At Perficient, we believe that championing innovation begins with the bold leaders who live it every day. Today, we’re proud to recognize Madhu Mohan Kommu, a key driver in our Databricks Center of Excellence (CoE), for being named a Databricks Champion, one of the most coveted recognitions in the Databricks ecosystem.

This honor represents more than technical mastery; it reflects strategic impact, thought leadership, and the power to drive transformation across industries through smart architecture and scalable data solutions. Achieving Databricks Champion status unlocks priority access to exclusive events, speaking engagements, and community collaboration. It’s a mark of excellence reserved for those shaping the future of data and AI, with Madhu as a stellar example.

The Journey Behind the Recognition

Earning Champion status was no small feat. Madhu’s five-month journey began with his first Databricks certification and culminated in a nomination based on real customer impact, platform leadership, and consistent contributions to Perficient’s Databricks CoE. The nomination process spotlighted Madhu’s technical depth, thought leadership, and innovation across enterprise engagements.

From Spark to Strategy: A Legacy of Impact

Since 2019, Madhu has led initiatives for our enterprise clients, delivering platform modernization, transformation frameworks, and cutting-edge data quality solutions. His expertise in the Spark Distributed Processing Framework, combined with deep knowledge of PySpark and Unity Catalog, has made him a cornerstone in delivering high-value, AI-powered outcomes across industries.

“Personally, it’s a proud and rewarding milestone I’ve always aspired to achieve. Professionally, it elevates my credibility and brings visibility to my work in the industry. Being recognized as a Champion validates years of dedication and impact.” – Madhu Mohan Kommu, Technical Architect

Strengthening Perficient’s Position

Madhu’s recognition significantly strengthens Perficient’s role as a strategic Databricks partner, expanding our influence across regions, deepening pre-sales and enablement capabilities, and empowering customer engagement at scale. His leadership amplifies our ability to serve clients with precision and purpose.

Looking Ahead: Agentic AI & Beyond

Next up? Madhu plans to lead Perficient’s charge in Agentic AI within Databricks pipelines, designing use cases that deliver measurable savings in time, cost, and process efficiency. These efforts will drive value for both existing and future clients, making AI innovation more accessible and impactful than ever.

Advice for Future Champions

Madhu’s advice for those on a similar path is to embrace continuous learning, collaborate across teams, and actively contribute to Perficient’s Databricks CoE.

What’s Hot in Databricks Innovation

From Lakehouse Federation to Mosaic AI and DBRX, Madhu stays at the forefront of game-changing trends. He sees these innovations not just as tools, but as catalysts for redefining business intelligence.

Madhu’s story is a powerful reflection of how Perficient continues to lead with purpose, vision, and excellence in the Databricks community.

Perficient + Databricks

Perficient is proud to be a trusted Databricks elite consulting partner with 100s of certified consultants. We specialize in delivering tailored data engineering, analytics, and AI solutions that unlock value and drive business transformation.

Learn more about our Databricks partnership.

We are proud to share that Perficient has been recognized among notable providers in The Commerce Services Landscape, Q3 2025, Forrester’s authoritative overview of 40 global providers authored by Principal Analyst Chuck Gahun. We believe this recognition highlights Perficient’s role as a systems integrator driving innovation across the commerce ecosystem.

Why This Recognition Matters

We believe Forrester’s inclusion reflects more than market presence. To us, it signals our strategic alignment with the future of enterprise commerce. As organizations shift from legacy platforms to intelligent AI-first ecosystems, Perficient is helping clients reimagine how value is created, sustained, and scaled.

Forrester asked each provider included in the Landscape to select the top business scenarios for which clients select them and from there determined which are the extended business scenarios that highlight differentiation among the providers. Perficient is shown in the report for having selected B2B2B commerce, B2B2C commerce, and Extended Reality and Augmented Reality Commerce as the top reasons clients work with us out of those extended use cases. Notably, Perficient was only one of three providers included in the Landscape to have selected Extended Reality and Augmented Reality Commerce. We believe this is a differentiator for us.

We were also listed with a focus on three industries:

• Manufacturing and production of consumer products
• Manufacturing and production of industrial products
• Retail

What This Means for Perficient and Our Clients

Several themes have emerged from within the commerce landscape that Perficient is poised to take by storm, especially as it relates to taking an AI-first approach to client’s challenges and goals.

AI-First Differentiation: There is a need for providers to move beyond buzzwords and clearly define what AI-first means. Perficient’s focus on operationalizing AI through proprietary intellectual property and composable architectures positions us to lead this shift.

Vertical-Specific Solutions: It is important to tailor AI offerings by industry. Our deep expertise and work across several industry verticals, notably commerce work for manufacturing and retail clients, reflects this strategic direction with solutions that drive real outcomes in product discovery, engagement, and experience.

A New Lens on Customer Journeys: Intelligent commerce is transforming the customer experience from AI-integrated search to immersive product journeys, and Perficient is building the infrastructure for what comes next.

Outcome-Based Engagements: As clients demand measurable impact, our ability to structure engagements around business outcomes powered by AI-driven insights sets us apart.

Embrace the New Era of AI-First Commerce

We believe Perficient’s inclusion in the Forrester landscape is more than recognition. For us, it is a signal to enterprise leaders that we are ready to help them transform legacy systems into intelligent platforms, activate AI across the full commerce lifecycle, and deliver personalized immersive experiences that drive growth.

Whether you are navigating B2B complexity, scaling retail innovation, or exploring extended reality commerce, Perficient is the partner to help you lead with confidence.

Ready to build what comes next in commerce? Let’s talk about how AI-first transformation can reshape your business.

Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. For more information, read about Forrester’s objectivity 

We’re proud to share that Perficient’s What If? So What? podcast has been named a Gold Stevie® Award winner in the Technology Podcast category at the 22nd Annual International Business Awards®. These awards are among the world’s top honors for business achievement, celebrating innovation, impact, and excellence across industries.

Winners were selected by more than 250 executives worldwide, whose feedback praised the podcast’s ability to translate complex digital trends into practical, high-impact strategies for business and technology leaders.

Hosted by Jim Hertzfeld, Perficient’s AVP of Strategy, the podcast explores the business impact of digital transformation, AI, and disruption. With guests like Mark Cuban, Neil Hoyne (Google), May Habib (WRITER), Brian Solis (ServiceNow), and Chris Duffey (Adobe), we dive into the possibilities of What If?, the practical impact of So What?, and the actions leaders can take with Now What?

The Stevie judges called out what makes the show stand out:

• “What If? So What? Podcast invites experts from different industries, which is important to make sure that audiences are listening and gaining valuable information.”
• “A sharp, forward-thinking podcast that effectively translates complex digital trends into actionable insights.”
• “With standout guests like Mark Cuban, Brian Solis, and Google’s Neil Hoyne, the podcast demonstrates exceptional reach, relevance, and editorial curation.”

In other words, we’re not just talking about technology for technology’s sake. We’re focused on real business impact, helping leaders make smarter, faster decisions in a rapidly changing digital world.

We’re honored by this recognition and grateful to our listeners, guests, and production team who make each episode possible.

If you haven’t tuned in yet, now’s the perfect time to hear why the judges called What If? So What? a “high-quality, future-forward show that raises the standard for business podcasts.”

Catch the latest episodes here: What If? So What? Podcast

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Meet our Host

Jim Hertzfeld is Area Vice President, Strategy for Perficient.

For over two decades, he has worked with clients to convert market insights into real-world digital products and customer experiences that actually grow their business. More than just a strategist, Jim is a pragmatic rebel known for challenging the conventional and turning grand visions into actionable steps. His candid demeanor, sprinkled with a dose of cynical optimism, shapes a narrative that challenges and inspires listeners.

Connect with Jim: LinkedIn | Perficient

These days, data is pretty much everywhere, and come 2025, it’s more valuable, more regulated, and honestly more complicated than ever. But with all this data comes responsibility. That’s where data governance steps in. Whether you’re running a business, working in IT, or just really care about your privacy, understanding data governance is key to navigating today’s digital world.

So, what is data governance, really?

Think of it as the rulebook for how your organization handles its data. It’s not just about keeping data safe – it’s about making sure it’s accurate, easy to access, and used responsibly.

That includes:

• Figuring out who owns and manages the data
• Setting rules for privacy and security
• Making sure data stays high quality and consistent
• Keeping up with all the changing laws
• Promoting a culture of accountability and trust

Privacy is now at the core of modern data governance.

It’s no longer just a checkbox – it’s a guiding principle. In 2025, organizations should focus on:

• Building privacy into every system and process (“privacy by design”)
• Controlling who can access sensitive data
• Giving users clear options and transparency, especially when AI makes decisions
• Stay compliant with global laws like GDPR (General Data Protection Regulation), CCPA (California Consumer Privacy Act), and CPRA (California Privacy Rights Act) – plus new regulations popping up every year

Let’s talk about some of the biggest challenges – and how to tackle them:

1. Balancing Access and Security

Challenge: Allowing teams to access data for innovation without risking breaches or misuse.
Solutions:

• Use role-based access controls (RBAC) so only authorized folks can see sensitive stuff.
• Automate policy enforcement and use data catalogues to classify and track data in real time.
• Create a single policy hub to manage rules consistently across the entire organization.

2. Keeping Up with Regulations

Challenge: Navigating a maze of global privacy laws and avoiding hefty fines.
Solutions:

• Automate compliance monitoring with AI tools.
• Regularly update your policies and train staff on new laws like GDPR, CCPA, and CPRA.
• Use dashboards to get a real-time picture of your compliance status.

3. Integrating Old and New Systems

Challenge: Legacy systems often don’t have modern governance features.
Solutions:

• Use open-by-design governance tools that work well with both old and new platforms.
• Gradually move critical data into cloud-based systems for easier management.

4. Managing Unstructured Data

Challenge: Emails, documents, and media files are tricky to govern.
Solutions:

• Use AI and natural language processing (NLP) to classify and monitor unstructured data.
• Set clear rules for how long to keep data and when to delete it.

5. Building a Data-Driven Culture

Challenge: People often see governance as a bureaucratic hassle.
Solutions:

• Make governance work for the team – show how it helps hit business goals, not just compliance.
• Include data responsibility in performance reviews and encourage collaboration across teams.
• Offer ongoing training and recognize those who champion data stewardship.

By focusing on these areas, organizations can turn data governance from a compliance chore into a strategic advantage that fuels innovation and trust in 2025 and beyond.

How Tech Is Shaping the Future

Over the next ten years, we’re going to see some pretty exciting tech changes that totally shake up how we handle data:

• AI & Automation: AI is really stepping up, helping us spot data errors faster and make sure we stick to the rules without the constant manual checks.
• Real-time monitoring: Think instant alerts – getting notified right away if something’s off or looks fishy.
• Cloud-native tools: Managing data smoothly across different cloud platforms without breaking a sweat.
• Blockchain & Zero Trust: Better, more secure ways to control and keep track of data – more transparent too.
• NLP (Natural Language Processing): Making sense of all those unstructured bits of data, like emails or social media posts.

Is Your Data Governance Really Working? Here’s How to Tell

You can’t really manage what you don’t measure, right? Here’s how some of the top organizations keep track:

• Data quality scores – like how accurate, complete, and timely the data is
• Results from compliance checks and audits
• Who’s accessing what data and how often
• Reports of incidents or data breaches
• Employee engagement – are people actually using governance tools and following policies?
• Real-time dashboards and alerts that keep everyone updated

What You Can Do to Help Out

Data governance is not just for the IT or compliance folks – it is everyone’s job. Here are some simple ways you can pitch in:

• Stick to your organization’s data policies.
• Speak up if you notice something odd or suspicious.
• Only collect or use data if you really need it.
• Help spread awareness about privacy among your coworkers.
• Stay updated on privacy laws – they change quickly!
• Volunteer for data stewardship or governance projects.

The Bottom Line

By 2025, data governance is not just some buzzword – it is what builds trust, drives innovation, and keeps organizations compliant. Accepting new tech, tackling challenges head-on, and making data privacy everyone’s concern will help us release our data’s full potential – safely and responsibly.

Drupal 11’s AI Features: What They Actually Mean for Your Team

If you’ve been following the Drupal community lately, you’ve probably heard about the excitement with AI in Drupal 11 and the new Drupal AI Initiative. With over $100,000 in funding and 290+ AI modules already available, this will be a game changer.

But here’s the thing, AI in Drupal isn’t about replacing your team. It’s about making everyone more effective at what they already do best. Let’s talk through some of these new capabilities and what they mean for different teams in your organization.

Content Teams: Finally, An Assistant That Actually Helps

Creating quality content quickly has always been a challenge, but Drupal 11’s AI features tackle this head-on. The AI CKEditor integration gives content creators real-time assistance right in the editing interface, things like spelling corrections, translations, and contextual suggestions as you type.

The AI Content module is where things get interesting. It can automatically adjust your content’s tone for different audiences, summarize long content, and even suggest relevant taxonomy terms. For marketing teams juggling multiple campaigns, this means maintaining brand consistency without the usual back-and-forth reviews.

One feature that’s already saving teams hours is the AI Image Alt Text module. Instead of manually writing alt text for accessibility compliance, it generates descriptions automatically. The AI Translate feature is another game-changer for organizations with global reach—one-click multilingual content creation that actually understands context.

The bottom line? Your content team can focus on strategy and creativity instead of getting bogged down in routine tasks.

Developers: Natural Language Site Building

Here’s where Drupal 11 gets really exciting for a dev team. The AI Agents module introduces something we haven’t seen before, text-to-action capabilities. Developers can now modify Drupal configurations, create content types, and manage taxonomies just by describing what they need in spoken english.

Instead of clicking through admin interfaces, you can literally tell Drupal what you want, “Create a content type for product reviews with fields for rating, pros, cons, and reviewer information.” The system understands and executes these commands.

The AI module ecosystem supports over 21 major providers, OpenAI, Claude, AWS Bedrock, Google Vertex, and more. This means you’re not locked into any single AI provider and can choose the best model for specific tasks. The AI Explorer gives you a testing ground to experiment with prompts before pushing anything live.

For complex workflows, AI Automators let you chain multiple AI systems together. Think automated content transformation, field population, and business logic handling with minimal custom code.

The other great aspect of Drupal AI, is the open source backbone of Drupal, allows you to extend, add and build upon these agents in any way your dev team sees fit.

Marketing Teams: Data-Driven Campaign Planning

Marketing teams might be the biggest winners here. The AI Content Strategy module analyzes your existing content and provides recommendations for what to create next based on actual data, not guesswork. It identifies gaps in your content strategy and suggests targeted content based on audience behavior and industry trends.

The AI Search functionality means visitors can find content quickly, no more keyword guessing games. The integrated chatbot framework provides intelligent customer service that can access your site’s content to give accurate responses.

For SEO, the AI SEO module generates reports with user recommendations, reviewing content and metadata automatically. This reduces the need for separate SEO tools while giving insights right where you can act on them.

Why This Matters Right Now

The Drupal AI Initiative represents something more than just new features. With dedicated teams from leading agencies and serious funding behind it, this is Drupal positioning itself as the go-to platform for AI-powered content management.

For IT executives evaluating CMS options, Drupal 11’s approach is a great fit. You maintain complete control over your data and AI interactions while getting enterprise-grade governance with approval workflows and audit trails. It’s AI augmentation rather than AI replacement.

The practical benefits are clear: faster campaign launches, consistent brand voice across all content, and teams freed from manual tasks to focus on strategic work. In today’s competitive landscape, that kind of operational efficiency can make the difference between leading your market and playing catch-up.

The Reality Check

We all know, no technology is perfect. The success of these AI features, especially within the open source community, depends heavily on implementation and team adoption. You’ll need to spend time in training and process development to see real benefits. Like any new technology, there will be a learning curve as your team figures out the best ways to leverage these new features.

Based on what we are seeing within groups that have done early adoption of the AI features, they are seeing a good ROI on improvement of team efficiency, marketing time as well as reduced SEO churn.

If you’re considering how Drupal 11’s AI features might fit your organization, it’s worth having a conversation with an experienced implementation partner like Perficient. We can help you navigate the options and develop an AI strategy that makes sense for your specific situation.

Empathy, Resilience, Innovation, and Speed: The Blueprint for Intelligent Healthcare Transformation

Forrester’s recent report, Becoming An Intelligent Healthcare Organization Is An Attainable Goal, Not A Lost Cause, confirms what healthcare executives already know: transformation is no longer optional.

Perficient is proud to be quoted in this research, which outlines a pragmatic framework for becoming an intelligent healthcare organization (IHO)—one that scales innovation, strengthens clinical and operational performance, and delivers measurable impact across the enterprise and the populations it serves.

Why Intelligent Healthcare Is No Longer Optional

Healthcare leaders are under pressure to deliver better outcomes, reduce costs, and modernize operations, all while navigating fragmented systems and siloed departments. The journey to transformation requires more than technology; it demands strategic clarity, operational alignment, and a commitment to continuous improvement.

Forrester reports, “Among business and technology professionals at large US healthcare firms, only 63% agree that their IT organization can readily reallocate people and technologies to serve the newest business priority; 65% say they have enterprise architecture that can quickly and efficiently support major changes in business strategy and execution.”

Despite widespread investment in digital tools, many healthcare organizations struggle to translate those investments into enterprise-wide impact. Misaligned priorities, inconsistent progress across departments, and legacy systems often create bottlenecks that stall innovation and dilute momentum.

Breaking Through Transformation Barriers

These challenges aren’t just technical or organizational. They’re strategic. Enterprise leaders can no longer sit on the sidelines and play the “wait and see” game. They must shift from reactive IT management to proactive digital orchestration, where technology, talent, and transformation are aligned to business outcomes.

Business transformation is not a fleeting trend. It’s an essential strategy for healthcare organizations that want to remain competitive as the marketplace evolves.

Forrester’s report identifies four hallmarks of intelligent healthcare organizations, emphasizing that transformation is not a destination but a continuous practice.

Four Hallmarks of An Intelligent Healthcare Organization (IHO)

To overcome transformation barriers, healthcare organizations must align consumer expectations, digital infrastructure, clinical workflows, and data governance with strategic business goals.

1. Empathy At Scale: Human-Centered, Trust-Enhancing Experiences

A defining trait of intelligent healthcare organizations is a commitment to human-centered experiences.

• Driven By: Continuous understanding of consumer needs
• Supported By: Strategic technology investments that enable timely, personalized interventions and touchpoints

As Forrester notes, “The most intelligent organizations excel at empathetic, swift, and resilient innovation to continuously deliver new value for customers and stay ahead of the competition.”

Empathy is a performance driver. Organizations that prioritize human-centered care see higher engagement, better adherence, and stronger loyalty.

Our experts help clients reimagine care journeys using journey sciences, predictive analytics, integrated CRM and CDP platforms, and cloud-native architectures that support scalable personalization. But personalization without protection is a risk. That’s why empathy must extend beyond experience design to include ethical, secure, and responsible AI adoption.

Healthcare organizations face unique constraints, including HIPAA, PHI, and PII regulations that limit the utility of plug-and-play AI solutions. To meet these challenges, we apply our PACE framework—Policies, Advocacy, Controls, and Enablement—to ensure AI is not only innovative but also rooted in trust.

• Policies establish clear boundaries for acceptable AI usage, tailored to healthcare’s regulatory landscape.
• Advocacy builds cross-functional understanding and adoption through education and collaboration.
• Controls implement oversight, auditing, and risk mitigation to protect patient data and ensure model integrity.
• Enablement equips teams with the tools and environments needed to innovate confidently and securely.

This approach ensures AI is deployed with purpose, aligned to business goals, and embedded with safeguards that protect consumers and care teams alike. It also supports the creation of reusable architectures that blend scalable services with real-time monitoring, which is critical for delivering fast, reliable, and compliant AI applications.

Responsible AI isn’t a checkbox. It’s a continuous practice. And in healthcare, it’s the difference between innovation that inspires trust and innovation that invites scrutiny.

2. Designing for Disruption: Resilience as a Competitive Advantage

Patient-led experiences must be grounded in a clear-eyed understanding that market disruption isn’t simply looming. It’s already here. To thrive, healthcare leaders must architect systems that flex under pressure and evolve with purpose. Resilience is more than operational; it’s also behavioral, cultural, and strategic.

Perficient’s Access to Care research reveals that friction in the care journey directly impacts health outcomes, loyalty, and revenue:

• More than 50% of consumers who experienced scheduling friction took their care elsewhere, resulting in lost revenue, trust, and care continuity
• 33% of respondents acted as caregivers, yet this persona is often overlooked in digital strategies
• Nearly 1 in 4 respondents who experienced difficulty scheduling an appointment stated that the friction led to delayed care, and they believed their health declined as a result
• More than 45% of consumers aged 18–64 have used digital-first care instead of their regular provider, and 92% of them believe the quality is equal or better

This sentiment should be a wakeup call for leaders. It clearly signals that consumers expect healthcare to meet both foundational needs (cost, access) and lifestyle standards (convenience, personalization, digital ease). When systems fail to deliver, patients disengage. And when caregivers—who often manage care for entire households—encounter barriers, the ripple effect is exponential.

To build resilience that drives retention and revenue, leaders must design systems that anticipate needs and remove barriers before they impact care. Resilient operations must therefore be designed to:

• Reduce friction across the care journey, especially in scheduling and follow-up
• Support caregivers with multi-profile tools, shared access, and streamlined coordination
• Enable digital-first engagement that mirrors the ease of consumer platforms like Amazon and Uber

Consumers are blending survival needs with lifestyle demands. Intelligent healthcare organizations address both simultaneously.

Resilience also means preparing for the unexpected. Whether it’s regulatory shifts, staffing shortages, or competitive disruption, IHOs must be able to pivot quickly. That requires leaders to reimagine patient (and member) access as a strategic lever and prioritize digital transformation that eases the path to care.

3. Unified Innovation: Aligning Strategy, Tech, and Teams

Innovation without enterprise alignment is just noise—activity without impact. When digital initiatives are disconnected from business strategy, consumer needs, or operational realities, they create confusion, dilute resources, and fail to deliver meaningful outcomes. Fragmented innovation may look impressive in isolation, but without coordination, it lacks the momentum to drive true transformation.

To deliver real results, healthcare leaders must connect strategy, execution, and change readiness. In Forrester’s report, a quote from an interview with Priyal Patel emphasizes the importance of a shared strategic vision:

“Today’s decisions should be guided by long-term thinking, envisioning your organization’s business needs five to 10 years into the future.” — Priyal Patel, Director, Perficient

Our approach begins with strategic clarity. Using our Envision Framework, we help healthcare organizations rapidly identify opportunities, define a consumer-centric vision, and develop a prioritized roadmap that aligns with business goals and stakeholder expectations. This framework blends real-world insights with pragmatic planning, ensuring that innovation is both visionary and executable.

We also recognize that transformation is not just technical—it’s human. Organizational change management (OCM) ensures that teams are ready, willing, and able to adopt new ways of working. Through structured engagement, training, and sustainment, we help clients navigate the behavioral shifts required to scale innovation across departments and disciplines.

This strategic rigor is especially critical in healthcare, where innovation must be resilient, compliant, and deeply empathetic. As highlighted in our 2025 Digital Healthcare Trends report, successful organizations are those that align innovation with measurable business outcomes, ethical AI adoption, and consumer trust.

Perficient’s strategy and transformation services connect vision to execution, ensuring that innovation is sustainable. We partner with healthcare leaders to identify friction points and quick wins, build a culture of continuous improvement, and empower change agents across the enterprise.

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4. Speed With Purpose and Strategic Precision

The ability to pivot, scale, and deliver quickly is becoming a defining trait of tomorrow’s healthcare leaders. The way forward requires a comprehensive digital strategy that builds the capabilities, agility, and alignment to stay ahead of evolving demands and deliver meaningful impact.

IHOs act quickly without sacrificing quality. But speed alone isn’t enough. Perficient’s strategic position emphasizes speed with purpose—where every acceleration is grounded in business value, ethical AI adoption, and measurable health outcomes.

Our experts help healthcare organizations move fast by:

• Designing for agility with composable architectures, MVP+ delivery models, and intelligent automation that compliantly accelerates time-to-value while maintaining clinical integrity
• Aligning digital transformation with value-based care goals, enabling organizations to shift from volume to value through strategic investments that integrate cloud platforms, interoperable systems, and advanced analytics
• Operationalizing AI responsibly to support equitable, secure, compliant, and scalable AI adoption

This approach supports the Quintuple Aim: better outcomes, lower costs, improved experiences, clinician well-being, and health equity. It also ensures that innovation is not just fast. It’s focused, ethical, and sustainable.

Speed with purpose means:

• Rapid prototyping that validates ideas before scaling
• Real-time data visibility to inform decisions and interventions
• Cross-functional collaboration that breaks down silos and accelerates execution
• Outcome-driven KPIs that measure impact, not just activity

Healthcare leaders don’t need more tools. They need a strategy that connects business imperatives, consumer demands, and an empowered workforce to drive transformation forward. Perficient equips organizations to move with confidence, clarity, and control.

Collaborating to Build Intelligent Healthcare Organizations

We believe our inclusion in Forrester’s report underscores our role as a trusted advisor in intelligent healthcare transformation. From insight to impact, our healthcare expertise equips leaders to modernize, personalize, and scale care. We drive resilient, AI-powered transformation to shape the experiences and engagement of healthcare consumers, streamline operations, and improve the cost, quality, and equity of care.

We have been trusted by the 10 largest health systems and the 10 largest health insurers in the U.S., and Modern Healthcare consistently ranks us as one of the largest healthcare consulting firms.

Our strategic partnerships with industry-leading technology innovators—including AWS, Microsoft, Salesforce, Adobe, and more—accelerate healthcare organizations’ ability to modernize infrastructure, integrate data, and deliver intelligent experiences. Together, we shatter boundaries so you have the AI-native solutions you need to boldly advance business.

Ready to advance your journey as an intelligent healthcare organization?

We’re here to help you move beyond disconnected systems and toward a unified, data-driven future—one that delivers better experiences for patients, caregivers, and communities. Let’s connect and explore how you can lead with empathy, intelligence, and impact.

Perficient has officially announced a groundbreaking partnership with WRITER, the leader in agentic AI for the enterprise. This 360-degree collaboration marks a pivotal moment in our AI-first journey, combining WRITER’s powerful end-to-end agent platform with Perficient’s deep consulting expertise to deliver scalable, secure, and transformative AI solutions to the Global 2000. 

To explore the significance of this partnership, I sat down with Bill Davis, Perficient’s Senior Vice President and Head of Partners and Ecosystem, to discuss what this means for our clients, our colleagues, and the future of enterprise AI. 

Connor Stieferman: Bill, what makes this partnership with WRITER so significant for Perficient and the broader market? 

Bill Davis: This partnership represents a major milestone not just for Perficient and WRITER, but for the enterprise AI landscape as a whole. WRITER is gaining serious momentum in the market, and their agentic AI platform is redefining how organizations think about productivity, automation, and intelligence at scale. By combining WRITER’s cutting-edge technology with Perficient’s deep industry expertise and global implementation capabilities, we’re creating a force multiplier for enterprise transformation. Together, we’re enabling organizations to move beyond isolated AI experiments and into scalable, secure, and measurable deployments.  

To put it simply, this partnership sets a new standard for how AI can be adopted and operationalized across industries. 

Connor: What makes Perficient a strong partner for a company like WRITER? 

Bill: WRITER is leading the way in agentic AI, and companies at that level need partners who can match their pace and deliver enterprise-grade execution. Perficient brings deep industry expertise, a global delivery model, and a strong track record of helping large organizations adopt emerging technologies at scale. We understand how to translate innovation into business outcomes with speed and precision. We also add a critical strategic layer, helping clients identify where agentic AI can drive the most value, designing tailored solutions, and ensuring successful adoption. By jointly going to market with WRITER, we’re co-developing best-in-class, industry-specific agentic solutions that deliver real outcomes for enterprise customers.  

Connor: How does this partnership reflect Perficient’s AI-first strategy? 

Bill: Our AI-first strategy is about embedding intelligence into everything we do — from internal operations to client solutions. By broadly deploying WRITER agents across our own enterprise, we’re demonstrating a top-down and bottom-up commitment to transformation. We’re not just advising clients on AI; we’re living it. This partnership allows us to build and deploy custom agents that automate our own workflows, generate contextual content, and deliver insights, showcasing what’s possible when AI is fully integrated into a business. 

Connor: What’s the significance of WRITER being Perficient’s first 360-degree partner? 

Bill: It’s a testament to the depth of our collaboration. Our relationship extends well beyond jointly going to market together. Each organization is deeply committed to the other’s success. The alignment extends across our executive, sales, marketing, and technology teams and reflects the strength of our shared vision. This level of partnership is rare — and it positions us to lead the market in agentic AI adoption. 

Connor: What kind of value can clients expect from this collaboration? 

Bill: Clients will see accelerated time-to-value through rapid deployment of tailored AI agents. They’ll benefit from embedded intelligence that integrates seamlessly with their existing systems, along with strategic guidance from our AI experts to ensure adoption and ROI. Plus, WRITER’s platform offers enterprise-grade security and governance, which is critical for large-scale deployments. Together, we’re helping clients cut through the noise and focus on fast, secure outcomes. 

Connor: What excites you most about what’s ahead? 

Bill: Honestly, it’s the opportunity to help our clients become agent builders themselves. We’re not just delivering tools — we’re enabling transformation. And we’re doing it alongside the exceptional team at WRITER. They’re agile, collaborative, and deeply committed to moving fast and getting things done right. Partnerships work best when both sides are aligned, and WRITER brings the same obsession over client outcomes that we value at Perficient. Together, we’re empowering organizations to reinvent how they work, innovate, and grow. The future of enterprise AI is agentic, and Perficient and WRITER are at the forefront of making that future real. 

Final Thoughts 

Perficient’s partnership with WRITER is a bold step forward in our mission to transform enterprises through AI. By combining cutting-edge technology with deep consulting expertise, we’re helping clients unlock the full potential of agentic AI. 

Stay tuned for more updates as we roll out new solutions, launch innovation labs, and continue to lead the way in enterprise AI transformation. 

Modern Healthcare has once again recognized Perficient among the largest healthcare management consulting firms in the U.S., ranking us ninth in its 2025 survey. This honor reflects not only our growth but also our commitment to helping healthcare leaders navigate complexity with clarity, precision, and purpose.

What’s Driving Demand: Innovation with Intent

As provider, payer, and MedTech organizations face mounting pressure to modernize, our work is increasingly focused on connecting digital investments to measurable business and health outcomes. The challenges are real—and so are the opportunities.

Healthcare leaders are engaging our experts to tackle shifts from digital experimentation to enterprise alignment in business-critical areas, including:

• Digital health transformation that eases access to care.
• AI and data analytics that accelerate insight, guide clinical decisions, and personalize consumer experiences.
• Workforce optimization that supports clinicians, streamlines operations, and restores time to focus on patients, members, brokers, and care teams.

These investments represent strategic maturity that reshapes how care is delivered, experienced, and sustained.

Operational Challenges: Strategy Meets Reality

Serving healthcare clients means working inside a system that resists simplicity. Our industry, technical, and change management experts help leaders address three persistent tensions:

1. Aligning digital strategy with enterprise goals. Innovation often lacks a shared compass. We translate divergent priorities—clinical, operational, financial—into unified programs that drive outcomes.
2. Controlling costs while preserving agility. Budgets are tight, but the need for speed and competitive relevancy remains. Our approach favors scalable roadmaps and solutions that deliver early wins and can flex as the health care marketplace and consumer expectations evolve.
3. Preparing the enterprise for AI. Many of our clients have discovered that their AI readiness lags behind ambition. We help build the data foundations, governance frameworks, and workforce capabilities needed to operationalize intelligent systems.

Related Insights: Explore the Digital Trends in Healthcare

Consumer Expectations: Access Is the New Loyalty

Our Access to Care research, based on insights from more than 1,000 U.S. healthcare consumers, reveals a fundamental shift: if your healthcare organization isn’t delivering a seamless, personalized, and convenient experience, consumers will go elsewhere. And they won’t always come back.

Many healthcare leaders still view competition as other hospitals or clinics in their region. But today’s consumer has more options—and they’re exercising them. From digital-first health experiences to hyper-local disruptors and retail-style health providers focused on accessibility and immediacy, the competitive field is rapidly expanding.

• Digital convenience is now a baseline. More than half of consumers who encountered friction while scheduling care went elsewhere.
• Caregivers are underserved. One in three respondents manage care for a loved one, yet most digital strategies treat the patient as a single user.
• Digital-first care is mainstream. 45% of respondents aged 18–64 have already used direct-to-consumer digital care, and 92% of those adopters believe the quality is equal or better to the care offered by their regular health care system.

These behaviors demand a rethinking of access, engagement, and loyalty. We help clients build experiences that are intuitive, inclusive, and aligned with how people actually live and seek care.

Looking Ahead: Complexity Accelerates

With intensified focus on modernization, data strategy, and responsible AI, healthcare leaders are asking harder questions. We’re helping them find and activate answers that deliver value now and build resilience for what’s next.

Our technology partnerships with Adobe, AWS, Microsoft, Salesforce, and other platform leaders allow us to move quickly, integrate deeply, and co-innovate with confidence. We bring cross-industry expertise from financial services, retail, and manufacturing—sectors where personalization and operational excellence are already table stakes. That perspective helps healthcare clients leapfrog legacy thinking and adopt proven strategies. And our fluency in HIPAA, HITRUST, and healthcare data governance ensures that our digital solutions are compliant, resilient, and future-ready.

Optimized, Agile Strategy and Outcomes for Health Insurers, Providers, and MedTech

Discover why we been trusted by the 10 largest U.S. health systems, 10 largest U.S. health insurers, and 14 of the 20 largest medical device firms. We are recognized in analyst reports and regularly awarded for our excellence in solution innovation, industry expertise, and being a great place to work.

Contact us to explore how we can help you forge a resilient, impactful future that delivers better experiences for patients, caregivers, and communities.

Azure Cosmos DB for NoSQL now supports hybrid search, it is a powerful feature that combines full-text search and vector search to deliver highly relevant and accurate results. This blog post provides a comprehensive guide for developers and architects to understand, implement, and leverage hybrid search capabilities in their applications.

• What is hybrid search?
• How hybrid search works in Cosmos DB
• Vector embedding
• Implementing hybrid search
  • Enable hybrid search.
  • Container set-up and indexing
  • Data Ingestion
  • Search Queries
• Code Example

What is Hybrid Search?

Hybrid search is an advanced search technology that combines keyword search (also known as full-text search) and vector search to deliver more accurate and relevant search results. It leverages the strengths of both approaches to overcome the limitations of each when used in isolation.

Key Components

• Full-Text Search: This traditional method matches the words you type in, using techniques like stemming, lemmatization, and fuzzy matching to find relevant documents. It excels at finding exact matches and is efficient for structured queries with specific terms. Employs the BM25 algorithm to evaluate and rank the relevance of records based on keyword matching and text relevance.
• Vector Search: This method uses machine learning models to represent queries and documents as numerical embeddings in a multidimensional space, allowing the system to find items with similar characteristics and relationships, even if the exact keywords don’t match. Vector search is particularly useful for finding information that’s conceptually similar to the search query.
• Reciprocal Rank Fusion (RRF): This algorithm merges the results from both keyword and vector search, creating a single, unified ranked list of documents. RRF ensures that relevant results from both search types are fairly represented.

Hybrid search is suitable for various use cases, such as:

• Retrieval Augmented Generation (RAG) with LLMs
• Knowledge management systems: Enabling employees to efficiently find pertinent information within an enterprise knowledge base.
• Content Management: Efficiently search through articles, blogs, and documents.
• AI-powered chatbots
• E-commerce platforms: Helping customers find products based on descriptions, reviews, and other text attributes.
• Streaming services: Helping users find content based on specific titles or themes.

Let’s understand vector search and full-text search before diving into hybrid search implementation.

Understanding of Vector Search

Vector search in Azure Cosmos DB for NoSQL is a powerful feature that allows you to find similar items based on their semantic meaning, rather than relying on exact matches of keywords or specific values. It is a fundamental component for building AI applications, semantic search, recommendation engines, and more.

Here’s how vector search works in Cosmos DB:

Vector embeddings

Vector embeddings are numerical representations of data in a high-dimensional space, capturing their semantic meaning. In this space, semantically similar items are represented by vectors that are closer to each other. The dimensionality of these vectors can be quite large. We have separate topics in this blog on how to generate vector embedding.

Storing and indexing vectors

Azure Cosmos DB allows you to store vector embeddings directly within your documents. You define a vector policy for your container to specify the vector data’s path, data type, and dimensions. Cosmos DB supports various vector index types to optimize search performance, accuracy, and cost:

• Flat: Provides exact k-nearest neighbor (KNN) search.
• Quantized Flat: Offers exact search on compressed vectors.
• DiskANN: Enables highly scalable and accurate Approximate Nearest Neighbor (ANN) search.

Querying

• Azure Cosmos DB provides the VectorDistance() system function, which can be used within SQL queries to perform vector similarity searches as part of vector search.

Understanding Full-Text Search

Azure Cosmos DB for NoSQL now offers full-text search functionality (feature is in preview at this time for certain Azure regions), allowing you to perform powerful and efficient text-based searches within your documents directly in the database. This significantly enhances your application’s search capabilities without the need for an external search service for basic full-text needs.

Indexing

To enable full-text search, you need to define a full-text policy specifying the paths for searching and add a full-text index to your container’s indexing policy. Without the index, full-text searches would perform a full scan. Indexing involves tokenization, stemming, and stop word removal, creating a data structure like an inverted index for fast retrieval. Multi-language support (beyond English) and stop word removal are in early preview.

Querying

Cosmos DB provides system functions for full-text search in the NoSQL query language. These include FullTextContains, FullTextContainsAll, and FullTextContainsAny for filtering in the WHERE clause. The FullTextScore function uses the BM25 algorithm to rank documents by their relevance.

How Hybrid Search works in Cosmos DB

• Data Storage: Your documents in Cosmos DB include both text fields (for full-text search) and vector embedding fields (for vector search).
• Indexing:
  • Full-Text Index: A full-text policy and index are configured on your text fields, enabling keyword-based searches.
  • Vector Index: A vector policy and index are configured on your vector embedding fields, allowing for efficient similarity searches based on semantic meaning.

• Querying: A single query request is used to initiate hybrid search, including both full-text and vector search parameters.
• Parallel Execution: The vector and full-text search components run in parallel.
  • VectorDistance() measures vector similarity.
  • FullTextContains() or similar functions find keyword matches, and `FullTextScore()` ranks results using BM25.

• Result Fusion: The RRF function merges the rankings from both searches (vector & full text), creating a combined, ordered list based on overall relevance.
• Enhanced Results: The final results are highly relevant, leveraging both semantic understanding and keyword precision.

Vector Embedding

Vector embedding refers to the process of transforming data (like text, images) into a series of numbers, or a vector, that captures its semantic meaning. In this n-dimensional space, similar data points are mapped closer together, allowing computers to understand and analyze relationships that would be difficult with raw data.

To support hybrid search in Azure Cosmos DB, enhance the data by generating vector embeddings from searchable text fields. Store these embeddings in dedicated vector fields alongside the original content to enable both semantic and keyword-based queries.

Steps to generate embeddings with Azure OpenAI models

Provision Azure OpenAI Resource

• Sign in to the Azure portal: Go to https://portal.azure.com and log in.
• Create a resource: Select “Create a resource” from the Azure dashboard and search for “Azure OpenAI”.

Deploy Embedding Model

• Navigate to your newly created Azure OpenAI resource and click on “Explore Azure AI Foundry portal” in the overview page.
• Go to the model catalog and search for embedding models.
• Select embedding model:
  • From the embedding model list, choose an embedding model like text-embedding-ada-002, text-embedding-3-large, or text-embedding-3-small.

• • 
  • Azure open AI embedding models https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions#embeddings
• Deployment name: Provide a unique name for your deployment. This name is crucial for accessing the model via the API.
• Deploy the model: Click “Create” to deploy the embedding model.
• After deployment, the resource will appear under the ‘Deployments’ section.
  • 

Accessing and utilizing embeddings

• Endpoint and API Key: After deployment, navigate to your Azure OpenAI resource and find the “Keys and Endpoint” under “Resource Management”. Copy these values as they are needed for authenticating API calls.
• Integration with applications: Use the Azure OpenAI SDK or REST APIs in your applications, referencing the deployment name and the retrieved endpoint and API key to generate embeddings.

Code example for .NET Core

Note: Ensure you have the .NET Core 8 SDK installed

using Azure;
using Azure.AI.OpenAI;
using System;
using System.Linq;

namespace AzureOpenAIAmbeddings
{
    class Program
    {
        static async Task Main(string[] args)
        {
            // Set your Azure OpenAI endpoint and API key securely
            string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? "https://YOUR_RESOURCE_NAME.openai.azure.com/"; // Replace with OpenAI endpoint
            string apiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY") ?? "YOUR_API_KEY"; // Replace with OpenAI API key

            // Create an AzureOpenAIAClient
            var credentials = new AzureKeyCredential(apiKey);
            var openaiClient = new OpenAIClient(new Uri(endpoint), credentials);

            // Create embedding options
            EmbeddingOptions embeddingOptions = new()
            {
                DeploymentName = "text-embedding-ada-002", // Replace with your deployment name
                Input = { "Your text for generating embedding" },  // Text that require to generate embedding 
            };

            // Generate embeddings
            var returnValue = await openaiClient.GetEmbeddingsAsync(embeddingOptions);

            //Store generated embedding data to Cosmos DB along with your text content
            var embedding = returnValue.Value.Data[0].Embedding.ToArray()
        }
    }
}

Implementing Hybrid search

Implementing hybrid search in Azure Cosmos DB for NoSQL involves several key steps to combine the power of vector search and full-text search. This diagram illustrates the architecture of Hybrid Search in Azure Cosmos DB, leveraging Azure OpenAI for generating embedding, combining both vector-based and keyword-based search:

Step 1: Enable hybrid search in the Cosmos DB account

To implement hybrid search in Cosmos DB, begin by enabling both vector search and full-text search on the Azure Cosmos DB account.

• Navigate to Your Azure Cosmos DB for NoSQL Resource Page

• Access the Features Pane:

  • Select the “Features” pane under the “Settings” menu item.

• Enable Vector Search:

  • Locate and select the “Vector Search for NoSQL.” Read the description to understand the feature.
  • Click “Enable” to activate vector indexing and search capabilities.
  • 

• Enable Full-Text Search:

  • Locate and select the “Preview Feature for Full-Text Search” (Full-Text Search for NoSQL API (preview)). Read the description to confirm your intention to enable it.
  • Click “Enable” to activate full-text indexing and search capabilities.

                Notes:

• • • Once these features are enabled, they cannot be disabled.
    • Full Text Search (preview) may not be available in all regions at this time.

Step 2: Container Setup and Indexing

• Create a database and container or use an existing one.
  • Note: Adding a vector index policy to an existing container may not be supported. If so, you will need to create a new container.

• Define the Vector embedding policy on the container
  • You need to specify a vector embedding policy for the container during its creation. This policy defines how vectors are treated at the container level.
  • 

    {
       "vectorEmbeddings": [
           {
               "path":"/contentvector",
               "dataType":"float32",
               "distanceFunction":"cosine",
               "dimensions":1536
           },
    }
    

    • Path: Specify the JSON path to your vector embedding field (e.g., /contentvector).
    • Data type: Define the data type of the vector elements (e.g., float32).
    • Dimensions: Specify the dimensionality of your vectors (e.g., 1536 for text-embedding-ada-002).
    • Distance Function: Choose the distance metric for similarity calculation (e.g., cosine, dotProduct, or euclidean)

• Add Vector Index: Add a vector index to your container’s indexing policy. This enables efficient vector similarity searches.
  • 

• • • Path: Include the same vector path defined in your vector policy.
    • Type: Select the appropriate index type (flat, quantizedFlat, or diskANN).

• Define Full-Text Policy: Define a container-level full-text policy. This policy specifies which paths in your documents contain the text content that you want to search.
  • 

• • • Path: Specify the JSON path to your text search field
    • Language: content language

• Add Full-Text Index: Add a full-text index to the indexing policy, making full-text searches efficient
  • 

Hybrid search index (both Full-Text and Vector index)

{
  "indexingMode": "consistent",
  "automatic": true,
  "includedPaths": [
    {
      "path": "/*"
    }
  ],
  "excludedPaths": [
    {
      "path": "/_etag*/?"
    },
    {
      "path": "/contentvector/*"
    }
  ],
  "fullTextIndexes": [
    {
      "path": "/content"
    },
    {
      "path": "/description"
    }
  ],
  "vectorIndexes": [
    {
      "path": "/contentvector",
      "type": "diskANN"
    }
  ]
}

Exclude the Vector Path:

• To optimize performance during data ingestion, you must add the vector path to the “excludedPaths” section of your indexing policy. This prevents the vector path from being indexed by the default range indexes, which can increase RU charges and latency.

Step 3: Data Ingestion

• Generate Vector Embeddings: For every document, convert the text content (and potentially other data like images) into numerical vector embeddings using an embedding model (e.g., from Azure OpenAI Service). This topic is covered above.
• Populate Documents: Insert documents into your container. Each document should have:
  • The text content in the fields specified in your full-text policy (e.g., content, description).
  • The corresponding vector embedding in the field is specified in your vector policy (e.g., /contentvector).
  • Example document
  • 

Step 4: Search Queries

Hybrid search queries in Azure Cosmos DB for NoSQL combine the power of vector similarity search and full-text search within a single query using the Reciprocal Rank Fusion (RRF) function. This allows you to find documents that are both semantically similar and contain specific keywords.

SQL:  SELECT TOP 10 * FROM c ORDER BY RANK RRF(VectorDistance(c.contentvector, @queryVector), FullTextScore(c.content, @searchKeywords))

VectorDistance(c. contentvector, @queryVector):

• VectorDistance(): This is a system function that calculates the similarity score between two vectors.
• @queryVector: This is a parameter representing the vector embedding of your search query. You would generate this vector embedding using the same embedding model used to create document vector embeddings.
• Return Value: Returns a similarity score based on the distance function defined in your vector policy (e.g., cosine, dot product, Euclidean).

FullTextScore(c.content, @searchKeywords):

• FullTextScore(): This is a system function that calculates a BM25 score, which evaluates the relevance of a document to a given set of search terms. This function relies on a full-text index on the specified path.
• @searchKeywords: This is a parameter representing the keywords or phrases you want to search for. You can provide multiple keywords separated by commas.
• Return Value: Returns a BM25 score, indicating the relevance of the document to the search terms. Higher scores mean greater relevance.

ORDER BY RANK RRF(…):

• RRF(…) (Reciprocal Rank Fusion): This is a system function that combines the ranked results from multiple scoring functions (like VectorDistance and FullTextScore) into a single, unified ranking. RRF ensures that documents that rank highly in either the vector search or the full-text search are prioritized in the final results.

Weighted hybrid search query:

SELECT TOP 10 * FROM c ORDER BY RANK RRF(VectorDistance(c.contentvector, @queryVector), FullTextScore(c.content, @searchKeywords), [2, 1]).

• Optional Weights: You can optionally provide an array of weights as the last argument to RRF to control the relative importance of each component score. For example, to weight the vector search twice as important as the full-text search, you could use RRF(VectorDistance(c.contentvector, @queryVector), FullTextScore(c.content, @searchKeywords), [2,1]).

Multi-field hybrid search query:

SELECT TOP 10 * FROM c ORDER BY RANK RRF(VectorDistance(c.contentvector, @queryVector),VectorDistance(c.imagevector, @queryVector),

FullTextScore(c.content, @searchKeywords, FullTextScore(c.description, @searchKeywords,  [3,2,1,1]).

Code Example (.NET Core C#)

• Add Cosmos DB and OpenAI SDKs
• Get Cosmos DB connection string and create Cosmos DB client
• Get the OpenAI endpoint and key to create an OpenAI client
• Generate embedding for user query
• A hybrid search query to do a vector and keyword search

using Microsoft.Azure.Cosmos;
using System.Collections.Generic;
using System.Linq;
using System.Threading.Tasks;

namespace CosmosHybridSearch
{
    public class Product
    {
        public string Id { get; set; }
        public string Name { get; set; }
        public float[] DescriptionVector { get; set; } // Your vector embedding property
    }

    public class Program
    {
        private static readonly string EndpointUri = "YOUR_COSMOS_DB_ENDPOINT";
        private static readonly string PrimaryKey = "YOUR_COSMOS_DB_PRIMARY_KEY";
        private static readonly string DatabaseId = "YourDatabaseId";
        
        // Set your Azure OpenAI endpoint and API key securely.
        string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? "https://YOUR_RESOURCE_NAME.openai.azure.com/"; // Replace with your endpoint
        string apiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY") ?? "YOUR_API_KEY"; // Replace with your API key

        public static async Task Main(string[] args)
        {
            using CosmosClient client = new(EndpointUri, PrimaryKey);
            Database database = await client.CreateDatabaseIfNotExistsAsync(DatabaseId);
            Container container = database.GetContainer(ContainerId);

            // Create an AzureOpenAiEmbeddings instance - not online :)
            var credentials = new ApiKeyServiceClientCredentials(apiKey);
            AzureOpenAiEmbeddings openAiClient = new(endpoint, credentials);

            // Example: search your actual query vector and search term.
            float[] queryVector;
            string searchTerm = "lamp";

            EmbeddingOptions embeddingOptions = new()
            {
                DeploymentName = "text-embedding-ada-002", // Replace with your deployment name
                Input = searchTerm,
            };

            var queryVectorResponse = await openAICient.GetEmbeddingsAsync(embeddingOptions);
            queryVector = returnValue.Value.Data[0].Embedding.ToArray()

            // Define the hybrid search query using KQL
            QueryDefinition queryDefinition = new QueryDefinition(
              "SELECT top 10 * " +
              "FROM myindex " +
              "ORDER BY _vectorScore(desc, @queryVector), FullTextScore(_description, @searchTerm)")
           .WithParameter("@queryVector", queryVector)
           .WithParameter("@searchTerm", searchTerm);

           List<Product> products = new List<Product>();

           using FeedIterator<Product> feedIterator = container.GetItemQueryIterator<Product>(queryDefinition);

           while (feedIterator.HasMoreResults)
           {
              FeedResponse<Product> response = await feedIterator.ReadNextAsync();
              foreach (Product product in response)
              {
                  products.Add(product);
              }
           }

           // Process your search results
           foreach (Product product in products)
           {
              Console.WriteLine($"Product Id: {product.Id}, Name: {product.Name}");
           }
        }
    }
}

Balancing AI Strategy With Human Wisdom 

“AI-first” has become a buzzword in executive conversations, but what does it really mean? Is it about using artificial intelligence at every turn, or applying it with intention and purpose? For analyst and researcher Susan Etlinger, it’s clearly the latter. 

On the latest episode of “What If? So What?”, Susan joins host Jim Hertzfeld to explore what it takes to build AI strategies that are both innovative and responsible. With a background that bridges the humanities and technology, she makes a compelling case for the critical role of human insight in an AI-driven world. 

When (and When Not) to Automate 

AI’s power lies not just in what it can do, but in knowing when not to use it. Susan argues that leaders must assess whether automation truly improves outcomes or risks eliminating valuable learning opportunities. 

She shares a story from early in her career, when manually compiling business data helped her develop essential skills like stakeholder management, strategic thinking, and financial literacy. Her point: AI can accelerate, but only human experience gives results meaning. 

From Generative to Agentic AI: Who’s in Control? 

The conversation explores the evolution from machine learning to Generative AI, and now to Agentic AI. Susan encourages leaders to ask:  

Who sets the goals? Who ensures alignment?  

While AI agents can handle tasks from start to finish, intention, ethics, and judgment remain the responsibility of humans. 

Smarter AI Strategies, Not Just More AI 

Susan’s key takeaway is clear:  

Organizations don’t need more AI; they need better AI strategies. 

Start with a clear use case, implement with intention, and learn from the outcome. The most effective approaches respect the limits of automation while amplifying human strengths. 

Keep People at the Center of Your AI Strategy 

For leaders shaping AI strategy, Susan offers a clear reminder:  progress isn’t about replacing human decision making, it’s about enhancing it. AI can accelerate outcomes, but it’s people who ensure those outcomes are purposeful, ethical, and aligned to your business goals. 

Listen to the full conversation

Subscribe Where You Listen

Apple | Spotify | Amazon | Overcast | Watch the full video episode on YouTube

Meet our Guest – Susan Etlinger

Susan Etlinger is a globally recognized expert on the business and societal impact of data and artificial intelligence and senior fellow at the Centre for International Governance Innovation, an independent, non-partisan think tank based in Canada. Her TED talk, “What Do We Do With All This Big Data?” has been translated into 25 languages and has been viewed more than 1.5 million times. Her research is used in university curricula around the world, and she has been quoted in numerous media outlets including The Wall Street Journal, The Atlantic, The New York Times and the BBC. Susan holds a Bachelor of Arts in Rhetoric from the University of California at Berkeley. 

Follow Susan on LinkedIn  

Learn More about Susan Etlinger

Meet our Host

Jim Hertzfeld is Area Vice President, Strategy for Perficient.

For over two decades, he has worked with clients to convert market insights into real-world digital products and customer experiences that actually grow their business. More than just a strategist, Jim is a pragmatic rebel known for challenging the conventional and turning grand visions into actionable steps. His candid demeanor, sprinkled with a dose of cynical optimism, shapes a narrative that challenges and inspires listeners.

Connect with Jim:

LinkedIn | Perficient

In recent years, AI chatbots like ChatGPT have gone from fun tools for answering questions to serious helpers in workplaces, education, and even personal decision-making. With ChatGPT-5 now being the latest and most advanced version, it’s no surprise that people are asking a critical question:

“Is my personal data safe when I use ChatGPT-5?”

First, What Is ChatGPT-5?

ChatGPT-5 is an AI language model created by OpenAI. You can think of it like a super-smart digital assistant that can:

• Answering questions across a wide range of topics
• Drafting emails, essays, and creative content
• Writing and debugging code
• Assisting with research and brainstorming
• Supporting productivity and learning

It learns from patterns in data, but here’s an important point – it doesn’t “remember” your conversations unless the developer has built a special memory feature and you’ve agreed to it.

How Your Data Is Used

When you chat with ChatGPT-5, your messages are processed to generate a response. Depending on the app or platform you use, your conversations may be:

• Temporarily stored to improve the AI’s performance
• Reviewed by humans (in rare cases) to train and fine-tune the system
• Deleted or anonymized after a specific period, depending on the service’s privacy policy

This is why reading the privacy policy is not just boring legal stuff – it’s how you find out precisely what happens to your data.

Real Security Risks to Be Aware Of

The concerns about ChatGPT-5 (and similar AI tools) are less about it being “evil” and more about how your data could be exposed if not appropriately handled.

Here are the main risks:

1. Accidental Sharing of Sensitive Information

Many users unknowingly type personal details – such as their full name, home address, phone number, passwords, or banking information – into AI chat windows. While the chatbot itself may not misuse this data, it is still transmitted over the internet and may be temporarily stored by the platform. If the platform suffers a data breach or if the information is accessed by unauthorized personnel, your sensitive data could be exposed or exploited.

Best Practice: Treat AI chats like public forums – never share confidential or personally identifiable information.

2. Data Retention by Third-Party Platforms

AI chatbots are often integrated into third-party platforms, such as browser extensions, productivity tools, or mobile apps. These integrations may collect and store your chat data on their own servers, sometimes without clearly informing you. Unlike official platforms with strict privacy policies, third-party services may lack robust security measures or transparency.

Risk Example: A browser extension that logs your AI chats could be hacked, exposing all stored conversations.

Best Practice: Use only trusted, official apps and review their privacy policies before granting access.

3. Misuse of Login Credentials

In rare but serious cases, malicious AI integrations or compromised platforms could capture login credentials you enter during a conversation. If you share usernames, passwords, or OTPs (one-time passwords), these could be used to access your accounts and perform unauthorized actions – such as placing orders, transferring money, or changing account settings.

Real-World Consequence: You might wake up to find that someone used your credentials to order expensive items or access private services.

Best Practice: Never enter login details into any AI chat, and always use two-factor authentication (2FA) for added protection.

4. Phishing & Targeted Attacks

If chat logs containing personal information are accessed by cybercriminals, they can use that data to craft highly convincing phishing emails or social engineering attacks. For example, knowing your name, location, or recent purchases allows attackers to impersonate trusted services and trick you into clicking malicious links or revealing more sensitive data.

Best Practice: Be cautious of unsolicited messages and verify the sender before responding or clicking links.

5. Overtrusting AI Responses

AI chatbots are trained on vast datasets, but they can still generate inaccurate, outdated, or misleading information. Relying on AI responses without verifying facts can lead to poor decisions, especially in areas like health, finance, or legal advice.

Risk Example: Acting on incorrect medical advice or sharing false information publicly could have serious consequences.

Best Practice: Always cross-check AI-generated content with reputable sources before taking action or sharing it.

How to Protect Yourself

Here are simple steps you can take:

• Never share sensitive login credentials or card details inside a chat.
• Stick to official apps and platforms to reduce the risk of malicious AI clones.
• Use 2-factor authentication (2FA) for all accounts, so even stolen passwords can’t be used easily.
• Check permissions before connecting ChatGPT-5 to any service – don’t allow unnecessary access.
• Regularly clear chat history if your platform stores conversations.

Final Thoughts

ChatGPT-5 is a tool, and like any tool, it can be used for good or misused. The AI itself isn’t plotting to steal your logins or credentials, but if you use it carelessly or through untrusted apps, your data could be at risk.

Golden rule: Enjoy the benefits of AI, but treat it like a stranger online – don’t overshare, and keep control of your personal data.

Have you ever wondered what happens if you ask AI to create an image, write a poem, or draft an email?
Most of us picture “the cloud” working its magic in a distant location. The twist is that the cloud is physical, real, and thirsty. Data centers require water, sometimes millions of gallons per day, to stay cool while AI is operating.

By 2025, it is impossible to overlook AI’s growing water footprint. But don’t worry, AI isn’t to blame here. It’s about comprehending the problem, the ingenious ways technology is attempting to solve it, and what we (as humans) can do to improve the situation.

Why does AI need water?

Doesn’t your laptop heat up quickly when you run it on overdrive for hours? Now multiply that by millions of machines that are constantly in operation and stacked in enormous warehouses. A data centre is that.

These facilities are cooled by air conditioning units, liquid cooling, or evaporative cooling to avoid overheating. And gallons of fresh water are lost every day due to evaporative cooling, in which water actually evaporates into the atmosphere to remove heat.

Therefore, there is an invisible cost associated with every chatbot interaction, artificial intelligence-powered search, and generated image: water.

How big is the problem in 2025?

Pretty Big—and expanding. According to a 2025 industry report, data centers related to artificial intelligence may use more than 6 billion cubic meters of water a year by the end of this decade. That is roughly equivalent to the annual consumption of a mid-sized nation.

In short, AI’s water consumption is no longer a “future problem.” The effects are already being felt by the communities that surround big data centers. Concerns regarding water stress during dry months have been voiced by residents in places like Arizona and Ireland.

But wait—can AI help solve this?

Surprisingly, yes. It is being saved by the same intelligence that requires water.

optimised cooling: Businesses are utilising AI to operate data centers more effectively by anticipating precisely when and how much cooling is required, which can reduce water waste by as much as 20–30%.

Technology for liquid cooling: Some new servers are moving to liquid cooling systems, which consume a lot less water than conventional techniques.

Green data centers: Major corporations, such as Google and Microsoft, are testing facilities that use recycled water rather than fresh water for cooling and are powered by renewable energy.

Therefore, “AI is the problem” is not the story. “AI is thirsty, but also learning how to drink smarter,” it says.

What about us—can regular people help?

Absolutely.Our decisions have an impact even though the majority of us do not manage data centers. Here’s how:

More intelligent use of AI: We can be aware of how frequently we execute complex AI tasks, just as we try to conserve energy. (Is 50 AI-generated versions of the same image really necessary?)

Encourage green tech: Selecting platforms and services that are dedicated to sustainable data practices encourages the sector to improve.

Community action: Cities can enact laws that promote the use of recycled water in data centers and openness regarding the effects of water use in their communities.

Consider it similar to electricity, whose hidden costs we initially hardly noticed. Efficiency and awareness, however, had a significant impact over time. Water and AI can have the same effect.

What’s the bigger picture?

AI is only one piece of the global water puzzle. Water stress is still primarily caused by industry, agriculture, and climate change. However, the emergence of AI makes us reevaluate how we want to engage with the planet’s most valuable resource in the digital future.

If this is done correctly, artificial intelligence (AI) has the potential to be a partner in sustainability, not only in terms of how it uses water but also in terms of how it aids in global water monitoring, forecasting, and conservation.

The Takeaway

The cloud isn’t magic. It’s water, energy, wires, and metal. And AI’s thirst increases with its growth. However, this is an opportunity for creativity rather than panic. Communities, engineers, and even artificial intelligence (AI) are already rethinking how to keep machines cool without depleting the planet.

Therefore, keep in mind that every pixel and word contains a hidden drop of water the next time you converse with AI or create an interesting image. Furthermore, the more information we have, the better decisions we can make to ensure the future continues.