AI Integration & Automation
Automate the routine work between your systems, with people kept in the decisions.
About the serviceAI workflow automation: a practical guide for operations teams
AI 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.
Start with the guideArticles and guides
AI workflow automation: what it costs to run and what it savesThe cost of AI workflow automation splits into five areas: discovery or a paid audit, implementation, infrastructure and model usage, integration licenses, and support and change. Model usage is usually the smallest recurring line and scales with volume and document length, while support, exception handling and adoption are the lines buyers underestimate. Savings come from hours returned at loaded cost, fewer errors and faster cycle times, but only count as cash when they change hiring, overtime or outside spend.Read How to calculate the ROI of AI automationCalculate the ROI of AI automation by measuring the current process first, then valuing hours saved at loaded cost, errors avoided and faster cycle times, and setting that against the full cost to build, run and support the system. Express the result as a payback period under conservative assumptions, and count saved hours as money only when they change hiring, overtime or contractor spend.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 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 In-house, agency or partner: how to staff an AI projectAn AI project needs a business owner and a process owner from inside your company, plus AI engineering, integration, data, cloud and change skills that you can hire, contract or bring in through a partner. For a first workflow, a specialist partner working with named internal owners is usually the fastest route, and hiring makes sense once there is steady work to keep a team busy. Whichever route you take, your company should own the code, the accounts and the documentation.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 Which business processes to automate firstChoose the first process to automate by scoring candidates on volume, repetition, how much judgment they need, data availability, systems access, cost of errors, whether someone owns the process and whether you can measure it. The best first candidate is usually a frequent, well understood, medium consequence process with one accountable owner, not the one people complain about most. Score three to five candidates, pick the highest, and scope it as a single workflow with a recorded baseline.Read
Tools and checklists
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 AI readiness self-assessmentTwelve questions across workflows, data, systems, governance and people. See your readiness by area and what to work on first.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 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 AI readiness checklistA practical AI readiness checklist covering workflow, data, systems, security, people and measurement, so you can see what to fix before an AI project starts.See the checklist
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