Common FAQs - AI basics
What is Artificial Intelligence (AI)?
AI refers to systems that can perform tasks typically requiring human intelligence — such as analysing data, generating content, recognising patterns, or supporting decision-making.
In practice, most businesses use AI through tools like Microsoft Copilot, ChatGPT, or embedded features within existing software.
Is AI the same as Automation?
No, but the outcome may feel to the end user to be the same.
- Automation = rule-based processes (e.g. moving data between systems)
- AI = adaptive systems that learn from data and support judgement
Most organisations benefit from a combination of both.
What is Generative AI?
Generative AI creates new content — text, documents, emails, reports, images or code — based on prompts.
For professional services firms, this is where the immediate value often sits (drafting, summarising, client comms).
Where should we start with AI?
Start with specific business problems, not technology.
Typical starting points: Document drafting, knowledge management, reporting and analysis, client/customer onboarding, and Internal workflows
Successful organisations begin by identifying clear use cases aligned to business goals.
Do we need an AI strategy first?
Yes, but it doesn’t need to be complicated.
At minimum, you need: Clear outcomes (what success looks like), priority use cases, data considerations and governance approach
Do we need in-house AI expertise?
Not initially - Start with existing tools, work with external advisors and build internal capability over time.
You only need specialist teams when scaling complex solutions.
How much does AI cost?
It varies from cimple tools: low monthly cost, department-level solutions: moderate investment, and finally enterprise transformation: significant investment
The key is starting small and scaling with proof of value.
How do we measure ROI from AI?
Typical metrics include:
- Time saved
- Increased billable capacity
- Improved turnaround times
- Reduced operational costs
- Enhanced client retention
Many firms struggle to clearly link AI to financial outcomes early on - which is why structured measurement matters.
What is AI governance and why do we need it?
AI governance ensures:
- Safe and compliant use
- Data protection
- Transparency in decisions
- Accountability for outputs
This is especially critical in regulated sectors like legal and finance.
How does GDPR affect AI?
AI systems must:
- Use data lawfully
- Be transparent about processing
- Protect sensitive information
- Avoid misuse of personal data
Compliance is not optional - it must be designed in from day one.
Will our team resist AI?
Often, yes - initially.
Common concerns:
- Job security
- Complexity
- Trust in outputs
Successful firms:
- Provide training
- Set clear policies
- Lead from the top
What skills do we need in an AI-enabled firm?
Key capabilities include:
- Prompting and AI usage
- Data literacy
- Critical thinking (verifying outputs)
- Governance awareness