AI in CRE - Trimont Fireside Chat

Setting the stage

When Smart Capital Center’s CEO, Laura Krashakova, joined Trimont’s global team for their quarterly fireside chat, the discussion wasn’t about distant possibilities. It was about how artificial intelligence is already reshaping commercial real estate (CRE) servicing and asset management — and why the firms that adapt now will be the ones still standing five years from today.

Trimont’s Beau Jones, Executive Managing Director of Global Business Development, set the tone by pointing out how client expectations have accelerated. What was once seen as innovation is now merely the baseline.  

Together, Jones and Krashakova explored where AI is driving value, the cultural and operational hurdles to adoption, and how Smart Capital Center is helping firms move from experimentation to embedded transformation.  

Q&A: Trimont and Smart Capital Center

With that context, here are the questions CRE teams are asking—and the answers from Trimont and Smart Capital Center.

Q: What changed about client expectations?

‍Laura Krashakova (Smart Capital Center): “Last year’s miracle is table stakes today. Lenders and equity owners now want AI agents as teammates—monitoring portfolios, flagging risks, and triggering workflows in real time, and we at Smart Capital Center have been heads down building it for our customers.”

Beau Jones (Trimont): “Our Triview portfolio analytics wowed clients five years ago. Today they want continuous updates and instant context—and they expect changes within a week. I used a chatbot recently to generate a full page with code in minutes. It only needed a final 5% human touch.”

Q: Where is AI delivering the most value right now?

‍Krashakova: “Underwriting. Automating data ingestions from documents  into DCFs is baseline; the real step-change is deep research—AI scanning tenant health, local news, financial variances, and market signals to surface risks and assumptions with commentary. Analysts step up to review, validate, and decide.”

Q: What blocks adoption?

‍Krashakova: “Data quality still matters, but the bigger barrier is change management. Build a culture where people think AI first. We run demos, hold office hours, and require engineers to note how AI was (or wasn’t) used on each ticket—so the default question becomes: Can AI help here?”

Q: How do we stay safe and compliant?

‍Krashakova: “Balance enablement with guardrails: limit sensitive data to external systems, keep human-in-the-loop review for ownership, and design workflows to be as deterministic as possible to reduce prompt-injection risk. Newer protocols (like MCP) are promising but should be tested internally before exposure.”

From dialogue to takeaways

From this conversation, several themes emerged that will shape CRE over the next five years. Here are the key takeaways.

Takeaway 1: Expectations have shifted from reports to actions

Five years ago, Trimont’s Triview platform — a client-facing portfolio analytics tool — was seen as groundbreaking. Today, clients want more. They expect systems that not only deliver insights but also monitor portfolios continuously, flag risks, and initiate workflows automatically.

“Last year’s miracle is table stakes today. Lenders and equity owners now want AI agents to function like teammates — always on, constantly watching, and capable of taking the first step toward resolution. Smart Capital Center’s team has been heads down building our Smarty Assistant and underwriting and surveillance agents.” - Laura Krashakova, CEO, Smart Capital Center

Jones confirmed the shift from his vantage point:

“When we first showed Triview, it was a wow factor. Now clients say, ‘I like it — but can it do more, and can you update it next week?’ Expectations are accelerating.”

This new baseline is where Smart Capital Center positions itself: embedding AI directly into workflows so insights are paired with next-step actions, ensuring clients stay ahead of rising demands.

Takeaway 2: AI adoption happens in stages

Both leaders agreed that AI adoption isn’t a single leap — it’s a staged process:

1. Experimentation with generic tools: Firms begin with ChatGPT or copilots, using them to summarize documents or draft emails. Useful, but surface-level.

2. Fine-tuning with context: The next step is layering organizational data into those tools, improving their relevance and uncovering the workflows where AI makes the biggest impact.

3. Deeply embedded, workflow-specific solutions: True transformation comes when AI is customized for underwriting, servicing, or asset management — connected to the right data and designed to act, not just report.

“Most of CRE is still in the first two stages,” Krashakova noted. “But the real gains come at stage three. That’s where firms stop experimenting and start scaling.”

Smart Capital Center has built its platform specifically for that third stage, tailoring solutions for the complexities of CRE underwriting and asset management.

Takeaway 3: Underwriting is the ripest area for change

While AI can add value across the CRE lifecycle, both speakers zeroed in on underwriting as the area of greatest immediate impact.

• Repetitive tasks like data ingestions from documents and data entry are already being automated.

• Deep research is where AI truly shines — scanning tenant health, financial variances, market news, and local signals in seconds, then surfacing commentary no single analyst could match.

• Quality uplift: Analysts move from mechanical work to higher-value review and decision-making.

“It’s not just about saving time. AI can generate better assumptions for models and highlight risks humans might miss. Analysts don’t disappear — they step up to validate, steer, and decide.” - Laura Krashakova, CEO, Smart Capital Center  

Takeaway 4: Culture is as important as technology

Tools alone won’t shift an organization. Adoption depends on culture.

Some firms take a hardline approach — requiring engineers to use AI coding tools or risk termination. Smart Capital Center favors a structured but gradual approach:

• Hosting demos to showcase AI in action.

• Creating forums where teams share how they used AI.

• Requiring developers to explain how AI supported their work — or why they chose not to use it.

“The point is to create a mindset where the first question is always, Can AI help here?” Krashakova explained. “Even if the answer is no that shift in thinking matters.”

Jones reinforced the importance of exposure, sharing his own “aha” moment when a colleague generated a complete web page — with code — through a chatbot:

“I had no idea it could do that. It only needed a last 5% human touch, but it was better than what I could have built myself.”

Moments like these help teams see AI as more than a tool for small tasks — as a partner in higher-value work.

Takeaway 5: Security and accountability can’t be overlooked

Adoption is also slowed by concerns over data security and compliance. Both Trimont and Smart Capital Center emphasized the importance of guardrails:

• Limit sensitive data when using external AI tools.

• Keep humans in the loop to ensure ownership.

• Design deterministic outputs where possible to reduce risks like prompt injection.

• Pilot new technologies internally first before exposing them externally.

“You can’t just say, ‘AI made the mistake,’” Krashakova stressed. “Accountability still belongs to the firm. That’s why we design workflows where review and approval are required. Human judgment remains central.”

This philosophy underpins Smart Capital Center’s platform — ensuring outputs are not only fast and intelligent but also auditable, defensible, and secure.

Takeaway 6: Build vs. Buy is an enterprise dilemma

Trimont raised a question many firms face: should they build AI solutions in-house or adopt third-party tools?

Krashakova noted that while many enterprises start by building, they quickly find the pace of change overwhelming. Bloomberg famously invested millions in a finance-focused large language model, only to be outpaced by public releases of GPT-4.

The emerging pattern is hybrid: companies experiment with internal prototypes to learn but increasingly turn to external partners or co-developers for production-ready, workflow-embedded systems. Time to market is existential — waiting three years to perfect an internal tool could mean falling behind permanently.

Takeaway 7: Everyone will manage AI assistants

Perhaps the most striking prediction was how roles themselves will change.

In the near future, every employee may have one or more AI assistants — an underwriting agent, a portfolio monitor, an insurance review assistant.

That shift redefines work:

• Professionals move from doing tasks to managing and validating outputs.

• Job descriptions blur as assistants extend employees into adjacent responsibilities.

• Small teams can achieve outsized results by leveraging multiple assistants.

“Everyone becomes a manager — not just of people, but of AI assistants. The work shifts from execution to review and direction. Accountability doesn’t go away, but the leverage grows dramatically.” - Laura Krashakova, CEO, Smart Capital Center  

Many industry leaders echo the speed and scale: well-known investors forecast a future where most work is automated and humans manage the 20% that matters most—which aligns with the “pro as AI-manager” model discussed here.

Takeaway 8: The next five years are existential

Both Trimont and Smart Capital Center leaders agreed: the clock is ticking.

The velocity of AI adoption is faster than any prior technology wave — faster than PCs, the internet, or mobile. Within five years, the market will look very different. Firms that embed AI deeply into their workflows will thrive; those that hesitate may not survive.

“AI adoption isn’t just about efficiency. It’s existential. The next five years will define who leads and who exits the market.” - Laura Krashakova, CEO, Smart Capital Center

Turning urgency into action

The Trimont conversation made one thing clear: AI adoption carries both promise and pressure. The playbook is still being written, but several principles are already emerging.

• Move quickly from experimentation to workflow-specific solutions. Generic tools spark curiosity, but competitive advantage comes when AI is embedded in underwriting, servicing, and asset management.

• Build a culture that thinks AI first. Adoption sticks when employees see high-value use cases, share “aha” moments, and treat AI as a default part of the workflow.

• Keep humans in the loop. Security, accountability, and review guardrails ensure firms maintain trust while scaling automation.

• Expect new roles. Every employee will soon manage AI assistants, shifting focus from manual execution to oversight and decision-making.

For Smart Capital Center, the mission is to deliver secure, workflow-embedded AI that elevates CRE professionals and helps firms thrive in an industry on the brink of reinvention.

AI won’t replace enterprise. But enterprises that fail to embrace AI may find themselves replaced.  

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