AI for professional services
Client intake handled, so your people spend their time on judgment.
See the industry overviewWhere we usually start
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.
Related reading
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.
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