https://www.enter20.com/en/blog/enter 2.0 articles on practical AI, data models, dashboards, document automation and IT governance.en-GB https://www.enter20.com/en/blog/organisational-memory-keep-knowledge-when-changing-ai-tools/https://www.enter20.com/en/blog/organisational-memory-keep-knowledge-when-changing-ai-tools/Mon, 09 Nov 2026 08:00:00 GMTKnowledge and context for AIEvery new joiner and every change of tool means explaining all over again how the organisation works. If that knowledge were curated as the organisation's own cards, in plain text with sources, an owner, a version and a review date, any AI assistant could use it and the organisation could take it along when it changes vendor. The hard part is not the format. It is resolving contradictions between documents, deciding who approves each card and withdrawing from use anything that has expired or depends on a source that has changed. https://www.enter20.com/en/blog/working-with-ai-assistants-rules-memory-in-repository/https://www.enter20.com/en/blog/working-with-ai-assistants-rules-memory-in-repository/Fri, 23 Oct 2026 08:00:00 GMTWorking method with AIAn AI assistant that writes code is only as reliable as the context it receives. If the project's rules and status live in the repository, versioned alongside the code, any assistant or person can start where the previous one left off, and every change leaves a trail. The method fits in a handful of files: a document of binding rules, a lightweight memory folder, three session prompts (start, close and handover) and a guard that runs before every commit. https://www.enter20.com/en/blog/layered-security-internal-tools-identity-at-edge-fail-closed/https://www.enter20.com/en/blog/layered-security-internal-tools-identity-at-edge-fail-closed/Tue, 06 Oct 2026 08:00:00 GMTSecurity of internal toolsNo security layer should be trusted on its own. If one fails or is misconfigured, the next one must still block the way. For a small internal tool, that means identity before anything can be reached, a service that re-checks the session on every request, and data with its own permissions. The most useful principle is to fail closed: if configuration is missing, the service serves nothing. A visible error is preferable to a tool left open without anyone knowing. https://www.enter20.com/en/blog/from-pdf-to-reviewed-data-fingerprint-evidence-review-queue/https://www.enter20.com/en/blog/from-pdf-to-reviewed-data-fingerprint-evidence-review-queue/Fri, 18 Sep 2026 08:00:00 GMTDocument automationA reliable document workflow does not start with the model that reads the PDF. It starts by identifying each document with a fingerprint, keeping the original untouched, storing alongside each extracted data point the evidence of where it came from and letting a person approve what matters. With those four pieces in place, it makes no difference whether extraction is done by rules, OCR or a language model: the result can be checked, corrected and explained. https://www.enter20.com/en/blog/backup-only-as-good-as-its-restore/https://www.enter20.com/en/blog/backup-only-as-good-as-its-restore/Mon, 31 Aug 2026 08:00:00 GMTIT continuity and operationsA backup is not verified by reading its log, but by restoring it. Some backups are reported as fine by the system for months and then, when the moment comes, cannot take you back to the point you need. Continuity is built in layers: a fast local backup, a backup that a central machine ‘pulls’ and that the machine being backed up cannot delete, and an off-site backup with a restricted key. And a test restore in the calendar. https://www.enter20.com/en/blog/permissions-live-in-the-database-not-the-screen/https://www.enter20.com/en/blog/permissions-live-in-the-database-not-the-screen/Thu, 13 Aug 2026 08:00:00 GMTInternal tools and permissionsAn access rule that exists only in the interface is a suggestion. If someone calls the API directly, opens another screen or a bug lets a query through, the rule disappears. The rules that matter must be enforced in the database. The same applies to validations and auditing: ensuring that a person cannot be allocated at 120%, that an old clock-in record cannot be edited or that every change leaves a trace are guarantees best applied where the data lives. https://www.enter20.com/en/blog/ai-proposes-a-person-decides-inventory-ai-use/https://www.enter20.com/en/blog/ai-proposes-a-person-decides-inventory-ai-use/Sun, 26 Jul 2026 08:00:00 GMTAI governanceGoverning AI in a small organisation does not require a committee or a fifty-page document. It requires an honest table: for each tool, what the AI does, what it never does, where it runs and which person reviews what it proposes. That table clarifies more than any generic policy. It shows that in some tools the AI only transcribes, in others it prepares a record that someone validates and in others, deliberately, there is no AI. https://www.enter20.com/en/blog/danger-of-automating-without-redesigning-processes/https://www.enter20.com/en/blog/danger-of-automating-without-redesigning-processes/Wed, 08 Jul 2026 08:00:00 GMTTechnology adoption and processesIn small organisations, foundations and SMEs, using advanced tools to do faster what is already done manually simply moves the original disorder onto a faster digital platform. Building the strategy around cheap execution of common tasks creates a silent problem: the complete loss of differentiation and of real value in how the organisation is managed. https://www.enter20.com/en/blog/why-ai-projects-fail-without-understanding-data-model/https://www.enter20.com/en/blog/why-ai-projects-fail-without-understanding-data-model/Tue, 23 Jun 2026 08:00:00 GMTAI and the data modelAn AI project fails when the model is expected to settle decisions the organisation has not yet defined: what each entity is, who may see it, which data item is the official source and which indicator counts as valid. Before automating, it is worth mapping out the operating model: people, projects, documents, statuses, roles, permissions, reporting outputs and exceptions. That map reduces risk and gives the AI a verifiable foundation to work on. https://www.enter20.com/en/blog/from-excel-chaos-to-useful-indicators/https://www.enter20.com/en/blog/from-excel-chaos-to-useful-indicators/Tue, 24 Mar 2026 08:00:00 GMTIndicators and reportingMoving from Excel to useful indicators does not start with buying a dashboard. It starts with agreeing definitions, official sources, who is responsible for updates and rules for correcting errors. A spreadsheet can be a good tool if it is part of a clear system. It becomes fragile when it serves as database, application, report, historical archive and permissions control all at once. https://www.enter20.com/en/blog/what-ai-adoption-diagnostic-should-include-before-buying-tools/https://www.enter20.com/en/blog/what-ai-adoption-diagnostic-should-include-before-buying-tools/Thu, 18 Dec 2025 08:00:00 GMTAI diagnosticAn AI adoption diagnostic should review processes, data, documents, permissions, risks, in-house skills, vendors and success metrics before any tools are bought. The aim is not to decide whether the organisation will use AI, but where it can use it usefully, under control and in proportion. Some opportunities will be technical, others will be organisational and others will have to wait. https://www.enter20.com/en/blog/using-ai-safely-with-sensitive-documents/https://www.enter20.com/en/blog/using-ai-safely-with-sensitive-documents/Fri, 19 Sep 2025 08:00:00 GMTSafe AI and documentsUsing AI with sensitive documents requires classifying documents, minimising the data sent, reviewing contracts, recording decisions, keeping human review in place and choosing local or controlled processing when the risk is high. The question is not only whether an AI tool is powerful. The question is what data it receives, where that data is processed, who has access, how long it is kept, how it is audited and which human decision depends on the output. https://www.enter20.com/en/blog/dashboards-are-the-visible-part-of-the-data-system/https://www.enter20.com/en/blog/dashboards-are-the-visible-part-of-the-data-system/Fri, 20 Jun 2025 08:00:00 GMTDashboards and the data systemA dashboard does not fix a data system. It displays it. If the sources, definitions and responsibilities are weak, the dashboard simply makes that fragility visible with better design. The important part usually sits before the screen: what is captured, who validates it, when it is updated, how it is corrected, what permissions exist and what decisions will be made with each indicator.