Compliance work often breaks in ordinary moments: a manager searching through an old tracker, a policy lead trying to find the latest rule, or a legal reviewer asking who owns a change that has already moved across the business. The global compliance software market was estimated at $35.82 billion in 2025 and is projected to reach $78.85 billion by 2033, but spending alone will not fix a system built around scattered ownership.
Nabarun Bandyopadhyay, Senior Delivery Consultant, Data & AI, at Amazon Web Services and an IEEE Senior Member, has spent 19 years building enterprise-scale data and AI platforms for large global organizations across sectors. To understand how enterprises can rebuild compliance from a reactive search exercise into a scalable operating discipline, we spoke with Bandyopadhyay about the state of regulatory intelligence and what comes next.
The Failure Starts With Ownership
“Compliance does not fail only because a rule is missed,” says Bandyopadhyay. “It fails when nobody can clearly say where the rule lives, who owns it, and what has already been done about it.” That distinction matters because large companies rarely suffer from a lack of effort. They suffer from scattered effort, repeated effort, and quiet gaps that only become visible when a deadline is close.
The enterprise compliance management market is expected to rise from $3.66 billion in 2025 to $7.11 billion by 2030, reflecting the push toward structured compliance operations. Bandyopadhyay has performed technical lead roles on regulation-intensive industry initiatives where growing pipelines of regulatory obligations could no longer be managed reliably through fragmented trackers, siloed ownership, and manual review paths. Some obligations had not yet received initial assessment, while others crossed multiple business areas without a clear owner. His work translated that scattered operating model into a centralized regulatory intelligence approach where regulatory exposure, early assessment, and ownership could be viewed in one place. The same pattern applies across industries such as retail, transport, logistics, healthcare, and other regulated sectors, where accountability must be visible before gaps turn into escalations.
Regulatory Text Has To Become Usable Data
Once ownership is visible, the next problem is the content itself. Regulations arrive as long documents, often across languages, full of provisions, penalties, deadlines, and applicability questions that do not fit neatly into a spreadsheet. “The hard part is not just summarizing a rule,” Bandyopadhyay says. “The hard part is turning the rule into structured content that a compliance team can trust and act on.”
The intelligent document processing market was estimated at $2.30 billion in 2024 and is projected to reach $12.35 billion by 2030, and the reason is practical: enterprises need reliable extraction from document-heavy obligations. For the regulatory intelligence platform, Bandyopadhyay designed a GenAI and LLM content pipeline that turned raw regulatory text into summaries, key provisions, penalties, compliance requirements, and metadata. He guided the engineering approach around chunking, prompt design, entity extraction, summarization, and evaluation while keeping the work focused on accuracy, scale, and integration. The first production iteration showed measurable evaluation progress, with optimization work focused on making generated regulatory content more reliable over time.
That operating choice kept the work grounded. The point was not to produce an impressive answer in isolation, but to create a trusted content layer that compliance teams could review, route, and improve. In a large enterprise, that feedback loop is often the difference between an AI demo and a system people can responsibly use.
Search Must Lead To Action
Better extraction still does not solve compliance if the answer sits in another isolated tool. Teams need to find the right regulation, understand why it matters, and route the output into the systems where accountability already lives. Search had to lead somewhere. Existing risk processes still mattered, and the new layer had to respect them.
The enterprise search market reached $7.47 billion in 2026 and is projected to climb to $11.66 billion by 2031 as companies replace keyword lookup with more intelligent retrieval. Bandyopadhyay’s design used semantic search across stored regulatory content, with a fallback path for emerging regulations not yet indexed. More importantly, he shaped the platform to feed existing compliance systems instead of becoming another dashboard people had to remember. In the earlier operating model, compliance teams had to move across multiple disconnected sources before warning creation, ownership follow-up, or assessment work could begin. In “Is the Data Warehouse Dead? 3 Patterns From Enterprise Architecture That Answer This Question,” Bandyopadhyay makes a related architecture point: poorly maintained enterprise data layers can become a governance burden when they multiply similar views, duplicate information, and drift away from real usage patterns. In the regulatory context, search only matters when it connects fragmented information to ownership, workflow, and action.
Environmental Compliance Needs Forward Motion
As regulatory intelligence matures, the strongest use cases will be those where delay carries real-world consequences. Environmental assurance is one of them. A missed update is not just a paperwork issue when it affects responsible trade practices, workplace safety, or environmental obligations across markets.
The environmental compliance software market was valued at $3.94 billion in 2025 and is predicted to reach $10.59 billion by 2035. The platform was built for that kind of burden. It introduced multilingual regulatory scanning, AI-powered relevancy scoring, automated compliance requirement creation, and alerting for new or changing regulations. For program teams, that meant less noise because each function could see obligations relevant to its specific scope. For compliance managers, it meant faster triage across environmental assurance, trade services, occupational safety, and responsible business practices. The work directly targeted a compliance environment where a meaningful share of regulatory updates were not being implemented consistently, creating risk of penalties, reputational damage, and operational disruption.
“One early review stayed with me,” Bandyopadhyay says. “People were not arguing about whether compliance mattered. They were trying to reconcile different views of the same obligation, and that is where teams get hurt.” The lesson was plain: multilingual scanning and alerts only matter if they help the right team act before the risk becomes urgent.
The Fix Is A System People Actually Use
The future of compliance will not be won by adding another place to check. It will be won by making regulatory intelligence part of normal work. That means the system must handle discovery, extraction, relevancy, ownership, and downstream handoff without forcing every team to invent its own process.
AI in regulatory technology was valued at $18.50 billion in 2025 and is predicted to reach $144.26 billion by 2035. Bandyopadhyay’s work shows what that direction looks like inside a large enterprise. The initial deployment supported geo-specific compliance needs, with planned expansion across other geographies and compliance domains. The program also created meaningful projected third-party licensing savings and substantially reduced manual regulatory search and warning-creation effort. Those are not vague efficiency claims. They are the operating result of replacing scattered search, manual review, and unclear ownership with a centralized compliance data backbone.
“Enterprises need compliance systems that remember, route, and explain,” Bandyopadhyay says. “If a regulation is important enough to create risk, it is important enough to be visible, owned, and connected to action.”
