AI for professional services
Client intake handled, so your people spend their time on judgment.
See the industry overviewWhich professional services workflows should you automate first?
Three workflows that come up again and again in professional services.
Client onboardingClient details collected into a record ready for review.
Document collectionRequested documents tracked and chased automatically.
Review workflowsWork routed for review, with the final call kept by your professionals.
Free tools
AI fit checkGet a structured read on one workflow: the service it calls for, what AI could take on, what stays with a person, and how ready it looks to start.Open the tool Automation priority scorerList up to five processes and see them ranked for automation, with a score, the hours each takes today and the reason for its place.Open the tool AI automation ROI calculatorEstimate what automating one workflow could be worth in hours and annual value, and how long it would take to pay back.Open the tool Human approval plannerDescribe one AI task and see the level of human control it needs, with a control pattern your team can set up.Open the tool
Related reading
Client document collection for accounting, legal and advisory firms: from request to ready fileClient document collection automation sends each client a tailored request list, tracks what arrives, checks that each document is the right one and complete, chases what is missing and hands the professional a ready file. It cuts the administrative back and forth, while the professional reviews the documents, decides what they mean and owns the advice.Read RAG, fine-tuning or prompting: how to give AI your company's knowledgeUse retrieval (RAG) when the AI must answer from facts that change and show where each answer came from. Use fine-tuning when you need the model to behave consistently, for example a fixed output format or a narrow classification. Start with prompting, because a small, stable body of context often needs neither.Read How to write an RFP for an AI implementation projectAn RFP for an AI implementation project works when you need to compare several partners on the same basis, and works badly as a compliance formality that never asks whether a firm understands your workflow. This guide sets out what the RFP should contain, the questions worth putting to every bidder, and how to score what comes back using weighted criteria rather than gut feel. It also covers when a lighter conversation beats a formal RFP.Read AI automation handover checklist and runbook templateAn AI automation handover should leave your team able to operate, check, pause and recover the workflow without relying on its builder. Use this checklist to request the evidence behind each responsibility, then copy the runbook template to document one workflow before accepting it.Read How to write an AI acceptable use policy for employeesAn AI acceptable use policy tells employees which AI tools they may use, what data may go into them, how closely to check the output, and who to ask when a new tool shows up. It matters now because most employees already use AI on their own initiative, often through personal accounts the company cannot see. A workable policy fits on two pages, matches its rules to how sensitive the data is, and names one owner who keeps it current.Read AI agents with human approval: how to keep a person in controlAI agents with human approval means deciding in advance which actions a person must see before they happen, and how much of the work still reaches them. Money, legal terms, customer messages and irreversible changes need a person by default, while routine, reversible work can run on sampled review instead. Get the design wrong and you either slow the agent to a crawl or approve items nobody actually checked.Read What drives the cost of an AI readiness auditThe cost of an AI readiness audit comes from scope, not company size: how many workflows and teams are reviewed, how many systems and data sources need checking, and how deep the security and governance review goes. A proposal that names these choices can be compared and negotiated; one that quotes a single number for "an AI audit" usually cannot. Kastling prices its own audit the same way, after a free discovery call.Read Client onboarding automation for professional services firms: intake to engagement without the chasingClient onboarding automation for professional services firms means combining a single intake form, document checklists and automated field checks with a person who still makes every acceptance, conflict and risk decision. Done well, it replaces chasing missing documents and re-keying the same details into three systems with one record that is ready for a reviewer to approve. The result shows up as fewer touches per client and a shorter gap between a signed engagement and billable work.Read Data security and governance for AI in the enterpriseAI data security and governance means knowing which data each AI workflow touches, approving the sources it may use, checking what your AI provider does with prompts and outputs, and making the AI respect the same permissions as the person using it. It also covers defenses against prompt injection, logging with sensible retention, human approval for consequential actions and a clear usage policy for staff. Frameworks such as the NIST AI RMF and ISO/IEC 42001 give that work a structure.Read AI document processing for invoices, contracts and formsAI document processing combines three techniques: OCR to turn images into text, layout models to understand where values sit on a page, and language models to extract and interpret fields that vary between documents. The technique matters less than what surrounds it: validation rules that check extracted values against your own records, confidence thresholds that decide what a person sees, and a review queue somebody owns. Measure field level accuracy and the share of documents that pass without a human touch, not a single headline accuracy number.Read Building an AI assistant on your company knowledgeAn AI knowledge assistant answers questions using your own approved documents rather than general model training, a pattern called retrieval-augmented generation. The work is less about the model and more about choosing which sources count as authoritative, enforcing the permissions those sources already carry, showing citations people can check, and deciding what happens when information is missing or two documents disagree. Buy Microsoft 365 Copilot, Gemini for Workspace or Glean when your knowledge lives in one mainstream suite, and build when it is spread across line of business systems or the answers must trigger work.Read What happens in an AI readiness assessmentAn AI readiness assessment is a structured review of specific workflows, the data and systems behind them and the people who run them, to decide where AI or automation will help, what must be fixed first and in what order to act. A good one ends with named, prioritized opportunities and a roadmap, not a maturity score or a deck of industry trends.Read AI workflow automation: a practical guide for operations teamsAI workflow automation puts AI models inside a defined business process to read, classify and prepare work, while fixed rules, integrations and named people handle the steps that must be predictable. It suits repetitive, document-heavy or request-heavy work such as intake, routing, document checks and approvals. Start with one measurable workflow, keep consequential approvals with an accountable person, and expand only once the numbers show it works.Read AWS or Azure for AI workloads: how to chooseFor most companies, the right cloud for AI is the one where their identity, data and technical team already sit. As of September 2026, Amazon Bedrock and Microsoft Foundry both offer models from OpenAI, Anthropic and other providers, so model choice rarely settles the question alone. Identity, data location, compliance scope, skills and existing agreements usually do.Read How to choose an AI implementation partner: 10 questions to askChoosing an AI implementation partner comes down to ten questions about scoping, testing, human control, ownership, lock-in, security, integration feasibility, who does the work, how success is measured and what happens after launch. Ask every shortlisted firm the same questions and compare the answers side by side. Then verify with reference calls and a small, paid first piece of work before committing to a large build.Read What drives the cost of custom softwareThe cost of custom software is set mostly by scope: how many user roles the software serves, how many systems it connects to, how much existing data has to move, and what the security and compliance requirements are. Technology choices and hourly rates matter far less than most buyers expect. Budget for the years after launch as well, because hosting, support and maintenance continue for as long as the business depends on the software.Read Internal tools and customer portals: planning the first releaseThe first release of an internal tool or customer portal should cover one workflow for one group of users, end to end, rather than a thin version of everything. Decide roles, permissions and how people sign in before design starts, because those choices are expensive to retrofit. Then measure adoption against the work actually moving through the tool, not against logins.Read Off-the-shelf, no-code or custom software: when to use eachBuy an off-the-shelf product when the process is standard and a vendor already handles it well. Use a no-code or low-code platform for internal tools and team workflows where speed matters more than scale, and govern those apps the same way you govern any other business system. Build custom software when the process is specific to your business, when data volume or integration depth exceeds what a platform can carry, or when the app must live inside a system of record you already own.Read How to write a requirements brief a development partner can useA good requirements brief is a decisions document, not a design document. In two to four pages it states the business problem, who uses the software and in what role, how the process runs today, what must be in the first release, which systems and data are involved, the constraints, how success will be measured and who decides. Written that way it produces comparable quotes, a realistic plan and far less rework.Read How to test AI before it touches your operationsTesting AI before deployment means scoring it against a set of real past cases, with the business owner agreeing in advance what counts as correct and what target has to be met. Run it in shadow mode or a limited pilot before it can change anything, keep human review on consequential decisions, and rerun the same test set every time a prompt or model changes.Read
Other industries
RetailOrders, stock requests and product updates that move without anyone re-keying them.LogisticsShipping documents checked and every exception routed to the person who owns it.TelecommunicationsActivations, field visits and support requests that reach the right team first time.Real estateInquiries answered, applications complete and maintenance moving before anyone chases.FMCGDistributor orders, promotions and launches coordinated from brief to sign-off.
See how an idea becomes a working solution.
Explore how we scope, build and hand over AI projects, with clear decisions at each stage.