How Can My Company Use AI?
AI adoption doesn't need to be expensive, technical, or confusing. This page answers the most common questions CEOs and business leaders ask when figuring out how their company can start using AI - even if they have no technical staff or existing AI strategy.
The goal is to help you move from curiosity to action. Whether you're aiming to reduce costs, increase revenue, or simply make better use of your data, the answers below offer a practical starting point.
Contents
Getting Started
Strategy & Resourcing
Use Cases & Value
Risks & Pitfalls
Implementation
Scaling & Growth
Getting Started
What's the first step for implementing AI in my company?
Start by solving a real business problem using simple tools. You don't need to over-engineer - focus on quick, visible wins.
- Identify a clear business problem.
- Choose a low-risk, high-impact use case.
- Use existing AI tools before building anything custom.
- Assign someone to own the process internally.
How do I know if my business is ready for AI?
You need the right foundations before investing time or money. If these elements aren't in place, start there.
- Clean, accessible data.
- Clear pain points that AI can address.
- Leadership support.
- A budget for experimentation.
How can I use AI if I don't have technical staff?
You don't need in-house developers to get started. Modern AI tools are designed for non-technical users, and you can bring in external support where needed.
- Use off-the-shelf tools like ChatGPT, Notion AI, or Salesforce Einstein.
- Start with no-code or low-code platforms.
- Work with a consultant or agency to fill skill gaps.
How much does it cost to start using AI?
Costs vary, but entry is affordable. Most businesses start with low-cost tools and pilot projects before scaling up.
- Off-the-shelf tools: Free to a few hundred pounds/month.
- Custom solutions: £5k - £50k+ depending on scope.
- Consultant support: £2k - £20k+ for strategy or MVP builds.
Do I need a lot of data to use AI effectively?
No - you can start with very little. Many tools don't require internal data and work well out of the box.
- You can use AI even with limited data if you apply it to simple tasks.
- Many tools work well with low or no internal data.
Strategy & Resourcing
Should I hire an AI consultant or build a team in-house?
This depends on your resources, goals, and timeline. The right choice comes down to whether you need to move fast or build long-term capability.
Hire a consultant if:
- You want to move fast.
- You lack internal skills.
- You need strategy support.
Build in-house if:
- You have a long-term roadmap.
- You want to control IP and skills.
- You can afford the overhead.
How do I find the right AI partner or vendor?
The right partner brings real-world experience and communicates clearly. Test them with a small project before making a long-term commitment.
- Look for proven industry experience.
- Ask for real case studies.
- Check for transparency around data handling and IP.
- Start with a small project to assess fit.
At Vertex Agility, we have tangible experience with proven results. Get in touch now to find out how we can help.
What roles or skills do I need internally to support AI?
You'll need both technical and business roles to make AI projects work. Even small teams should cover the basics.
- Product owner
- Data analyst or engineer
- Software engineer (for integration)
- Someone to manage change and adoption
How can I build an AI roadmap for my company?
A good roadmap focuses on value and evolves as you learn. Start small and build momentum.
- List pain points and inefficiencies.
- Prioritise based on impact and feasibility.
- Plan short sprints to test solutions.
- Assign owners and track metrics.
How do I get my leadership team on board with AI?
Leaders care about outcomes. Speak their language by linking AI to revenue, efficiency, and competitive edge.
- Show practical business cases.
- Highlight cost savings or revenue impact.
- Start small and prove value fast.
Use Cases & Value
What are the best AI use cases for small or mid-sized companies?
Start with areas where AI can save time or improve consistency. These use cases usually bring the fastest returns.
- Customer service chatbots
- Sales forecasting
- Marketing personalisation
- Document summarisation
- Internal reporting automation
Where can AI make the biggest impact in my business?
Look for areas with lots of data or repetitive tasks. AI performs best where people spend too much time on low-value work.
- Repetitive manual tasks
- Customer-facing workflows
- Data-heavy decision-making
- Areas where speed matters (e.g. sales or operations)
Can AI help me reduce costs or increase revenue?
Yes - it can do both. The key is to target inefficiencies or missed revenue opportunities first.
- Reduce costs through automation.
- Increase revenue via faster decisions and better targeting.
- Improve customer satisfaction through 24/7 support.
How are other companies in my industry using AI?
Many businesses are already benefiting from AI. Use their successes as a model.
- Retail: Inventory prediction and personalisation
- Finance: Fraud detection and client onboarding
- Manufacturing: Predictive maintenance
- Services: Proposal generation and lead scoring
How long does it take to see ROI from AI?
Returns vary by project size, but pilots show impact quickly. Larger rollouts take time but deliver greater returns.
- 2 to 6 months for small pilots
- 6 to 18 months for full implementations
- Faster if starting with automation or customer service
Risks & Pitfalls
How do I avoid wasting money on AI?
Avoid common traps by staying focused on outcomes. Don't invest heavily without early signs of value.
- Start small
- Focus on business value, not tech hype
- Track clear metrics
- Avoid open-ended R&D projects
What are common mistakes companies make with AI?
Learn from others' missteps. These are the errors that derail most projects.
- Starting without a business case
- Over-engineering simple solutions
- Ignoring adoption and training
- Failing to track ROI
What legal or ethical issues should I be aware of?
You're responsible for how AI behaves in your business. Stay ahead of compliance and reputation risks.
- Data privacy (GDPR)
- Transparency in decision-making
- Bias in algorithms
- Accountability for AI outputs
How do I keep control over AI projects without being technical?
You don't need to write code to lead AI efforts. Just keep visibility on results and ask the right questions.
- Focus on outcomes, not methods
- Use simple tools with clear dashboards
- Work with partners who can explain what they're doing
Implementation
Can I start using AI with tools we already have?
Yes - you may already own AI-enabled software. Use these features before exploring new platforms.
- Microsoft, Google, Salesforce, and HubSpot now include AI.
- Use plugins and extensions to test AI in your current stack.
How do I test AI in one part of my business before scaling it?
Start small and controlled. Choose a single process to improve and measure its success.
- Choose one workflow or department
- Define success metrics
- Run a 4-8 week test
- Document results and issues
How do I measure success or failure in an AI pilot?
Use simple, visible metrics. Avoid overcomplicating - just show that the AI made a difference.
Track:
- Time saved
- Costs reduced
- Quality improvements
- Team or customer feedback
What's a realistic timeline for implementing AI?
Expect weeks for pilots and months for broader rollouts. Complexity, data, and skills all affect the timeline.
- 2 weeks to 2 months for a small pilot
- 3 - 12 months for wider rollout
- Timeline depends on data quality and team readiness
Scaling & Growth
How do I scale AI once I've proven it works?
Repeat what works and build on it. The goal is to make success repeatable and sustainable.
- Standardise what worked in the pilot
- Document processes and training
- Roll out to similar areas
- Monitor and adjust
What infrastructure or processes need to change as we adopt AI?
AI needs clean data and clear ownership. Expect to adjust how teams and systems work together.
- Better data pipelines
- Cross-functional collaboration
- Governance and risk management
- Continuous improvement mindset
How do I train my team to work effectively with AI?
Training should be short, practical, and role-specific. People learn best when they can apply skills immediately.
- Give hands-on access
- Run short workshops
- Focus on use cases, not theory
- Make training role-specific
Can I use AI across multiple departments or just one?
You can expand once you've shown value in one area. That's the safest and most effective way to scale.
- Start with one area
- Expand to others based on ROI
- Common expansion paths: operations, marketing, customer service