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AI how-to guides
How to do it with AI: practical guides for enterprise teams
One guide per task: what AI does and what stays with people, the steps to put it in place, what you need, the risks and when it is not worth it. Each one is backed by a solution Thinkia delivers.
Operations and processes
6 guides-
How to automate email with AI
Connect an AI agent to the mailbox so it reads each email, works out what it is about, pulls out the data it needs, checks your systems and drafts a reply or routes it. People approve what matters and handle the exceptions. Start with one shared inbox and a handful of email types, measure, then widen.
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How to automate processes with multiple AI agents
Split the process into steps with a clear job each, give each step to an agent with its own tools and permissions, and let an orchestrator pass work between them and escalate when something is unclear. People approve the decisions that carry risk. Start with one process that crosses several systems, run it in parallel with the team, then widen.
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How to do predictive maintenance with AI
Connect sensor readings and maintenance history for your most critical assets, train models that spot the early signs of degradation and turn alerts into work orders in your maintenance system. Technicians validate the alerts and decide the intervention. Start with a few assets where failures are costly and the telemetry is trustworthy.
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How to find AI use cases in your company
Start from business priorities and real pain, not from the technology: run structured workshops with business, data and IT, list where work is slow, repetitive or error-prone, and score each idea on value, feasibility and risk. Give every shortlisted case a baseline metric and a named owner. End with a sequenced backlog that delivery can pick up, not a list of ideas.
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How to forecast demand and inventory with AI
Train forecasting models on your sales history together with promotions, prices, calendar and external signals, producing coherent forecasts by product, location and channel with a confidence range. Planners review the forecast, override it with a reason when they know something the model does not, and the result feeds replenishment. Start with one category, compare against your current forecast and widen.
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How to triage insurance claims with AI
Let AI read everything that arrives with a claim, extract the key data with its source, score severity and complexity, pre-check it against the policy and route it to fast track, standard handling or an experienced adjuster. The handler opens a claim that is already summarised and flagged, with the reasons visible. Decisions on cover and payment stay with people.
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Customers and sales
4 guides-
How to analyse survey comments with AI
Feed the open-ended answers from your surveys (NPS, CSAT, employee or product surveys) to an AI model that groups them into themes, scores sentiment and links each theme to segments and to the scores people gave. People define the questions, check the themes against real comments and decide what to do. Start with one survey and a theme list you validate by hand.
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How to build an AI contact centre
Put an AI layer in front of your channels that understands each query, answers from your knowledge and systems, and hands over to a person with the full context when it should. People take the complex, sensitive and high-value conversations. Start with a few frequent intents on one channel, measure quality, then add intents, languages and channels.
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How to personalise customer experience with AI
Build a single, consented view of each customer, let AI rank products, content and messages from their behaviour and context in the moment, and serve that decision to every channel from one place. Prove the lift with holdout tests before scaling. People set the rules, the brand limits and what must never be personalised.
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How to prepare sales meetings with AI
Connect an AI assistant to the calendar, CRM, email and call notes so it builds a brief before each meeting: who is coming, what has happened so far, open deals, likely needs and suggested questions and content. After the meeting it drafts the follow-up and logs next steps in the CRM. The rep checks the brief, runs the conversation and owns the relationship.
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Finance
3 guides-
How to automate accounts payable with AI
Use AI to capture invoices from every channel, extract their data, match them against purchase orders and goods receipts, and send only the exceptions to people. Approvers see the context, postings go to the ERP and every step leaves evidence for audit. Start with one entity and your highest-volume suppliers, measure, then widen.
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How to do financial forecasting with AI
Feed a forecasting model the signals that move your numbers (sales, ERP, pipeline, FX, operations) so it recalibrates as new data arrives and explains which drivers changed the figure. Finance reviews, overrides and approves before anything is published. Start with one forecast that matters, test it against past cycles, then widen.
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How to use AI in procurement
Bring purchase orders, invoices and contracts together, let AI classify the spend and point out savings, supplier issues and upcoming renewals, and automate the routine transactions. Buyers keep the negotiation, the supplier relationship and the final sourcing decision. Start with spend visibility in a few categories, because every other use depends on it.
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People and HR
2 guides-
How to answer HR policy questions with AI
Index your handbook, country addenda and role policies so an AI assistant answers employees in plain language, cites the exact article and version, and picks the variant that applies to them. When there is no source or the case needs nuance, it hands over to HR with the full conversation. Start with the most asked topics in one country, then extend.
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How to automate employee onboarding with AI
Give new joiners an AI assistant that follows a journey built for their role, answers their questions from your own policies and documents, and reminds them and their manager of what is pending. HR and managers stay in charge of the welcome, the judgement calls and anything sensitive. Start with one function or site, measure, then roll out.
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Knowledge and documents
3 guides-
How to build a company knowledge base with AI
Connect the repositories where knowledge already lives, index them with their permissions and let an AI assistant answer questions by retrieving the relevant passages and citing them. People own the content and fix what the assistant shows is missing or out of date. Start with one team and the sources it uses every day, then add more as trust in the citations grows.
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How to keep expert knowledge when people retire
Find the roles where critical know-how sits with one or two people, capture it in AI-assisted sessions while they are still there, and turn it into verified checklists, guides and answers others can search. The experts and their peers validate what is captured. Start with the most exposed roles and keep the knowledge alive with owners and review dates.
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How to manage company documents with RAG
Retrieval-augmented generation (RAG) indexes your documents so an AI model first finds the relevant passages and then answers or extracts data from them, citing where each answer came from. People own the sources, the permissions and the review of low-confidence results. Start with one document type and one question set, measure accuracy, then add more.
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Technology and data
6 guides-
How to automate the IT service desk with AI
Put an AI agent in front of the service desk so it answers common requests from your knowledge base, runs safe, approved actions such as password resets or access requests, and classifies and routes the rest with full context. Engineers handle complex incidents and approve sensitive changes. Start with the most frequent ticket types, measure against your baseline, then widen.
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How to improve data quality with AI
Profile your critical datasets, turn freshness, completeness and validity rules into automated checks with named owners, and use AI to propose rules, spot anomalies, classify sensitive columns and suggest fixes. Lineage shows where each problem comes from and what it affects. People decide the rules, own the data and approve corrections.
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How to modernise legacy code with AI
Use AI first to understand the legacy system: extract business rules, data flows and dependencies into documentation engineers can query. Then translate and refactor module by module, with regression tests generated from how the current system actually behaves, and cut over one capability at a time with rollback. Engineers review every change and decide the target architecture.
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How to move an AI pilot to production
Design the pilot to end in a decision: agree the exit criteria, the baseline and the metrics before building, evaluate on real cases and wire governance in from the start. Then turn 'go' into a production path with data access, integrations, monitoring and a business owner. Most pilots stall because these were never scoped, not because the model failed.
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How to query your data in natural language
Put a governed semantic layer between the AI and your data, defining metrics, dimensions and joins once, and let a language model translate each question into a query on that layer, run it with the user's permissions and explain how the number was built. Data owners keep control of the definitions; business users self-serve. Start with one domain and the questions people already ask the data team.
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How to use AI across the software development lifecycle
Use AI at every stage, not only for writing code: to turn requirements into specs, draft code and tests, review pull requests and keep documentation current. Engineers keep design decisions, review and accountability for what ships. Roll it out with governed tools, an evaluation harness in the pipeline and a baseline so you can measure the real effect.
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Risk, compliance and governance
6 guides-
How to automate AI governance
Automating AI governance means turning policy into a living register and a workflow: every AI system is inventoried, classified by risk, taken through review gates and monitored, with evidence collected as it runs. Tools do the collecting, checking and reminding; named people decide, approve and answer for each system. Start with an inventory and one gate, then add controls.
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How to automate ESG reporting with AI
Connect your operational systems to a single governed ESG dataset, apply a documented and versioned methodology, and let AI collect, check and draft while people approve. Every disclosed figure should trace back to its source records, factors and approver. Start with one framework and a few metrics, then widen by reporting cycle.
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How to detect fraud with AI
Train models on your transaction and behaviour data so they score each event as it happens, learn what normal looks like for each customer or account and flag what deviates. Analysts review ranked alerts with the reasons behind each score and their decisions feed the models. Start with the flows that lose the most money, run in shadow against your current rules, then switch on.
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How to monitor regulatory change with AI
Connect the official sources for your jurisdictions so AI detects each new rule, guideline or consultation, summarises what changed, classifies it and proposes which of your controls, policies and processes it touches. Compliance reviews, adjusts and assigns owners, and every step is kept as evidence. Start with a few sources and one topic, then add jurisdictions.
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How to red team your AI agents automatically
Write down what each agent must never do, then use a battery of attacks, partly generated by another model, to try to make it do exactly that, and score every run automatically. Run the battery on every change to prompts, tools or models, and block releases that regress. Automation gives coverage and repeatability; people still design the threat model and look for the attacks nobody has scripted yet.
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How to review contracts with AI
Load your contracts into an AI system that extracts clauses, obligations and key dates, compares them with your standard positions and flags what deviates. Lawyers review the flagged items, decide and negotiate. Start with one contract family and a short list of clause types, validate on real agreements, then widen.
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