To Harness GenAI, Law Firms Need Teams Built for Collaboration

Here is what’s new in the AI world.

Your AI Strategy's Missing Piece: The Multi-Disciplinary Team

Generative AI (GenAI) is already driving profound change in your industry, reshaping how legal services are delivered.

For your firm to excel, you must move beyond the hype and adopt a cohesive client-service model built on the strengths of multiple disciplines from across your firm.

A Paradigm Shift in Your Staffing Approach

GenAI is dismantling traditional assumptions about professional roles. The old advice to "think outside the box" no longer applies, because there is no box. There is no manual or single set of instructions to move from today’s practice to a better future.

In this environment, your lawyers cannot fully harness GenAI's power alone. As the Thomson Reuters Future of the Professionals 2025 report states, you must move quickly to establish new roles and rethink old ones to hire the versatile, tech-savvy professionals needed for continued success.

While your firm has long drawn on expertise in finance, marketing, and IT, historically, these professionals were rarely integrated into direct client-service teams. That has to change. The simple but powerful lesson is this: diverse perspectives and expertise produce better results.

You now have the opportunity and the necessity to embed professionals like innovation leaders, data analysts, and technologists directly into your client-service model, moving them from the periphery to the core of client engagement.

Why Multi-Disciplinary Teams Matter for You Now?

Capturing GenAI's potential demands a breadth of expertise. A multi-disciplinary approach balances innovation (what the technology can do) with professional responsibility (what it should do).

For you, this approach reduces risk, accelerates adoption, and ensures tools deliver real value to both your clients and your firm.

From your client's vantage point, this approach is increasingly expected. In-house legal departments are no longer evaluating you solely on legal expertise. They’re asking harder questions that you cannot answer convincingly with lawyers alone:

• How does your firm protect our data when using AI?

• What governance frameworks ensure quality control?

• How do you measure the ROI on your technology investments?

Your clients want to see the team behind the technology. When they understand your AI capabilities are backed by genuine cross-functional expertise, not just vendor promises, trust deepens, and your competitive differentiation becomes real.

What Your Truly Multi-Disciplinary Model Should Look Like

For your model to succeed, you need to integrate the following range of expertise into your client-service teams:

Moving from Pilot to Sustained Practice in Your Firm

Despite the interest, skepticism remains high. Your lawyers may hesitate to trust unfamiliar technology or share control. They want proof of value.

The answer to this skepticism lies in who builds and deploys the tools.

They are the ones capable of producing sustainable, trusted solutions.

However, building these teams is one thing; sustaining them is another. The difference between a flashy pilot and a lasting transformation is integration.

You must ensure your technologists and process experts work alongside your lawyers on real client matters, solving actual problems in real time.

This embedded approach creates natural feedback loops: your lawyers see immediate value, and your technologists understand practical constraints. When this collaboration becomes how work gets done, sustainability follows.

GenAI is a transformative force shaping the next era of legal service. To lead, you must embrace the multi-disciplinary model by bringing together diverse experts as integrated, client-facing teams. Those who choose not to do this risk being left behind.

The destination may not be marked on a map, but your path forward is clear: Innovation in your law firm cannot succeed without collaboration across disciplines.

How Artificial Intelligence Will Change Builders, Brokers, and Beyond

The traditionally tech-slow real estate and construction industry is now rapidly adopting artificial intelligence, driven by clear evidence of its operational and cost-saving benefits.

Major legacy players like CBRE and JLL are launching their own large language models (LLMs) and internal AI tools.

This momentum is reflected in venture capital: since late 2022, PitchBook has tracked 670 new real estate and construction tech companies, with over 37% (249 companies) classified as AI/ML-focused.

These span the entire property value chain from sales and marketing to construction, property management, and investment due diligence.

Four Key Adoption Trends

The article identifies four major GenAI-related trends since 2022:

Practical AI Use Cases in the Industry

Applications are emerging across the property lifecycle:

• Conversational AI for property management and tenant support.

• AI-generated documents like leases, offer letters, and marketing content.

• Market analytics and prediction tools.

• Document retrieval, summarization, and analysis.

• AI-assisted design tools, such as converting floor plans into technical drawings.

Current State: The "Co-Pilot" Phase

Most industry AI applications are currently at the "condition agency" or "co-pilot" stage, where humans and AI work closely together. Examples include AI assistants for knowledge management, compliance checking in construction drawings, and tools that help analysts review investment opportunities.

Humans remain essential for supervision and final decision-making.

The Future: Toward Agentic AI

The next phase will be "agentic AI with supervision," enabling AI-powered deal sourcing and transaction support for faster, more efficient global investing. In the long term, fully autonomous AI could manage entire functions like overseeing building operations or leading the architectural design of new developments.

Success will depend on developing domain-specific LLMs tailored to real estate's unique terminology and processes. The future real estate professional will spend less time on manual desktop analysis and more time supervising AI agents and applying real-world experience where human judgment is critical.

For industry leaders, the imperative is clear: identify these targeted AI opportunities now and act to avoid being left behind.

Nvidia Aims to Democratize AI with Open Source Acquisition and New Models

Nvidia is significantly expanding its open-source AI ecosystem through a strategic acquisition and the launch of a new family of AI models, reinforcing its commitment to open innovation.

Acquisition: Taking Over the Leading Open-Source Workload Manager

Nvidia announced the acquisition of SchedMD, the primary developer behind Slurm, the dominant open-source workload management system used globally in high-performance computing (HPC) and AI clusters.

Originally launched in 2002, Slurm is a critical infrastructure for scheduling and managing complex computing jobs across massive GPU clusters.

Nvidia plans to continue operating Slurm as open-source and vendor-neutral software, while accelerating its development and ensuring compatibility across diverse systems.

The company has collaborated with SchedMD for over a decade and views Slurm as essential infrastructure for scaling generative AI workloads.

New Release: Introducing the "Nemotron 3" Model Family

Simultaneously, Nvidia launched Nemotron 3, a new family of open AI models it describes as the "most efficient family of open models" for building accurate AI agents. The family includes three specialized tiers:

• Nemotron 3 Nano: A small, efficient model for targeted, lightweight tasks.

• Nemotron 3 Super: A model optimized for orchestrating multi-agent AI applications.

• Nemotron 3 Ultra: A high-capability model built for complex reasoning and advanced tasks.

CEO Jensen Huang stated,

Nvidia's Broader Open-Source Push

These moves are part of Nvidia's intensified focus on open-source AI. Last week, the company released Alpamayo-R1, an open reasoning model for autonomous driving research, and expanded resources for its Cosmos family of open-source world models for physical AI.

This strategy reflects a dual bet:

1. On open ecosystems: By strengthening foundational open-source tools like Slurm and releasing open models, Nvidia aims to foster developer adoption and lock in its hardware as the preferred platform.

2. On physical AI: Nvidia is positioning itself as the indispensable supplier of AI software and hardware for the next frontier, robotics, autonomous vehicles, and other embodied AI systems that interact with the physical world.

OpenAI to Educate Journalists: Building Trust or Shaping Narratives?

OpenAI has unveiled a new educational platform, the OpenAI Academy for News Organizations, to equip journalists and publishers with the skills to integrate artificial intelligence into their work.

The digital hub, developed with the American Journalism Project and The Lenfest Institute for Journalism, was announced at the recent AI and Journalism Summit.

Designed specifically for newsrooms, the academy provides practical training, technical guides, and real-world case studies. Core offerings include:

The platform encourages collaboration by offering open-source projects and adaptable resources, allowing news organizations to tailor tools to their needs. It also emphasizes responsible AI use, providing frameworks for internal governance and ethical policies to address concerns around accuracy and transparency.

This initiative builds on OpenAI’s existing partnerships with major media companies such as News Corp, Axel Springer, and The Financial Times.

Future plans include expanding the academy with live programming and additional courses, supporting the long-term adaptation of the news industry to an AI-driven landscape.

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-Shen & Towards AGI team