The team here at Hawksman Technology have seen more and more agents being widely adopted across industries, offering scalable automation, around-the-clock availability, and reduced operational costs. From sales enablement to internal support desks, these systems have become foundational tools for modern businesses. However, we have also seen that not every implementation has gone smoothly. A growing number of companies are reevaluating or even removing AI agents—particularly in customer service—after realizing that people can often tell when they’re not speaking to a real human. And that recognition, however subtle, can lead to frustration, mistrust, or even reputational damage.
Where AI Agents Are Adding Real Value
- Sales and Lead Qualification
AI agents have been successfully deployed to engage website visitors, qualify leads, and route them to the appropriate sales reps—often booking meetings automatically. This has enabled sales teams to handle a much higher volume of inbound leads without increasing headcount.
However, an interesting dynamic has emerged: AI agent vendors are quick to highlight statistics on the number of additional leads they’ve generated, but they rarely acknowledge the potential reputational damage caused by over-automation. In many cases, prospects are bombarded with aggressive follow-ups or generic messaging—leading to frustration and diminishing the brand’s credibility. What looks like “lead generation” in the data can sometimes feel like spam in the real world. - Internal IT and HR Support
Companies like Slack, ServiceNow, and Atlassian have integrated AI agents into their internal systems to handle repetitive support queries—ranging from password resets and software access requests to common HR policy questions and onboarding workflows. These agents act as the first line of support, resolving issues instantly or routing them to the appropriate human team when necessary. This internal deployment of AI has led to measurable gains: faster ticket resolution times, lower IT and HR workloads, and improved employee satisfaction. Teams no longer need to spend hours each week answering routine queries, allowing them to focus on more strategic work.
That said, there’s a subtle challenge here too: while most internal users appreciate fast answers, the quality of those answers still matters. When AI agents give vague or inaccurate responses—or fail to escalate properly—it can result in confusion and time lost chasing the wrong solution. Smart companies mitigate this by clearly flagging when a human is (or isn’t) involved, and by maintaining strong feedback loops to continuously improve their AI’s performance. - Financial Services and Trading
In the world of hedge funds, proprietary trading firms, and asset managers, AI agents are increasingly being deployed to augment decision-making and streamline operations. These agents monitor vast streams of market data in real time, flag anomalies, track news sentiment, and—in some cases—execute pre-approved trades on ultra-low-latency systems.
By acting as 24/7 watchers, AI agents can surface insights that humans might miss, especially in fast-moving or illiquid markets. For example, an agent might detect unusual price action in a particular asset class and alert a trader before it impacts the broader portfolio. In more sophisticated setups, AI agents can also adjust risk parameters, optimize execution strategies, or dynamically rebalance portfolios based on predefined logic.
This level of automation gives firms a critical edge—especially in high-frequency environments where milliseconds matter. However, while the upside is clear, the risks are too: poorly designed agents or “black box” models with unclear decision logic can create unintended consequences, including flash crashes or overfitting to short-term noise. Many firms now use AI agents not as autonomous decision-makers, but as highly intelligent co-pilots—always under the watchful eye of human quants and traders.
Why Are Some Companies Dropping AI Agents?
AI agents are proving less effective in areas that require emotional intelligence, contextual awareness, and trust-building—like customer service.
- Air Canada was taken to court after its chatbot misled a customer about bereavement fare policies.
- McDonald’s is shutting down its AI-powered drive-thru system after a wave of social media videos showed it placing absurd or incorrect orders.
- Lush and Frontier Airlines have both stepped back from AI-based chatbots following customer complaints.
These failures often stem from a simple truth: people can usually tell when they’re not speaking to a human.
Why Humans Still Matter in Customer Service
Despite advances in natural language processing, AI often gives itself away:
- Stilted or overly formal responses
- Inability to handle follow-up questions gracefully
- Lack of empathy or humor
- Struggling with nuance, sarcasm, or emotional context
According to recent reporting from the BBC, companies are now hiring people specifically to “humanize” AI responses. People like Miller—a writer tasked with making AI sound more natural—are part of a growing effort to reduce the uncanny valley between bots and real people. But even with that effort, users are quick to notice the difference.And when customers sense they’re speaking to AI and not getting real help, they’re more likely to lose trust in the brand.
Implications for Recruitment
This dynamic matters in recruitment for two key reasons:
- Role Evolution: As AI handles more repetitive tasks, recruiters must focus on sourcing candidates who bring human strengths—empathy, judgment, adaptability, and communication—especially for client- or candidate-facing roles.
- Candidate Experience: AI tools can streamline parts of the hiring process, like resume screening and interview scheduling, but overuse can make the experience feel cold and impersonal. Ensuring a human touch in candidate communication can differentiate your brand in a competitive market.
Conclusion: AI Should Support, Not Replace
AI agents are powerful tools when used in the right context: high-volume, low-stakes tasks; internal support; and structured workflows. But where emotional nuance, trust, and human intuition are required—particularly in candidate or client relationships—human interaction remains essential.
At Hawksman Technology, we’ve embraced AI thoughtfully: it helps us optimize internal processes like research, scheduling, and database management. These behind-the-scenes efficiencies free up our team to do what we do best—build genuine relationships.
