OPINION: AI adoption is accelerating — user readiness, not so much

The views expressed in this column are solely those of the author.

There is a quiet shift happening within organizations right now.

On one side, leading companies are accelerating efforts to capture employee knowledge and make it available to AI-powered systems. On the other, a growing number of professionals are becoming more cautious about how that knowledge is documented and shared.

This tension is not accidental. It signals that AI is fundamentally reshaping how work is structured.

Some organizations are moving towards leaner operating models in which smaller teams oversee increasingly automated workflows.

For brokerages, that could mean fewer traditional entry points for new talent, compressed development paths and greater pressure on experienced professionals to supervise more activity. The question is not simply how much work AI can automate, but how brokerages will continue to develop the judgment, experience and client-service skills their businesses depend on.

At the same time, many organizations are rushing implementation.

In real estate, we are seeing the rapid rollout of AI-powered sales assistants, marketing tools, voice interfaces, transaction support systems and autonomous agents, often without a cohesive strategy behind them.

That introduces risk in very practical ways:

• Client information flowing through third-party AI tools
• Automated communications being sent without proper validation
• AI agents receiving excessive access to email, calendars, CRM records, documents and other business systems
• Reputational, financial and privacy consequences when systems behave unpredictably

Recent research and software supply-chain incidents have also shown that AI systems can be influenced through the information, tools and infrastructure on which they depend.

This is not theoretical.

In a business built on trust, confidentiality and financial accuracy, small failures can have outsized consequences.

The key insight is simple: AI does not fix broken processes. It amplifies them.

This raises a more important question: what does responsible adoption actually look like in practice?

For brokerages and real estate professionals, the answer is not to slow down innovation. It is to bring structure and discipline to how AI is introduced into the business.

It starts with clarity

Before deploying an AI tool, organizations should define exactly what problem they are solving and where automation adds value.

Not every workflow benefits from AI, and not every task should be delegated.

Mapping core processes, from lead generation to transaction management, allows leaders to identify where human judgment is essential and where AI can safely assist.

Organizations should also distinguish between systems that generate recommendations and systems that can take action.

An AI tool that drafts an email presents one level of risk. An AI agent that can send the email, update a CRM record, retrieve documents or initiate a workflow presents another.

The more authority an AI system receives, the stronger its identity, access, logging and approval controls must be.

Data and vendor governance become non-negotiable

Professionals should know what information is being shared with AI systems, where that data is stored and who ultimately has access to it.

This includes establishing clear policies on what can and cannot be entered into third-party tools, particularly when dealing with client financial information, personal information or confidential transaction details.

Organizations should prioritize services with appropriate administrative controls, defined retention and deletion practices, clear data boundaries and explicit commitments regarding whether customer data is used for model training.

That does not necessarily mean choosing only the largest enterprise providers. Smaller vendors may be more agile, more specialized and closer to the cutting edge of innovation. But they may also have fewer resources devoted to security, privacy, compliance and operational resilience.

The objective is not to favour size over innovation. It is to ensure that the level of due diligence, contractual protection and oversight matches the sensitivity of the data and the authority being given to the system.

Organizations should also understand which vendors, subprocessors and external models can access their information, what permissions connected tools receive and how organizational data can be retrieved or deleted when the relationship ends.

Every integration expands the attack surface. Every connection should have a clear business purpose.

Human validation remains essential

AI-generated content, whether it is a client email, listing description, image or market insight, should never automatically be treated as final output.

Mandatory human review should be built into client-facing, financial, legal, listing and transaction workflows, especially where mistakes could have significant consequences.

This helps ensure that accuracy, tone and compliance are maintained, and that accountability remains with the professional rather than the system.

Organizations should also determine when the use of AI should be disclosed, particularly when it influences advice, communications, images or representations provided to clients.

Training may be the most underestimated lever

Safe AI adoption is not just a technology issue. It is a people issue.

Agents and staff need to understand how to use these tools and how to question them.

This includes recognizing hallucinations, bias and sycophantic responses, identifying suspicious outputs, protecting confidential information and knowing when human judgment must take over.

Employees also need to understand that an AI system can be manipulated through the information it processes, the instructions it receives and the external tools it is permitted to use.

Ongoing training, rather than one-time sessions, is what builds that level of fluency and confidence.

For brokerages, this training also has a broader purpose. It helps ensure that AI supports the development of professional judgment rather than replacing the opportunities through which that judgment is built.

Accountability must be formalized

Clear guidelines should define where AI can operate independently and where human sign-off is required, particularly in financial decisions, legal documentation and client communications.

For agentic systems, organizations should define which applications the agent may access, what actions it may take, how those actions are recorded and when approval is mandatory.

When something goes wrong, there should be no ambiguity about ownership. AI may assist with the work, but it cannot carry professional accountability.

None of these steps are complex on their own. Together, however, they represent a shift from reactive adoption to operational readiness.

Businesses that move deliberately rather than reactively will be better positioned when they:

• Standardize workflows before automating them
• Control where data flows and who has access
• Apply least-privilege access to applications and AI agents
• Keep humans accountable for client, legal and financial decisions
• Limit unnecessary integrations
• Involve teams in redesigning workflows rather than treating knowledge capture as an extraction exercise

AI is a powerful tool that works best when paired with experienced, engaged professionals, not used as a substitute for them.

That puts recruitment, training and talent development back at the centre of the strategy.

Brokerages that recruit adaptable people, develop their skills and keep them meaningfully involved in evolving workflows will be better positioned than those attempting to automate around them.

The goal is not full automation.

It is better execution.

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