As part of that evolution, we faced a choice: build our own way to measure how we show up in AI-driven search, or learn from the platforms already emerging in this space. We chose the latter, and that’s what led us to Scrunch.

Naturally, the next step was validation. Were we showing up for the conversations that matter?

So we did something simple. We opened up an AI assistant and asked a question our clients ask every day: who are the best digital experience consultancies?

We expected to see ourselves somewhere in the mix.

We weren’t there at all.

That moment stuck with us. Not because of ego, but because it revealed something deeper. Even after improving our site and strengthening our content, there was a growing surface where we simply didn’t exist.

That surface was AI-driven search.

A Different Kind of Search

At first, it’s easy to treat this as just another extension of SEO. But the more we explored it, the clearer it became that this is fundamentally different. People aren’t browsing lists of links the way they used to. They’re asking direct questions and getting summarized answers, often without ever clicking through multiple sites.

Which raises a new challenge: if AI is shaping the answer, how do you make sure your brand is part of it?

That’s where our work with Scrunch started.

Scrunch gave us a way to move from guesswork to visibility. Instead of wondering whether we showed up across platforms like ChatGPT, Claude, Gemini, and Perplexity, we could actually measure it. We could see which questions triggered mentions of our brand, where we appeared in the response, and how often competitors were recommended instead.

It didn’t take long to notice a pattern. There was a clear gap between how we performed in traditional search and how we appeared in AI-generated answers. Content that ranked well and consistently drove traffic wasn’t always being picked up or cited by these systems.

That disconnect forced us to rethink how we define visibility.

Search rankings still matter. But being included in a synthesized answer is a different kind of success. It depends on how clearly you answer a question, how complete your perspective is, and how consistently your brand shows up across the broader web, not just on your own site.

One of the most valuable things Scrunch surfaced for us was context. Not just whether we were mentioned, but how. Were we positioned as a leading recommendation or simply listed among others? Were competitors showing up more frequently? Were our strengths being represented accurately?

Those subtleties matter more than they might seem. Because for many users, that AI-generated answer is their first impression.

Grounding It in Data

At the same time, we knew Scrunch wasn’t the only platform exploring this space. Tools like Conductor are also building capabilities around AI visibility, and we’ve been actively exploring them as well. Not to replace one with another, but to understand how each approaches the problem differently. The category itself is still evolving, and part of the opportunity right now is learning where each tool creates unique value.

Alongside that, we’ve leaned on tools we already trust. SEMrush, for example, has become a natural complement in this process. It helps us ground what we’re seeing in AI responses with actual search demand and competitive positioning. When we identify gaps in AI visibility, SEMrush helps us understand whether those gaps align with high-value topics and where we already have momentum.

And then there’s the piece that makes all of this real: traffic.

Through Google Analytics, we’re able to see when AI platforms actually drive visitors to our site. Referrals from sources like ChatGPT and Perplexity, which used to be almost invisible, are now something we actively look for. When we connect that data with what we see in Scrunch, it gives us a much clearer picture. Not just whether we’re being recommended, but whether those recommendations lead to meaningful engagement.

Making It Practical with MCP

One of the things that changed this from a monthly exercise into something we can run consistently is how these platforms let you access their data programmatically. Scrunch offers what they call their AXP platform, and both Scrunch and SEMrush support MCP (Model Context Protocol) integrations, which means you can pull their data directly into tools like Claude and work with it conversationally.

In practice, that means instead of logging into three separate dashboards and manually comparing numbers, we can ask questions in plain language. Things like: where did our visibility change this week? Which competitors gained ground? What keywords have high search demand but low AI presence? The answers come back with the actual data attached, ready to act on.

That shift matters more than it might sound. When the friction of pulling data goes away, the frequency goes up. We went from reviewing this monthly to weekly. We started catching patterns we would have missed before, things like a single content update moving our position on a specific prompt within days, or a competitor’s new page immediately showing up in AI recommendations while ours didn’t.

The MCP access also means the analysis isn’t locked to one person who knows the dashboards. Anyone on the team can ask questions about the data in natural language and get useful answers. That democratizes the insight in a way that traditional BI tools rarely manage.

That combination, visibility, demand, traffic, and the ability to query it all in one place, starts to turn this into something actionable.

We’re no longer just asking if we show up. We’re asking where to focus, what to improve, and how to create content that works across both traditional and AI-driven discovery.

Some of the changes have been simple. Making answers more direct. Structuring content more clearly. Expanding depth where needed. None of it is radical on its own, but together it starts to shift how our content is interpreted and surfaced.

And importantly, we can see that shift happening.

Why This Moment Matters

Stepping back, what’s most interesting is how early this still feels. Most teams are still anchored in traditional SEO metrics. AI visibility isn’t widely tracked, and in many cases, it isn’t even on the radar yet.

But that’s exactly what makes this moment valuable.

The behaviors are changing before the processes are. And that creates space for teams willing to explore, test, and adapt.

For us, this started with a simple question and turned into a broader shift in how we think about discovery. It’s no longer just about where you rank. It’s about whether you’re part of the answer at all.

Scrunch helped make that visible. Conductor and others are helping us broaden our understanding. SEMrush keeps us grounded in demand. And Google Analytics tells us whether it’s actually working.

Together, those pieces are shaping how we approach what comes next.

From Visibility to Action

While these tools are incredibly valuable for understanding where you stand in AI-driven search, the harder part is knowing what to actually do with that information. Data on its own doesn’t drive change. A dashboard that tells you your presence score dropped three points this week doesn’t tell you which page to rewrite, what question to answer more directly, or which content gap is actually costing you opportunities.

That’s where we’ve been focusing our efforts. Not just measuring AI visibility, but translating those insights into clear, specific steps that marketing teams can act on. Helping people understand not just where they stand, but why it matters for their particular goals and what to do about it in a way that connects directly to the marketing outcomes they’re already measured on.

Because at the end of the day, visibility is only valuable if it leads to better decisions, stronger performance, and measurable growth. The tools give you the picture. The work is in making that picture useful.

If you haven’t looked at your brand through this lens yet, it’s worth the exercise. Ask the questions your customers would ask. Look closely at the answers.

Because whether you’re there or not, those answers are already shaping decisions.

The post Scrunch and the Search We Didn’t Know We Were Missing appeared first on Perficient Blogs.

This award highlights the strength of our Digital Experience and Platforms (DXP) practice and our dedication to helping organizations modernize, accelerate, and scale their web infrastructure with Vercel’s industry‑leading capabilities. 

“Perficient delivered a record year with us,” said Jennifer Chang, Vice President of Partnerships, Vercel. “They pair deep DXP expertise with consistently excellent implementations, and that’s exactly what Solution Partner of the Year recognizes.” 

Jeanne Grosser, Chief Operating Officer, Vercel, echoed that sentiment, noting, “Enterprises come to Perficient because they deliver transformation, not just software.” She highlighted the craftsmanship and reliability behind our work on the Vercel platform — qualities that contributed directly to this year’s award. 

Together, these perspectives underscore our shared commitment to helping organizations build faster, ship smarter, and modernize their digital experiences with the speed and flexibility today’s landscape demands. 

We’re proud of what our teams have accomplished and excited for what’s ahead as we continue to grow our partnership with Vercel. 

The post Perficient Named Vercel’s Solution Partner of the Year for the Americas appeared first on Perficient Blogs.

MobileWright is a modern open-source mobile test automation framework built as the ‘Playwright for Mobile’. It provides a unified and developer-friendly way to automate both iOS and Android applications across real devices and emulators with near zero setup complexity.

Why MobileWright

Traditional tools like Appium require complex setup involving servers, drivers, and platform-specific configurations. MobileWright eliminates this overhead through a simple npm-based installation and a Playwright-like API that developers already understand.

Key Benefits

• Playwright-style API (getByRole, getByLabel, expect)
• Zero configuration setup
• Cross-platform support (iOS & Android)
• Built-in auto-waiting for stable execution
• AI-agent first architecture
• Cloud-ready with real device integration

Quick Start & Installation

Prerequisites:

• Node.js >= 18
• iOS simulator / Android emulator / real device
• Xcode or Android SDK

Installation Commands:
npm install mobilewright @mobilewright/test

Verification:
npx mobilewright doctor

Configuration
MobileWright uses a clean configuration model:

• platform: ios | android
• deviceName: Example (iPhone 16)
• timeout and retries supported
• testDir and file patterns
• reporter support (html, json, junit)

Locator Strategies

MobileWright provides powerful element locators:

• getByLabel – accessibility labels
• getByRole – semantic roles
• getByText – visible text
• getByTestId – accessibility id
• getByType – UI element type

AI Agent Integration

MobileWright is designed for AI-native automation:

• Exposes accessibility tree for LLM understanding
• Eliminates screenshot-based parsing
• Works with tools like Copilot, Claude, Cursor
• Integrates with MCP servers for mobile automation
• Enables deterministic and token-efficient execution

Cloud Device Support

MobileWright integrates with cloud platforms to run tests on real devices at scale without changing scripts.

Proof of Concept (POC)

Two POC scenarios were successfully executed using MobileWright:

1. Rapido App – Metro Flow

• Automated navigation and validation of Metro booking flow
• Demonstrated stable execution with auto-waiting
• Execution logs confirmed successful test runs

2. Rapido App – Parcel Flow

• Automated parcel booking flow
• Showcased element detection and interaction consistency
• Demonstrated reliability across UI interactions

Example Usage

Below command is used across POC scenarios:

npm install mobilewright @mobilewright/test

Demo & Reference

Official Website: https://mobilewright.dev/
Refer to attached POC recordings and screenshots for execution proof and practical demonstration.

MobileWright -mobilewright.dev/
Example – Rapido App-Metro
npm install mobilewright @mobilewright/test

Example – Rapido App-Parcel
npm install mobilewright @mobilewright/test

Reference link –  https://mobilewright.dev/

The post MobileWright Overview: Simplifying iOS and Android Test Automation appeared first on Perficient Blogs.

This recognition reflects our people-driven culture, where colleagues grow through meaningful work, connect through purposeful collaboration, and build community—all while making a real impact.  

LEARN MORE: Perficient’s Award-Winning Culture

Our St. Louis Office Powered by Connection and Community

As Perficient’s global headquarters, our St. Louis office brings our culture to life through the relationships colleagues build and the connections we carry beyond the workplace.

The St. Louis office feels both familiar and deeply personal, shaped by everyday moments and a ‘small, big city’ connection where colleagues are often also neighbors or friends outside of work. That closeness shows up every day in how we celebrate together, support each other through life’s moments, and genuinely care about each other.” – Joey Mixon, Lead Vendor Coordinator, Finance Business Partners

Throughout the year, the St. Louis office hosts events that energize and engage teams. Our annual holiday party is a standout—we’ve teed it up at Puttshack, where colleagues enjoy tech-infused mini golf, cocktails, and appetizers in a fun, relaxed setting. Crowd favorites, from chili cook-offs and Halloween costume contests in the fall to March Madness “Dips and Dunks” gatherings packed with snacks and friendly competition, give teams a chance to build relationships and recharge.

That same sense of connection shows up daily through team lunches, happy hours, and office-wide celebrations. Outside the office, our teams turn that culture into action through volunteer initiatives like highway cleanups and Hunger Action Month. By translating connection into collective impact, we strengthen our communities and reinforce our shared purpose to make a difference.

Even working remotely, the St. Louis office social events create meaningful opportunities to connect and build relationships beyond our day-to-day projects. The encouragement and camaraderie here inspire confidence and help everyone bring their very best to our clients.” – Christine Wolz, Senior Project Manager, AI Data Platforms Delivery

These experiences strengthen the relationships and trust that help us collaborate more effectively, think more creatively, and ultimately deliver stronger outcomes for our clients.

The St. Louis office is highly collaborative, with colleagues who are supportive and always willing to help one another succeed. The culture is friendly, welcoming, and built on strong relationships, creating a great balance between delivering excellent work and enjoying time together as a team.” – Ellie Huss, Director, Custom Product Development Delivery 

Our New York City Office Built on Collaboration and Momentum 

Stronger teams drive stronger outcomes. In New York City, the same foundation of trust and collaboration reflects the energy of its surroundings—fast-paced, highly connected, and dynamic. It’s where teams come together to innovate and tackle complex challenges, delivering high-impact solutions for our clients.

What stands out is how frequently our teams choose to come into the New York office to collaborate. Conference rooms are consistently full, and we regularly host partners and clients, bringing energy into the space. Our management team is often in the office, making themselves available for team members to drop by and chat.” – David Wiesel, Vice President, Banking, Financial Services, and Insurance

Traditions play a key role in shaping that culture. Bagel Thursdays offer a regular moment to connect, while outings to Yankees games, team dinners, and after-work gatherings give colleagues time to step away from project work and connect personally. Seasonal celebrations, including holiday mixers with white elephant exchanges, Super Bowl luncheons, and off-site events like our brand launch pizza-making and cocktail party at Kesté Pizzeria, further strengthen the sense of community.

The New York office reflects a strong, people-first culture. It is welcoming, inclusive, and collaborative—colleagues are consistently friendly, supportive, and willing to help one another. Even after spending time working remotely, I’ve always felt a strong sense of belonging and connection when in the office, which speaks to the authenticity of the culture.” – Ken Fishman, Associate Vice President, Banking, Financial Services, and Insurance

Together, these experiences create an inclusive environment where colleagues show up for one another, build meaningful relationships, and drive our collective success.  

Culture in our New York office connects the team across every level and sector of the business, empowering everyone to contribute to Perficient’s mission to Think Different.” – Maeve Martin, Corporate Office Administrator, Finance Business Partners

Being named a 2026 Top Workplace in both St. Louis and New York City reinforces our commitment to an exceptional colleague experience where every individual can thrive. This recognition reflects what makes Perficient different: our people and the award-winning culture we build together through collaboration, continuous growth, and meaningful impact.  

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. 

The post Perficient Named a 2026 Top Workplace in St. Louis and New York City appeared first on Perficient Blogs.

Organizations have invested heavily in data platforms and AI models. They can sense and reason, but when it comes to acting on that intelligence in a secure, scalable way, progress stalls.

The Real Problem: AI That Doesn’t Translate to Action

Across conversations with customers and Google teams, the same issues continue to surface. AI initiatives are stuck in pilot mode, APIs aren’t designed for agent-driven workflows, and there’s no clear model for governance, security, or cost control at scale.

The result is consistent: AI that generates insight but fails to deliver outcomes.

This isn’t a tooling issue—it’s an architecture issue.

“What we’re seeing across the market is that organizations don’t struggle to generate insights anymore — they struggle to act on them. The real opportunity in the Agentic Data Cloud is closing that gap. When your data is governed, ready, and connected to agentic AI, it stops being a reporting layer and starts driving real business outcomes.”
— Jonathan Crockett, VP over

Where Most Approaches Fall Short

Many organizations are trying to layer AI onto existing systems without rethinking how execution happens. They invest in data. They experiment with models. But they lack a controlled, scalable way to operationalize AI across the enterprise.

Without that foundation, every use case becomes a one-off—and nothing truly scales.

Our Point of View: The “Act” Layer Is the Differentiator
Perficient delivers across Google’s Agentic Data Cloud model—Sense, Reason, and Act—bringing end-to-end capabilities that connect data, intelligence, and execution. While we work across the full lifecycle, we see the greatest opportunity for differentiation in the “Act” layer, where organizations move from insight to impact and drive measurable business outcomes.

Apigee, as the AI Gateway, becomes the foundation of this execution layer. It acts as the control plane for how AI systems interact with enterprise data and services—bringing structure, governance, and scalability to what is otherwise fragmented.

Specifically, Apigee enables:

• Secure, governed interactions between agents, models, and enterprise systems
• APIs transformed into agent-ready interfaces
• Built-in policy enforcement, identity, and traffic control
• Cost optimization through routing, caching, and token tracking

If data is how you sense and AI is how you reason, Apigee is how you act.

A More Complete Architecture—Built for Execution

What we’re seeing in the market is that fragmented approaches don’t scale. Success requires connecting the system end to end:

• Sense: Governed data foundation (BigQuery, Dataplex)
• Reason: AI and agents (Gemini, Vertex AI)
• Act: Secure Orchestration layer (Apigee)

This is how we’re helping clients move AI into production environments with governance built in from day one—not bolted on later.

Where We’re Going Deeper

Three areas are quickly becoming critical—and where we are actively investing our focus.

Knowledge Catalog as the semantic core: Organizations need more than access to data—they need context, governance, and trust. Knowledge Catalog is emerging as the layer that defines how data is understood and used.

Unified data and API execution: The real unlock comes from connecting governed data with controlled execution. Knowledge Catalog defines what is trusted, and Apigee enforces how it is accessed and acted on.

Together, they create the foundation for scalable, enterprise-grade AI.

Why It Matters Now

The market is at an inflection point. Companies that continue focusing only on data and models will struggle to scale. Those that invest in execution architecture will move faster—and pull ahead.

This is where we’re focused, and where we’re helping our clients differentiate.

AI maturity is no longer defined by how much you can analyze—it’s defined by how effectively you can act.

Insight without execution has no impact.

The organizations that close that gap will define the next generation of enterprise AI.

Stuck with an Agentic Pilot with no Destination? 
Connect with our experts.

The post Activating the Agentic Data Cloud: Closing the Gap Between Insight and Action appeared first on Perficient Blogs.

Held on June 17 at the Javits Center, this year’s event brought together cloud leaders, developers, and business decision-makers to explore what’s next in AI, cloud, and application modernization. Across keynotes, announcements, and customer stories, AWS delivered a focused message: organizations are moving rapidly from experimentation to production-scale, agent-driven transformation.

Agentic AI Takes Center Stage

In the keynote, AWS emphasized a major shift happening across the enterprise landscape: businesses are moving beyond chat-based AI tools toward intelligent agents capable of reasoning, executing tasks, and driving outcomes autonomously.

As Swami Sivasubramanian, VP of Agentic AI at AWS, emphasized, “Better outcomes means you build more trust. Agents help you drive outcomes quickly.”

AWS highlighted real-world examples of organizations deploying agents to improve operations, automate workflows, and enhance customer experiences.

The takeaway is that organizations that successfully adopt agentic AI will gain a compounding advantage as agents learn, improve, and unlock greater efficiency over time.

Key Announcements from AWS Summit NYC

AWS backed up its vision with a wave of product announcements aimed at making agentic AI easier to build, deploy, and scale.

1. Expanding Amazon Bedrock for Enterprise AI

AWS introduced new capabilities in Amazon Bedrock AgentCore, including:

• Managed knowledge bases for faster enterprise RAG pipelines
• Built-in web search to ground AI responses in real-time data
• Enhanced governance and controls for production-scale agents

These updates help organizations move from experimentation to enterprise-grade AI applications with less complexity.

2. New Tools to Build and Run AI Agents

Several new and enhanced services focused on accelerating development and deployment:

• Amazon Q enhancements for productivity and decision-making
• New features on Kiro iOS, a new agentic development experience
• AWS DevOps Agent for autonomous testing and release readiness
• AWS Transform for continuous modernization

Together, these tools create a more seamless path from idea to build to deploy to optimize.

3. Introducing AI-Native Security with AWS Continuum

Security remains a top priority in an agent-driven world. AWS introduced AWS Continuum, a new AI-native security service designed to:

• Continuously detect and prioritize vulnerabilities
• Automate remediation at scale
• Keep pace with AI-speed threats

This reflects a broader trend: security must evolve just as quickly as the systems it protects.

4. A Full Stack for Agentic Innovation

Across announcements like AWS Context, Amazon Quick, and enhancements to existing services, AWS is building a comprehensive ecosystem for agent-based systems from development to deployment to governance.

 As Southwest Airlines CIO Lauren Woods noted, “Don’t let your infrastructure be the ceiling on your ambition.”

The goal is simple: remove barriers so organizations can focus on business outcomes instead of infrastructure complexity.

What This Means for Organizations

The announcements at AWS Summit NYC point to a clear inflection point:

• AI Is Moving Into Core Business Operations – Agents are becoming embedded in workflows across customer service, development, security, and operations.
• Speed to Value Is Becoming a Competitive Differentiator – With tools that automate coding, testing, and deployment, organizations can dramatically reduce the time from concept to production.
• Governance and Trust Are Non-Negotiable – As agents take on more responsibility, organizations must build in security, observability, and governance from day one.
• The Gap Between Leaders and Laggards Will Widen – AWS emphasized that agentic systems create compounding momentum. This means that early adopters will accelerate faster than those still in experimentation.

Why Perficient: Turning AWS Innovation into Business Outcomes

While the technology is evolving quickly, organizations still face a familiar challenge: how to translate innovation into measurable business value. That’s where Perficient comes in.

We partner with clients & AWS to operationalize their ideas, transforming proofs of concept into production-ready, outcome-driven AWS solutions, including:

Cloud & Platform Modernization

• Application modernization and cloud-native architecture
• DevOps transformation and CI/CD acceleration
• Scalable, secure AWS infrastructure

Data, AI, and Generative AI

• Enterprise data platforms and governance
• Generative AI and agentic architecture design
• Amazon Bedrock implementation and optimization
• Responsible AI frameworks

Industry-Focused Solutions

• Financial services (risk, compliance, customer experience)
• Healthcare & life sciences (data interoperability, patient engagement)
• Commerce and digital experience platforms

Driving Measurable Outcomes

Aligned with AWS’s growing focus on business value realization, Perficient helps clients:

• Identify high-impact use cases
• Accelerate implementation timelines
• Measure and optimize ROI from AI and cloud investments

Final Thoughts

AWS Summit New York 2026 reinforced a pivotal shift: AI is starting to do the work, not just augmenting it.

Organizations that embrace this shift, by combining the right technology, strategy, and partner, will be best positioned to lead in the next era of digital transformation.

Read more about Perficient’s AWS practice here.

The post AWS Summit New York 2026 Recap: From AI Assistants to Autonomous Agents appeared first on Perficient Blogs.

What Is the Databricks Partner Champion Program? 

The Databricks Partner Champion Program is an invitation-only initiative that recognizes the most technically distinguished and community-driven individuals across the Databricks partner network. Champions are more than just certified professionals — they are evangelists, thought leaders, and trusted advisors who drive meaningful outcomes for clients and inspire those around them to push the boundaries of what’s possible with unified data and AI. 

At every stage, candidates are evaluated not just on their platform knowledge, but on their ability to deliver genuine business value and foster growth within the broader Databricks community. 

Ramalinngam’s path to becoming a Databricks Champion reflects years of hands-on experience, intellectual curiosity, and a passion for helping organizations unlock the full power of their data. As a Senior Technical Architect at Perficient, Ramalinngam has been a driving force in the company’s Databricks practice, working alongside clients to solve complex data engineering challenges and architect scalable, production-grade solutions on the Lakehouse platform. Ramalinngam has been a driving force in the company’s Databricks practice, working alongside clients to solve complex data engineering challenges and architect scalable, production-grade solutions on the Lakehouse platform. 

“Becoming a Databricks Champion represents not just my technical journey, but my commitment to continuously learn, share knowledge, mentoring teams and help organizations unlock the true value of their data through scalable and modern Lakehouse architectures,” he said. 

Driving Client Impact Through the Databricks Platform 

His work has consistently translated technical capability into measurable business outcomes — whether reducing time-to-insight for executive stakeholders, improving operational efficiency through optimized Spark workloads, or enabling data teams to deliver more reliable, governed, and secure analytics at scale. Ramalinngam’s ability to bridge the gap between technical architecture and strategic business goals makes him an invaluable partner for clients navigating the evolving data and AI landscape. 

Looking Ahead 

As a Databricks Champion, Ramalinngam is eager to build on his achievement and continue pushing the boundaries of what Perficient’s clients can accomplish with the platform.  

“I’m particularly excited about emerging capabilities such as Lakeflow Designer, Genie, future Autopilot capabilities, Databricks Apps, and Lakebase,” he said. “I’m aiming to leverage these innovations to drive next‑generation, AI‑powered data platforms while strengthening adoption and best practices within Perficient’s Databricks Center of Excellence.” 

He is also committed to paying it forward — mentoring colleagues who are pursuing their own certifications and Champion nominations, contributing to Perficient’s Databricks Center of Excellence, and helping to grow the technical community both inside Perficient and across the broader Databricks ecosystem. 

Perficient and Databricks: A Winning Partnership 

Ramalinngam’s achievement adds to Perficient’s already impressive roster of Databricks Champions, which now stands at seven — spanning the United States, Latin America, and India. This growing group of Champions reflects Perficient’s deep, sustained commitment to the Databricks platform and its mission to help enterprises build the data foundations that power the AI era. 

Perficient is proud to be a Databricks Gold Partner with more than 130 certified consultants delivering tailored data engineering, analytics, and AI solutions across Azure, AWS, and Google Cloud. Our Champions are at the heart of that mission: driving adoption, enabling innovation, and ensuring our clients get the most from every investment they make in the Databricks platform. 

Interested in learning more about Perficient’s Databricks practice? Visit our Databricks partnership page to explore how we can help your organization accelerate its data and AI transformation. 

The post Celebrating Perficient’s Newest Databricks Champion: Ramalinngam Ganesh appeared first on Perficient Blogs.

And this year’s panel continued that tradition. The event featured leaders driving real change across industries and brought together a mix of strategic vision and frontline experience that was grounded in the message that progress doesn’t happen by accident. It happens when people decide to move together.

Guests at the event heard from:

• Marissa Rexer, General Manager of CX and Agentic Solutions at Adobe
• Tauni Crefeld, Head of Customer Success at Perficient
• Sam Teibel, AVP of Digital Products and Platforms at Enterprise Mobility
• Noelle Taylor, Director of Operations and Portfolio Management at Ford
• Magy Kramer, Senior Director of Ecommerce and Digital Marketing at Caterpillar

Guiding the discussion was Lynn Brading, Global Alliance Director at Perficient, who helped shape this event into a decade-long platform for connection, mentorship, and momentum.

They spoke about how they cut through AI hype, reshaped what career growth looks like, and how to confront the invisible barriers that still shape who gets ahead.

Watch the Recorded Discussion

You can watch the full panel discussion here or read on to learn more about this year’s panel discussion.

AI Isn’t the Story. How You Use It Is.

Unsurprisingly, AI was a big part of the discussion.

Marissa Rexer shared how Adobe is embedding AI into its sales workflows, not as a headline feature, but as a quiet accelerator. Think less manual updates, more time for meaningful work.

At Caterpillar, Magy Kramer described launching an AI assistant that pulls together fragmented systems. The real challenge wasn’t the tech. It was the change management and getting teams to rethink how work gets done.

Across the board, the takeaway was simple: AI isn’t magic. It’s leverage. When used well, it removes friction. It creates space for things, like leadership, creativity, and decision-making, that actually move a business forward.

Stop Waiting. Start Getting Sponsored.

Another interesting topic that was discussed was the idea of mentorship versus sponsorship.

Tauni Crefeld said, “Mentors are coaches. They will give you advice. We all need mentors. I’ve had some great mentors in my career, but sponsors are people that have power. They are people who are in positions of authority, and they can help move you into a better role.”

And that distinction matters. Career-defining moments don’t always come from good advice. They come from someone opening a door.

During this part of the discussion, Sam Teibel shared her story about a career pivot that looked like a step backward at the time. But it wasn’t. It accelerated everything that came after. She said, “I had a sponsor when I first started my family, who made a suggestion for a role that I wasn’t even thinking about. And that role that I took has accelerated me into my current career. But at that time, that role was a step back, a significant step back from what I thought I was on the path of achieving. So, the sponsor really urged me to take a look at that and I did. I took a step back in my career for about two years and it was the best decision I’ve made since then.”

The pattern showed up again and again:

• Growth rarely looks linear
• The right sponsor can change your trajectory
• And waiting to be “ready” is usually a losing strategy

The Bias Problem Nobody Assigns

One of the most resonant themes during the discussion was about gender bias and how to handle it in the workplace.

Women often get the work that “just needs to get done.” Notes. Coordination. Follow-ups. It’s efficient. It’s reliable. It’s also limiting.

As Tauni put it, the more you do it, the more you’re seen as the person who does it. And that perception compounds over time, impacting visibility, influence, and promotion paths.

The fix isn’t complicated. It’s just hard:

• Share the work
• Say no (or at least “not always”)
• And be explicit about the role you want to play

Because if you don’t define it, someone else will.

Movement Beats Perfection

Noelle Taylor captured another hard truth about how most leaders don’t have all the answers. Yet, they move anyway.

She said, “Every day we are forced with keeping things moving, right? … What I am there to do is help make sure we are moving one way or the other. We have to move based on the best of the information we have together. Otherwise, we’ll just be paralyzed … And oftentimes the risk of not doing anything at all is worse than making the choice and taking a step forward, getting learnings and, and pivoting from there.”

In a world of constant change, especially with AI, waiting for certainty is a liability. Progress comes from making informed decisions, learning fast, and adjusting.

Or put more bluntly, the risk of doing nothing is often greater than getting it wrong.

The Leadership Shift That Actually Matters

Across every story shared, one leadership pattern stood out: The best leaders aren’t climbing faster. They’re building better.

That means:

• Developing successors
• Advocating for others
• Creating environments where teams, not individuals, win

It’s less about personal advancement and more about collective momentum.

And ironically, that’s what drives individual growth.

Progress Starts With the First Move

The throughline from this year’s Women in Digital Breakfast is simple: progress tends to come from the people willing to go first. The ones who lean into AI to make space for leadership, who advocate for sponsorship over waiting on mentorship, and who call out bias even when it shows up in subtle, structural ways. It’s not always easy or comfortable, but it’s how real change happens.

We feel privileged to continue bringing women together to learn and lift each other up, and we hope to see you at the next Women in Digital event.

The post What Women Leaders Are Doing About AI, Growth, and Getting Unstuck appeared first on Perficient Blogs.

Our team guided important discussions about modernization drag, governance pressure, cost control, and uncertainty about how emerging AI capabilities should fit into real business workflows. 

Across those conversations, the same issue kept showing up: 

Many organizations are moving faster on AI planning than they are on the architecture, operating model, and controls required to support execution at scale.

That matters because the current phase of enterprise AI is operational. Leaders are being asked to improve speed, lower costs, strengthen customer and employee experiences, and show measurable returns.

Why does workflow readiness matter for AI strategy? 

Workflow readiness matters because brittle process architecture, delayed modernization, and weak governance make it harder to move AI from planning into controlled execution across service, operations, customer engagement, and decisioning. That is why workflow readiness deserves more attention in digital investment planning. The organizations best positioned to do that are the ones that can place the right capabilities into the right workflows and govern how those workflows perform over time. 

Why Modernization Kept Coming Up 

Across conversations, it was abundantly clear that operational realities shape how quickly organizations can act on new priorities: 

• Legacy platform estates 

• Upgrade complexity 

• Cloud migration delays 

• Stalled executive buy-in as practical barriers to progress  

Leaders are now evaluating whether their platforms and processes can carry intelligence into execution across the enterprise and beyond.  

“Enterprises that address workflow architecture early have a clearer path to value. Enterprises that delay it keep absorbing friction into every new initiative.” 

Governance Must Be Part of the Business Case 

Compliance. Risk. Explainability. Observability. Reliability. Difficulty of estimating AI-related costs. These issues shape how leaders evaluate confidence, control, and future investment decisions. 

This aligns with Perficient’s broader perspective on governing AI at scale. Governance defines where AI belongs, what it is allowed to do, how outputs are validated, and how performance is measured over time.  

As organizations move toward more agentic patterns, that structure becomes even more important. Autonomy requires boundaries, validation steps, and observability if it is going to hold up in production. 

Choose AI Based on the Work 

PegaWorld conversations also revealed a practical challenge that deserves more direct attention: fit-for-use clarity. Confusion between agentic AI, orchestration, deterministic automation, and more traditional forms of AI has real consequences.  

When organizations apply the wrong level of intelligence to the wrong type of work, they increase cost, complexity, and delivery risk. 

How should enterprise leaders choose between different AI approaches?   

Start with the nature of the work.  

• Deterministic automation supports repeatable steps. 

• Predictive AI supports scoring and forecasting. 

• Generative AI supports summarization and drafting. 

• Agentic patterns are better suited for governed coordination across systems and roles.  

Intelligent process orchestration connects those choices.  

It helps leaders determine where rules belong, where models belong, where generative tools can accelerate work, and where agentic capabilities can operate within clear boundaries. That discipline improves how work moves through the business and gives leaders a stronger basis for sequencing investments. 

What PegaWorld 2026 Revealed About AI Investment Priorities 

PegaWorld 2026 reinforced that enterprise leaders are prioritizing workflow readiness, modernization, governance, and fit-for-use AI decisions because those factors determine whether AI can perform reliably inside real business processes. 

• CIOs and platform leaders: The priority is architecture that can support governed execution. That includes upgradeability, cloud readiness, integration flexibility, and observability across workflow performance.  

• Operations leaders: The focus should be high-volume workflows where orchestration can reduce delays, improve consistency, and lower manual effort.  

• Customer and marketing leaders: The question is whether decisioning and execution can happen in real time across the moments that shape loyalty, conversion, and service outcomes—including moments you don’t necessarily control. 

• Transformation leaders: The issue is readiness for a blended human-and-agent operating model, including adoption, role clarity, and trust. 

Industry nuance still matters. Regulated industries like financial services and healthcare share heavier burdens around explainability, trust, and compliance. Even so, the broader pattern held across industries: Workflow readiness, governance, cost discipline, and practical fit-for-use decisions are emerging as common enterprise priorities. 

The Real Takeaway 

PegaWorld 2026 highlighted a more demanding phase of enterprise AI. Leaders are being asked to move faster while making choices that will hold up under scale, complexity, affordability and accountability. The organizations that advance most effectively will be the ones that modernize the workflow layer, govern AI where it acts, and apply fit-for-use thinking to every investment decision. That is how digital and AI priorities become measurable business outcomes rather than a growing backlog of disconnected initiatives.  

Discover how Perficient equips leaders to choose and act with clarity—connecting strategy, data, workflows, and AI into systems designed for real, measurable execution. 

The post What PegaWorld 2026 Revealed About AI Investment Priorities appeared first on Perficient Blogs.

At DAIS 2026, Databricks announced Lakehouse//RT, powered by Reyden, a ground-up engine rewrite and not an update to Photon. It runs against existing Delta and Iceberg tables without restructuring, requires Unity Catalog, and keeps data in open formats. 

Databricks frames the value proposition around three costs of maintaining a separate serving layer: data duplication into proprietary storage, governance policies that don’t travel with the data out of Unity Catalog, and the ongoing engineering burden of owning a second pipeline. 

Official performance claims include up to 16x better performance versus real-time serving layers, with response times as low as 10 milliseconds on smaller datasets and sub-100 milliseconds on larger ones.

How We’re Using It 

A healthcare client came to us with a straightforward problem: their FHIR data was already in Databricks, and they didn’t want another system to manage it. Standing up a dedicated FHIR server (HAPI, Azure FHIR API, Smile CDR) means another deployment, another copy of PHI, another governance boundary, another bill, and another platform someone has to debug. The question was whether the lakehouse they already had could do the job. 

The system follows a three-layer pattern. Bronze handles raw ingestion and is never modified; it’s the source of truth, and its immutability is what makes compliance and audit defensible. Silver holds cleaned, validated data optimized for queries. The API layer, built on FastAPI, serves FHIR REST endpoints to consuming systems. Keeping these concerns separated means ingestion, processing, and serving don’t interfere with each other, and each layer scales independently. In a healthcare setting, that clean boundary between raw and transformed data is useful well beyond performance. 

DBIgnite feeds Bronze directly, pulling from EHRs and APIs or consuming pre-staged landing zones depending on the source system. 

The request flow: a FHIR client calls an endpoint → FastAPI validates and translates the FHIR parameters → the service layer generates optimized SQL → Databricks queries Bronze or Silver → results are mapped to the FHIR schema → a JSON response is returned. Each step is independently observable and testable. 

What We Built 

We explored storing raw FHIR resources in JSON, NDJSON, and Variant format directly in Delta tables. Unity Catalog handled governance. The problem was query performance: deeply nested FHIR JSON in a lakehouse doesn’t behave like a transactional FHIR server, and the gap on patient-specific lookups was real. 

To put numbers to it, we built a claims query system for Explanation of Benefit (EOB) records: what a patient’s plan covered, what was billed, how the claim was adjudicated. Runs on Databricks App, ingests via DBIgnite into Delta tables under Unity Catalog. Baseline performance using Databricks SQL: sub-300 milliseconds for search queries, sub-150 milliseconds for direct record retrieval. Workable, but not fast enough to feel invisible. 

What We Observed 

Patient-specific EOB lookups across a few billion records, each query returning 50–100 records scattered across all data files with no clean partition shortcut. 

On individual EOB lookups, DBSQL averaged approximately 550 milliseconds. Reyden on a small cluster averaged 86 milliseconds, roughly 84% lower latency. All seven of the seven individual EOB queries improved. Reyden’s ceiling (111 milliseconds) didn’t reach DBSQL’s floor (488 milliseconds). Scaling to a large cluster (4x the compute) produced no meaningful gain: 93 milliseconds versus 86 milliseconds, statistically indistinguishable. One collated EOB query pattern was slower on Reyden across all cluster sizes. One collated EOB query pattern was slower on Reyden across all cluster sizes. Query shape matters, and there are areas worth investigating for further optimization. 

DBSQL vs. Reyden Execution Times

Where This Leaves Us 

The case for consolidating FHIR services natively in Databricks is now a serious engineering conversation, not a wish list item.

Reyden delivered sub-100 millisecond patient-specific lookups across billions of records on a small cluster, without a separate serving layer, without a second copy of PHI, and without a second governance boundary to maintain. For healthcare teams already running on Databricks with Unity Catalog deployed, the path to retiring a third-party FHIR server or real-time serving layer is shorter than it has ever been. As Reyden moves toward general availability, we see a real opportunity to consolidate the full FHIR stack: ingestion, governance, and low-latency serving inside a single platform. We will be building on it. 

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