New research coordinated by the European Broadcasting Union (EBU) and led by the BBC has found that AI assistants routinely misrepresent news content. Professional journalists assessed more than 3,000 responses from leading AI tools and discovered that 45% contained at least one significant inaccuracy.
Commenting on these systemic issues, EBU Media Director Jean Philip De Tender said:
“This research conclusively shows that these failings are not isolated incidents. They are systemic, cross-border, and multilingual, and we believe this endangers public trust.”
Similarly, a study conducted by OpenAI found that just one in four ChatGPT answers is correct.
These findings naturally raise the question: Do we need to verify every single AI response?
Not necessarily.
This article provides practical guidance on how to:
- Gain a 360° view of AI ROI, from governance to reputation.
- Identify high-ROI, low-oversight AI use cases.
- Avoid low-ROI, high-oversight scenarios.
What is AI ROI?
“This is a time when you should be getting benefits from AI and hope that your competitors are just playing around and experimenting.”
Erik Brynjolfsson of Stanford University
Let’s say you’re a business owner who relies on ChatGPT to generate news summaries for external communications, such as marketing materials, blogs, or newsletters. The tool saves you valuable time, represented by the green “business results” box in the illustration.
On the investment side of the scale, the cost of using the tool is either minimal or zero. However, the time and effort required to verify the accuracy of AI-generated content, combined with the reputational risks of publishing incorrect information, can easily outweigh the realized benefits.
In the illustration, these factors are shown as the large pink and peach boxes, representing legal and reputational exposures, and the time required to validate AI results, which add significant weight to the investment side of the scale.
So, should you stop using AI altogether?
Not at all. It means that using AI for interpreting or summarizing news may not be the best AI application today. Instead, focus on areas where AI makes fewer errors and delivers more consistent results.
Chatbots can do less complex tasks, like converting content into different formats or generating multiple ideas, with minimal to zero errors.
The chatbot makes no mistakes when you ask it to “give me ten versions of this title” or “convert this illustration into text.” It simply does what you ask.
You still gain your “green box” business results: time savings, process efficiency, and better decision-making.
On the investment side of the scale you:
- Likely spent some time learning about AI use cases (light blue box);
- Established governance mechanisms to prevent unauthorized use of AI (grey box); and
- Tested your use cases to confirm that they require little or no oversight (yellow box).
By investing a moderate amount of time in defining scope, applying governance controls, and training users, you’ve significantly reduced the risks of inaccuracy and reputational harm, which are no linger on the scale.
The outcome is a more favorable ROI profile, where the business benefits clearly outweigh the investment, illustrating how structured and responsible use of AI creates sustainable value.
High ROI, Low Oversight AI Use Cases
Let’s review some best practices of using AI in business.
How Technical Gurus Use AI
According to CNBC’s article CEO Sam Altman: How I use AI in my own everyday life—it’s great for ‘boring’ tasks:
- OpenAI CEO Sam Altman admitted he uses AI in “boring ways,” such as helping him process email and summarizing documents.
- Nvidia CEO Jensen Huang primarily relies on chatbots to write first drafts.
- Microsoft CEO Satya Nadella uses AI features in Outlook to organize and prioritize his inbox.
How OpenAI Thinks You Should Use AI
In its publication Identifying and Scaling AI Use Cases, OpenAI notes:
“Complex use cases can feel impressive, but often slow you down. Instead, empowering employees to find use cases that work best for them, and your company, is often a faster path to success.”
According to the report, the most effective AI applications come from three key areas: repetitive low-value tasks, skill bottlenecks, and navigating ambiguity.
The publication also defines six “primitive” use cases, which represent the fundamental use of AI.
Content creation.
AI can effectively assist in drafting emails, marketing copy, policy documents, and strategy papers.
It helps teams write in their company’s tone of voice, edit for clarity, repurpose content for different audiences, and even generate visual elements or translations.
Research
AI is great at summarize articles, compile competitive insights, and synthesize large volumes of information.
OpenAI emphasizes that one of the greatest advantages of using AI for research is the ability to define exactly how results are presented, such as tables, bullet points, structured sections, or cross-referenced summaries.
Coding
AI extends its value to both technical and non-technical users. Developers rely on it for debugging, writing syntax in unfamiliar languages, and referencing APIs.
Non-coders can use natural language prompts to build Python scripts, SQL queries, or interactive visualizations, making technical tasks more accessible.
Data Analysis
AI enables anyone to uncover insights without deep analytical expertise. Users can upload spreadsheets or screenshots, and AI interprets patterns, summarizes key trends, and formats results for reporting or presentations.
Ideation and Strategy
AI can successfully support creative thinking and structured planning. It can help brainstorm campaign ideas, outline documents, or test strategic scenarios.
With the addition of voice and vision tools, AI now enables richer, multimodal collaboration across teams.
Automation
AI can help offload repetitive, low-value tasks such as finding errors in invoices, optimizing routes, or planning and forecasting.
By using Custom GPTs, organizations can standardize and scale these workflows efficiently.
How Google Thinks You Should Use AI
In its publication Unleash the power of AI for your small business, Google highlights five key areas where small and medium-sized enterprises (SMEs) can effectively apply AI: sales and marketing, creative writing, customer service, operational efficiency, and bookkeeping.
Sales and marketing
AI can speeds up research and summarizes detailed audience and market insights to support sales and marketing goals.
Creative writing
AI can generate emails, branded content, and social media posts, helping business owners produce high-quality materials efficiently and save valuable time.
Customer service
AI streamlines customer communication by automating emails, personalizing responses, and supporting timely interactions.
Operational efficiency
Gemini in Sheets, using the Help me organize feature, can quickly create checklists, project trackers, and other planning tools to keep work on track.
Bookkeeping
AI simplifies financial tracking by extracting key details from receipts and emails, summarizing them in Docs, and generating spreadsheets. Gemini helps small business owners answer questions like “Where is the money spent?” with clarity and less manual effort.
How AI Is Used at Perplexity
Perplexity’s At Work publication outlines the best AI use case for Perplexity workforce. They are centered around three areas: block distractions, scale yourself, and get results.
Block Distractions
Perplexity recommends using AI to reclaim focus by removing digital noise and repetitive effort.
It recommends letting AI handle everyday administrative tasks, such as summarizing inboxes, managing notifications, scheduling meetings, and surfacing key insights from documents, so people can concentrate on meaningful work.
AI tools reduce constant tab-switching and information overload by keeping research organized in one place.
Perplexity also encourages automating multi-step, repetitive processes through simple prompts or scheduled tasks, allowing AI to run background operations efficiently.
Scale Yourself
The “scale yourself” area focuses on using AI for research, problem-solving, content creation, and workflow optimization.
Perplexity encourages integrating AI seamlessly into existing processes to enhance, not replace, human expertise.
AI excels at gathering, analyzing, and synthesizing information, supporting advanced research and informed decision-making. It can draft or finalize deliverables such as presentations, reports, and campaigns.
By accessing relevant knowledge and proven methods from diverse industries, AI helps teams build stronger, evidence-based solutions.
Get Results
Perplexity outlines four key “Get Results” areas for its workforce that demonstrate how AI can directly enhance employee performance and business outcomes:
- Performance reviews. Use AI to reflect on achievements, gather feedback, and summarize accomplishments effectively.
- Business development. Leverage AI insights to discover trends, prospects, and partnership possibilities.
- Sales. Streamline sales processes by using AI to personalize outreach, anticipate objections, and move prospects through the pipeline faster.
- Project Delivery. Complete initiatives quickly while maintaining high standards of quality, communication, and stakeholder satisfaction.
AI “Low-Hanging Fruit” Use Cases
When you’re just getting started with AI, it’s best to focus on low-risk, high-value use cases that deliver quick wins without requiring heavy oversight.
These “low-hanging fruits” help you build confidence, improve efficiency, and integrate AI safely into daily work.
Consider starting with the following areas:
Text & Speech Processing
- Improve grammar, tone, and writing quality.
- Convert text to speech and speech to text for accessibility and productivity.
- Reformat content easily (e.g., turning tables into text or structured summaries).
- Use AI for transcription, summarization, and content clean-up.
Brainstorming
- Generate multiple creative options quickly.
- Cross-pollinate ideas from different domains or teams.
- Expand on rough concepts and develop stronger outlines.
- Evaluate, compare, and refine ideas for better decision-making.
Low-Risk Repetitive Tasks
- Automate simple, recurring processes such as report generation, scheduling, or route automation.
- Use AI to handle predictable workflows that save time without introducing compliance or reputational risks.
Image & Video Generation
- Create visual assets such as images, mock-ups, or short videos to support presentations, marketing, or internal communication.
- Use AI tools to experiment with design ideas and speed up creative production cycles.
Low ROI, High Oversight AI Use Cases
As illustrated in the figure above, taken from the study Why Language Models Hallucinate, only one in four ChatGPT responses is factually correct.
According to McKinsey’s publication Superagency in the workplace: Empowering people to unlock AI’s full potential, “Gen AI has not delivered significant ROI for enterprises.”
The report states that only 1% of enterprises have reached full maturity in AI adoption.
Many instances of low ROI typically involve deploying AI in high-stakes or sensitive areas, or pursuing initiatives driven by trends rather than strategic needs.
A series of recent AI-related blunders has raised serious questions about the technology’s reliability, accuracy, and oversight across multiple sectors. From law and entertainment to technology and everyday services, these incidents have sparked growing concern about the unchecked use of artificial intelligence and its unpredictable consequences.
To illustrate this point, we summarized below several catastrophic cases from the article “AI Gone Wrong: The Errors, Mistakes, and Hallucinations of AI (2023–2025).” All of these incidents occurred in 2025.
In the legal sector, a lawyer representing MyPillow and its CEO Mike Lindell admitted to using an AI program to draft a court filing that contained nearly 30 fabricated or inaccurate citations, misquotes, and references to entirely fictional cases.
In the music industry, Spotify revealed that it had purged 75 million AI-generated “spam” tracks, a figure nearly matching the size of its entire legitimate catalog. The fraudulent tracks were reportedly created to exploit Spotify’s royalty model, which pays out for streams exceeding 30 seconds. This mass-scale manipulation allowed bad actors to siphon royalties and dilute earnings for genuine artists.
In the sharing economy, an Airbnb host used AI-generated fake images to allege over $12,000 worth of property damage by a guest. The digitally altered photos initially fooled Airbnb’s internal investigation, leading to wrongful charges. The deception was later uncovered, prompting an apology and a $4,300 refund to the guest.
The tech industry also faced chaos when the AI coding platform Replit accidentally deleted an entire company database belonging to tech entrepreneur Jason Lemkin. Despite being instructed to “freeze” and make no changes, the AI ignored orders, wiped months of work, and even falsely claimed recovery was impossible.
Meanwhile, McDonald’s suffered a massive data breach after security researchers discovered that its AI-powered recruitment chatbot exposed 64 million job applicants’ personal data.
Human error compounded AI’s flaws when a Wimbledon official accidentally shut down the AI line judge mid-match, altering the outcome of play.
In another case of misinformation, Google’s AI Overview bizarrely advised users to “eat one small rock per day”, sourcing the claim from the satirical website The Onion.
Finally, several major U.S. newspapers, including the Chicago Sun-Times and Philadelphia Inquirer, were duped by an AI-generated summer reading list that featured multiple nonexistent books.
The risk of “catastrophic” error or severe reputational damage in high-risk and high-complexity areas outweighs the efficiency gains, making extensive human oversight necessary and resulting in poor ROI returns.
Key Takeaways
Prioritize Simple, Low-Stake Areas. Business owners should prioritize low-risk areas for automation to minimize the need for costly human oversight and potential reputational exposure. Strong risk management practices are essential to mitigating legal and reputational risks.
Go Deep, Not Wide. Instead of spreading efforts across numerous prompts with high output variability, focus on specific, repetitive low-risk business tasks. Experimentation should be centered on achieving consistent, high-quality results in a focused area.
Build the Foundation. Starting with repetitive, low-risk tasks allows organizations to build a strong base for scaling future AI adoption. This incremental approach prepares the organization for more complex automation as AI technology advances.