At CCQ Tech, we’re currently introducing AI models into businesses as part of ongoing digital transformation projects. Clients often rely on time-consuming, manual processes to extract and copy data into their systems. At high volumes, human error becomes a problem: users make mistakes, skip fields, and often use inconsistent data definitions. These errors stack up quickly, impacting reporting and calculation accuracy, and decision making suffers. 

In light of this, the growing pressure to integrate AI makes sense: when properly trained in the right context, AI models can read documents, identify the right data to extract, and map fields automatically. AI not only saves time on these manual tasks, but notably (when implemented properly) it brings a level of consistency to data ingestion which now outperforms humans. 

The implementation makes all the difference. At CCQ Tech we start with a proof of concept to demonstrate we can successfully leverage the right model to automate tasks and ingest data into client systems, then move on to MVP and scale up ready for a live release. Our experience across projects and industries allows us to adapt to different business needs, and technology like AWS Bedrock allows us to stay agile and change models when needed.

Despite AI’s potential, our experience has taught us that there are also pressure points which need to be carefully considered when implementing AI. 

Three areas to consider

1. Thorough audit logging & review

Ensuring that data going into a model is accurate and that data coming out is mapped correctly is really important. We all know the dangers of AI hallucinations and oversimplifications, so audit logging and human review points in the workflow are essential. 

Proper logging is essential when AI is used to populate a field, and the system should log: 

• That AI generated that value

• The value itself

• Any previous values

• The AI’s source documentation

This logging process is critical, and a business needs to be able to distinguish an AI-generated value from a human-entered one, identify which outputs need review, and revert bad results without manually checking everything.

2. Overconfidence in the face of uncertainty

While AI models often output with unwavering confidence, out-of-the-box models often don’t have the accuracy to back this up. AI models often extract, map and populate data fields without proper certainty checks. When taken from outdated or incorrect sources, data outputs can often be believable but inaccurate, a dangerous combination for businesses. The consequences of these inaccuracies can lead to significant problems downstream and can be particularly detrimental when used to support decision-making and client-facing reports.

A well-built integration accounts for this. A confidence scoring system means that when a model is not confident about a value, it can leave the field blank and flag it for human review, surfacing where it sourced each piece of information so the human reviewing the output can make an informed decision without wasting time digging through documents. 

At CCQ we put significant thought into how AI outputs are tested, at what stage, and how human review is built into the process, ensuring that the final output of the project reliably meets business needs.

3. AI models lack the training your team has

Document formats can vary widely, with complexities and edge cases in specific contexts. The rules governing what to extract and where to map it can be complex, specific and often poorly documented.

In many businesses, this knowledge lives in the heads of key employees who have had years of training and experience. Having a person as a single source of truth is risky for a business, and it can also be an obstacle to proper training for AI models.

To reduce uncertainty and ambiguity as much as possible, AI needs to be trained rigorously with explicit rules and logic. Surfacing that institutional knowledge, turning it into documented logic, testing it against real cases, and embedding it as governing rules is an indispensable part of these projects. While this process can be time-consuming, it does have the additional benefit of dispersing and documenting any knowledge which might have been tied to individual employees (posing a resilience risk if they leave the business).

Many AI integration projects start with a proof of concept, and rightly so: it's a sensible way to test whether a model can handle the core task before committing to a full build. The problem is that most PoCs answer the easy question and skip the hard one.

Take a common scenario: a user adds a new order to the system, and every order needs a supplier. The business has a long list of existing suppliers, and avoiding duplicates matters. "Acme (UK)" might already exist, and an invoice that arrives listing "Acme UK" needs to be recognised as the same entity, not added as a new one. A human handling this would start typing "Acme" into an autocomplete field, see the existing supplier appear, and click it. In more ambiguous cases, they might open the supplier record to check the address or recent orders before deciding whether it's a match.

The final shape of the data submitted is simple: a supplier ID. But the process to arrive at that ID involves navigating the wider system, applying judgment, and using the software as a tool.

This is where most PoCs fall short. They focus almost entirely on data extraction, pulling fields out of a PDF into a structured format. AI handles this well: it's exactly what these models are built for, and a quick sense-check confirms it works. But demonstrating extraction is the easy part of the problem, and on its own, it doesn't tell you much about whether the integration will actually work in production.

The hard part is mapping the process. The data structure you exchange with the AI can't only describe the final stored shape: it has to encode the decisions and actions taken along the way. Instead of just returning a supplier name, the model might return something like {supplier: {action: 'lookup', name: 'Acme UK'}} when it needs the system to search existing records, or {supplier: {action: 'new', name: 'Acme UK', note: 'distinct from Acme (UK)'}} when it's confident this is a genuinely new entity, or, after a round of lookup and confirmation, {supplier: {action: 'existing', id: 'abc'}}.

You also can't simply hand the model your entire supplier list. With tens of thousands of records, that's not feasible. What you need is a pipeline that lets the model behave agentically within your data structure: querying, confirming, and committing actions step by step, while your business rules stay firmly in control of what's actually allowed to happen.

The underlying issue is that your software wasn't designed for AI to interact with, and AI wasn't designed to run loose inside your software. Bridging the two means building flows that suit how LLMs work rather than how human users work, and that design effort is what a serious PoC needs to surface, not gloss over.

A proof of concept should be scoped with the full integration in mind, otherwise a business risks doing a PoC performatively, just to confirm that it can perform basic ingestion tasks (which we already know is the case).

At CCQ, we approach all proof of concept projects with this in mind, as this helps to give our clients a realistic picture of project cost and timeline before a business commits, which is ultimately the goal of a PoC.

Three questions that help define a better PoC and project scope

Before you define the scope of an AI integration project, three questions will reveal more about what you are actually committing to.

1. Does your solution have a comprehensive audit trail?

If the model produces a wrong output, can you identify it, trace its source and revert it without manually reviewing everything? Building this capability should be part of your project scope from the start, and is not a concern to be addressed later. 

2. What happens when AI is uncertain?

Where will a human review the process to ensure consistency and accuracy? AI is undeniably useful when automating manual processes, but human review is still critical and must be factored into any project scope. Without accounting for this, the consequences can be detrimental to a business, posing a risk to accurate reporting and data led decision making.

3. Is your business logic written down?

Getting the right data out is often underestimated. There can be many different formats to handle, which vary by country, by supplier, by client. Certain document types or data points only apply in certain contexts. Each of these may require different logic. Mapping this variety before scoping begins is one of the most effective ways to protect a project timeline and budget.

These business rules need to be explicit before they can be implemented. That means documenting the decisions that need to be made, the exceptions that need to be handled and the context that currently exists only in the minds of the people doing the work. AI cannot apply rules it has not been given, and documenting these rules has the hidden benefit of decentralising business knowledge, which is at risk if key employees leave.