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Written By: Adam Dermer, Director, Product Marketing
Executive Summary
The commercial real estate (CRE) industry is moving past the initial hype of Artificial Intelligence. While the first wave of AI adoption brought "point solutions" that speed up isolated tasks like document extraction and deck generation, these fragmented tools fail to create compounding organizational intelligence. The next competitive advantage in CRE capital markets belongs to firms utilizing closed-loop platforms—systems of record like Altrio that seamlessly connect inputs, decisions, execution, and outcomes to turn everyday workflows into durable institutional learning.
What Actually Changed (and What Didn’t)
Baseline reality that hasn’t changed
CRE capital markets workflows are still dominated by three structural constraints:
- Unstructured data remains the norm. Deals still arrive as PDF offering memoranda (OMs), scanned leases, and inconsistent rent rolls and T12s that vary by broker, region, and sector. Even when the information is “there,” it is rarely in a format that is immediately reusable across underwriting, reporting, and marketing.
- The process flow is still ad hoc. Diligence is frequently driven by email threads. Approvals often happen informally. And critical deal context lives in role-specific spreadsheets and private notes. This makes execution dependent on individual discipline rather than consistent operating rhythms.
- Most firms still operate across fragmented systems. CRM sits apart from underwriting. Marketing sits apart from CRM. Data rooms sit apart from both. Teams move information manually between tools, creating duplication, gaps, and version drift.
What materially changed
The major shift is not that “CRE became AI-native.” The shift is that AI adoption arrived as a wave of task-specific products that can dramatically speed up specific jobs, document extraction, underwriting first-pass, deck drafting, outreach drafting, and related “time-to-first-draft” work.
In many firms, these tools create faster first drafts, but they do not reliably create two things that determine whether advantage compounds:
- A durable institutional record of decisions
- A feedback loop from outcomes back into future workflows
As a result, the dominant benefit is often local speed, not compounding organizational intelligence.
Ultimately, the next wave of advantage accrues to firms that can connect:
inputs → decisions → execution → outcomes inside a single system of record (a “closed loop”), not just bolt AI onto individual steps.
Core Definitions: Understanding the CRE Tech Landscape
To understand the current market, it is vital to define the architecture of these tools.
A. “Point solution”
A point solution is a tool that primarily optimizes one job or one stage (e.g., extraction, underwriting, deck creation, outreach) and then exports the output elsewhere. These tools can be extremely effective within their lane, but they typically do not own the lifecycle.
Examples by category include:
- Deck/OM generation: Henry, Crellus, OMGPT
- Underwriting acceleration: Cactus, Archer, IntellCRE
- Document extraction / structuring: Clik.ai, Proda.ai, Fifth Dimension
B. “Closed loop”
A closed-loop platform is one like Altrio, unifys the lifecycle, where:
- Deals are first-class records (with documents + structured fields + stage + tasks + decisions)
- Counterparties/CRM are linked to deals (with investment requirements, interactions, engagement, outcomes)
- Workflow state and outcome events are recorded in the same system
Result: The practical implication is that each deal naturally produces structured data that improves the next deal.
C. “System of record”
A system of record is where the firm expects to find the authoritative version of:
- The current truth of deal status
- The latest version of assumptions and metrics
- The record of who was contacted, who engaged, and what happened
When these answers require reconstruction from email threads and spreadsheets, the firm does not have a true system of record.
What the Market Built: AI as Point Solutions
A. The standard adoption pattern
Across firms, the most common AI adoption path has been consistent:
- Identify a repetitive bottleneck
- Apply AI to compress time-to-output
- Drop the tool into existing workflows (email + Excel + CRM) rather than replacing the lifecycle
This approach is rational. It produces fast ROI. It avoids major change management. It also explains why the market looks the way it does.
B. Why this pattern dominates
It is easier to build and adopt a point solution than to replace the end-to-end lifecycle components that define how deals actually get executed, such as:
- Deal tracking
- Approvals
- CRM
- Reporting
- Engagement and outcomes capture
C. Practical map of point solutions
As a result, the market now contains clusters of AI tools focused on:
- Deal narrative and presentation generation
- Underwriting and financial ingestion
- Transaction workflows / marketplaces
- Listing and marketing content generation
- Capital targeting and deal packaging
- Debt-side intelligence / broker automation
- Data integration / normalization layers
- Relationship intelligence and CRM copilots
- Investor relations workflows
D. Shared takeaway
Most current CRE AI tools win by compressing time from raw inputs to usable artifacts: a model draft, deal summary, deck, buyer list, or lender package. That is real value but it is just not the same thing as compounding advantage.
Where Point Solutions Predictably Fall Short
These limitations are mostly architectural, not “AI quality problems.” Even strong point solutions tend to run into the same friction once the output has to move into real execution.
1) They optimize a step, not the lifecycle
What this means: the tool outputs something useful, but the output is not automatically:
- tied to the authoritative deal record
- reflected in the pipeline stage
- linked to the workflow tasks and approvals
- connected to downstream execution (sharing, tracking, outcomes)
Examples:
- A deck generator can produce a draft OM quickly, but the artifact often ends up emailed as a PDF, edited outside the system, and versioned in multiple places. The system that tracks the deal may not know what changed, which version is current, or which claims were approved.
- An underwriting accelerator can create a first-pass model quickly, but it typically does not capture partner feedback and outcomes tied to those assumptions, or place the model in the context of other deals the firm has evaluated.
Ultimately, speed gains are real, but continuity across stages usually requires manual handoffs.
2) They don’t capture outcomes as reusable data
What this means: the “what happened” signals—engagement, pass reasons, conversion—are not captured in a structured way that:
- updates counterparty profiles
- improves targeting
- improves future evaluation decisions
Example:
Data rooms often provide “activity analytics” (views, downloads), but those signals commonly remain siloed unless the firm has a disciplined process for writing them back into CRM. Outreach tools can show opens and clicks, but they typically do not connect engagement to deal stage advancement, bidding behavior, or pass reasons and requirements updates.
Without structured outcomes capture, firms repeat work—rebuilding target lists, re-arguing positioning, and re-learning which buyers convert for which strategies.
3) They push governance back onto humans
What this means: generative AI can draft compelling copy, but “claims hygiene” is still a human responsibility unless the system can tie claims to verified data.
Example:
Listing and marketing copy assistants can draft text quickly, but they rarely guarantee that stated rents match the rent roll, stated cap rates match the model, or market claims are sourced from traceable datasets. Some tools emphasize citations and provenance, but the broader market is still inconsistent on “ground every claim.”
In most stacks today, governance happens via manual review, spreadsheets, and email approvals—not via auditable, structured control loops.
4) They increase tool sprawl
What this means: every wedge adds another system, another workflow, another permissions model.
Example:
Underwriting numbers update due to an NOI adjustment or revised guidance. The model is updated in one place, the deck may still show old numbers, the data room contains multiple files, and buyer emails cite stale metrics. Teams then spend time reconciling versions instead of executing.
Tool sprawl doesn’t negate value, but it creates operational drag that limits compounding benefit.
The Structural Answer: Closed-Loop Platforms
Closed-loop platforms win because they connect the pieces that are usually fragmented. When deals and assets, workflow state, CRM activity, and outcomes all live in one model, work stops being a series of disconnected outputs and starts becoming a durable operating record. That’s what allows learning to compound: each deal produces structured signals that make the next deal faster, cleaner, and more defensible.
This structure also fixes the common breakdowns caused by point solutions.
- Outputs stay anchored to a single deal record, which reduces version drift.
- Engagement becomes a CRM signal by default, not an “analytics report” someone has to interpret and re-enter.
- Outcomes and pass reasons are captured in a reusable way, so targeting improves over time.
- And because execution events are recorded inside the same system, teams can analyze performance without manually reconstructing what happened from emails, spreadsheets, and scattered files.
Closed Loop in Practice
Here’s what it looks like in practice when the full deal lifecycle runs inside one platform:
A. Intake and extraction (opportunity becomes a structured deal)
Inbound files (OMs, rent rolls, T12s, leases) are ingested into a deal record. Extraction doesn’t just produce a table—it populates key deal fields, initial assumptions, and linked documents with source references. For example, rent roll ingestion populates unit mix, current rents, while a T12 maps line items into standardized revenue and expense categories.
B. Pipeline and workflow (deal moves through stages with owners and approvals)
Deal stages are explicit (screen → underwrite → IC prep → diligence → capital raising → close). Tasks and approvals are tied to stage progression. For example, IC cannot be scheduled until required fields and documents are present, and underwriting signoff is recorded before external sharing.
C. CRM integrated into dealflow (counterparties are not separate from deals)
Buyers and lenders are linked to deal records. Investment requirements live on counterparties and are used for targeting. For example, a buyer’s criteria (asset type, market, size, risk profile) directly informs who receives which deals.
D. Distribution + marketing (a downstream step, anchored in the same record)
When deals are shared externally, materials reflect the same structured data used in underwriting. Confidentiality gating, data room access, and distribution happen against the deal record. For example, a deal is shared to a targeted list, and NDAs/CAs are managed and tracked as part of the deal process.
E. Engagement and outcomes (execution produces learning)
Engagement events are recorded and attributed—who viewed what, who signed, who asked questions, who advanced to bids/IOIs/LOIs. Pass reasons and feedback update counterparty profiles. For example, if a lender passes due to DSCR constraints, that reason updates future lender matching for similar deals.
In closed-loop execution, learning is a byproduct of doing the work, not a separate manual process.
Why This Matters Now
Point solutions will keep getting better—and there will be more of them. Distribution costs are low, foundation models continue to improve, and CRE still has dozens of repetitive workflows that are expensive to do manually. As long as deals arrive through documents and fragmented communication, the market will keep producing tools that compress specific tasks into faster outputs.
But the competitive frontier is shifting.
The question is moving from:
Who can generate an artifact fastest? → Who can make each deal improve the next deal?
Speed matters, but speed alone is not a durable advantage. Durable advantage comes from turning execution into learning. That is why the real differentiator is the events and outcomes generated as a deal moves through evaluation, approval, distribution, and close.
Firms that capture those signals in a consistent, reusable way can tighten internal practices, reduce repeated mistakes, improve targeting and conversion over time, and strengthen governance and defensibility. In other words, they don’t just run deals faster—they build a system that gets smarter with every cycle.
Directionally: How Altrio will Add AI
It’s easy to take the wrong lesson from the recent wave of AI adoption. The goal isn’t to bolt a generic chatbot onto existing workflows, and it isn’t to make flashy claims about predicting rents or cap rates with false precision. Those approaches may demo well, but they don’t address the structural limitations that have kept AI from compounding inside real CRE execution.
The more durable path is to embed AI where closed-loop structure provides leverage—where the system already has a master deal record, clear workflow state, counterparty context, and outcomes history. Practically, that means prioritizing AI in areas like:
- Better extraction and structuring at intake (with review controls): reduce manual cleanup and ensure deal data enters the system in a consistent, reusable form.
- Automated deal summaries and first-pass highlights grounded in verified data: generate narratives from structured fields and source documents, not free-form drafting.
- Workflow-aware assistance that reduces dropped handoffs: suggest next actions based on stage, missing requirements, and approvals in flight.
- Feedback-aware targeting based on outcomes, not guesswork: use engagement, pass reasons, and conversion history to refine who to send deals to and why.
- Governance features that increase provenance and defensibility over time: preserve what changed, what was approved, and what each claim is based on as part of the execution record.
This approach keeps the focus on production value: AI that makes teams faster, but also makes the organization more consistent, more auditable, and smarter with each cycle.
Bottom Line
The industry has been upgraded by AI point solutions that accelerate discrete jobs. But point solutions often stop short of compounding intelligence because they sit outside the lifecycle record. Closed-loop platforms create advantage because they unify deal data, workflow, counterparties, and outcomes—turning execution into reusable institutional learning.
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