How to Assess AI Readiness and Plan for ROI (Beginner-Friendly Guide)

Steps to achieve AI readiness

Follow these five steps to prepare your business for AI:

1. Define goals and priorities – Identify where AI can save time or improve results. Look for repetitive tasks (such as data entry, report generation, or customer support) and align them with business goals. Start with small, high-impact use cases instead of a large transformation.
2. Build skills – Conduct a skills assessment to find gaps. Offer training for non-technical staff on AI basics and hire or partner with specialists if needed.
3. Strengthen data management – Establish processes for collecting, cleaning, and integrating data across departments. Put governance policies in place to ensure privacy, security, and compliance.
4. Invest in scalable infrastructure – Assess your existing hardware and software to see if it can handle AI workloads. Use cloud platforms or other scalable solutions to grow as needed.
5. Create a pilot project – Test your readiness with a small pilot. Use this project to refine workflows, check model accuracy, and measure benefits before scaling AI across the business.

Small wins: quick wins to build momentum

Early success builds trust and demonstrates value.

Here are examples of quick wins you can try:

• Automate repetitive tasks – Use AI to handle data entry or document processing. This frees up employees to focus on higher-value work.
• Add a chatbot for customer support – Deploy a simple AI chatbot to answer common customer questions. This reduces response times and provides measurable cost savings.
• Clean up your data – Implement tools that cleanse and organize your data. Clean data improves future AI projects.
• Generate AI insights – Use AI to analyse market trends or customer segments. Early insights can support better decisions and build internal confidence.
• Focus on a single business function – AI tools can help with sales, marketing, or operations. For example, 90 percent of SMBs use AI to automate customer interactions, and one company saved sales reps five hours a day by automating lead tracking. In operations, 88 percent of small businesses use AI for data integration and process automation.

By starting small, you show quick results and build momentum for larger projects. Highlight these successes to gain stakeholder support.

Calculating ROI

To decide if an AI tool is worth the investment, calculate its return on investment using the formula below:

ROI = (Net Benefits Total Costs) / Total Costs × 100

Net benefits are the financial gains from using AI. They include cost savings, increased revenue, and productivity gains. Total costs include one-time setup costs, ongoing subscription fees, training, and any hidden costs. Companies adopting AI often see cost reductions of about 15 percent, revenue increases of around 16 percent, and productivity gains of about 40 percent.

Simple ROI worksheet

 Use the steps below to estimate ROI using a productivity task like AI-generated reports. Adjust the numbers for your business.

1. Identify time saved per day – Estimate the minutes your team will save by using AI. In one survey, AI users saved about 69 minutes per week searching for information. Suppose your team saves 30 minutes per day.
2. Calculate annual time saved – Multiply daily time saved by the number of work days in a year (about 249 days). Example: 0.5 hours × 249 days = 124.5 hours saved.
3. Assign an hourly value – Multiply hours saved by the hourly wage of the employee doing the work. If the employee earns $20 per hour, the value of saved time is $2 490.
4. Estimate cost of the AI tool – Add setup costs, monthly subscription fees, and any training expenses. For example, setup might cost $500 and monthly fees $50 × 12 = $600. Total costs = $1 100.
5. Compute ROI – Net benefits = $2 490 (value of saved time) – $1 100 (costs) = $1 390. ROI = ($1 390 ÷ $1 100) × 100 = 126 percent. This means the project returns more than its cost.

Other metrics to track

 Besides direct financial gains, track operational and customer experience metrics:

• Task completion time – Measure how long a task takes before and after AI adoption.
• Error rates – Record reductions in mistakes after using AI.
• Customer satisfaction scores (CSAT/NPS) – Monitor improvements in customer sentiment and loyalty.
• Employee satisfaction – Ask employees how AI affects their workload and job satisfaction.

Use these metrics to capture both tangible and intangible value. If benefits outweigh costs, the project is likely a good investment.

Evaluating AI vendors: questions to ask

Selecting a vendor is not just about price. Use the checklist below to find a trustworthy partner. Each category includes questions drawn from legal and industry experts

1. Understand the technology

• Overview – Ask for a clear explanation of how the AI works and what data it uses.
• Data inputs – Find out what data sources train the model and whether they are properly licensed.
• Use of customer data – Clarify how your data will be used, de-identified, and protected.

2. Check data privacy and security

• Policies on web scraping – Ask if the vendor uses scraped data and how they comply with copyright and privacy laws.
• Guardrails and bias checks – Inquire about processes that review accuracy and bias.
• Incident response plan – Confirm there is a plan for handling security incidents.
• Regulatory compliance – Ask how they comply with laws such as GDPR or CCPA.
• Insurance – Determine whether the vendor has insurance for AI-related losses.

3. Evaluate alignment and ease of use

• Business fit – Does the tool solve your specific problem
• Training on your data – Can the AI be trained on your proprietary data for better results?
• Scalability – Will the tool scale as your needs grow?
• User experience – Ask to see how non-technical users interact with the tool and how long onboarding takes.
• Trial period – Request a pilot or trial so you can test the tool before committing.

4. Review deployment and integration

• Technical requirements – Understand any hardware or software you need.
• Compatibility – Check how well the tool integrates with your current systems.
• Performance monitoring – Ask how the vendor tracks model performance and drift over time.
• Implementation timeline – Get a clear integration timeline.

5. Consider internal skills and support

• Skill requirements – Determine what skills your team needs to implement and maintain the tool.
• Training resources – Ask what training and onboarding materials the vendor provides.
• Third-party support – See if they recommend partners for implementation and ongoing support.

6. Examine data management and ownership

• Data connection – Find out how the AI connects to your data and whether preparation is needed.
• Data storage – Ask where your data is stored and whether it leaves your organisation.
• Training data – Confirm the vendor had the right to use training data and that it is appropriate for your use case.
• Ownership of model and outputs – Clarify who owns the model, inputs and outputs.

7. Verify security and ethical commitments

• Data protection measures – Ensure encryption and other controls protect your data.
• Access and permissions – Review security settings and user permissions.
• Legal and regulatory compliance – Ask how the vendor monitors new regulations.
• Bias and explainability – Ask how they prevent bias and make their models explainable.
• Ethical standards – Inquire about their ethical guidelines.

8. Assess customer support and ROI

• Support and resources – Ask about ongoing support, access to experts, and documentation.
• Pricing model – Understand how they price their service and whether there are hidden fees.
• ROI estimate – Request an estimate of potential ROI for your use case.
• Case studies – Ask for success stories from similar companies.

9. Check model stability

• Model source – Find out which large language models power the tool.
• Backup plans – Ask about fail-over plans if the primary model is unavailable.
• Update frequency – Know how quickly they incorporate model updates.
• Decommissioning plans – Ensure they have a plan for handling your data and systems when the service ends.

Use these questions to compare vendors. A trustworthy vendor will provide clear answers and written commitments in their contract.

Moving forward

You do not have to master everything at once. Start by identifying one or two areas where AI can help you save time or improve customer experience.

Check your readiness by reviewing the four pillars, then run a small pilot project. Calculate ROI using the simple worksheet and track both financial and non-financial benefits.

Finally, use the vendor checklist when talking to providers. With careful planning and small wins, you can integrate AI into your business with confidence.