Know what to automate
before you build.
Practical guides, tools and checklists for putting AI and custom software to work in your business.
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Three in-depth guides, one for each service.
AI 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.
Read the guideRunning AI in production on AWS and AzureRunning AI in production on AWS or Azure means giving a model the same controls as any business system: identity, secrets, logging, cost limits, fallbacks and a named owner for every operating task. Managed services such as Amazon Bedrock and Azure OpenAI in Microsoft Foundry host the models, but the architecture, data boundaries and day-to-day operations remain your decisions to make and assign.Read Build, buy or integrate: deciding on custom business softwareBuy standard software when the process is common and you can adapt to the product, integrate when your existing systems already hold the data and only the connections are missing, and build custom software only where the process sets you apart or no product fits its core steps. Most growing companies land on a hybrid: buy the core system of record, then integrate or build the edges around it. Compare the options on 3 to 5 year total cost of ownership, not on the first invoice.Read
Library
Guides, articles and checklists for each part of the work.
AI Integration & Automation
Automate the routine work between your systems, with people kept in the decisions.
All 15 on AI Integration & AutomationAI 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
AI & Cloud Infrastructure
The cloud, data and model foundations an AI workload needs to run in production.
All 8 on AI & Cloud InfrastructureData 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 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 Connecting AI to your ERP, CRM and data warehouseConnect AI to your ERP, CRM and data warehouse through the vendor's supported APIs, webhooks, event streams or an integration platform, not by writing straight to the database. Start with read access, send every write through a named approver until the workflow has proven itself, and check API limits, licenses, permissions and data quality before you commit to a design.Read
Custom Solutions
When to build, buy or integrate, and how to get internal tools and portals right.
All 7 on Custom SolutionsWhat 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
Case studies
Tools and checklists
Work out what a workflow is worth, check your readiness and take a checklist into your next planning meeting.
Tools
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.AI readiness self-assessmentTwelve questions across workflows, data, systems, governance and people. See your readiness by area and what to work on first.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.Build, buy or integrate decision toolEight questions about one process. Get a recommendation to build, buy or integrate, the reasons behind it and what to check next.Human approval plannerDescribe one AI task and see the level of human control it needs, with a control pattern your team can set up.AI data readiness checkCheck the data behind one workflow across five areas, and get the three steps that would make it ready for AI.AI cloud readiness checkEight questions on running an AI workload on AWS or Azure. See your readiness by area and what to set up first.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.
Checklists
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.AWS or Azure for AI: decision checklistQuestions to answer before choosing AWS or Azure for an AI workload, covering identity, data location, model access, skills, support and commercial terms.Build, buy or integrate: decision checklistA decision checklist for choosing whether to build custom software, buy an off-the-shelf product or integrate the business systems you already have.AI adoption playbookA 16-week AI rollout checklist for operations leaders, with a free playbook on readiness, leadership, communication, training and measuring adoption.
Browse by industry
Where AI and automation help in your sector.
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.
Professional servicesClient intake handled, so your people spend their time on judgment.AI and software terms, explained
Plain definitions for the words that come up in every AI project.
Agentic workflowAI agentAI gatewayAI governanceAI readiness assessmentAI workflow automationAPI (application programming interface)Cloud migrationCRM (customer relationship management)Data pipelineData warehouseEmbeddings
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The services, industries and process behind everything in this library.
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