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The U.S. tax system is entering a structural imbalance: K‑1 volume is compounding, regulatory disclosures are expanding, and the talent base responsible for processing both is shrinking faster than firms can replace it. The result is not a seasonal capacity problem — it is a workflow model that can no longer absorb the load it was designed for.

For the tax and finance professionals at the center of that burden, no single segment illustrates the breaking point more clearly than the processing of Schedule K-1s.

The IRS reported over 4.5 million partnership returns filed for tax year 2023, representing more than 28.8 million individual partners, each of whom may receive a K-1 running anywhere from five to 500 pages of structured and unstructured data.

The SEC’s Division of Investment Management counts over 54,000 private funds holding $26.5 trillion in gross assets, a base that continues to grow as alternative investments become more broadly accessible.

The profession tasked with processing this volume is contracting at the same time. The AICPA’s Trends Report shows accounting bachelor’s degrees have fallen 17 percent since 2015/16, and CPA exam candidates have declined more than 32 percent since 2016.

The U.S. Bureau of Labor Statistics projects over 124,200 accounting and auditing openings per year through 2034, a pipeline that the current enrollment trends cannot fill.

The math no longer works. Tax and finance leaders are now confronting a structural question: not how to manage more volume with the same methods, but how to redesign the workflow itself.

Emerj recently hosted conversations with Ken Powell, Chief Revenue Officer at K1x, Neal Schneider, Co-Founder and CTO at K1x, and Juan Orlandini, CTO of North America at Insight, on how document‑heavy, regulated workflows such as K‑1 processing must evolve from manual triage to straight‑through automation.

This series distills the workflow changes required to move from document‑bound processes to scalable, automation‑driven operations:  

• Workflow modernization for cycle‑time compression:  Redesigning K‑1 processes around automation absorbs rising volume, reduces manual review, and prevents filing‑season bottlenecks from overwhelming limited staff.
• Digitized K‑1 data as the foundation for AI accuracy:  Structuring and standardizing unstructured K‑1 footnotes eliminates rework and enables AI tools to operate with the precision, auditability, and speed tax workflows require.
• Maturity progression from extraction to straight‑through processing:  Advancing from PDF triage to platform‑level automation concentrates human judgment on exceptions and turns week‑long cycles into hours‑long throughput.

Listen to the full episodes below:

Episode 1 : Why Manual K-1 Workflows Are Breaking Under Modern Tax Complexity

Guest: Ken Powell, Chief Revenue Officer atK1x

Expertise: Revenue Growth, Tax Technology, Enterprise Software, K1 Automation

Brief Recognition: With more than 25 years of experience driving commercial growth across SaaS and tech-enabled services, Ken Powell is Chief Revenue Officer at K1X, Inc., where he leads the company’s growth strategy for its AI-powered K-1 tax platform. He also serves as a board member at Logically. Previously, Ken held senior leadership roles including Chief Commercial Officer at EverView and Operating Executive at Cerberus Capital Management, driving commercial transformation across portfolio companies. He holds an executive certification from Columbia Business School and a Master’s in Technology Management from Stevens Institute of Technology.

Episode 2 : How Digital K1 Data Changes Tax Workflow Maturity

Guest: Neal Schneider, Co-founder and CTO atK1x

Expertise: Product Engineering, Tax Data Infrastructure, API Integration, Software Architecture

Brief Recognition: Neal is Chief Technology Officer at K1X, where he leads the design and development of scalable, AI-enabled platforms supporting digital K-1 tax preparation. Prior to this, he spent over 15 years at Crowe LLP as a Principal, delivering web-based solutions across financial services, government, manufacturing, and healthcare, and leading full software development lifecycles from design through deployment. Neal began his career in technical and consulting roles, including at JPMorgan Chase, and holds a B.S. in Computer Science and Engineering from The Ohio State University.

Episode 3 : Scaling Regulated Data Workflows Without Lock‑In – with Juan Orlandini of Insight

Guest: Juan Orlandini, Chief Technology Officer, North America at Insight

Expertise: Enterprise AI, Financial Technology, Data Engineering, Cloud Architecture

Brief Recognition: Juan is Chief Technology Officer for North America and Distinguished Engineer at Insight Enterprises, where he leads architecture and innovation across cloud, data center, edge, and enterprise IT strategy. With nearly three decades of experience, he has held multiple leadership roles at Insight and Datalink, driving large-scale infrastructure transformation and technical strategy. Known for his blend of deep technical expertise and mentorship, Juan has built and led high-performing engineering teams across complex, enterprise environments. He studied Computer Science at Georgia Institute of Technology.

Workflow Modernization for Cycle‑Time Compression

Ken Powell argues that the traditional K‑1 workflow has reached its structural limit. He describes the current perfect storm which is brewing in this industry:

“The profession is being hit by three forces at once: fewer people entering the field, more regulatory disclosures, and a K‑1 volume curve that keeps accelerating. The work is getting more unstructured and more time‑compressed every year, and firms simply don’t have the staffing model to keep up. Straight‑through processing isn’t an upgrade — it’s the only way to stay ahead of the workload.”

— Ken Powell, Chief Revenue Officer, K1x

The constraint is no longer tax complexity; it is the workflow model itself.

Powell notes that democratization of alternative investments is driving a doubling of K‑1s, while changes like the expansion of the K‑3 have multiplied the manual review burden. Modernization, in his view, means redesigning the workflow so automation handles extraction, validation, and routing — and human judgment is reserved for true exceptions.

Neal Schneider emphasizes that cycle‑time compression starts with eliminating the PDF as the organizing unit of work. When data is trapped in documents, firms are forced into rigid, sequential processes. When data is digitized at intake, the workflow becomes parallelized and machine‑assisted from the start.

“The real shift isn’t just digitizing documents — it’s moving from closed, point‑solution workflows to an open ecosystem where tools can actually talk to each other. When firms work inside isolated PDF‑driven processes, every step becomes a handoff. But when the data sits in a shared schema and a connected environment, you unlock interoperability, faster communication, and the ability to plug into modern AI tools that depend on real data connectivity.”

— Neal Schneider, Co‑Founder & CTO, K1x

Powell makes the operational impact concrete. A K‑1 can run from five to 500 pages, with the first page structured and the remaining footnotes entirely unstructured. Historically, staff had to:

• Read each page
• Interpret footnotes and white‑paper statements.
• Key data into workpapers.
• Review the extracted information.
• Escalate exceptions up the chain.

Modernization replaces that chain with:

• Drag‑and‑drop ingestion of PDFs.
• Automated extraction of structured and unstructured data.
• Direct population into tax applications.

This shift absorbs rising volume without overwhelming limited staff.

Juan Orlandini adds that automation only accelerates cycle time when the underlying data flows are sound. If reconciliation breaks because inputs are inconsistent or poorly structured, firms risk re‑introducing manual verification or doubling the workload.

“Finance leaders need to remember that generative AI is not good at math — it gives statistically plausible answers, not guaranteed correct ones. That’s why your architecture matters more than the model. If the underlying data flows aren’t governed, verified, and consistent, you don’t just fail to automate the work — you create more of it, because people now have to verify both the system and the output.”

— Juan Orlandini, CTO North America, Insight

Across all three conversations, it becomes clear that cycle‑time compression comes from eliminating manual work, not accelerating it. Firms need to redesign their K‑1 workflows around automation to absorb rising volume, reduce review hours, and prevent filing‑season bottlenecks from overwhelming limited staff.

Digitized K‑1 Data as the Foundation for AI Accuracy

Traditional K‑1 data is overwhelmingly dependent on unstructured footnotes — narrative disclosures, attachments, and issuer‑specific language that vary widely in format. This variability makes consistent interpretation difficult and blocks AI tools from operating with precision, as Ken Powell explains:

“The footnotes hold the real tax logic, and issuers express the same concepts in completely different ways. Unless you standardize that information at intake, every system downstream is interpreting nuance instead of working with facts. AI can’t deliver accuracy or auditability when the inputs don’t align.”

— Ken Powell, Chief Revenue Officer, K1x

This inconsistency also drives avoidable rework. Even when firms apply automation, they often have to correct or reconcile outputs because the underlying data wasn’t normalized. Digitization removes that ambiguity and creates a single, consistent representation of the K‑1 that downstream tools can trust.

Neal Schneider underscores that AI systems do not operate on documents — they operate on structured data:

“AI isn’t reading a PDF — it’s consuming the structured data you’ve created from it. If that data isn’t normalized into a consistent schema, you’re asking the model to infer meaning it was never designed to interpret. Once the information is standardized, you get precision, audit trails, and the ability to plug into more advanced AI capabilities.”

— Neal Schneider, Co‑Founder & CTO, K1x

Standardization also reduces the verification burden. When data is digitized and validated at intake, firms avoid the cascading rework that occurs when inconsistencies surface late in the process.

Juan Orlandini reinforces the architectural requirement behind AI accuracy:

“AI only works when the data underneath it is governed and consistent. If the inputs aren’t aligned, the model produces answers that look plausible but aren’t correct — and now people have to verify both the system and the output. Good data architecture is what makes AI reliable.”

— Juan Orlandini, CTO North America, Insight

The conversations bring to light that AI accuracy is downstream of data quality and that AI systems do not operate on documents — they operate on structured data.

Maturity Progression From Extraction to Straight‑Through Processing

Across the conversations, a clear progression surfaces in how K‑1 data moves through the tax lifecycle:

• Manual triage: Teams review PDFs, interpret footnotes, and key data line by line.
• Assisted extraction: Tools pull fields from documents, but humans still reconcile, normalize, and validate.
• Platform‑level automation: Intake, normalization, and distribution are orchestrated centrally, with the system enforcing consistency.
• Exception‑only review: Human judgment concentrates on discrepancies surfaced by the platform rather than full‑file validation.
• Straight‑through processing: End‑to‑end automation collapses week‑long cycles into hours‑long throughput.

Ken Powell describes the inflection point where this progression becomes transformational:

“Most firms start with tools that help them extract data, but the real transformation happens when the entire process is automated end‑to‑end. Once the platform is handling intake, normalization, and distribution, people are only looking at the outliers. That’s when you go from week‑long cycles to hours‑long throughput.”

— Ken Powell, Chief Revenue Officer, K1x

This progression also changes the nature of review. Instead of validating every line item, teams focus on discrepancies surfaced by the system. The platform becomes the control layer, ensuring consistency across issuers, entities, and reporting periods. There is a technical shift that enables this as laid out by Neal:

“Extraction is just the first step. Straight‑through processing requires a platform that understands the relationships across the entire K‑1 — how the footnotes tie to the schedules, how the allocations tie to the entities, how the data flows into downstream systems. When the platform handles that logic, you’re not automating tasks, you’re automating the lifecycle.”

— Neal Schneider, Co‑Founder & CTO, K1x

Once the platform is running the lifecycle, the human role shifts from operator to arbiter. Juan Orlandini makes clear why that distinction determines whether the model can scale:

“Straight‑through processing is what lets you scale without adding people. The system handles the volume, and your team handles the exceptions. That’s the only sustainable model when the data keeps growing, and the timelines keep shrinking.”

— Juan Orlandini, CTO North America, Insight

Juan wraps up the series on a poignant note that straight‑through processing becomes the only sustainable model as data grows and timelines tighten.