I think it's always a good idea for issuers in the municipal bond market to pay attention to the trends and best practices of issuers in other capital markets. When I was debt manager for the Commonwealth of Massachusetts, I modeled a number of new initiatives and programs on what I observed corporate issuers doing. Today, it's worth watching how corporate issuers are responding to the changing demands of artificial intelligence-armed institutional investors.
Municipal bond issuers have always known that primary market offering documents and continuing disclosures are the lifeblood of investor confidence. For decades, those words were read by people — rating analysts, sales & underwriting, and credit teams on the buyside — whose professional judgment guided credit decisions. But today, a new reader has entered the scene: AI.
Across the municipal market, institutional investors and data providers now rely on large language models and natural language processing tools to parse, categorize, and interpret issuer disclosures at scale. These systems scan thousands of pages of financial statements and management discussions in seconds — extracting data, detecting sentiment, and benchmarking issuers across peers.
That shift fundamentally changes how municipal disclosures are consumed. Human readers still matter, but increasingly, so do machines. AI-driven credit research platforms ingest EMMA filings, scrape investor relations websites, and analyze the Management Discussion & Analysis narratives in annual comprehensive financial reports. They're not just pulling ratios. They're assessing clarity, tone, and risk language used by issuers.
This means that ambiguous or inconsistent phrasing can trigger unintended signals. A sentence like "the City may consider budgetary adjustments if necessary" might sound cautious to a person, but an algorithm could interpret it as fiscal stress. Similarly, using different terms like "reserves," "fund balance," or "unrestricted assets" for the same concept can fragment an issuer's credit profile in automated databases.
Algorithms detect sentiment, measure readability, and can flag key themes such as governance, fiscal stress, climate exposure, or cyber risk. If disclosure narrative is disorganized, overly technical, or heavy with boilerplate language, AI may tag it as opaque or high-risk. Clear, well-structured disclosure with defined sections, labeled assumptions, and consistent terminology likely produces cleaner machine interpretations that result in building confidence among investors.
AI doesn't just read a single issuer's disclosure in isolation. Instead, it compares it to the disclosures of peer issuers. Credit-data platforms can then benchmark municipal issuers based on the completeness and tone of their disclosures. For example, if a discussion of pension liabilities or climate resilience lacks detail, while peers provide thorough quantitative context, AI may score that issuer's narrative as relatively weaker, even if the credit fundamentals are in fact strong.
In this environment, maintaining parity in robust disclosure quality and data structure is essential to ensuring fair representation in automated credit assessments.
This isn't about writing "for robots." It's about writing in a way that both humans and algorithms can accurately understand an issuer's story, which is a win-win for market transparency.
This is also happening in the corporate markets, and corporate CFOs and IR professionals are adjusting. According to interesting research by Keren Bar-Hava of the Hebrew University, quarterly reports are being written for AI. In short, corporate finance professionals are writing disclosure in a way to optimize algorithmic interpretation.
Municipal issuers should work with their disclosure counsel to adjust content in similar fashion. By writing with consistency, precision, and structure, it will help ensure that AI tools interpret disclosures correctly, reducing the risk of misrepresentation. The same best practices that support regulatory compliance (precision, transparency, and consistency) are now critical defenses against algorithmic misinterpretation.
Issuers may also want to monitor how their data appears on public credit-data platforms, much like monitoring media coverage or rating agency commentary. Understanding one's "AI footprint" will become a key investor relations function.
This new reality is an opportunity for municipal issuers, and not a threat. It's also not merely a compliance exercise. I think it should be viewed as a strategic opportunity. Issuers that communicate in clear, data-friendly formats can enhance visibility in investor screening tools, improve comparability, and have their disclosure scored accurately, which over the long term may even lower borrowing costs. Just as user-friendly IR websites and continuing disclosures once distinguished proactive issuers, AI-readable narrative will become the new hallmark of transparency.
Municipal issuers have always worked to communicate faithfully to the market. Today, it is essential to realize that the audience includes both humans and algorithms. By modernizing how we write and structure our disclosures — through clarity, consistency, and accessibility — we can ensure that the story of a municipal issuer's credit is told accurately, no matter who, or what, is reading it.
Colin MacNaught is CEO and co-founder of BondLink.
