Preparing Your Business for AI: An AI Readiness Guide

A practical approach to adopting AI securely, responsibly and successfully across your organisation

Artificial intelligence can help staff work faster, improve customer service and make better use of the information a business already holds. However, adopting AI for everyone is not simply a matter of buying licences and switching the technology on. AI can search, summarise and combine information at a speed and scale that exposes weaknesses in permissions, data governance and working practices that may previously have gone unnoticed.

A successful AI programme therefore starts with the business, its people and its data. The objective should be to create a controlled environment in which approved tools deliver measurable value without exposing confidential information, personal data, intellectual property or client records.

1. Establish leadership, ownership and acceptable use

Nominate a senior owner for AI and create a small steering group involving business leadership, IT, information security, data protection and representatives from the teams that will use AI. Define which outcomes matter, such as reducing administration, improving response times or helping staff find information, and agree how success will be measured.

• Publish an AI acceptable-use policy covering approved services, permitted data, human review and prohibited activities.
• Create a simple approval process for new AI tools, browser extensions, agents, connectors and integrations.
• Define accountable owners for each use case and record its purpose, data sources, users, supplier and risk rating.
• Set clear rules for customer-facing content, automated decisions and any use involving personal or sensitive information.

2. Discover and classify the information AI could access

Build an information map covering Microsoft 365, line-of-business systems, shared drives, cloud storage, email, collaboration platforms and archives. Identify where personal data, commercially sensitive material, HR records, financial information, credentials and client data are stored. Assign owners and retention requirements, remove redundant data and introduce sensitivity labels where appropriate.

Why this matters: an AI assistant may make information dramatically easier to find, but it does not correct an unsafe permissions model. Content that was technically accessible but difficult to discover can become available through a straightforward natural-language request.

3. Review permissions and reduce oversharing

Carry out a structured permissions audit before connecting AI to business data. Review SharePoint sites, Teams, OneDrive, shared mailboxes, network folders and business applications. Pay particular attention to organisation-wide access, “anyone” links, the “Everyone except external users” group, inherited or broken permissions, guest access, stale accounts and sites without an active owner.

• Apply least privilege: staff should have access only to information required for their role.
• Replace broad sharing links with named users or controlled groups.
• Review privileged roles, service accounts and third-party application permissions.
• Remove obsolete content and access left behind by former employees, suppliers or completed projects.
• Introduce recurring access reviews rather than treating remediation as a one-off exercise.

4. Assess every AI supplier and service

Do not assume that a free or inexpensive AI service is suitable for business use. The commercial model may depend on retaining prompts, uploaded files or conversation history and using them to improve services or train models.

Even where an opt-out exists, it may depend on the account type, settings or contract. Staff accepting consumer terms on behalf of themselves can unintentionally disclose company or client information outside approved controls.

For each supplier, establish in writing:

• Whether prompts, outputs and uploaded data are used for model training or service improvement.
• Where data is processed and stored, how long it is retained and how it can be deleted or exported.
• Whether administrators can control accounts, sharing, connectors, agents and audit logs.
• Which subprocessors are involved and what contractual, privacy and security assurances apply.
• How the service handles encryption, incident notification, business continuity and account termination.
• Whether the intended use requires a data protection impact assessment, contractual review or client approval.

5. Find and control shadow AI

Shadow AI occurs when employees adopt AI tools, agents, meeting assistants, browser extensions or personal accounts without the knowledge or approval of the business. This may already be happening before a formal AI project begins.

Risks include uncontrolled data transfer, excessive application permissions, poor authentication, unrecorded automated actions, intellectual-property leakage and suppliers that cannot meet the organisation’s legal or contractual obligations.

• Survey staff in a constructive, non-punitive way to understand what they use and why.
• Review sign-ins, enterprise applications, OAuth consent, browser extensions, network or cloud-security logs and expense claims.
• Maintain an approved AI catalogue and give staff a quick route for requesting alternatives.
• Block or restrict high-risk services where proportionate, while explaining the approved options.
• Revoke unknown integrations and investigate any exposure of confidential or personal information.

6. Put technical guardrails around approved AI

Use business-grade services under centrally managed accounts. Enforce multi-factor authentication, conditional access, least-privilege administration and controlled app consent. Apply data loss prevention, sensitivity labels, retention controls, audit logging and alerting. Limit agents and connectors to approved systems, credentials and scopes; test what information they can retrieve and what actions they can take.

Treat an autonomous or semi-autonomous agent as a privileged digital worker. Give it a named owner, a defined purpose, the minimum data and permissions required, spending or transaction limits where relevant, complete logging, and a reliable method to stop or revoke it.

7. Start with controlled, valuable use cases

Select a small pilot group and begin with repeatable tasks that have clear value and manageable risk: drafting routine communications, summarising approved documents, finding internal procedures or assisting with meeting notes. Establish a baseline, define expected benefits and monitor quality, time saved, adoption, support demand and incidents.

Require human verification of outputs. AI can produce inaccurate, incomplete or fabricated answers, and plausible wording should never be mistaken for evidence. High-impact decisions, legal or financial advice, customer commitments, security changes and publication of sensitive material should remain subject to competent human approval.

8. Train staff and create safe working habits

Training should cover more than prompting. Staff need to understand information classification, approved tools, privacy, copyright and intellectual property, hallucinations, bias, social engineering, secure sharing and how to report a mistake. Use role-based examples and provide reusable prompt patterns that avoid unnecessary personal or confidential data.

9. Prepare for incidents and ongoing assurance

Update incident-response procedures to include accidental prompt disclosure, unsafe output, compromised AI accounts, malicious integrations, agent misbehaviour and supplier incidents. Staff should know whom to contact and should preserve the relevant prompt, output, tool, account and time without redistributing sensitive content.

Review the AI register, permissions, suppliers, logs, policies, training needs and business benefits regularly. Terms, features and risks change quickly, so approval should never be permanent by default.

What does this mean for your business

AI readiness is not about preventing innovation. It is about making sure the organisation understands its information, chooses trustworthy services and gives staff a safe, productive route to use them. Before deploying AI to all staff, your business should consider the following steps:

1. Appoint an AI owner and agree the outcomes you want to achieve.
2. Discover and classify the data your organisation holds.
3. Audit file, folder, site, mailbox and application permissions.
4. Identify any AI services, agents or extensions already being used.
5. Create an approved-tools list and an AI acceptable-use policy.
6. Review supplier terms, privacy, training, retention and data-location commitments.
7. Implement identity, data-protection, logging and application-control guardrails.
8. Pilot a small number of low-risk, high-value use cases.
9. Train staff to use AI safely and verify its outputs.
10. Measure value, review risk and improve controls continuously.

The Silver Cloud Business can help. Our AI Readiness service can assess your Microsoft 365 and / or server environment, review permissions and data exposure, help to identify shadow AI, evaluate proposed platforms, develop practical policies and create a phased roadmap for secure adoption.

Call us on 01722 411 999 to discuss an AI Readiness Assessment and make sure your organisation is ready to gain the benefits of AI without exposing the data it depends on.