5 Questions To Ask Before Implementing Your Next AI Solution

by Abdullah Khalid

Jan 15, 2026 | 03 min read

Hi there,

AI has moved fast from experimentation to boardroom priority.

But as more businesses adopt AI, a pattern is emerging: the biggest challenges aren’t about intelligence, they’re about reliability, practicality, and fit for real-world operations. Many AI initiatives stall not because the models aren’t powerful, but because the fundamentals were never questioned upfront.

Before selecting your next AI vendor, it’s worth anchoring your evaluation around five critical questions. These questions don’t just assess technology, they reveal whether a solution can actually deliver value at scale.

Here’s what to ask, and Bizom’s point of view on why each one matters.

1. How secure is your AI and who owns the data?

AI solutions often process highly sensitive information: sales numbers, outlet-level data, pricing, inventory movement, and customer insights. Any compromise here can quickly turn into a business risk.

Ask your vendor:

Where is the data stored?
Who has access to it?
Is your data used to train external or shared models?
What compliance and security standards are followed?

Bizom POV:
Security isn’t a feature, it’s a prerequisite. AI must be built with enterprise-grade data protection from day one. Businesses should retain full ownership of their data, with clear boundaries on how it’s stored, processed, and used. Trust in AI starts with trust in how your data is handled.

2. Will the solution scale as my business grows?

Many AI tools perform well in controlled pilots but struggle when rolled out across regions, teams, or larger datasets. What works for 20 users may fail for 2,000.

Ask your vendor:

How does performance change with scale?
Can the system handle growing data volumes and user bases?
What happens to cost and complexity as you expand?

Bizom POV:
AI should grow with your business, not slow it down. True scalability means consistent performance across geographies, product lines, and field teams, without frequent rework or escalating costs. If AI can’t handle scale, it becomes a bottleneck instead of an advantage.

3. How do you prevent hallucinations and incorrect recommendations?

AI can sometimes produce confident but incorrect outputs. In a business setting, acting on such recommendations can lead to lost revenue, poor decisions, or erosion of trust among teams.

Ask your vendor:

How are outputs validated?
What checks exist to reduce incorrect or misleading recommendations?
When is human intervention required?

Bizom POV:
In business, accuracy matters more than novelty. AI should be designed to prioritise reliability over flashy responses, using guardrails, validations, and context-aware logic. When uncertainty exists, the system should flag it, not guess. Responsible AI earns adoption by being dependable.

4. Is the solution affordable in the long run and not just at launch?

AI value compounds over time, but so can costs. Many solutions appear affordable during pilots and early stages, only to become expensive as usage increases.

Ask your vendor:

How does pricing change with more users or data?
Are there hidden infrastructure or usage-based costs?
What does total cost of ownership look like over 2–3 years?

Bizom POV:
AI should deliver ROI at scale, not just in demos. Sustainable affordability ensures that businesses can expand usage confidently without constantly re-evaluating costs. The best AI solutions are those where value grows faster than spend.

5. Can it work reliably in offline or low-connectivity environments?

In real-world sales and distribution, connectivity is often inconsistent. If AI depends entirely on constant internet access, adoption breaks down where it matters most i.e. on the ground.

Ask your vendor:

What functionality is available offline?
How does data sync once connectivity is restored?
Is the experience designed for field realities?

Bizom POV:
AI built for emerging markets and on-ground execution must be resilient by design. Offline readiness isn’t an edge case; it’s essential for consistent usage, accurate data capture, and frontline adoption. Practical AI works wherever your teams work.

Final Thoughts

AI isn’t just a technology choice, it’s a long-term operational decision. Asking the right questions early can mean the difference between AI that looks impressive and AI that actually drives outcomes.

At Bizom, we believe AI works best when it’s secure, scalable, accurate, affordable, and built for real-world conditions, because that’s what it takes to move the needle in sales and distribution.

If you’d like, you can connect with our Chief AI officer who can help you deep dive on the above points before selecting your next AI solution.