The hype around artificial intelligence sounds tempting, but a jungle of point solutions can quickly waste money and energy. SMEs that want to get real value from AI should not start with code or tools, but with the right questions. Below are the core questions every wholesaler, distributor, accounting firm, or local manufacturer should ask in 2025 before approving any budget.

Why asking good questions is essential

McKinsey recently estimated that companies miss around 40% of potential AI value because they think too late about process selection, data quality, and change management. For SMEs, where margins are thinner and specialists are scarce, a wrong implementation can hurt even more. By asking critical questions upfront, you avoid:

• expensive proof of concepts that never reach production,

• hidden integration costs after purchase,

• employee resistance because benefits and risks remain unclear.

1. Strategy and goals

1. Which specific business objective (time savings, error reduction, revenue growth) will the AI solution support?

2. Does automation fit our long term vision, or is it an ad hoc patch?

3. How will we measure success? Think of KPIs such as quote to order lead time, or average time to collect payments.

4. Does the process have enough volume to make automation profitable? An accounting firm producing twenty monthly reports will benefit faster than one producing two.

Tip: In the article AI versus manual work: which saves more time and money? you can read how to quantify the impact of repetitive tasks.

2. Data and integrations

1. Which data sources (CRM, ERP, spreadsheets) are needed, and how clean is the data?

2. Can the AI tool connect out of the box with our software, or is custom work required?

3. Does the supplier support API access so we can add our own agents later?

4. Where is data stored, and how is encryption handled?

For wholesale companies with thousands of SKUs, product data quality is crucial. Without standardized fields, an AI quote generator can send out incorrect pricing.

3. Compliance and security

1. Does the solution comply with GDPR and industry specific regulations (for example, accounting: NBA guidance, real estate: Wwft)?

2. How are audit logs stored so we can reconstruct decisions afterward?

3. Does the vendor have ISO 27001 certification or comparable standards?

4. Are models hosted internally, or does everything run in a third party cloud where data residency becomes a risk?

4. ROI and cost structure

1. What is the total cost of ownership (TCO): licenses, setup, integration, and maintenance?

2. How quickly do we expect to reach break even given our order volumes or hourly rates?

3. Is there a pay per use model so we can start small?

4. What savings in FTE hours or external consulting can we realistically achieve?

Also read: 5 Must-Have AI Tools to Streamline Your Business tasks for inspiration on scaling in a modular way.

5. Change management and skills

1. Which roles disappear, change, or emerge because of automation?

2. What training budget do we reserve for employees, for example prompt engineering or data quality?

3. How do we ensure knowledge stays in house and not entirely with the supplier?

4. Is there an internal process owner who monitors adoption and collects feedback?

6. Scalability and future proofing

1. Can the system grow from one department (for example accounts receivable) to a full end to end sales chain?

2. Does the supplier support new LLM versions without forcing us to migrate again?

3. Can we add our own models or agents for niche tasks, such as customs documents for export?

4. Is there a roadmap for features like multimodal input (voice, video) or real time analytics?

7. Ethics and governance

1. How does the solution handle bias, for example in lead scoring where women or new businesses are disadvantaged?

2. Is there a human review step built in for decisions with legal implications?

3. Can we explain model outcomes to customers and auditors (explainability)?

4. Is there an escalation procedure when the AI shows abnormal behavior?

8. Vendor evaluation and support

1. What success stories does the vendor have in similar SME sectors?

2. Is there 24/7 support or an SLA with clear response time guarantees?

3. Do we get periodic optimization reviews, or must this be purchased externally?

4. Does the provider offer a pilot project so we can limit risk?

How to move from questions to action

1. Map all processes and prioritize based on impact and feasibility.

2. Put the questions above to at least three vendors so you can compare apples to apples.

3. Start with one clear use case (for example automated follow up emails from the CRM pipeline) and measure results.

4. Communicate early with employees, make wins visible, and scale steadily.

Conclusion: the shortest route to a profitable AI implementation

AI is not a magic wand, but it can be a powerful tool that only pays off when the basics are in place. By systematically asking the questions above, you build a business case that holds up in board meetings and on the work floor.

Want to see how these questions translate into concrete automations for sales, marketing, and operations? B2B Groeimachine helps SMEs with plug and play workflows or fully custom builds. Schedule a no obligation strategy call today and discover how your team can do less manual work tomorrow and close more deals.