Professional service firms are waking up to the fact that the “AI hype” may, in fact, be the “AI transformation” they’ve been waiting for.
If you’re here, you’re likely asking: “Where can AI really make a difference in my day-to-day work, without compromising quality or trust?”
We understand that when your service business is built on deep expertise, judgment calls, and tight deadlines, the answer can make or break your operations.
That’s why, in this blog post, we’ll show you concrete use cases of AI in professional services industry, from consulting analysis to legal research, financial auditing, and client delivery. You’ll also see how firms are getting it right (and where they’re stumbling)…plus the nuts-and-bolts of adopting AI with a Converged AI Workspace like .
What Is AI in Professional Services?
AI in professional services refers to the tools and systems—frequently including machine learning, natural language processing, large-language models, and automation pipelines—that help with knowledge-intensive tasks, such as research, document generation, risk analysis, summarization, and decision support.
Why does AI matter in professional services?
Because professional services firms live or die by precision, credibility, and efficiency.
Clients expect fast, correct, and thoughtful work. Rework is costly. It drains time and monetary resources, including time spent on research, manual drafting, compliance, and audits.
Rather than replacing professionals’ judgment or expertise in professional service delivery, artificial intelligence amplifies them. It speeds up the routine work, surfacing valuable insights faster and reducing the margin of error.
👀 Did You Know? People confirm having reallocated the time saved using AI to higher-level client work (42% of respondents in an Intapp survey) and strategic planning (33% of respondents).
Benefits of AI in Professional Services
The most compelling advantages that make AI worth investigating for any professional services include:
- Time saved on repetitive or manual work: Having AI perform mundane tasks such as automating document drafts, summarizing large reports, and pulling together research frees people from tedious data entry or formatting so they can focus on deeper, more complex analytical and strategic work
- Improved quality and consistency: Using AI often results in fewer errors, more standardized outputs, and more consistent knowledge-base usage across teams in the legal, accounting, and consulting fields, with 82% of people admitting that AI-generated work is at least as good as their own
- Scalability and efficiency: Professional service firms using AI report being able to scale up work volume without linearly driving headcount and see faster turnarounds for proposals, audits, legal briefs, etc.
- Better client outcomes & competitiveness: AI not only unlocks aster insights and more timely advice for servicing clients but also the ability to offer services in new ways, potentially lowering costs or risk for clients.
📌 Example: A law firm uses AI to analyze thousands of past contracts in minutes to flag unusual clauses or compliance risks. Instead of a junior lawyer manually reviewing each contract for hours, the firm can quickly provide clients with detailed risk assessments and recommendations. This not only speeds up delivery (timely advice) but also reduces the chance of costly errors (lower risk) and allows the firm to offer subscription-based contract review services at a lower price point (new ways of serving clients)
- Talent attraction, satisfaction, and retention: Top-tier professionals prefer working where tools make their lives easier, allow more interesting work, and reduce burnout from dull tasks. This is where firms investing in AI can differentiate themselves
The writing on the wall is clear: For service-centric companies, AI’s benefits extend to nearly all business areas. Those who embrace it early stand to gain a real competitive edge. 💪🏼
📮 Insight: 88% of our survey respondents use AI tools for personal tasks every day, and 55% use them several times a day.
What about AI at work? With a centralized AI powering all aspects of your project management, knowledge management, and collaboration, you can save up to 3+ hours each week, which you’d otherwise spend searching for information, just like 60.2% of users!
Key Use Cases of AI in Professional Services
AI’s real power becomes obvious when you see where it’s applied. Below are five domains where AI is redefining professional services.
For each domain, we’ll show you how you, too, can make the most of it—with practical tips and tools you can start using right away.
AI in Consulting
Traditionally, consultants would spend weeks stitching data, building slide decks, doing competitor scans, drafting multiple scenarios, and then handing the analysis and recommendations to clients.
AI consulting tools are reducing that time to mere days, helping consultants quickly draw insights from convoluted data.
📌 For example, McKinsey’s internal chatbot “Lilli”, simplifies access to over 100 years of institutional knowledge for over 70% of the employees who use it.
Strategy firms can use AI to prototype a business model in minutes, turning a raw client brief into multiple alternatives, each with revenue projections. The consultant can then refine the best fit. That gives the client confidence and frees the senior team to focus on framing, risks, and custom value.
📌 At BCG, for example, employees have created over 3,000 GPTs (Generative Pre-trained Transformers), which address tasks ranging from document summarization and proposal writing to slide creation, scenario modeling, and internal knowledge retrieval.
Thanks to AI usage for straightforward tasks, new hire productivity at BCG jumped by 30-40% while experienced consultants also saw a 20-30% increase. One major caveat? For complex tasks, productivity sometimes fell due to the need for debugging AI output.
You, too, can replicate these benefits using , the world’s first Converged AI Workspace, bringing together all work apps, data, and workflows. eliminates all forms of Work Sprawl to provide 100% context and a single place for humans and agents to work, together.
🦄 How to use AI for consulting
Brain, the world’s most complete and Contextual AI assistant, links your internal knowledge base, past decks, industry databases, Slack threads, and other troves of organizational information in your Workspace together.
With one prompt, you can query all of this data together to deliver a consolidated answer or insight (“What are the top risks and blockers flagged this sprint?”).
Want to move from insights to action?
Brain can also create actionable Tasks based on its own recommendations or your explicit commands. Try asking Brain to generate a slide deck outline from the insights it surfaces. Then prompt it to add talking points, and embellish the details in a Doc that gets prefilled with this data. You and your team can co-edit in real time or even expand and summarize sections with AI built into Docs.
If you’re having a client kickoff Chat discussion, you can also transform any message from the thread into a Task using AI with just one click and assign it to the right person.
Meanwhile, ’s Autopilot Agents autonomously keep the project moving. They learn from your projects and can perform complex, context-aware actions: creating subtasks based on project phases, assigning tasks intelligently based on workload, summarizing updates, or suggesting next steps without you explicitly programming each rule.
AI in Legal Services
Research, document drafting, precedent matching, contract review, and compliance checks are major friction points in law firms and legal departments.
Your AI-powered legal tech stack can preprocess due diligence by flagging anomalies, potential liabilities, and deviations. The human team can do the detailed review, but because the AI handles the “first pass,” lawyers have more time for strategy and framing the client communications.
No wonder that the legal sector’s AI adoption is accelerating: In corporate legal teams, 38% already use AI tools, and another 50% are actively exploring them. Also, among lawyers using AI, 45% use it daily, and 40% weekly.
🦄 How to use AI for Legal Services
When every case starts with a mountain of docs, it makes sense to keep them all together in a unified workspace, such as one in . Brain then has access to everything it needs to learn from—your key case documents, prior rulings, internal memos, client briefs, and more.
Now, a partner may input: “Summarize depositions in Case A, highlighting discrepancies with the client’s version.” Brain delivers a draft summary and identifies anomalies for human review.
When your legal team finishes a summary, use Custom Autopilot Agents (no-code) to enforce review workflows: e.g., when a draft contract moves to “Ready for Review”, the agent notifies the review partner or triggers a checklist.
Meanwhile, during case meetings, ’s AI Meeting Notetaker captures discussions into neatly labelled transcripts and auto-generates action items like “File motion on clause X by 5/20.” You can ask Brain to convert these into Tasks with AI Autofill Task Properties (due dates, tags, stakeholders) automatically.
In your Dashboards, AI Cards summarize how many documents remain, what sections need human review, or the details of the legal risks flagged.
💡 Pro Tip: Use Write with AI in Docs to draft initial contract templates, memos, or summaries of case research without starting from a blank page.
🗣️ Don’t want to type? You can also dictate content using Talk to Text in Brain MAX—your desktop AI companion. Watch this video to learn how!
🧠 Fun Fact: In litigation, 100% of respondents to a Kaplan survey agreed document analysis is the most impactful AI use, followed by transcript management (90%), chronology (87%), and case strategy (77%).
AI in Finance & Accounting
🦄 How to use AI for Finance & Accounting
Does your shared services finance and accounting firm support multiple business units? If so, chances are you wrestle with journal reconciliations, variance explanations, anomaly detection, and reporting every month.
Start by bringing all unit financials, spreadsheets, General ledger feeds, and commentary into .
You can then query Brain to flag anomalous journal entries during month-end close. The AI would surface “outliers”, accelerating reviews and preventing material misstatements. You can also use AI Cards on Dashboards to monitor real-time anomalies or KPI deviations across multiple accounting projects and highlight patterns or recurring themes.
💡 Pro Tip: Use Recurring Tasks in to schedule periodic checks, such as monthly variance analyses or reconciliations, so nothing slips through the cracks.
Combine this with Autopilot Agents to automatically create follow-up subtasks when anomalies are detected—like investigating unusual journal entries or requesting clarification from a business unit. This keeps your team focused on high-value work while AI handles routine monitoring, pattern detection, and task orchestration across all accounting projects.
🤝 Friendly Reminder: You don’t need separate AI dashboard tools, anomaly detectors, or scheduling software. All you need is one workspace with Brain, Agents, AI Cards, and integrated logic. This approach keeps context centralized for all your projects, minimizing AI Sprawl and its associated costs.
AI in Marketing Agencies
When you need campaign planning, market research, creative testing, and reporting on the fly, AI comes to the rescue. Agencies that value agility can’t afford to move forward without aggressively deploying AI.
Marketing agencies can use AI to fast-track and optimize every stage of the workflow—from creative brainstorming and ideation to actual content creation, A/B testing experiments, and campaign refinements using AI-generated variants.
They can even automate reporting, pulling in performance numbers and generating client-friendly dashboards overnight, rather than manually drilling into hundreds of rows of spreadsheet data.
AI also helps agencies become proactive rather than reactive. They can use predictive analytics to segment audiences better, forecast customer behavior, and decide which channels and messaging to prioritize in upcoming campaigns, cutting wasted spending.
🦄 How to use AI for Marketing Agencies
- Tap into Brain’s generative AI superpowers to create campaign briefs, content drafts, taglines, ad copy, and even image assets from just an idea or prompt
- Use Autopilot Agents to sequence campaign workflows: e.g., when a piece of content is submitted, a Content Review Agent does the first round of reviews against established playbooks and sends it back or moves it forward as applicable
- Ask Brain to cluster past campaign performance data and suggest lessons or patterns (“What content formats historically performed best for this segment?”)
- Set up AI Cards to display campaign-level performance summaries
- Try Chat-based task creation: When someone drops a content idea in Chat, a “Create task with AI” option lets you capture it instantly
AI lets the agency scale creative experimentation without exploding headcount, while maintaining control over direction.
💡Pro Tip: Here’s how our teams at plan their marketing campaigns end-to-end. Hear it from Mike, one of our Strategic Solutions Engineers.👇🏼
AI in Client Service Delivery & Operations
Picture a professional services firm whose client support team spends half the day updating clients on status, chasing internal handoffs, and reconciling ticket logs.
Many firms see the biggest win from using AI in customer service. You’ve got automated triage, AI teammates that answer repetitive client queries, internal handoffs that take place on their own, plus consolidated reporting and status updates.
📌 Walmart, for example, has launched “Sparky,” an AI-powered shopping assistant designed to act as a personalized digital companion for customers. Sparky will offer features such as product discovery, personalized recommendations, cart management, order tracking, and reordering frequently purchased items. This initiative is part of Walmart’s broader strategy to enhance the shopping experience through agentic AI technologies.
🦄 How to use AI for Client Service Delivery and Operations
- Use the Auto-Answers Autopilot Agent within Chat channels to respond to routine client FAQs (or internal ops queries) using your project knowledge base
- Build Custom Agents to monitor and triage incoming client requests (via Chat or email). Agents can autonomously classify/reroute routine ones using predefined criteria
- Ask Brain to answer status questions across project tasks (“Where is the compliance module in Project A?”)
- Use AI Autofill and AI Fields to keep status tags, SLAs, and next steps updated automatically
- Get operational overviews with AI Cards that summarize open tickets, escalations, and bottlenecks on your Dashboards
- Use the AI Meeting Notetaker in client or internal ops meetings to capture action items, decisions, and follow-ups with context
- Turn any client request or conversation directly from Chat into a task using AI, with the context, due date, and owner assigned
- Build custom, no-code Automations using natural language commands in Brain: e.g., “When task progress > 80% and no comment is added in 3 days, assign owner to check in”
In service delivery and ops, the main role of AI lies in reducing friction and making the client feel responsive without overloading the team.
🧠 Fun Fact: 58% of business functions are likely to have AI agents handling at least one process or sub-process daily between 2025 and 2028.
Challenges of AI Adoption in Professional Services
Here are the major challenges professional services organizations typically face when adopting AI, and what often trips people up:
- Skill & literacy gaps: Many firms lack staff who know how to prompt, refine, or govern AI tools properly. In a 2025 survey, nearly 58% of L&D leaders said skill gaps are their biggest barrier to adopting AI
Integration into existing workflows: AI applied outside established processes tends to generate friction. According to BCG’s ‘GenAI in Professional Services’ report, only ~38% of respondents currently use specialized GenAI tools; many cite difficulty aligning AI with their processes - Output quality & consistency: AI tools often produce content that requires heavy human refining—especially in high-stakes client deliverables (legal, finance, consulting). General tools are still seen as lower fidelity vs. domain-specific ones
- Data security, confidentiality & AI governance: In professional services, customer data is often sensitive. Ensuring privacy, compliance, and avoiding data leaks is a big concern. Surveys show data privacy/confidentiality among the top barriers for AI adoption in legal, tax, and accounting sectors
- Change resistance and change management: Professionals are used to certain ways of working. There’s often internal resistance—fear of loss of control, worries about correctness and trust. Plus, the cost (time, effort) to retrain and revamp processes may make AI adoption tougher
All of these challenges are real, but none are insurmountable—with the right practices.
Best Practices for Leveraging AI in Professional Services
Let’s look at the concrete practices that help different professional services sectors navigate those challenges above and unlock real value:
Start with high-impact, small-scale use cases
To start with, pare usage down to 1-2 use cases as you evaluate AI applications These should be areas where ROI is more certain (e.g., status updates, content drafting, research). Doing too many AI-assisted pilots at once dilutes attention and impedes consistency.
Invest in team training & AI literacy
Train employees not just on tool usage but also on prompt engineering, critically evaluating AI output, and ethics and governance. Make learning an ongoing process rather than a one-time activity. This helps reduce resistance to AI adoption and improves the quality of outcomes you’re expecting from the technology.
Embed AI into existing processes rather than replacing them
Don’t bolt AI on as an afterthought. Map current workflows, identify your biggest bottlenecks, and then design automation/augmenting steps. This prevents chaos and ensures you’re able to build trust gradually.
Define clear governance, security & quality controls
Set policies for what data can/cannot be used in AI, especially when client data is involved. Have review checks in place, enforce version control, and allow independent audits where possible.
👀 Did You Know? AI prioritizes your data privacy 🔐
When using AI features like Brain, Brain MAX, and Autopilot Agents, your data is handled with the utmost care:
- No data retention: ’s AI partners are bound by zero data retention agreements. They do not store or use your Workspace data for training purposes
- Role-based access control: AI respects your Workspace’s permissions and roles, ensuring that users only access data they’re authorized to view
- Compliance with global standards: complies with major data protection regulations, including GDPR, CCPA, and HIPAA (for Enterprise customers with a BAA)
Measure the right metrics from day one
Don’t only track the hours AI saves you; also measure error rates, client satisfaction, turnaround time, and adoption rates to gauge AI’s true effectiveness. Regularly revisit metrics so you can tell what’s working and what isn’t.
Use integrated tools to avoid “tool sprawl”
Using many disconnected AI tools may seem flexible, but it causes context loss. You also have to deal with multiple security risks. Centralizing workflows in a Converged AI Workspace like helps maintain consistency, control, clarity, and reduces switching costs.
Lead change through leadership & communication
Leaders need to set expectations, show examples of early wins, encourage feedback, and make it safe to experiment. Transparent communication around AI adoption and performance helps build trust and reduces fear.
AI
Think of AI as your full workflow backbone—starting from brief to delivery—all within one platform, minimizing handoffs and maximizing context.
According to the Forrester Total Economic Impact™ (TEI) Study, organizations using achieve a 384% ROI over three years and save 92,400 productive hours through AI + automation, with a break-even point in under six months.
Here’s how a sample workflow might look using AI end-to-end:
- Project brief & kickoff
You get a new engagement—say, a consulting project. You create a dedicated project Space in . Using Write with AI in Docs, you generate the first version of the project brief from a prompt (client goals, scope, deadlines). Brain/Brain MAX helps pull in relevant prior briefs or internal knowledge, so you don’t start from zero
- Task creation & planning
From that brief, Brain can auto-generate tasks/subtasks—research, stakeholder interviews, deliverables—and AI Autofill Task Properties can add the context (owners/assignees, due dates, tags, priorities, dependencies, and more). Use ’s AI-powered Calendar to schedule milestones, plan work, and align deliverables. It even suggests the best times for client meetings and schedules them for you using plain English commands.
- Collaboration & updates
As teams work, their Chat + Docs + Comments are all in one place inside —no tab hopping required between Slack, Google Docs, and limited professional service project management platforms. Firms can use the AI Meeting Notetaker during kickoff or progress calls to capture action items and decisions. Use Brain to ask questions like “What deliverables are blocked?”, “Which tasks haven’t had updates this week?”, pulling up-to-date insights from across your Chats, Tasks, and Docs in . - Reporting & dashboards
Use AI Cards in Dashboards to surface status summaries, flagged risks, overdue items, and upcoming milestones. Use Doc Summaries / Enterprise AI Search to compile suggestions or review past projects. - Review & delivery
Before client delivery, use Brain/Write with AI to prepare deliverables (templates, slides, reports), then use Agents or task automations to schedule peer reviews, legal checks, etc. Finally, package the deliverables, and attach all context (research docs, meeting notes) within the relevant Tasks so the client sees a coherent, polished output.
Everything stays inside one Converged AI Workspace, which reduces context switching, handoffs, and potential info loss—so your team focuses more on strategy, less on logistics. The Forrester TEI report also notes that consolidating work into (versus juggling several apps) drove much of the productivity gains.
Industry-specific AI tools
While AI is the perfect, industry-agnostic choice for bringing AI to your workflows, sometimes you may need specialized tools to do the job.
Here are some such tools, with their key use cases and features mapped out for you:
| Vertical | Tool | What it does / suitable for | Top features / Use cases |
| Legal | Harvey AI | An AI-platform for legal teams/in-house counsel, offers LLMs trained on legal content, helps with contract drafting, risk scoring, precedent retrieval. | • Drafting/redlining contracts faster • Surfacing relevant case law/precedent based on prompts • Risk scoring clauses or terms to highlight exposure • Faster responses to common legal questions |
| SpotDraft | Contract Lifecycle Management (CLM) tool aimed at automating contract generation, reviewing, and workflow for legal teams. Especially strong in metadata extraction and compliance. | • Automated contract generation and templating • Lifecycle tracking (versioning, approvals) • Extracting metadata (dates, obligations, parties) automatically • Alerts/reminders on renewals or obligations |
|
| Consulting / Marketing Agencies | Otterly.ai | For marketing/SEO teams, monitors how brand/product content gets surfaced in AI/LLM responses and helps adjust for AI search visibility. Useful when agencies are optimizing content for visibility in AI-driven search / conversational interfaces. | • Monitor brand presence in LLM/AI responses • Alerts when content is misrepresented or missing • Insights into which content types are working versus being ignored • SEO and content strategy optimizations specific to AI search contexts |
| Adobe generative AI + Adobe Firefly / Creative Cloud tools | Helps generate visuals, design variants, speed up creative iteration. Reduces time between ideation and production. Useful for agencies with high creative demands. | • Generate design variants from prompts • Rapid iteration of creative options • Content repurposing (e.g. adapt one visual into social, display etc.) • Collaboration tools + review workflows embedded |
|
| Finance & Accounting | UiPath Document Understanding (or similar IDP tools) | Automates data extraction from invoices, receipts, expense reports; classification, validation, exception handling. Very useful in shared services or finance ops. | • OCR/IDP for unstructured financial documents • Exception workflows for mismatched data • High accuracy extraction to reduce manual data entry • Scalable throughput (large volume processing) |
| Hebbia | Knowledge retrieval tool focused on financial & legal research, allows for search over documents, with higher accuracy using embeddings, etc. Useful for consultants needing fast, precise insights. | • Document search over large corporate knowledge • Embeddings/similarity retrieval of relevant content • Faster research, snippet extraction for reports • Integration with existing data sources for research continuity |
Examples of AI in Action Across Industries
In this blog post, we’ve already seen so many ways in which AI is making life simpler for service professionals.
If you’re still unsure about piloting AI for yourself, maybe these examples will help you cement the decision:
| Org / Case | The problem | The AI solutions | The impact |
| Omega Healthcare Management Services (Healthcare / Revenue Cycle) | Employees manually processed insurance claims, medical documents, correspondence; lots of manual data entry, slow turnarounds. | They deployed UiPath’s AI-powered Document Understanding tools to automatically extract relevant data from medical documents, classify items, and route them for approval. Thus, much of their repetitive documentation work was automated. | Saved 15,000+ hours per month; document processing time down ~40%; turnaround time slashed ~50%; accuracy ~99.5%; delivered ~30% ROI for clients |
| A&O Shearman + Harvey | Senior lawyers spent time doing low‐billing but high effort tasks: drafting info requests for regulatory filings, analyzing financial data across many jurisdictions, checking for missing data manually. | They built a joint tool with Harvey to automate those tasks: identify needed filings, generate data requests, flag missing info; reduce manual effort. | The tool is expected to lower costs, especially in senior associate/partner hours and improve margins on these traditionally tedious tasks. |
| ATB Financial | Various internal and external stakeholder workflows, ad hoc information requests, routine tasks scattered across systems; slow research and report generation. | Deployed Google Workspace + Gemini AI to automate routine tasks, enable agent-based tools for research and document generation, letting employees concentrate on higher-value tasks. | More efficient collaboration; faster decision cycles; better access to accurate info earlier in the process. |
Each of these examples moves firms from “AI as experiment” to “AI as workflow.” The pattern is the same: remove busywork, keep humans in the loop where judgment matters, and, finally, orchestrate agents and automations with clear governance so outputs are auditable and defensible.
The Future of AI in Professional Services
The next several years will be about scaling AI from employee experiments into dependable, governed workflows that change how services are created and priced. The trendlines and expert signals below show why.
- Widening adoption, fast growth: McKinsey’s 2025 State of AI shows 78% of respondents now use AI in at least one business function (up from 72% in early 2024 and 55% a year earlier), with service operations among the fastest-growing use areas. That’s a clear signal that professional-services workflows are prime for AI integration
- Investment momentum: McKinsey also reports 92% of companies plan to increase AI investments, so vendors and integrators will keep building specialized tools and agent orchestration platforms. Expect more firms to move beyond pilots to production
- Integration and economic gain: Consolidation pays. Forrester’s TEI for found massive value when organizations ditched tool sprawl in favor of a Converged AI Workspace—measured as 384% ROI and tens of thousands of hours saved—showing the economic case for integrated platforms rather than dozens of disconnected AI apps
- Agentic AI & orchestration: The next wave in AI adoption will be agentic AI. PwC, Deloitte, EY, and others are building “agent OS” or agent frameworks to orchestrate multiple agents into enterprise workflows. Expect this to accelerate complex multi-step automation across the consulting, legal, audit, and finance sectors
- Risk & regulation scrutiny: Regulators and standards bodies are catching up. Recent reviews show auditors and big firms are using AI more, but often aren’t sufficiently tracking how these tools affect audit quality—so governance, KPIs, and auditability will be mandatory, not optional. Firms must prove AI improves outcomes and doesn’t introduce unknown risks
In a nutshell:
- AI will move from “assistant” to “workflow partner.” More firms will stitch LLMs, extraction engines and agents into end-to-end flows (research → draft → review → delivery) with humans gating judgment
- Integration wins over best-of-breed for many firms. Tool sprawl will create security, context loss, and cost problems; converged workspaces (like ’s TEI study suggests) will be appealing for firms that value auditability and lower total cost of ownership
- Governance and measurable KPIs will be the divider between successful and failed programs. Regulators and clients will demand measurable evidence that AI improves quality, not just productivity
Industry leaders (PwC, Deloitte, McKinsey, KPMG) are already public about moving from experiments to scaled platforms and agent orchestration—so firms that invest now in governance, data hygiene, and converged work platforms will likely be the winners.
Your Next Step: Putting AI in Professional Services into Practice
AI in professional services is no longer about “what if.” It’s about “how soon.”
The firms getting ahead are the ones that treat AI as a workflow partner, not a sidekick—embedding it into research, drafting, reporting, and client delivery. Done right, it saves thousands of hours, improves quality, and gives professional services workers more time to focus on the work clients actually value: judgment, strategy, and trust.
But you don’t need a dozen disconnected apps to make this leap. A converged AI workspace like gives you everything in one place—agents to automate routine tasks, Brain to surface context instantly, and AI Cards, Notetakers, and Calendars to keep the team aligned. Less tool-sprawl, more context, faster adoption. That’s how you make AI work for your firm today—and scale it tomorrow.
The best part? You can tap into this AI advantage for free. Try today!
Frequently Asked Questions (FAQs)
AI impacts client service delivery by making it faster, more consistent, and more transparent. Instead of waiting days for updates or reports, clients can get near real-time insights powered by automation and dashboards. AI also reduces errors in routine work and ensures smoother handoffs between teams. The result: less back-and-forth, more proactive communication, and client relationships built on responsiveness and trust.
AI will not replace consultants, lawyers, and accountants—it will augment them. AI can draft a first version of a proposal, scan contracts, or flag anomalies in data, but it cannot replace professional judgment, strategy, or client trust. The highest-value work in professional services—advising, interpreting, persuading—remains uniquely human. Firms that adopt AI wisely see it as an assistant that removes grunt work so professionals can focus on higher-value contributions.
Professional services firms adopt AI successfully by starting with targeted, high-impact use cases like reporting or research, embedding AI directly into workflows, and training staff to evaluate and refine outputs. They also need governance around data privacy and output quality. Success comes from scaling slowly—piloting, measuring impact, then expanding—rather than chasing every new tool. Integrated platforms like help firms avoid AI Sprawl while delivering measurable gains in efficiency and quality.
You balance AI with human expertise in professional services by using AI for speed and scale while keeping humans in charge of judgment, trust, and context. AI handles drafting, summarizing, or automating repetitive steps; humans refine, validate, and decide. The best firms treat AI as a “workflow partner” rather than a replacement—freeing professionals to focus on strategic insights and client relationships while ensuring the output remains accurate, ethical, and contextually sound.
Everything you need to stay organized and get work done.
