This is a loaded question, which tech optimists might embrace wholeheartedly while sceptics would be appalled at the idea of an application becoming more human-like. It is a fascinating area of research which I am delving into as I study for my PhD. I am looking at the interconnection between artificial intelligence (AI), psychology and enterprise applications. AI is becoming smarter every day with some commentators pronouncing that artificial general intelligence (AGI) is not far away. Yet others, as revealed by the Association for the Advancement of Artificial Intelligence (AAAI) report on the future of AI, question whether AGI should even be the long-term goal.
Ask Microsoft Co-Pilot and it cautions against believing applications will become sentient, but it does suggest that AI is a powerful tool to automate tasks, relieve workloads and provide more personalized interactions for users with technology. And yes, it may well be that in the future you have a natural human-like conversation with your AI Agent about your weekend; it might remind you about Grandma’s birthday while offering advice on how best to manage your workload to ensure you have downtime for personal commitments. That may feel sentient – but it won’t be.
The illusion of Sentience
Let’s be clear: AI isn’t conscious. It doesn’t experience emotions, make choices or have intentions. What it does do is simulate these behaviours with high fluency.
The illusion of sentience, where a system appears to understand, empathize, or relate is already here, and it’s becoming more convincing. That illusion has powerful implications for how we interact with enterprise technology. AI won’t become sentient in the human sense. But it will become more attuned to us, to our behaviors, preferences, needs, and even our moods. Over time, enterprise software will feel more “human” not because it thinks, but because it increasingly mirrors how we think, communicate, and make decisions.
Rather than a linear path toward sentience, we’re seeing three concurrent dimensions of AI transformation in enterprise applications:
1. Automation with Empathy
AI is already excelling at automating workflows, surfacing insights, and optimizing performance. But it’s not just about efficiency, it’s about doing so in ways that enhance well-being and equity.
Take Unilever’s use of chatbot-led interviews, which help eliminate human biases in early-stage hiring. The result? A 16% increase in workplace diversity. Or AI-powered workload management systems that balance task allocation to avoid burnout and create more sustainable team dynamics. These systems don’t just automate workflows, they support humans in a more thoughtful, responsive way.
2. Personalization that Learns You
We are already seeing examples where AI is being used to make suggestions based on employees’ preferences and work history. One area is personalized learning. AI-powered adaptive learning platforms can personalize training based on individual progress, ensuring efficient skill development.
McKinsey’s research indicates that personalized learning pathways can enhance student engagement by as much as 60% while improving academic performance by 30%. It can support employee well-being with AI-powered sentiment analysis tools assessing workplace morale based on analysis of internal communications. This leads to more proactive engagement from HR with employees to address concerns.
Consequently, enterprise AI is becoming more personal, and not just in the way it adapts content or training materials. Adaptive learning platforms tailor professional development based on real-time progress. McKinsey reports that personalized learning pathways can boost engagement by 60% and performance by 30%.
More sophisticated systems analyze tone, sentiment, and historical data to support emotional well-being, too. Imagine HR proactively reaching out when an employee’s communications suggest disengagement or stress. Not to monitor, but to offer help.
3. Human-like Interactions
Here’s where things get interesting. AI can already mimic emotional intelligence — recognizing facial expressions, tone, and language patterns. But in future applications, it will also predict emotional states and respond in ways that feel empathetic, timely, and intuitive.
Does that make it sentient? No. But it will feel sentient to the user.
Of course, this leads to an important follow-up question, how comfortable are we with an AI tool knowing so much about us? We are already accustomed to consumer devices tracking our heart rate and software that can read emotional responses, as well as scan transcripts of Teams calls to evaluate the emotional state of participants.
This must be handled with sensitivity, but emotional AI is looking to better understand our psychological response to technology, which could be beneficial for employees. Combining different sources of data could enable an AI tool to make recommendations about taking a break or attending a mindfulness session by scanning various biometric data points as we use applications.
Imagine the possibility of using an AI Agent for onboarding processes or an AI-enabled coach for professional development? There are questions about what boundaries should be in place to protect an employee, that’s where frameworks like the EU AI Act come in and why it is critical to adhere to their parameters. This ensures transparency in how data is used especially when it comes to decision making. This also ensures users have the ability to override the AI and prevent sensitive data leaking.
If we can ensure the right protections are in place, there are hugely exciting opportunities ahead to do new things. More human-like interactions between users and enterprise applications will undoubtedly change the way we use technology. It brings about entirely new ways of working, but more importantly it will create far more stimulating and rewarding work because our applications are more attuned to us. Your enterprise application may not quite be able to read your mind, but thanks to AI it may well become your indispensable work colleague.
