Making AI Safety First in a
New Era of Intelligence 

Published on May 22, 2025
Last Updated on May 22, 2025

Responsible builds. Rigorous benchmarks. Real-world evaluating and testing. These are, undeniably, the most important foundational steps in making AI safe. Research shows, however, that “few companies report having fully implemented key responsible AI capabilities.” 

And, as AI grows more intelligent, more capable of acting on its own and more widely deployed, the risks grow even faster. Getting ahead starts from the beginning, building in safety and human-in-the-loop processes.

The smarter the AI, the bigger the risks

As AI gets smarter, adoption is finally matching the hype. At the same time, the stakes are higher and concerns bigger.

Misinformation is prolific and frequently accepted as fact, creating a ripple effect of global mistrust. AI-generated content is fueling a wave of large-scale fraud with off-the-charts financial consequences, while also stirring up challenges around copyright, fair use and repetitive, one-note output.

Egregious content can go unchecked resulting in mental and physical harm for people and reputational damage for organizations. Personal data is being captured without consent and exploited to new levels. Even something as benign as delivering the right customer experience (CX) can backfire.

It’s no wonder that 47% of organizations have already had a negative experience from using AI.  

When AI fails, the consequences are exponential: widespread political and societal ramifications, increased regulatory scrutiny and irreparable brand damage. The answer is simple, but getting there is more complex. 

To scale AI responsibly, safety must be a priority at every stage — from training and testing to real-world deployment.

AI first means safety first

Just like when every company became a tech company, every company today is becoming an AI company. Yet, in the rush to be AI first, safety is too often an afterthought. It’s surface-level rather than embedded: a singular QA checkpoint, an HR policy or another compliance box to check. 

That’s not even close to enough oversight to meet the scale and speed at which AI systems are applied, operate and improve. The risks are on a similar trajectory, but the ones inherent to how AI models are built and learn can be mitigated.

Creating safe AI models

For one, LLMs hallucinate (make up facts with confidence) and produce outputs that mislead, offend or even harm. Without intentional safety, false information is normalized, bias is inevitable and CX can go haywire.

Keeping AI safe and reliable starts, minimally, with these best practices:

  • Using high-quality training data: LLMs are only as reliable as the data they’re trained on. Using diverse, properly annotated and reviewed datasets improve overall model precision, reducing the risk of misleading or inconsistent answers. This is especially critical in high-stakes environments like healthcare, finance and legal services.
  • Training with human feedback: Human judgement is essential to shape model behavior. Techniques like Reinforcement Learning with Human Feedback (RLHF) allow annotators to rate and guide responses, helping LLMs generate outputs that are safe and aligned with human values.
  • Stress testing before launch: Before a model is released, red teams provoke unsafe behavior using adversarial prompts, edge cases and attempted jailbreaks (also known as red teaming). This exercise exposes vulnerabilities so engineers and developers can fix issues before they reach the public.

Safety must also be ongoing. LLMs evolve as they interact with users and data. Regular monitoring and audits help catch new risks, while ongoing updates ensure models stay aligned with safety and performance goals. 

Deploying trustworthy AI agents

Emerging Agentic AI is the latest test of trust, as it handles customer interactions  autonomously — managing workflows and making its own decisions. AI agents evaluate situations and determine appropriate actions independently but within defined parameters.

“Our clients are eager to realize the benefits of AI,” says Joe Anderson, leader of the new Agentic AI Consulting practice, “but doing so isn’t simple. They need an advisor and system integrator that really understands their operating environments – their customer experience strategy, their policies, processes, and systems to create positive customer experiences and realize business benefits.”

To make the right decisions, AI agents need a deeper understanding of a user's data. This new level of autonomy unlocks new use cases but leaves more room for mistakes (e.g., scheduling a wrong appointment, flagging a user as fraudulent or exposing private data) if not properly deployed. Agentic AI must also be trained to know it reaches the limits of its own expertise — when it detects uncertainty, encounters ethical dilemmas, faces novel situations or confronts higher-stakes decisions.

Responsible agents need strong safeguards:

  • Monitoring task execution in real time to detect errors, failures or unexpected behavior
  • Evaluating and validating outcomes, especially for sensitive actions, through automated checks or human review
  • Keeping a human in the loop for high-stakes decisions where judgement, empathy or ambiguity are involved
  • Designing handoffs to humans when AI agents get stuck, encounter edge cases or escalate issues

Making AI safety first with people

Even (or especially) the most powerful AI needs a human compass. Behind every safe, reliable system is a team of expert annotators, red teams, trust & safety professionals, customer experience (CX) specialists and QA testers to ensure the technology is accurate, reliable and inclusive.

As creators, deployers and users of AI, TaskUs helps enterprises achieve breakthrough results while maintaining the highest standards of security and trust. 

Our AI data services experts partner with technologists and engineers to create safer, more accurate systems — curating training data and establishing rigorous safety benchmarks that guide development from initial build to release and fine-tuning. 

Our agentic AI specialists help clients automate confidently, deploying AI agents built on best-in-class partner technology platforms.

We also apply our own proprietary tools to CX workflows, augmenting our teammates’ capabilities while enforcing guardrails to protect sensitive customer and client data.

As we progress deeper into an AI era marked with greater intelligence, safety first must be a top business imperative. Creating AI systems, deploying new solutions and applying tools that are transparent and reliable will enable businesses to truly benefit from the technology’s potential — innovating and protecting the customers and communities they serve.

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