Businesses often come to us knowing they want to use AI, but not always knowing exactly how.

That is normal.

The most successful AI projects rarely begin with, “Which model should we use? ChatGPT, Claude, Gemini, etc.” They begin with a much more important question:

What are you trying to accomplish?

At Pixo, our discovery process is designed to understand the business problem first, then determine whether AI is the right solution, what kind of AI makes sense, and what it will take to build something useful, secure and measurable.

Here are 10 questions we ask early in the conversation, and why they matter.

1. What would you like AI to help your business do?

This question shifts the conversation away from technology and toward outcomes.

Maybe you want to reduce repetitive work, improve customer response times, analyze large amounts of information, automate a workflow, help employees find answers faster or create a more intelligent customer experience.

We want to understand the result you are after before recommending a particular technology.

AI should serve the business objective, not become the objective itself.

2. Tell us about the problem or process you want to improve.

This is often the most important discovery question.

We want to understand where the friction exists today.

Is a process too slow? Too manual? Too expensive? Too dependent on one person? Are employees spending hours searching for information? Are customers waiting too long for answers?

The clearer we understand the problem, the better we can determine whether AI can meaningfully improve it.

3. How are you handling this today?

Before we design something new, we need to understand the existing workflow.

That includes the people involved, the steps they follow, the tools they use and the parts of the process that create the most frustration.

Sometimes the best AI opportunity becomes obvious once we map what is already happening.

A process that requires five employees to manually copy information between systems may need automation. A process that requires an expert to interpret documents may benefit from AI-assisted analysis. A team that repeatedly searches through thousands of files may need an intelligent knowledge system.

Understanding the current process helps us identify where AI can create the most value.

4. Who would use the solution?

A custom AI tool for 10 internal employees is very different from a customer-facing AI system used by thousands of people.

We want to know:

• Who will use it?

• How often will they use it?

• What level of technical experience do they have?

• Will customers interact with it directly?

• Will employees review the AI's work before anything happens?

These answers influence the user experience, infrastructure, security requirements and overall architecture.

5. What systems or software would it need to work with?

AI usually does not operate in isolation.

A useful AI solution may need to connect with your CRM, ERP, accounting platform, website, ecommerce system, document management system, customer portal or internal database.

For example, an AI agent might need to:

Read customer information from Salesforce.

Search documents stored in SharePoint or Google Drive.

Create support tickets.

Update records in an internal application.

Pull product data from an ecommerce platform.

Send information into an existing workflow.

Understanding integrations early helps us accurately evaluate complexity and design the right architecture.

6. Where does the information it would need live today?

AI is only as useful as the information it can access.

That information might live in:

Databases.

PDFs and documents.

Emails.

Spreadsheets.

Customer records.

Product catalogs.

Internal websites.

Cloud storage.

Legacy systems.

APIs.

Or sometimes all of the above.

We also want to understand whether that data is organized, current and accessible.

This gives us an early indication of your AI readiness and whether additional work may be needed to prepare, connect or structure the information before AI can use it effectively.

7. What would a successful result look like?

AI projects should have measurable business outcomes.

Success might mean:

Reducing a process from two hours to 15 minutes.

Handling 50% of common customer questions automatically.

Helping employees find information in seconds instead of searching through hundreds of documents.

Reducing manual data entry.

Improving lead response time.

Increasing conversion rates.

Reducing errors.

Lowering operational costs.

When we define success early, we can design the solution around a meaningful business metric rather than simply delivering an interesting AI demo.

8. Do you have a desired timeline?

Some AI initiatives are exploratory. Others are tied to a product launch, operational deadline or strategic initiative.

Knowing the timeline helps us determine the appropriate development approach.

In some cases, the best first step may be a focused proof of concept that validates whether the idea works before investing in a larger implementation.

In others, the technology and data may already be ready for a production solution.

The timeline helps us recommend the right path.

9. What investment range are you considering?

Custom AI solutions can vary dramatically in complexity.

A focused internal automation may be relatively straightforward. An enterprise AI platform integrating multiple systems, user roles, proprietary data and advanced security controls can be substantially more complex.

Understanding the investment range helps us recommend an approach that matches the opportunity.

It also allows us to identify whether the project should begin with a smaller proof of concept, a phased implementation or a broader production build.

The goal is not simply to build AI. The goal is to build something that produces enough value to justify the investment.

10. Anything sensitive, regulated or security-related we should know about?

This question is especially important with AI.

Your solution may involve personally identifiable information, financial records, healthcare information, proprietary business data, customer information or other sensitive content.

That can affect:

Where data is stored.

Which AI models can be used.

What information can leave your systems.

User permissions.

Audit requirements.

Data retention.

Compliance requirements.

Security architecture.

Human review.

We want to identify these requirements early so security and governance are part of the architecture from the beginning rather than something added later.

Good AI Development Starts With Good Discovery

One of the biggest mistakes businesses can make is starting with the technology.

“We need ChatGPT.”

“We want an AI agent.”

“We want to build something with Claude.”

Those may eventually be part of the solution, but they are not the starting point.

The better starting point is:

What business problem are we solving, and what would meaningful improvement look like?

From there, we can evaluate the workflow, users, systems, data, integrations, risks and economics of the opportunity.

Sometimes the answer is a custom AI agent.

Sometimes it is an AI-powered search system.

Sometimes it is intelligent workflow automation.

Sometimes it is a custom application that combines traditional software development with AI.

And sometimes AI is not the best answer at all.

That is exactly why discovery matters.

Exploring a Custom AI Project?

You do not need to have the technical details figured out before talking with us.

You just need to understand the business problem you would like to solve.

Pixo can help evaluate the opportunity, determine what is technically feasible and design a practical path from idea to implementation.

Tell us what you would like AI to help your business do. We will help you figure out what comes next.