Across industries, organizations are racing to integrate generative AI into their workflows. No one wants to fall behind competitors who are already leveraging AI to boost productivity and accelerate innovation. The promise is clear: faster output, greater efficiency, and more time for strategic work.
But there’s a growing tension beneath the surface. While AI can produce high-quality work at machine speed, it also has the potential to generate misinformation, fabricate sources, or misinterpret data—errors that often sound polished and credible enough to evade traditional review.
According to Wakefield’s (2025) research*, many companies are expanding AI adoption despite lacking governance protocols. Case in point, 75% of business leaders plan to increase GenAI use this year, yet 78% admit they aren’t fully prepared to manage its risks.
The result is a widening gap between the speed of AI-assisted work and the safeguards required to ensure accuracy at scale. Read on to see how organizations can keep pace with AI without sacrificing accuracy or reputation.
How is GenAI adoption helping and testing organizational workflows?
Turnitin’s latest research shows just how deeply AI has become embedded in organizational workflows. 52% of business leaders say GenAI use is already “widespread”, concentrated in the following areas:
- 51% use it for data analysis
- 47% for technical research
- 47% for creative ideation
- 43% for marketing content
These use cases touch nearly every function that shapes external brand perception and internal decision-making. The tools aren’t sitting on the margins—they’re influencing insights, shaping communication, and informing strategy.
Three out of four business leaders (75%) say they plan to increase GenAI use this year. When asked what was the greatest business need, training focused on using GenAI responsibly versus training on using GenAI to achieve business outcomes, a majority (55%) chose the latter.
This signals an imbalance: organizations are prioritizing speed and scope of AI integration, but not always strengthening the safeguards needed to support transparent, ethical use.
What risks do business leaders see with GenAI output?
As AI becomes a standard part of daily work, we’re seeing the rise of AI-driven blindspots. Compared with traditional publishing violations, AI errors are subtle, pervasive, and can propagate quickly across workflows. Wakefield’s research identifies four key risks top of mind for executives:
- Misinformation (43%)
- IP infringement (39%)
- Unverifiable information (33%)
- Plagiarism (33%)
These concerns are not theoretical. Over the past two years, we’ve seen high-profile cases of fabricated citations, hallucinated legal references, misattributed quotes, and AI-generated material being unknowingly published. These incidents created legal exposure, forced public corrections, and in some cases, damaged reputations.
Human reviewers simply aren’t equipped to catch every AI-generated error. Research and real-world outcomes alike show that AI hallucinations can be highly believable and that traditional review workflows weren’t built for the scale or the complexity of machine-generated output.
What “trust at scale” really means in the AI era
For organizations, trust at scale means ensuring the content they produce—whether internal analysis or external communication—is verifiable, transparent, and built on reliable information.
It’s not enough to trust that AI tools are accurate by default. Instead, organizations must be able to show:
- How AI contributed to content
- What sources or data informed its output
- Who reviewed or validated the material
- Which guardrails were used to ensure the integrity of the final product
Trust at scale isn’t about limiting innovation; it’s about making sure AI innovation doesn’t compromise accuracy, compliance, or credibility. It allows teams to move quickly with confidence in what they produce and avoid the knock-on effects mistakes can have on morale.
How can organizations balance speed and accuracy with GenAI?
1. Build clear GenAI governance that keeps pace with usage
Organizations need transparent, accessible guidelines that outline:
- Where AI can be used
- What types of data are safe to input
- Which tools are approved
- What level of validation is required before publication
Good governance isn’t restrictive—it reduces guesswork and revisions, prevents errors, and ensures consistency across teams.
2. Invest in AI literacy—not just productivity gains
With 55% of leaders’ primary training objective on business outcomes rather than responsible use, there’s an emerging development gap.
Teams need to understand:
- The likelihood of AI hallucinations.
- The difference between plausible-sounding text and verified information.
- What kinds of tasks require extra validation.
- How to critically evaluate AI-generated outputs.
3. Use automated integrity and verification tools to match AI’s speed
Of course, manual review alone can’t keep up with the sheer volume of AI content. Attempting to do so would tip the balance and erode the very efficiency that AI is meant to deliver.
Fortunately, iThenticate offers a solution. With in-built AI detection plus automated checks against the most comprehensive content database available, it adds a critical layer of protection to your workflow by:
- Flagging possible IP risks, plagiarism, or unattributed reuse.
- Using algorithms to identify synthetic text patterns and potential hallucinations or unverifiable sources.
- Empowering teams to use AI confidently, supported by always-on AI detection.
- Equipping reviewers with instant insights that enhance judgment and review.
iThenticate ensures content verification becomes a seamless part of the process—not dependent on humans’ capacity for detection, nor an obstacle to productivity.
Overview: Turning AI safeguards into strategic advantage
As GenAI reshapes how organizations work, the businesses that succeed will be those that build trust directly into their workflows. Measures for accuracy, transparency, and accountability are foundational to operating at scale in the AI era.
Organizations that can verify information as quickly as they generate it will move faster, innovate more confidently, and communicate with credibility. Those that rely on speed alone risk falling behind—or worse, facing preventable reputational or legal consequences.
Trust at scale isn’t just a safeguard. It’s a competitive advantage. And with the right combination of governance, literacy, and automated integrity checks, organizations can unlock the full potential of AI while protecting the integrity of every piece of content they produce.
*Turnitin provided compensation to Wakefield Research to conduct this research