Trustworthy AI Starts with Trustworthy Data
Most internal artificial intelligence (AI) projects fail, according to a recent study by MIT’s Networked Agents and Decentralized AI (NANDA) initiative. However, the curious reason behind this failure is not because the algorithms are weak or the technology isn’t ready. They fail because of something far more basic: bad data. The MIT study entitled “The GenAI Divide: State of AI in Business 2025” found that 95% of in-house generative AI projects don’t deliver real value, and the reason is almost always the same. Companies build sophisticated models on top of messy, unverified, or legally questionable data, then wonder why their AI can’t find the right acquisition targets or keeps serving up outdated company profiles.
Bad data doesn’t just waste time or slow down your process. It actively distorts valuations, hides genuine opportunities behind false negatives, and points you toward deals that will never close because the fundamentals were wrong from the start. When you’re trying to find acquisition targets in emerging sectors where information is scarce and hard to verify, one corrupted data point can send your entire analysis down the wrong path.
As we noted in our previous blog, the real competitive advantage in AI deal sourcing software doesn’t come from having the fanciest algorithm or the most computing power. It comes from having trustworthy data feeding that algorithm, and building that foundation is harder than most dealmakers realize. It requires legal expertise, ongoing maintenance, quality assurance protocols, and a clear understanding of where every data point comes from and whether you have the right to use it. Without that foundation, you’re just building expensive tools that produce unreliable results.
Reliable M&A Software
Even legitimate data needs work before it’s useful. Raw information is full of duplicates, inconsistencies, and errors that weaken any analysis. Quality assurance isn’t optional if you want your M&A software to actually work.
Here’s what separates functional deal origination software from noise:
- Validation: Check for accuracy and completeness before data enters your systems.
- Deduplication: Identify and remove repeated or redundant entries that skew results.
- Entity Resolution: Ensure companies, investors, and founders are correctly identified and not mistakenly merged or split.
- Anomaly Detection: Use statistical checks or AI-driven validation to find outliers that could distort analysis.
- Benchmarking: Compare datasets to market references to maintain competitive accuracy.
These steps are critical in private equity deal origination software where a single bad data point can mess up a valuation or hide a legitimate opportunity.
The Best Financial AI Runs on Compliant Data
Companies often miss a critical legal minefield, which is that public data is not automatically free to use. Online datasets frequently contain proprietary, confidential, or copyrighted content that looks accessible but carries significant restrictions. When organizations scrape vast amounts of web data and feed it into models under the assumption that “public” equals “permissible”, they expose themselves to intellectual property issues and regulatory consequences that could have been avoided with proper due diligence. The strongest financial AI should be built on curated, compliant data pipelines designed to handle sensitive information responsibly.
This challenge becomes even more complex on a global stage. Dealmaking crosses borders; so do datasets, but the rules that govern them do not. A compliant AI finance system must track where data originates, how it’s processed, and whether it can legally move across jurisdictions, because mistakes lead not only to fines but also to eroded trust with partners who expect professional stewardship of their information. This is especially crucial for platforms that translate private-company data across several languages.
Ultimately, the value of AI-driven due diligence and valuation software depends entirely on the trustworthiness of its data. Strong privacy, security, and governance practices are more than compliance checkboxes; they are competitive advantages in a world where breaches and privacy concerns regularly make headlines. Organizations need robust governance policies, encryption standards, and transparent consent frameworks that demonstrate they understand the responsibility of handling sensitive financial intelligence.
When dealmakers rely on AI to guide multi-million dollar decisions, they need confidence that the insights are grounded in reliable, properly sourced data.
Deal Origination Platforms Need Maintenance
Data decays faster than people think, and in private markets the decay happens at an accelerating rate. Company ownership changes hands through secondary transactions that often don’t make headlines. Competitors may adjust their business models in response to market conditions. Funding rounds can close quietly, and the startups that were desperate for capital six months ago might now be flush with cash and disinterested in new investors. Private market information becomes outdated in days, not weeks, making stale data useless.
Most companies underestimate how resource-intensive this ongoing maintenance actually is when they’re building their business case for in-house AI. It’s not a one-time setup where you load the data, train the model, and just let things happen. It requires continuous technical infrastructure, dedicated data engineers, quality assurance processes, and long-term strategy. That’s why specialized platforms often deliver better results at lower total costs than trying to build everything in-house, especially for mid-market firms without dedicated AI teams available around the clock.
Building and maintaining compliant, continuously reliable data infrastructure requires technical skill, legal expertise, and deep market understanding across multiple jurisdictions and industries. Few organizations can sustain this level of complexity in-house while still focusing on their core strategy of finding deals, conducting due diligence, and closing transactions. The hidden costs of data maintenance, compliance monitoring, and quality assurance add up quickly, and many firms discover too late that what seemed like a manageable project has become a resource drain.
Partnering with Experts in Deal Sourcing Platforms
Without regular updates, even the smartest AI deal making origination tools lose their edge and start pointing you toward irrelevant, outdated opportunities.
That’s where purpose-built AI platforms make the difference for firms that want to move quickly without sacrificing quality. Cyndx provides businesses with a powerful deal sourcing platform to discover and evaluate investment opportunities, strategic partnerships, and acquisition targets using AI that evolves with changing markets. Each tool is built on ethically sourced, continuously maintained, and compliance-ready data that’s been verified through multiple quality checks.
- Finder helps teams explore companies and uncover opportunities, investors and comparables on over 31 million companies across industries and geographies.
- Acquirer identifies logical acquisition targets based on strategy and profile, explaining why each target fits the thesis.
- Raiser connects companies to potential investors based on industry and deal activity, matching firms with the right capital sources.
- Valer streamlines valuations with advanced financial modeling that produces professional-grade reports in minutes.
- Scholar delivers comprehensive, citation-backed research reports that combine proprietary and external data for thorough due diligence.
These tools give dealmakers confidence that their insights are grounded in data they can trust and defend. They reduce time spent on manual validation and verification while improving accuracy in every deal decision.
Owning the most data or the biggest dataset is good, but the competitive advantage in AI deal sourcing is much more than that. It’s about having the right data, verified and maintained the right way.
And that’s where trustworthy, useful AI really starts. Contact us to learn more.