Most enterprise AI projects don’t make it past the pilot stage. That’s the baseline reality heading into Appian World this year.

It’s not a lack of ambition or investment. Healthcare and government leaders are actively testing generative AI, automation, and intelligent workflows. The problem is execution. Promising proofs of concept stall out when they hit real workloads, compliance requirements, or the messiness of legacy systems.

The gap between experimentation and deployment is where most AI strategies break down.

When AI Meets Operational Reality

The pattern is consistent. A pilot works in isolation but fails when integrated into live workflows. Automation runs smoothly until it encounters regulatory pressure or data governance requirements that weren’t baked in from the start. Change management becomes a blocker because no one designed for how teams really do their work.

The organizations getting real value from AI aren’t just testing ideas—they’re grounding them in process from day one. They’re designing around how work actually gets done, not how it should get done in theory. And they’re building systems that hold up under volume, complexity, and scrutiny.

“Most organizations are still treating AI like a standalone tool. That’s the problem.” – Craig Peterson, Director, AI Workflows

That shift—from testing possibilities to deploying what works—is what we’re focused on at Appian World.

Where Automation Creates Leverage

The highest-impact use cases in healthcare and other industries aren’t flashy. They’re tactical.

Prior-authorization processes that move faster without compromising compliance. Referral workflows that reduce administrative burden instead of shifting it. Member portals that feel useful, not transactional.

“AI delivers real value when it’s embedded in process automation—whether you’re calling an AI agent or using generative AI inside an automated workflow. Our clients taking that approach are seeing measurable returns. The ones experimenting with AI in isolation keep getting stuck at the pilot stage.” – Craig Peterson, Director, AI Workflows

What makes these work isn’t AI alone. It’s intelligent automation combined with workflow orchestration and platform capabilities that let teams adapt without rebuilding from scratch.

At Perficient, our work with Appian reflects this approach. We’ve built accelerators using Appian Agents and Doc Center to automate document-heavy, high-friction processes—prior authorization, referrals, mailroom operations, and self-service portals. These solutions reduce friction across provider, insurer, and consumer interactions—enabling smoother experiences without compromising the governance and control these environments require.

The result isn’t just speed. It’s predictable workflows, consistent outcomes, and systems operational teams can trust.

AI Built To Perform Under Pressure

One thing most AI discussions gloss over: how solutions behave when scale, regulations, and variability all hit at once.

Healthcare and other industry leaders are prioritizing platforms and partners that support:

• AI-enabled decisioning without brittle custom code
• Automation that reduces effort while preserving transparency
• Experiences that improve satisfaction without adding operational complexity

This is where low-code platforms like Appian—and partners who’ve deployed them at scale—matter. Speed is important. Durability is what separates pilots from production systems.

Why We’re At Appian World

Appian World brings together teams actively working through these challenges. It’s a chance to see how organizations are using AI and automation to solve real operational problems—and learn from those who’ve already made the move from ambition to execution.

It’s also where the more useful conversations happen. What worked. What broke. What teams would do differently if they started over.

Perficient will be at Appian World 2026 sharing how we help organizations accelerate time to value while building solutions designed to last. If you’re attending, stop by booth #209. We’ll talk through what execution looks like in your environment—and what it takes to scale AI for measurable results.

Ready to move past the pilot stage?

See how we help organizations deploy AI that actually scales—from healthcare automation built for compliance to moving enterprise AI projects into production.

The post From AI Ambition to Real Outcomes: What We’re Watching at Appian World appeared first on Perficient Blogs.

Perficient earned the Cortex Code Preferred Partner Badge from Snowflake. Not because we attended a training or passed a certification. Because our engineers ran more than 3,000 requests through Cortex Code, Snowflake’s AI coding agent, applying it to everything from data pipelines to machine learning (ML) operations. 

“Achieving the Cortex Code Preferred Partner Badge reflects our team’s dedication to leveraging cutting-edge AI technology to accelerate development velocity and deliver exceptional value for our clients,” said Eric Walk, vice president, AI Data Platforms, Perficient. “We are committed to using Cortex Code to accelerate our work on 100 percent of our Snowflake client engagements.” 

What It Took 

Snowflake doesn’t hand out this badge for enthusiasm. It requires proof. 

Enterprise-scale adoption: More than 3,000 requests across Perficient. Our engineers use Cortex Code daily — for code generation, debugging, optimization, and documentation. It’s integrated into how we work, not treated as a side experiment. 

Full-platform proficiency: We used multiple bundled skills within Cortex Code, applying it across different technical contexts. That means our teams know how to deploy it where it matters, not just where it’s easy. 

Real applications: We provided usage examples from both internal engineering and client solutions. These aren’t hypotheticals. They’re live implementations proving how AI coding agents reduce development friction and accelerate delivery. 

Thought leadership: We committed to sharing what we’ve learned with the broader data and AI community. Experience is only useful if it moves others forward. 

What This Means for Clients 

Clients need partners who can deliver sophisticated AI solutions with speed and precision — not partners who are still figuring it out themselves. 

We’ve already done the work. Whether you’re modernizing legacy data pipelines, building intelligent applications on Snowflake, or accelerating ML model development, we bring proven experience — not promises. 

Cortex Code provides AI-powered acceleration. Perficient provides the strategy, implementation expertise, and enterprise experience to make it work in your environment. 

This badge reinforces our Snowflake Premier Partner status and reflects our ability to deliver comprehensive solutions across data architecture, analytics, AI, and application development. 

AI as a Force Multiplier 

The Cortex Code badge isn’t about a technical achievement. It’s about how we work. 

We integrated AI-powered coding assistance into standard development practices because it makes our teams faster, sharper, and more focused on high-value problem-solving. We’re not experimenting with AI. We’re using it to reimagine how we approach complex projects and deliver results. 

The future of software development is collaborative, augmented, and intelligent. We bring that approach to every client engagement. 

Credit to Our Team 

This happened because engineers, architects, and data scientists across Perficient chose to explore new frontiers instead of defaulting to traditional workflows. Every optimized workflow and every development challenge solved with Cortex Code reflects a team willing to push boundaries. 

We’re building on this momentum as a Cortex Code Preferred Partner and Snowflake Premier Partner. 

Learn more about our Snowflake expertise on our partner page. 

The post Perficient Earns Cortex Code Preferred Partner Badge from Snowflake appeared first on Perficient Blogs.

Our culture is built by our people, who are committed to fostering an environment of inclusion and belonging where every individual can thrive personally and professionally. With more than 7,000 global colleagues, we collaborate across borders, challenge assumptions, and learn through hands-on experience. This award underscores the pride our people take in the lasting impact we create for our clients, communities, and each other.

Behind the Top Workplace Recognition 

The USA Today Top Workplace awards are based completely on colleague feedback. Our U.S. colleagues previously shared their perspectives through a voluntary survey conducted by Energage, an employee and research technology company. The results were evaluated against national Top Workplace benchmarks, measuring colleague engagement and cultural alignment. 

This recognition reflects the real experience of our people and the role they play in building a supportive, high-impact culture. Being named a 2026 USA Today Top Workplace marks our fourth consecutive year of national recognition and reinforces our commitment to creating a place where colleagues can grow, connect, and contribute to meaningful work. 

Our people make us who we are, and how we deliver solutions and treat our clients are what allows us to feel the Perficient pride. We value our people and spend significant time focusing on our colleagues and clients because they are the foundation of everything we do.” – Lisa Hunter, Director, Sitecore and Optimizely

READ MORE: Perficient Earns Recognition on Forbes’ 2026 List of America’s Best Midsize Employers 

Empowering a Future-Ready, AI-Fluent Workforce

We continue to invest in our people by building career development around real work that strengthens the skills needed to stay ahead in the AI era. Through hands-on experience and targeted learning, we support growth at every career stage while reinforcing an AI-first mindset grounded in practical application.

At Perficient, we approach AI with purpose by leading with it when it adds value and recognizing when it does not. That level of thoughtfulness is part of what sets us apart.” – John Vylasek, Senior Solutions Architect, Data and Analytics

This approach comes to life through our Global AI-First Academy. Designed to equip colleagues with the capabilities and confidence to succeed in an AI-driven market, the Academy prioritizes hands-on learning across four core areas: AI technology, professional and consulting skills, project management and delivery excellence, and leadership development. 

Within the Global AI-First Academy, colleagues have access to LinkedIn Learning, where role-specific courses build relevant skills and help apply AI in day-to-day work. Beyond this learning program, we offer in-demand technology certifications and continue to expand learning initiatives across our focus areas in collaboration with our technology ecosystem partners. 

Building Community, Inclusivity, and Belonging 

Our global colleagues bring diverse perspectives and expertise that strengthen Perficient’s culture and drive better outcomes. That belief fuels our commitment to diversity, equity, and inclusion, shaping a workplace where colleagues listen and respect every voice.

We put that commitment into practice through our Employee Resource Groups (ERGs) and Innovation Groups (IGs). These communities connect colleagues across backgrounds and regions to share expertise, exchange ideas, and grow leadership skills through purpose-driven collaboration. Whether colleagues are interested in giving back, advancing well-being, or exploring emerging technology, there’s space for everyone to get involved and build lasting connections. 

Even in the age of AI, our human experience remains our greatest value. When we create and nurture safe environments where people feel they belong, we foster innovation, improvement, and collaboration.” – Erika Ibarra, Senior Technical Architect, Product Development 

One example of this in action is our Women in Technology (WiT) ERG, which brings women and allies together to share ideas, build skills, and support one another’s growth. Through mentorship and candid conversations, WiT fosters career development while advancing gender equality and helping shape a more inclusive future in technology.

It’s the people that make Perficient what it is. I love being part of a team with strong female leaders who create space for collaboration and support. The culture here is empowering, credible, and driven by people who truly care.” – Jill Boisvert, Project Manager, Salesforce 

LEARN MORE: Perficient’s Culture and Community 

Creating Positive Change in Our Communities 

United by a shared sense of purpose, our colleagues use their time and expertise to strengthen the communities where we live and work. Our community engagement initiatives focus on advancing science, technology, engineering, and mathematics (STEM) education and improving health and well-being.

These priorities drive our global volunteering initiatives, philanthropic partnerships, and programs that expand opportunity and empower the next generation of technology leaders. By applying our skills where they have the greatest impact, we help build brighter, healthier futures around the world.  

READ MORE: Hunger Action Month 2025: Perficient Donates More Than 8,600 Pounds of Food Globally 

Our award-winning culture is shaped by how our people show up for one another, their communities, and the work we deliver. Being honored as a national Top Workplace reflects the environment our colleagues create every day through collaboration, continuous learning, and real impact. As we look ahead, we remain focused on investing in our people and the experiences that set Perficient apart.

READY TO GROW YOUR CAREER?   

Our people cut through complexity, challenge outdated approaches, and grow through hands-on experience. Across teams, technologies, and time zones, our colleagues collaborate to build AI-first solutions that hold up in the real world and deliver outcomes that last. We’re always looking for problem-solvers who want responsibility early and turn bold thinking into results. Join us and be part of a culture defined by curiosity, ownership, and impact. 

Visit our Careers page to see career opportunities and more!   

Go inside Life at Perficient and connect with us on LinkedIn, YouTube, X, Facebook, and Instagram.

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Perficient was proud to participate onsite as a Platinum sponsor, allowing us to connect with hundreds of attendees, share our perspective at Booth #833 and our welcome happy hour, and deliver our session, “The Golden Thread: Architecting an End-to-End OneLake Fabric Ecosystem for Analytics, Real-Time Intelligence & RAG.”

Below is our take on the updates that matter most and what they mean for the next generation of enterprise data platforms.

1. OneLake Matures Into the Enterprise Data Foundation

One of the strongest signals at FabCon was the continued expansion of OneLake as the central data foundation for the enterprise. The introduction of the new Database Hub, which brings SQL Server, Azure SQL, PostgreSQL, MySQL, and Cosmos DB into the Fabric experience, cements Microsoft’s intention to unify transactional, operational, and analytical workloads under a single architecture.

Combined with expanded OneLake interoperability and deeper zero‑copy shortcuts, the direction is unmistakable: organizations should prepare for a world where movement of data becomes the exception, not the rule.

The platform is shifting toward metadata‑driven orchestration, simplified pipelines, and a significant reduction in the need for external ETL tools.

This is exactly the direction many of the teams we met with have been pushing toward: eliminating duplication, lowering operational complexity, and moving toward a foundation that is AI‑ready by default.

2. Fabric Evolves Into an Intelligence Layer, Not Just a Data Platform

Multiple keynotes highlighted Fabric’s evolution from a data platform into a unified intelligence layer was one of the clearest storylines at the event. The debut of Fabric IQ represents Microsoft’s commitment to advancing beyond storage and computation toward semantic understanding.

Fabric IQ introduces Ontologies, Graph, and semantic reasoning as first‑class platform capabilities. This unlocks an entirely new category of enterprise AI:

• Ontologies establish a shared business vocabulary
• Graph enables deep relationship reasoning
• Data Agents operate within those rules, grounding AI in business context rather than model guesswork

This architecture mirrors the hybrid RAG approach Perficient has implemented manually for years—where structured knowledge constrains the reasoning space and vector retrieval fills in the unstructured gaps. By making semantic grounding native, Fabric is moving toward AI systems that can take safe, auditable actions, not just produce outputs.

The shift is clear: the next frontier in enterprise AI isn’t bigger models but better grounding.

3. Real-Time Intelligence Becomes the Default

FabCon also made it clear that real‑time data is transitioning from an advanced capability to a default operational expectation.

Expanded Real‑Time Intelligence capabilities, unified batch + streaming processing, and deeper telemetry fusion into Medallion architectures all reinforce this trend. Many teams told us they’re outgrowing batch pipelines, and these updates reflect Microsoft’s commitment to delivering analytics and decision‑making while data is still in motion.

For Perficient, this aligns directly with the Golden Thread approach—where real‑time signals and historical context converge to unlock high‑value scenarios like anomaly detection, operational awareness, and predictive insights.

4. Fabric Becomes the Unified Data Control Plane

Perhaps the most important macro‑signal at FabCon was how deliberately Microsoft positioned Fabric as a unified control plane across the data estate. With databases, analytics, governance, security, orchestration, and AI all converging into a single platform, the architectural direction is now clear: reduce fragmentation, simplify the stack, and centralize control so organizations can move faster and operate more consistently.

This is more than consolidation—it’s the foundation for enterprise-scale AI. A unified control plane speeds insight development, lowers operational overhead, and enables organizations to standardize the architectural patterns that underpin AI‑ready data.

5. Skills for Fabric: Agentic Automation Raises the Bar for Developers

Another standout announcement was Skills for Fabric, an open‑source library that introduces agentic automation into the Fabric development workflow. Rather than “closing the skills gap,” these capabilities augment developers through automation and Model Context Protocol (MCP) support.

Skills for Fabric streamlines tasks that once consumed days: provisioning Lakehouses and Warehouses, configuring pipelines, setting up medallion scaffolding, and preparing developer environments. Through integrations with GitHub Copilot, Claude Code, VS Code, and the CLI, teams can now leverage AI to generate code, plan multi-step workflows, provision Fabric resources, validate patterns, and deploy artifacts end‑to‑end.

But this raises the bar, not lowers it. Organizations now need developers who understand how to:

• configure agentic workflows
• evaluate AI‑generated infrastructure
• work safely within MCP
• architect Fabric environments for agent operations

Perficient’s engineering teams are already leveraging these capabilities internally to shift client time away from infrastructure setup and toward modeling, semantics, and insight delivery.

Looking Ahead: How Perficient Helps Organizations Move Faster

Across FabCon, one theme came through clearly: Fabric is becoming the AI‑native operating layer for the enterprise. The future is zero‑copy, metadata-driven, real‑time, semantically grounded, and agent‑enabled.

To help teams move confidently into that future, Perficient offers our 5‑Week AI AMP, a focused engagement that helps organizations rapidly identify high‑value AI opportunities, prioritize RAG/RTI/ML scenarios, and build a Fabric‑aligned proof of concept that accelerates time to value.

With Database Hub, Real‑Time Intelligence, Dataflow Gen2, Fabric IQ, and agentic development now reshaping the platform, organizations have a rare opportunity to simplify their architecture and activate intelligence at scale. Perficient is ready to help teams turn this momentum into meaningful outcomes—moving from what’s possible to what’s proven.

Ready to Start Your Fabric Journey?

Connect with our experts to discuss your roadmap or explore how our expertise can help you move faster.

Download our “Golden Thread” session deck | Request an AI AMP briefing with our team

The post FabCon 2026: A Breakthrough Year for the Unified Data + AI Platform appeared first on Perficient Blogs.

Agentic CLIs

Most of the conversation around agentic AI focuses on graphical IDEs, but the CLI tools are worth understanding separately. They integrate more naturally into existing scripts and automation pipelines, and in some cases offer capabilities the GUI tools don’t.

The main options currently available:

Claude Code (Anthropic) works with the Claude Sonnet and Opus model families. It handles multi-file reasoning well and tends to produce more explanation alongside its changes, which is useful when the reasoning behind a decision matters as much as the decision itself.

OpenAI Codex CLI is more predictable for tasks requiring strict adherence to a specification — business logic, security-sensitive code, anything where creative interpretation is a liability rather than an asset.

Gemini CLI is notable mainly for its context window, which reaches 1–2 million tokens depending on the model. Large enough to load a substantial codebase without chunking, which changes what kinds of questions are practical to ask.

OpenCode is open-source and accepts third-party API keys, including mixing providers. Relevant for environments with restrictions on approved vendors.

Configuration and Permission Levels

Configuration is stored in hidden directories under the user home folder — ~/.claude/ for Claude Code, ~/.codex/ for Codex. Claude uses JSON; Codex uses TOML. The parameter that actually matters day-to-day is the permission level.

By default, most tools ask for confirmation before destructive operations: file deletion, script execution, anything irreversible. There’s also typically a mode where the agent executes without asking. It’s faster, and it will occasionally remove something that shouldn’t have been removed. The appropriate context for that mode is throwaway branches and isolated environments where the cost of a mistake is low.

Structuring a Development Session

Jumping straight to code generation tends to produce output that looks correct but requires significant rework. The agent didn’t have enough context to make the right decisions, so it made assumptions — and those assumptions have to be found and corrected manually.

Plan Mode

Before any code is written, the agent should decompose the task and surface ambiguities. This is sometimes called Plan Mode or Chain of Thought mode. The output is a list of verifiable subtasks and a set of clarifying questions, typically around:

• Tech stack and framework choices
• Persistence strategy (local storage, SQL, vector database)
• Scope boundaries — what’s in and what’s explicitly out

It feels like overhead. The time is recovered during implementation because the agent isn’t making assumptions that have to be corrected later.

Repository Setup via GitHub CLI

The GitHub CLI (gh) integrates cleanly with agentic workflows. Repository initialization, .gitignore configuration, and GitHub issue creation with acceptance criteria and implementation checklists can all be handled by the agent. Having the backlog populated automatically keeps work visible without manual overhead.

Context Management

The context window is finite. How it’s used determines whether the agent stays coherent across a long session or starts producing inconsistent output. Three mechanisms matter here: rules, skills, and MCP.

Rule Hierarchy

Rules operate at three levels:

User-level rules are global preferences that apply across all projects — language requirements, style constraints, operator restrictions. Set once.

Project rules (.cursorrules or AGENTS.md) are repository-specific: naming conventions, architectural patterns, which shared components to reuse before creating new ones. In a team context, this file deserves the same review process as any other documentation. It tends to get neglected and then blamed when the agent produces inconsistent output.

Conditional rules activate only for specific file patterns. Testing rules that only load when editing .test.ts files, for example. This keeps the context lean when those rules aren’t relevant to the current task.

Skills

Skills are reusable logic packages that the agent loads on demand. Each skill lives in .cursor/skills/ and consists of a skill.md file with frontmatter metadata, plus any executable scripts it needs (Python, Bash, or JavaScript). The agent discovers them semantically or they can be invoked explicitly.

The practical value is context efficiency — instead of re-explaining a pattern every session, the skill carries it and only loads when the task requires it.

Model Context Protocol (MCP)

MCP is the standard for giving agents access to external systems. An MCP server exposes Tools (functions the agent can call) and Resources (data it can query). Configuration is added to the IDE’s config file, after which the agent can interact with connected systems directly.

Common integrations: Slack for notifications, Sentry for querying recent errors related to code being modified, Chrome DevTools for visual validation. The Figma MCP integration is particularly useful — design context can be pulled directly without manual translation of specs into implementation requirements.

Validation

A task isn’t complete until there’s evidence it works. The validation sequence should cover four things:

Compilation and static analysis. The build runs, linters pass. Errors get fixed before the agent reports done.

Test suite. Unit and integration tests for the affected logic must pass. Existing tests must stay green. This sounds obvious and is frequently skipped.

Runtime verification. The agent launches the application in a background process and monitors console output. Runtime errors that don’t surface in tests are common enough that skipping this step is a real risk.

Visual validation. With a browser MCP server, the agent can take a screenshot and compare it against design requirements. Layout and styling issues won’t be caught by any automated test.

Security Configuration

Two files, different purposes, frequently confused:

.cursorignore is a hard block. The agent cannot read files listed here. Use it for .env files, credentials, secrets — anything that shouldn’t leave the local environment. This is the primary security layer.

.cursorindexingignore excludes files from semantic indexing but still allows the agent to read them if explicitly requested. The appropriate use is performance optimization: node_modules, build outputs, generated files that would pollute the index without adding useful signal.

For corporate environments, Privacy Mode should be explicitly verified as enabled rather than assumed. This prevents source code from being stored by the provider or used for model training. Most enterprise tiers include it; the default state varies by tool and version.

Hooks

Hooks are event-driven triggers that run custom scripts at specific points in the agent’s lifecycle. Not necessary for small projects, but worth the setup as the codebase grows.

beforeSubmitPrompt runs before a prompt is sent. Useful for injecting dynamic context — current branch name, recent error logs — or for auditing what’s about to be sent.

afterFileEdit fires immediately after the agent modifies a file. The natural use is triggering auto-formatting or running the test suite, catching regressions as they’re introduced.

pre-compact fires when the context window is about to be trimmed. Allows prioritization of what information should be retained. Relevant for long sessions where important context has accumulated, and the default trimming behavior would discard it.

Parallel Development with Git Worktrees

Sequential work on a single branch is a bottleneck when multiple tasks are running in parallel. Git worktrees allow different branches to exist as separate working directories simultaneously:

git worktree add ../wt-feature-name -b feature/branch-name

Each worktree should have its own .env with unique local ports (PORT=3001, PORT=3002) to prevent dev server collisions. The agent can handle rebases and straightforward merge conflicts autonomously. Complex conflicts still require human judgment — the agent will flag them rather than guess.

The model itself is less of a determining factor than it might seem. Rule configuration, context management, and validation coverage drive the actual quality of the output. A well-configured environment with a mid-tier model will consistently outperform a poorly configured one with a better model. The engineering work shifts toward writing the constraints and verification steps that govern how code gets produced, which is a different skill than writing the code directly, but the productivity difference once it’s in place is significant.

The post From Coding Assistants to Agentic IDEs appeared first on Perficient Blogs.

That shift sets the stage for TRANSACT 2026, where Perficient’s Director of Payments, Amanda Estiverne, will moderate “Keys, Tokens & Trust: How Crypto Wallets Unlock Tomorrow’s Payments,” unpacking how these technologies can finally push digital wallets into their next era. She’ll be joined by three industry leaders for the future-minded discussion:

• Wade Arnold, founder and CEO, Moov
• Chintan Turakhia, senior director of engineering, Coinbase
• Karen Hsu, founder and CEO, Blocksteer
  

“Beyond the session I’m moderating on crypto wallets—and how this technology is set to supercharge tokenization, transform digital identity, and reinvent the very idea of a mobile wallet—I’m fired up for several powerhouse conversations.” – Amanda Estiverne 

Here are three topics we’re looking forward to exploring—and why they matter now. 

Security That Actually Builds Trust 

Security remains one of the biggest obstacles to broader U.S. digital wallet adoption—but it’s also the area where crypto wallets offer the clearest blueprint forward. Having spent years securing billions in digital assets in high‑risk environments, crypto wallets have refined capabilities such as multi‑signature authentication, advanced biometrics, tokenization, and decentralized key management. They show how strong security and user‑friendly design can coexist.

“ETA Transact is the gathering place for the entire payments ecosystem. Banks, networks, fintechs, processors, and regulators all come together under one roof to explore what’s next in payments.” – Amanda Estiverne

Interoperability Across Rails and Borders

One of the most persistent challenges in payments is fragmentation—different rails, incompatible systems, and cross‑border friction that create cost and complexity for businesses and consumers alike. Crypto wallets, by contrast, were designed for interoperability from the start. A single wallet can span multiple networks, assets, and payment types without the user having to think about what’s happening behind the scenes.

Digital wallets are quickly becoming more than a place to store cards. With AI, they can deliver smarter, more contextual experiences—from personalized rewards to anticipatory recommendations to voice‑enabled commerce. But to power these experiences responsibly, wallets need identity models that balance personalization with user privacy and control.

“Agentic commerce, stablecoins and digital assets, digital identity, personalized payments, and instant payments are among the key themes shaping the conversation. The financial system is undergoing massive transformation, and these emerging areas will play a defining role in the infrastructure of tomorrow’s payments ecosystem.” – Amanda Estiverne 

Discover the Next Payment Innovation Trends 

Transact 2026 is where theory meets practice. Where banks, networks, fintechs, processors, and regulators pressure-test ideas and forge the partnerships that will define the next era of payments.

Amanda’s session focuses on how crypto‑wallet innovations—biometrics, tokenization, decentralized identity, and cross‑border interoperability—can help U.S. mobile wallets finally graduate from tap‑to‑pay conveniences into trusted, intelligent financial hubs.

“It’s where partnerships are forged, new ideas are pressure-tested, and the future of how money moves begins to take shape.” – Amanda Estiverne 

For payment leaders exploring what comes next, this conversation offers a grounded look at the capabilities most likely to redefine digital wallets across security, identity, interoperability, and user experience.

Attending TRANSACT 2026? Come by the Idea Zone at 1:40pm on Thursday, March 19th to hear the exclusive insights. Not attending? Contact Perficient to explore how we help payment and Fintech firms innovate and boost market position with transformative, AI-first digital experiences and efficient operations.

The post 3 Topics We’re Excited About at TRANSACT 2026 appeared first on Perficient Blogs.

Researchers from MIT and UC San Diego published a paper in Science describing LLM concept vectors and a new algorithm called the Recursive Feature Machine (RFM) that can extract these concept vectors from large language models. Essentially, these are patterns of neural activity corresponding to specific ideas or behaviors. Using fewer than 500 training samples and under a minute of compute on a single A100 GPU, researchers were able to steer models toward or away from specific behaviors, bypass safety features, and transfer concepts across languages.

Furthermore, the technique works across LLMs, vision-language models, and reasoning models.

Why LLM Concept Vectors Matter for Developers

This research points to a future beyond prompt engineering. Instead of coaxing a model into a desired behavior with carefully crafted text, developers will be able to directly manipulate the model’s internal representations of concepts. Consequently, that is a fundamentally different level of control. For context on how quickly the underlying models are evolving, Mercury’s diffusion-based LLM now generates over 1,000 tokens per second, which means techniques like concept vector steering could be applied in near real-time production workloads.

Additionally, it opens the door to more precise model customization and makes it easier to debug why a model behaves a certain way. The ability to extract and transfer concepts across languages is particularly significant for global teams building multilingual applications, since it sidesteps the need to curate separate alignment datasets for each language. For developers interested in building intuition for how models learn representations at a fundamental level, Karpathy’s microGPT project offers a minimal, readable implementation worth studying alongside this research. The practical takeaway is clear: the developers who learn to work with internal model representations, not just prompts, will therefore have a serious edge in building AI-powered applications.

The post LLM Concept Vectors: MIT Research on Steering AI Behavior appeared first on Perficient Blogs.

In today’s data‑driven economy, success isn’t just about keeping up – it’s about anticipating change and acting decisively. Oracle IPM Insights, a powerful capability within Oracle EPM Cloud, empowers organizations to uncover critical anomalies, forecast emerging trends, and recommend actions that drive performance. With AI‑driven narratives and real‑time intelligence embedded directly into financial workflows, IPM Insights transforms raw data into strategic guidance – helping businesses improve forecast accuracy, control costs, and stay ahead in a rapidly evolving market.

Transforming Data into Actionable Intelligence

Key Features of Oracle IPM Insights

Integrating IPM Insights Across EPM

IPM Insights works natively across Oracle Cloud EPM solutions – Planning, Financial Consolidation and Close, Enterprise Profitability and Cost Management , Tax Reporting, and FreeForm Planning. This integration eliminates silos and ensures consistency across processes. By connecting insights across the full financial lifecycle, organizations can trace the impact of assumptions, drivers, and anomalies from planning through consolidation and final reporting. This unified view reduces reconciliation effort, improves data reliability, and accelerates the close‑to‑forecast cycle.

Best Practices for Optimization

Unlocking the full potential of Oracle IPM Insights requires more than activation – it demands a disciplined approach. Follow these best practices to maximize value:

1. Define Insight Scope Strategically – Configure Insight Definitions for specific data slices aligned with business priorities to keep insights actionable.
2. Incorporate Calendars & Event Context – Annotate insights with business events to distinguish expected fluctuations from true anomalies.
3. Embed Insights into Everyday Workflows – Use Smart View and the Insights dashboard to make insights accessible where planners work.
4. Use Narratives to Strengthen Commentary and Executive Reporting – Incorporate AI‑generated explanations into management decks, close packages, and forecast summaries to improve speed and consistency. This reduces time spent drafting commentary while increasing clarity and precision.
5. Establish Governance & Ongoing Review – Create a monitoring team to fine-tune thresholds, validate models, and drive continuous improvement.

Future Trends in Enterprise Performance Management

1. Driver-Based Forecasting with AutoMLx – Trends are shifting toward intelligent, driver-based forecasting. Oracle EPM leads with Advanced Predictions powered by AutoMLx, enabling multivariate models that incorporate key business drivers for greater accuracy and transparency.
2. Conversational AI Agents for Finance – AI-driven assistants, will allow finance teams to query insights in natural language and receive instant recommendations – making planning more intuitive and collaborative. This shift will not only accelerates decision‑making but will also empower organizations to respond to market changes with greater agility, improving both financial accuracy and overall business performance.
3. Self-Learning Models and Continuous Improvement – Future models will learn from user actions and outcomes, improving accuracy over time. This adaptive capability ensures businesses stay ahead in an ever-changing market.

Why Insights Matter

The ability to detect, predict, and act on insights is no longer optional – it’s a competitive and existential necessity. In an environment where markets shift rapidly, budgets tighten, and expectations for accuracy increase, finance teams must operate with real‑time intelligence rather than backward‑looking reports. Organizations that can rapidly translate data into decisions gain measurable advantages in agility, cost control, and strategic alignment.

Ready to unlock the power of Oracle IPM Insights? Leave a comment or contact us to explore how Oracle EPM Cloud can help you anticipate change, optimize performance, and lead with confidence.

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For financial services executives and compliance professionals, this isn’t just a compliance headache—it’s a data infrastructure challenge. The days of manual spreadsheets and last-minute scrambles are over. The new requirements demand a level of agility and precision that legacy systems simply cannot support. If you’re still relying on manual processes to meet these evolving standards, you’re not just risking non-compliance; you’re risking your firm’s operational resilience.

The Shifting Landscape: More Data, More Often

The theme for 2026 is “more.” More frequent filings, more detailed disclosures, and more scrutiny. The SEC’s push for modernization is driven by a desire to better monitor systemic risk and protect investors, but for asset managers, it translates to a significant operational burden.

Take Form N-PORT, for example. What was once a quarterly obligation with a 60-day lag is transitioning to a monthly filing requirement due within 30 days of month-end. This tripling of filing frequency doesn’t just mean three times the work; it means your data governance and reporting engines must be “always-on,” capable of aggregating and validating portfolio data on a continuous cycle.

The “Big Three” for 2026: Form PF, 13F, and N-PORT

While there are numerous reports to manage, three stand out as critical focus areas for 2026: Form PF, Form 13F, and Form N-PORT. Each has undergone significant changes or is subject to new scrutiny that demands your attention.

Form PF: The Private Fund Data Deep Dive

The amendments to Form PF, adopted in February 2024, represent a sea change for private fund advisers. With a compliance date of October 1, 2026, these changes require more granular reporting on fund structures, exposures, and performance. Large hedge fund advisers must now report within 60 days of quarter-end, and the scope of data required—from detailed asset class breakdowns to counterparty exposures—has expanded significantly. This isn’t just another new report. It’s a comprehensive audit of your fund’s risk profile, delivered quarterly.

Form 13F: The Institutional Standard

For institutional investment managers exercising discretion over $100 million or more in 13(f) securities, Form 13F remains a cornerstone of transparency. Filed quarterly within 45 days of quarter-end, this report now requires the companion filing of Form N-PX to disclose proxy votes on executive compensation. This linkage between holdings and voting records adds a new layer of complexity, requiring firms to seamlessly integrate data from their portfolio management and proxy voting systems.

Form N-PORT: The Monthly Sprint

A shift to monthly N-PORT filings is a game-changer for registered investment companies. The requirement to file within 30 days of month-end means that your month-end close process must be tighter than ever. Any delays in data reconciliation or validation will eat directly into your filing window, leaving little margin for error.

The Operational Burden: Hidden Costs of Manual Processes

It’s easy to underestimate the time and effort required to produce these reports. A “simple” quarterly update can easily consume a week or more of a compliance officer’s time when you factor in data gathering, reconciliation, and review.

For a large hedge fund adviser, we at Perficient have seen a full Form PF filing taking two weeks or more of dedicated effort from multiple teams. When you multiply this across all your reporting obligations, the cost of manual processing becomes staggering. And that’s before you consider the opportunity cost—time your team spends wrangling data is time they aren’t spending on strategic initiatives or risk management.

The Solution: Automation and Cloud Migration

The only viable path forward is automation. To meet the demands of 2026, asset managers must treat regulatory reporting as a data engineering problem, not just a compliance task. This means moving away from siloed spreadsheets and towards a centralized, cloud-native data platform.

By migrating your data infrastructure to the cloud, you gain the scalability and flexibility needed to handle large datasets and complex calculations. Automated data pipelines can ingest, validate, and format your data in real-time, reducing the “production time” from weeks to hours. This isn’t just about efficiency; it’s about accuracy and peace of mind. When your data is governed and your processes are automated, you can file with confidence, knowing that your numbers are right.

Key Regulatory Reports at a Glance

To help you navigate the 2026 reporting calendar, we’ve compiled a summary of the key reports, their purpose, and what it takes to get them across the finish line.

Your Next Move

If your firm would like assistance designing or adopting regulatory reporting processes or migrating your data infrastructure to the cloud with a consulting partner that has deep industry expertise – reach out to us here.

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This specialization reflects both our strategic commitment to advancing health innovation through data and AI, and our proven track record of helping clients modernize with speed, responsibility, and measurable outcomes.

Our combined expertise in Healthcare & Life Sciences and the Databricks platform uniquely positions us to help customers achieve meaningful impact, whether improving patient outcomes or accelerating the clinical data review process. This specialization underscores the strength of our capabilities across both the platform and within this highly complex industry. – Nick Passero, Director Data and Analytics

How We Earned the Specialization

Achieving the Databricks Brickbuilder Specialization requires a deep and sustained investment in technical expertise, customer delivery, and industry innovation.

Technical Expertise: Perficient met Databricks’ stringent certification thresholds, ensuring that dozens of our data engineers, architects, and AI practitioners maintain active Pro and Associate certifications across key domains. This level of technical enablement ensures that our teams not only understand the Databricks platform, but can apply it to clinical trials, healthcare claims management, and real world evidence, leading to AI-driven decisioning.

Delivery Excellence: Equally important, we demonstrated consistent success delivering in production healthcare and life sciences use cases. From enhancing omnichannel member services to migrating complex Hadoop workloads to Databricks for a large midwest payer, building a modern lakehouse on Azure for a leading children’s research hospital, and modernizing enterprise data architecture with Lakehouse and DataOps for a national payer, our client work demonstrates both scale and repeatability.

Thought Leadership: Our achievement also reflects ongoing thought leadership, another core requirement of Databricks’ specialization framework. Perficient continues to publish research-driven perspectives (Agentic AI Closed-Loop Systems for N-of-1 Treatment Optimization, and Agentic AI for RealTime Pharmacovigilance) that help executives navigate the evolving interplay of AI, regulatory compliance, clinical innovation, and operational modernization across the industry.

Why This Matter to You

Healthcare and life sciences organizations face unprecedented complexity as they seek to unify and activate data from sensitive datasets (EMR/EHR, imaging, genomics, clinical trial data). Leaders must make decisions that balance innovation with security, scale with precision, and AI-driven speed with regulatory responsibility.

The Databricks specialization matters because it signals that Perficient has both the technical foundation and the industry expertise to guide organizations through this transformation. Whether the goal is to accelerate drug discovery, reduce clinical trial timelines, personalize therapeutic interventions, or surface real-time operational insights, Databricks provides the engine and Perficient provides the strategy, implementation, and healthcare context needed to turn potential into outcomes.

A Thank You to Our Team

This accomplishment is the result of extraordinary commitment across Perficient’s Databricks team. Each certification earned, each solution architected, and each successful client outcome reflects the passion and expertise of people who believe deeply in improving healthcare through better data.

We’re excited to continue shaping the future of healthcare and life sciences with Databricks as a strategic partner.

To learn more about our Databricks practice and how we support healthcare and life sciences organizations, visit our partner page.

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