Top 5 Jobs in Real Estate That Are Most at Risk from AI in Round Rock - And How to Adapt

Round Rock's real estate market is waking up to a hard truth in 2025: AI is already streamlining the paperwork and analytics that underpin everyday property work, with Morgan Stanley estimating 37% of real estate tasks are automatable and roughly $34 billion in industry efficiency gains by 2030 (Morgan Stanley report on AI in real estate).

Local impacts matter because AI activity concentrates near tech hubs and reshapes demand for assets and services - a dynamic JLL says will alter where and how space is used (JLL analysis of AI implications for real estate).

Tasks like lease administration and repetitive marketing that once took days can be done in minutes, while high-touch “trusted advisor” work remains harder to automate.

For Round Rock professionals, practical reskilling - for example, learning to prompt and manage AI tools - is a fast, realistic hedge; Nucamp's AI Essentials for Work bootcamp offers a targeted pathway to those skills (AI Essentials for Work bootcamp at Nucamp).

“Our recent works suggests that operating efficiencies, primarily through labor cost savings, represent the greatest opportunity for real estate companies to capitalize on AI in the next three to five years,” - Ronald Kamdem, Morgan Stanley.

Methodology: rankings blend three practical signals to reflect Round Rock and wider Texas realities - (1) published automation-risk scores and local labor stats (we pulled calculated risk, wage and growth snapshots from Will Robots Take My Job? pages for roles like property managers and brokers), (2) task-level AI applicability and industry expert observation (Microsoft's generative-AI exposure lists and Ylopo's field notes on back‑office automation guided which repetitive tasks are most vulnerable), and (3) local market exposure where Round Rock/Austin drivers matter (Nucamp's Round Rock use cases for predictive rent forecasting and AI site‑selection show which roles intersect heavily with data and therefore automation).

Jobs that score high on automation metrics, handle repeatable data tasks, and report modest projected growth were ranked higher on the “at risk” list; roles requiring sustained human judgment or high-touch negotiation scored lower.

This hybrid approach keeps the list grounded in numeric risk signals while honoring on-the-ground Texas market dynamics and practical AI use cases.

“Data entry, phone dialers, transaction management, title work, just a lot of the backend processes are really going to streamline.” - Barry Jenkins, Realtor in Residence at Ylopo

Jesse, the transaction coordinator in Round Rock who spends hours chasing missing disclosures and juggling email threads, is among the most exposed to AI because core TC work - contract parsing, checklist generation, deadline tracking and data-room organization - is precisely what today's AI agents automate; firms like Datagrid note that 60–70% of coordinators' time is tied up in manual data tasks that AI can classify, tag, and secure automatically (AI agents for data-room organization and automated contract parsing).

Tools such as Nekst can extract key dates and contact details from a signed contract in under 90 seconds, turning tedious file setup into a quick review task (Nekst AI transaction creation and contract data extraction), while roundups like AgentUp show both efficiency gains and cautionary examples - from faster deadline-tracking to nightmare bugs (an AI that emailed buyers every minute for seven hours) - underscoring why TCs who don't upskill toward AI oversight, quality-control, and client-facing judgment risk being sidelined (AgentUp analysis of AI impacts on transaction coordinators).

"The potential for AI to replace transaction coordinators in real estate is a topic of ongoing discussion, but complete replacement is unlikely in the near future. Real estate transactions involve complex negotiations and human judgment, which AI currently cannot fully replicate. While AI can automate certain tasks, the evolution towards AI replacing transaction coordinators will be gradual as technology advances and industry dynamics evolve."

Maria, the property marketer who builds basic listing pages and runs local Facebook ads, is squarely in the crosshairs of 2025's marketing automation: AI now writes tailored listing copy, spins listing videos, stages interiors, and even nurtures leads automatically, shrinking the time between “new lead” and “showing” from days to minutes.

Patten Title's guide shows how tools like ChatGPT can craft polished listing descriptions and email campaigns in seconds when given precise prompts, while Full Circle's roundup traces AI across smarter searches, predictive analytics and virtual tours that shape buyer demand in Texas markets.

HousingWire's catalog of agent tools - think Ylopo for AI lead nurturing, Virtual Staging AI and Reimaginehome for instant staging, and LazyEditor for turnkey listing videos - means a lot of the entry-level marketing tasks Maria relied on are now low-cost automations; the memorable risk is this: an algorithm can produce a staged-photo, video, SMS follow-up, and a CMA faster than a single afternoon open house.

Upskilling into AI prompt design, creative strategy, or high-touch client advising is the practical hedge for Round Rock marketers who want to stay indispensable.

Carlos, the Round Rock leasing agent who once earned steady commissions by answering calls, showing units, and doing on-the-spot pre‑qualifying, faces growing pressure as low‑touch leasing work becomes a modular stack of automations: AI‑powered chatbots and virtual assistants now handle inquiries, schedule tours and even pre‑qualify prospects, cutting the busiest slice of a leasing day into instant digital interactions (AI-powered chatbots and virtual assistants for multifamily leasing - Draper & Kramer); comprehensive leasing platforms automate screening, rent collection, and lease processing so paperwork that once kept Carlos in the office can be closed online (Leasing automation platforms for tenant screening and rent collection - Leasey.ai).

The result: many prospects tour via 3D walkthroughs or self‑showing apps and never meet an agent until paperwork time, meaning Carlos' “show-up” shifts risk turning into quality‑control and relationship work - skills that local agents must lean into if they want to convert automated leads into long‑term renters.

“Among all AI development requests in real estate, 87% of our clients want to develop AI chatbots to automate communication with stakeholders… Chatbots deliver the most value and ROI.” - Wesley Snow, CEO and Co-founder of Ascendix Technologies

Priya, the appraisal assistant in Round Rock who spends hours pulling comps, entering MLS fields, and assembling valuation reports, is squarely in AI's path because automated valuation models (AVMs) and AI valuation tools can ingest public records, MLS data, photos and market trends and spit out fast, repeatable estimates that once required manual data‑entry and days of analysis: modern AVMs give near‑instant valuations that lenders and investors use for underwriting and portfolio checks (Investopedia: Automated Valuation Model (AVM) overview and how AVMs work), and AI property‑valuation platforms condense a month‑long manual workflow into days by automating data collection, analytics and reporting (AscendixTech: AI property valuation tools and appraisal automation).

That doesn't spell the end for humans - Clear Capital and HouseCanary emphasize that AVMs come with confidence scores and perform best as part of hybrid workflows (lending‑grade vs.

marketing‑grade AVMs), so Priya's practical path is to become the team's AVM auditor: flag low‑confidence cases, validate outliers with targeted inspections, and own hybrid appraisal coordination and geospatial checks.

The memorable takeaway: an algorithm can produce a baseline value in seconds, but human judgment still wins where confidence is low or a house's condition tells a different story - turning data‑entry roles into quality‑control and field‑inspection careers tuned to Round Rock's fast market.

Marcus, the property-management assistant who spends afternoons posting rent payments, chasing late checks, and reconciling owner statements, is squarely in AI's sights because those routine finance and communication tasks are being swept into tenant portals and AP automation stacks: CIP Texas even flags “automated billing and reminders; prompt follow‑ups for late payments” as standard expectations for modern managers (CIP Texas guide to automated billing and payment reminders for property managers in Texas), while AvidXchange's platform playbook shows how accounts‑payable automation and e‑payment integrations (Yardi, Rent Manager, QuickBooks and more) remove manual invoice routing and reconciliation from the daily to‑do list (AvidXchange AP automation and e-payment integrations overview).

Local operators already market tenant portals and electronic rent collection - Bay Property Management Group highlights streamlined rent collection and owner reporting as core services in Texas - so the practical risk for Marcus is losing hours to software unless the role pivots to exception handling, tenant relationship work, and vendor oversight that automation can't fully own (Bay Property Management Group Texas tenant portals and owner reporting services).

The memorable takeaway: when a portal can post a midnight payment and trigger owner disbursements by morning, routine clerical shifts from full‑time work to quality control and people‑first problem solving.

Where opportunity meets automation in Round Rock is practical, local, and immediate: start by auditing which daily tasks (lease admin, basic marketing, rent posting) are routable to software, pilot one small automation, and reserve time to learn how to manage the results - Scale Up Round Rock's hands‑on workshops are a local place to get concrete, actionable steps and experiment with AI tools (Scale Up Round Rock workshop details and registration); for a deeper skills play, Nucamp AI Essentials for Work - 15-week AI Essentials for Work bootcamp (register) teaches prompt design and job‑based AI skills so professionals can move from being automated to overseeing and improving automations (early bird $3,582).

Finally, pair learning with tools that reflect Round Rock realities - predictive rent models for a market where average home price sits near $491,688 and average rent around $2,346, and modern proptech/tenant portals so routine clerical hours become exception handling (a portal that posts a midnight payment and triggers owner disbursements by morning is now real).

That combination - local workshops, targeted reskilling, and selective tech adoption - turns risk into a route for higher‑value work.