Blog 1
Getting Started With Trust
This first blog addresses the key points for getting started at a high level before going deeper into specific trust, security, business, and rollout questions.
At CDIA Connect 2026, a great event with over 250 practitioners focused on credit data furnishing and disputes, Bridgeforce Data Solutions co-sponsored a lunch focused on AI adoption. We polled attendees on a straightforward question: What is your biggest AI adoption concern?
The results were telling. But what made them even more meaningful were the conversations that followed at the lunch tables, where the concerns behind the poll answers came through even more clearly.
Top AI Adoption Concerns
Source: CDIA Connect 2026 | Bridgeforce Data Solutions
The takeaway: the industry's hesitation isn't about whether AI has great potential. It's about whether AI can be trusted to work correctly in a regulated, high-stakes process like credit bureau disputes.
This aligns with what we see more broadly. McKinsey's 2026 AI Trust Maturity Survey found that 74% of organizations cite inaccuracy as a top AI risk, and nearly two-thirds say security and risk concerns are the top barrier to scaling agentic AI.
We have invested heavily in recent years in developing AI-enabled solutions, including our AI Research Assistant and our AI Resolution Engine for credit bureau disputes. We have learned quite a bit along the way, building on our experiences in a highly regulated environment inspecting billions of Metro 2® records.
The combination of the insights from the poll and the follow-on conversations made us feel that a series of blogs could be helpful for professionals in this space. So here we go.
In this first blog, I will address the key points for getting started at a high level, with the follow-up blogs going deeper on specific parts.
Blog 1
This first blog addresses the key points for getting started at a high level before going deeper into specific trust, security, business, and rollout questions.
Blog 2
The second blog will focus on the information security aspects, since addressing those concerns is the price of admission to being able to even consider the business outcomes.
Blog 3
The third blog will focus on evaluating business outcomes and related compliance questions by asking the right questions at the right level of detail.
Blog 4
The fourth blog will focus on processes for rolling out solutions that do real things, from getting started to the iterative enhancements that follow.
This is a commonly asked question, and it is an important one. But the first key to getting useful answers starts with improving the question itself. "Can I trust AI?" is incomplete and therefore cannot be answered with the specificity needed.
To have more meaningful answers, we need to add two things to "Can I trust AI...":
This is the most critical step. AI capabilities are impressive overall but remain very uneven across different use cases. The term “AI” is also used to cover a very wide variety of tools and technologies, and the relevant types of AI are determined by what exactly we are asking the technology to do.
Being precise about what you need a solution to do is therefore critically important.
We will address this in two broad categories:
Trusting AI to keep data and intellectual property secure.
Trusting AI to execute the in-scope tasks.
This is also a step where many people and organizations struggle, especially on the business outcomes side. AI's capabilities can now potentially be helpful in many use cases that are currently highly manual and not very well defined.
If you can be precise in your business use cases regarding exactly what you need from the solutions, then you have created the foundation for evaluating whether to trust the results.
This is also why highly regulated activities with many specific rules can be great places to start with AI. The definitions of what are, and are not, required to be done are often well defined, which gives you a much stronger starting point for evaluation.
For questions about business outcomes, this involves a mindset shift from prior eras of automation, and it is driven by the nature of the use cases that AI can now address. Many of them are highly imperfect today.
The required definition of "how well" will vary greatly based on the answers to the first question, which is why we need both parts. This means that the evaluation criteria in many instances need to be focused on percentiles, not Six Sigma.
In a Six Sigma process, the goal is to be under 3 defects per million, so getting it right 99.9997% of the time. Contrast that to a highly manual process with significant variance in quality across individuals.
For perspective: we see that human agents typically address 60-85% of the data issues in the dispute requests that are detected by the Data Quality Scanner disputes module and then create new data inconsistencies about 10% of the time. An AI solution that consistently achieves >90% correction rates and creates new data inconsistencies <1% of the time on the same tasks represents a meaningful improvement.
It is not Six Sigma, but it is materially better than the baseline, and the consistency itself has value. The right benchmark depends entirely on where you are starting from and what you are trying to accomplish.
I will expand on this in the next two blogs, but the short answer is that for most business people the right level is all about the business logic layer, completely independent of the technology being used.
Getting this right is critical, which is why it requires deeper focus in a separate blog. Staying at too high a level exposes you to avoidable problems, but trying to go too deep creates confusion without helping the quality of the answer. There is a productive middle ground, and we will walk through it.
This is the approach we took when building our AI Resolution Engine for credit bureau disputes and our AI Research Assistant for furnishing, and that we continue to take as we evolve and refine them.
Helps dispute teams assemble the right context, identify evidence gaps, and support more consistent, documented reviews while keeping human oversight central.
Helps furnishing teams investigate account history, review fields over time, explore DQS results in plain English, and better understand what is driving an issue.
We started with precisely defined use cases, a ruleset that has been refined over many years, and measures of success against benchmarks relevant to what these tools are for.
In the next blog, we will start with the question:
"Can I trust AI tools to keep our data secure with as much confidence as I have today in other technologies?"
Throughout this series, please let me know your feedback. I'm trying to avoid going too lengthy in any single post but want to share enough to be helpful.