need to focus on three core areas: tools, guidelines, and outputs.

Limit your toolset

Alex Read, senior enterprise product manager for data at energy provider EDF UK, told ZDNET that the best way for professionals to reap the benefits, not the challenges, of AI is to be uber-focused on tools that help you produce value in your roles.

While there are thousands of potential AI-enabled services on the market, Read said sensible professionals limit their horizons.

Also: How this travel company’s AI rollout drove a 73% satisfaction boost: A 5-step playbook for your business

In his own role, for example, Read focuses on how AI can help him build a data platform and update information accurately, efficiently, and productively: “Anything outside of that scope is noise for me.”

That sentiment resonated with Nick Pearson, CIO at technology specialist Ricoh Europe, who told ZDNET it’s important to take a step back and think carefully about how an AI tool can help you produce value in your role.

“If you think about the phrase ‘gen AI,’ the tech is very good, by definition, at generating outputs,” he said. “I could go to bed in the evening, set the model to work, and we could have four new IT strategies produced overnight.”

Also: Worried AI agents will replace you? 5 ways you can turn anxiety into action at work

However, quantity doesn’t necessarily mean quality. Pearson suggested it’s important to focus on AI’s blind spots, particularly as most models are trained on preexisting content.

“AI can’t inspire people, per se; it can’t naturally create something new, because it’s actually quite recursive,” he said.

“And the judgment you have to put in sometimes, on top of everything else, whether it be an ethical or a capability judgment, is not there automatically in the technology.”

It’s in this gap, said Pearson, that human experts play a critical role: “We’re toying with that concern as an organization and saying, ‘Where does AI really play an important role, versus where are we upskilling people in areas that AI probably won’t play for a long time?'”

Work to the guidelines

HBR’s research found that an initial productivity surge when AI is adopted can lead to lower-quality work, turnover, and other problems as people work harder rather than smarter.

To correct this issue, HBR said companies need to adopt an “AI practice,” or a set of norms and standards around AI use that help professionals ensure they use AI in a constrained but productive manner.

Also: 90% of AI projects fail – here are 3 ways to ensure yours doesn’t

At EDF UK, Read is part of an internal AI Center of Excellence in enterprise IT, which enables policy for the effective use of AI across the wider organization. 

In addition to Read, who contributes input from a data-use perspective, the group includes other tech representatives, such as the firm’s senior manager of AI, principal software engineer, and principal solution architect.

“The remit of this center is to make sure that, when the federated business units are looking to build, develop, and deploy AI services, they have platforms, guidance, best practices, architectural assets, and materials to guide them on how to safely and efficiently adopt AI and operationalize it at scale,” he said.

Some of the key themes the center considers when assessing AI tools are scalability and reusability, ensuring a proposed service doesn’t replicate one already in use.

Also: 5 ways to use AI when your budget is tight

“All new tools and services related to AI will go through that hopper and funnel to understand scope and ensure the security, regulatory, and ethical side of things are understood,” he said, suggesting that all professionals should use their organization’s pre-existing guidelines to foster an appropriate exploitation of emerging tech.

“The benefit that guided approach brings is that it allows us to be clear in our messaging around what AI services can be used, how they’re used from a use-case perspective, and ultimately, what personas are allowed to use them.”

Refine your outputs

Even when tools are assessed and considered acceptable, there can still be an overreliance on AI outputs. Worse, some professionals can drown in the insights they receive, leading to higher stress and fewer benefits.

Louise Newbury-Smith, head of UK&I at technology specialist Zoom, told ZDNET that one way to ensure your outputs are constrained is to focus on prompting.

“Use simple amendments to be specific, such as ‘Give me the top three things with the biggest impact.’ That approach should guide your prompt, rather than saying, ‘Give me everything you know about this topic.'”

Also: 5 ways to fortify your network against the new speed of AI attacks

Newbury-Smith said the successful use of AI is all about being smart about how it’s exploited, and that effectiveness comes down to enablement and engagement. If a prompt yields too much information, refine it until you get what you need. She said this should still be faster than trying to get answers without AI.

The basic message for professionals is that effective applications of AI are all about you staying in the loop, said Bernhard Seiser, vice president of digital, data, and IT at AOP Health.

Think before you use AI, and think again before you push your outputs around the organization.

“It doesn’t help the business if you get AI-generated emails that are many pages long, and then you need ChatGPT to summarize the text,” he told ZDNET.

Seiser said that while there are certain tasks generative AI is good at and worth using for, in the end, “you need to use your brain.”





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Digital Evidence Has Reshaped Criminal Defense – and the Defense Bar Is Still Catching Up

A decade ago, a felony case file might have run to a few hundred pages of police reports, witness statements, and lab results. Today, that same case can include a full cell phone extraction, hours of body-worn camera footage, surveillance video from multiple cameras, social media exports, license-plate-reader hits, and digital forensic reports running thousands of pages. The substantive law has not changed nearly as fast as the evidence it operates on.

For criminal defense practitioners, the shift is not just about volume. It is about how cases are investigated, how discovery is reviewed, how plea calculations are made, and how trials are tried. A defense lawyer who treats digital evidence as an afterthought — to be skimmed close to trial, with the cell phone dump opened only if something obvious surfaces — is no longer providing competent representation in most serious cases.

The Volume Problem

Modern law enforcement investigations generate digital evidence at a scale that traditional defense workflows were never designed to handle.

A single cell phone extraction using forensic tools commonly used by prosecutors can produce a report tens of thousands of pages long. Multiply that across co-defendants. Add cloud account data subpoenaed from providers. Add body-cam footage from every responding officer, often running an hour or more per officer per incident. Add interview recordings, surveillance video, ALPR records, and any wiretap or pen register data.

The defense lawyer’s obligation is to review all of it — or at least to review it competently enough to identify what matters. Doing that without a workflow is impossible. Cases get lost not because the exonerating evidence was hidden, but because it was buried in the third week of message history nobody had time to read.

The practical response involves a combination of technology and process: e-discovery review platforms scaled for criminal cases, paralegal-level review with defined search protocols, and clear allocation of which categories of evidence the attorney personally reviews versus which are screened first. Firms that handle digital-evidence-heavy cases without that infrastructure tend to discover, late in the process, that something important was missed.

Authentication and Chain of Custody Have Become Central

Volume is half the problem. The other half is that digital evidence is harder to authenticate than the physical evidence it has displaced.

A surveillance video recovered from a business has to be tied to a specific camera, on a specific system, with verified timestamps, with continuous custody from the moment of seizure to the moment of presentation. A cell phone extraction has to be tied to a specific device, performed using a documented forensic process, with hash values demonstrating that the data has not been altered. A social media export has to be authenticated either through the provider’s certification or through circumstantial evidence connecting the account to the defendant.

Each of these chains has potential breaks. Cameras get the wrong time. Forensic extractions get performed with outdated software. Social media accounts get used by people other than the registered user. Defense counsel who understands the technical underpinnings of how evidence was collected can identify gaps that opposing counsel may have assumed were settled.

Federal procedure in particular has evolved around these issues. Practitioners working in federal court should be familiar with the Federal Rules of Evidence governing authentication and the best-evidence rule, both of which apply to electronic records in ways that often surprise lawyers more accustomed to paper-era practice.

Discovery Obligations and the Brady Problem

The growth of digital evidence has also complicated the prosecution’s obligations under Brady and its progeny, which require disclosure of material exculpatory and impeachment evidence to the defense.

When the relevant evidence universe was a few hundred pages, prosecutors could reasonably review the file and identify Brady material. When the universe is a hundred thousand pages of cell phone data and dozens of hours of video, identifying what is exculpatory becomes a much harder problem — and not always a problem prosecutors solve well. Defense counsel cannot rely on the prosecution to flag what the defense will find useful. The defense has to find it themselves, which loops back to the volume problem.

Courts have been inconsistent in how they handle Brady obligations in the digital age. Some jurisdictions require prosecutors to provide searchable, organized productions; others permit document dumps that effectively shift the search burden to the defense. The practical implication is that defense lawyers in serious cases must budget significantly more time for discovery review than would have been required even a few years ago, and must do so on schedules that prosecutors and courts often have not adjusted to reflect the new reality.

How Digital Evidence Changes Plea Negotiations

Plea negotiations have always been driven by each side’s assessment of trial risk. Digital evidence has changed both sides of that calculation.

For the prosecution, video and digital records often appear to lock in factual elements that previously turned on witness credibility. A clear video of an alleged assault, or a series of incriminating messages, can shift a case from a battle of testimony into a battle of interpretation. Prosecutors evaluating cases with strong digital evidence often offer less, because they perceive their trial position as stronger.

For the defense, the same evidence frequently contains nuance that changes how a jury would actually receive it. Body-cam footage that the prosecution thinks is damning often shows context that supports the defense theory. Cell phone messages read in full rather than excerpted often tell a different story. The defense lawyer who has actually watched the video and read the messages — rather than relying on the prosecution’s characterization — is often in a meaningfully stronger negotiating position than the case file would initially suggest.

This is part of why pretrial preparation has become more decisive. The cases that resolve favorably are usually the cases where the defense did the digital evidence work early enough to see what was actually there, rather than what the police reports said was there. Resources from the California Courts and the State Bar of California outline the procedural framework within which this work has to happen, but the framework alone does not produce results — sustained attention to the evidence does.

What Effective Defense Looks Like Now

Competent criminal defense in 2026 looks different than it did even five years ago. The lawyers who get the best outcomes for clients tend to share a few characteristics: they take digital evidence seriously from intake forward, they have the infrastructure to review it at scale, they understand the technical questions well enough to challenge authentication where appropriate, and they treat plea calculations as something to be made after the evidence has been examined rather than after the police reports have been read.

For people facing serious charges in California, the practical implication is that the choice of counsel matters more, not less, in the digital evidence era. A firm like Angelo Reyes Law, built around trial-ready preparation rather than volume-driven plea processing, reflects what effective representation tends to look like in cases where the evidence record is large and where the difference between a good and a poor outcome turns on what defense counsel actually finds in the file.

The volume of evidence will keep growing. Defense practice has to keep up.



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