Ethical AI: The Critical Difference Between Artificial Intelligence and Information Technology

Originally published on Forbes by Introhive’s CEO, Lee Blakemore

For decades, information technology has helped businesses manage client data. Now, AI promises to unlock new insights from that data. But they are not the same. The fundamental difference between AI and IT isn’t technical, it’s ethical. While IT manages information, AI makes decisions, and understanding this difference is critical to building and keeping client trust.

When it comes to relationship building, AI also raises important concerns around data privacy, security, and governance. It’s not just about what AI creates; it’s also about what data AI consumes. Ethical use of AI depends on a strong commitment to protecting client information through robust policies, secure infrastructure, and clear governance. Without that foundation, it’s not only trust that is at risk, it’s the relationships that make business possible.

How AI can strengthen client relationships

AI can be a powerful ally in building stronger client relationships. It can manage client information, track communication history, and take care of repetitive tasks, so professionals have more time to do what really matters: listening, advising, and solving complex problems. And this is where AI diverges from traditional IT. IT stores data, but AI interacts with it, which creates an ethical responsibility to keep relationships human.

But with that opportunity comes a responsibility too: AI should never turn relationships into transactions. If it’s used in ways that feel impersonal or indiscriminate, it risks treating clients like data points instead of people.

Lately I’ve seen more and more automated LinkedIn messages that sound polished but hollow. They might be grammatically perfect, but they don’t feel human or genuinely interested. Real relationships depend on empathy, context, and intent – all the things AI still struggles with.

Used thoughtfully, though, AI can amplify the human touch. The key is to deploy it in ways that make engagement more meaningful, not as a shortcut that undermines sincerity.

Strengthening governance: the cornerstone of responsible AI

Strong governance is the cornerstone of responsible AI, and it’s also where the difference between artificial intelligence and information technology becomes clear. IT governance is mostly about keeping data safe, but AI governance also has to consider how that data is being used to drive insights. That shift makes compliance central to AI’s impact, because it shapes whether organizations earn and keep client trust.

Many companies still treat compliance as an exercise (a series of audits, checklists or security reviews) that happens in isolation. But real governance needs to be proactive, continuous and cross-functional. It’s about embedding ethical and regulatory considerations into the day-to-day. Not just responding to issues when they appear, but designing processes that prevent them in the first place and fostering a culture where governance is a shared organizational mindset.

One common challenge is that governance structures often become fragmented as organizations grow. With data flowing through more platforms, partners and tools than ever, it’s easy to lose track of how that data is being accessed, shared and used, especially by AI systems trained on sensitive or proprietary information. In this environment, compliance becomes the anchor that keeps organizations accountable and transparent, making sure data practices remain consistent even as complexity grows.

As a result, a critical challenge for organizations is knowing whether AI is secure or accurate, as well as having a firm handle on how data powers those systems. Being able to stand behind the decisions AI is helping to make means understanding where the data comes from, how models are trained, and what kinds of risks they introduce.

When designing our platform, we could have pursued many paths, some of which would have provided a quicker path to AI-driven insights into relationships, but after discussions with our clients, we chose not to use third-party APIs like ChatGPT so as to keep our customer data within our data centers and bring the LLM to the data instead. This took longer, but was more in line with our data residency and privacy commitments to our clients.

At the end of the day, AI is a powerful enabler, but it’s how we use it that defines its impact. In a business context, the future of AI will be shaped not just by innovation, but by our ability to use it responsibly, transparently and in alignment with our core values.

Practical steps to preserving authenticity and protecting data while using AI

When communication lacks authenticity, or when data isn’t handled with intentionality and care, it places an organization’s relationships and reputation at risk. Preserving authenticity and protecting data aren’t separate objectives; they’re twin priorities for any organization using AI responsibly.

Here are practical steps to ensure your organization upholds both authenticity and data security in its AI strategy:

Set clear guidelines for AI use

Ensure AI is used only for support tasks like data management or client insights, while personalized communication remains human-driven. Define and enforce clear boundaries to prevent over-reliance on automation.

Implement strong data governance

Develop strict policies around how client data is collected, stored, accessed and used by AI systems. Ensure compliance with data privacy regulations and industry standards.

Tailor AI-generated content

Require that AI outputs are edited by humans to add personalization and context before being shared externally, ensuring both the message and the data usage are thoughtful and appropriate.

Use AI for quicker paths to insights and signals, not conversations

Leverage AI for analyzing data and uncovering touchpoints, but always have a person follow up. For example, AI can highlight clients who haven’t been contacted recently, but the outreach should be human-driven.

Create ethical oversight committees

Form a team to review how AI is used in customer interactions and ensure it aligns with company values. This team can make decisions about when and where AI can be ethically deployed.

Maintain a human point of contact

Ensure that clients always know how to reach a human representative quickly, even if AI is being used for initial communications or data handling.

Audit and enhance data security regularly

Invest in security infrastructure to ensure client data is protected from unauthorized access and breaches. Ethical AI begins with secure AI.

As we integrate AI into how we work, the priority isn’t just innovation; it’s making sure that technology strengthens the connections that matter. Ultimately, the real difference between artificial intelligence and information technology is that IT protects your data, while responsible AI protects the trust needed to use it.

Ready to see how Introhive can help you put responsible AI into action? Book a demo and learn how we keep client trust at the center of innovation.