Generative AI (GenAI) is not just a buzzword anymore — it’s rapidly transforming the way we interact with technology. From content creation to design automation and synthetic media, GenAI apps are redefining productivity and creativity across industries. But what exactly are GenAI apps, and how are they impacting our digital landscape?

What Are GenAI Apps?

GenAI apps are applications powered by large-scale AI models that can generate new content. This could be text, images, code, music, or even 3D models. Unlike traditional apps that follow a fixed logic, GenAI apps use deep learning models (like GPT, DALL·E, Stable Diffusion, etc.) to generate output based on patterns learned from massive datasets.

Common Examples of GenAI Apps:

• Chatbots & Assistants: ChatGPT, Gemini, Claude — offering human-like conversational interfaces.
• Image Generation: Midjourney, DALL·E, Canva Magic Design — transforming text prompts into detailed images.
• Code Generators: GitHub Copilot, CodeWhisperer — helping developers write and debug code faster.
• Writing Tools: Jasper, Notion AI — assisting with blog posts, emails, and marketing copy.
• Video & Voice Synthesis: Synthesia, ElevenLabs — enabling AI-generated video avatars and voiceovers.

Key Capabilities

1. Content Creation: Drafting marketing copy, blog posts, emails, social media captions.
2. Design & Visualization: Generating logos, UI mockups, or artwork.
3. Personalization: Tailoring user experiences using real-time data and predictive content.
4. Automation: Streamlining repetitive tasks in writing, data analysis, and software development.

Use Cases by Industry

Benefits of GenAI Apps

• Speed: Instant content and idea generation.
• Scalability: Handle large workloads without human limitations.
• Cost Efficiency: Reduce the need for manual content creation or analysis.
• Creativity Boost: Offer novel ideas and alternatives that humans may overlook.

Challenges & Considerations

• Accuracy: AI can “hallucinate” or produce false content.
• Bias & Ethics: Output can reflect biases in training data.
• Security: Sensitive data must be protected from misuse.
• Regulation: Legal frameworks around AI-generated content are still evolving.

The Future of GenAI Apps

We are at the cusp of a paradigm shift. GenAI apps are evolving from experimental tools to essential productivity companions. As they become more integrated with everyday platforms — from Microsoft Office to Adobe Creative Suite — we can expect more intelligent, context-aware, and multimodal experiences.

Companies are also exploring AI agents: autonomous apps that can plan and execute multi-step tasks, potentially transforming workflows and redefining what software can do independently.

Conclusion

Generative AI apps are more than just cool demos — they’re powerful tools reshaping industries and redefining creativity. As models become more advanced and accessible, the focus will shift toward building responsible, ethical, and highly customized GenAI solutions for real-world needs.

Whether you’re a business leader, developer, or content creator, embracing GenAI apps can offer a competitive edge — the key is to explore, experiment, and evolve.

After highlighting the key benefits of the AI Assistant for enterprise analytics in my previous blog post, I am sharing here a demo of what it looks like to use the AI Assistant. The video below demonstrates how a persona interested in understanding enterprise projects may quickly  find answers to their typical everyday questions. The information requested include profitability, project analysis, cost management, and timecard reporting.
A Perficient Demo of AI Assistant for Project Analytics

What to Watch Out For

With the right upfront configuration in place, the AI assistant, native to Oracle Analytics, can transform how various levels of the workforce find the insights they need to be successful in their tasks. Here are a few things that make a difference when configuring the AI Assistant.

• Multiple Subject Areas: When enterprise data consists of several subject areas, for example Projects, Receivables, Payables, Procurement, etc., performing Q&A with the AI Assistant across multiple subject areas simultaneously is not currently possible. What the AI Assistant does in this situation is prompt for the subject area to use for the response. That is not an issue when the information requested is from a single subject area. However, there are situations when we want to simultaneously gain insights across two or more subject areas. This can be handled by preparing a combined subject area that contains the key relevant information from other underlying subject areas. As a result, the AI Assistant interfaces with a single subject area that consists of all the transaction facts and conformed dimensions across the various transactional data sets. With a little semantic model adjustments this is an achievable solution.
• Be selective on what is included in AI prompts: Enterprise semantic models typically have a lot of information that may not be relevant for an AI chat interface. Therefore, excluding any fields from being included in an AI prompt improves performance, accuracy, and sometimes even reduces the processing cost incurred by AI when leveraging external LLMs. Dimension codes, identifiers, keys, and audit columns are some examples of things to exclude. The Oracle Analytics AI Assistant comes with a fine-grained configuration that enables selecting the fields to include in AI prompts.
• Metadata Enrichment with Synonyms: Use synonyms on ambiguous fields, for example to clarify what a date field represents (Is it the transaction creation date or the date it was invoiced on?). Another example of when synonyms are useful is when there is a need to enable proper interpretation of internal organization-specific terms. The AI Assistant enables setting up synonyms on individual columns to improve it’s level of understanding.
• Indexing Data: For an enhanced user experience, I recommend identifying which data elements are worth indexing. This means the AI LLM will be made aware of the information stored in these fields that you chose while setting up the AI Assistant. This is an upfront one-time activity. The more information you equip the AI Assistant with, the smarter it gets when interpreting and responding to questions.

For guidance on how to get started with enabling GenAI for your enterprise data analytics, reach out to mazen.manasseh@perficient.com.

At Perficient, we believe in celebrating the people behind our success. Today, we proudly spotlight Juan Cardona Ramirez, a Technical Consultant and leader of our Latin America Data Sub-Practice. Since joining in 2023, Juan has consistently demonstrated a remarkable blend of technical expertise, strategic thinking, and genuine client dedication. 

Based in Medellín, Colombia, Juan brings passion for data and a deep commitment to delivering meaningful business impact. His energy, leadership, and innovative mindset have played a pivotal role in expanding our data capabilities across the region. His journey reflects how purpose and skill can elevate both individual careers and team performance. 

From Intern to Leader: The Beginning of His Journey 

Juan first discovered Perficient while studying Systems and Computer Engineering at EIA University. When the company visited his campus to engage emerging talent, Juan saw more than a job opportunity—he saw a path to becoming a data engineer. 

“What struck me most was that from day one, I wasn’t treated like an intern,” said Juan. “I was immediately included in a real project and began contributing right away.” 

His internship quickly evolved into a fast-paced professional journey. Today, Juan is the leader of the Data + Intelligence Sub-Practice for Latin America and embraces a multifaceted role.

He builds technical solutions like data pipelines, leads internal training sessions such as Chill & Learn, supports pre-sales activities, and contributes to talent recruitment. No two days look the same. 

 “My job is very dynamic,” said Juan. “Some days focus on client work, others on internal initiatives. If I could design my ideal day, I’d spend most of it coding and learning.” 

Databricks: A Partnership Beyond Borders 

Juan played a key role in strengthening Perficient’s relationship with Databricks in Latin America. As one of the first to build a technical bridge with the platform in the region, he developed a strategy to support training, enablement, and positioning.

“I saw the potential to grow the Databricks partnership in Latin America,” said Juan. “I got certified, helped others get certified, and realized that certifications aren’t just validations—they’re the key to speaking the same technical and business language as Databricks.” 

He now serves as a crucial link between technical and commercial teams and maintains direct communication with Databricks’ global leadership.

Insights and Achievements from Juan’s Journey in Consulting

Juan’s view of consulting evolved through his experience at Perficient. Initially, he thought his role would revolve around coding. Over time, he discovered the broader impact of consultants. Juan learned to embrace proactivity—going beyond expectations, driving purpose-led contributions, and continuously improving. He has grown significantly at Perficient, not only as a data engineer, but also as a well-rounded professional. 

“Being a consultant goes beyond code,” said Juan. “It’s about empathy, adaptability, strategic thinking, and finding ways to deliver real value.” 

Among his personal milestones, Juan highlights his honors thesis: an AI system designed to detect pituitary adenomas (benign brain tumors) using MRI scans. He developed it in collaboration with his university’s medical faculty, and the project earned him the opportunity to present at an international medical conference in Peru. 

“It was both a technical and human challenge—navigating a complex medical domain with limited data while integrating into a team of doctors as an engineer.” – Juan Cardona, Technical Consultant

Currently, Juan prepares to join the Databricks Championship Panel, the highest technical certification available. If successful, he would become the first Databricks Champion in Latin America within Perficient.

Balance, Passion, and Future Vision 

Juan has found a unique balance between personal passion and professional excellence. Outside of work, he dedicates time to self-directed learning and stays updated on emerging technologies. He also enjoys a very different pursuit: driving. Exploring new places, especially small towns, allows him to disconnect and recharge. 

“Driving gives me a sense of freedom,” said Juan. “It’s about enjoying the landscape and feeling like I can just move, explore, and breathe. One of my favorite experiences was driving from Mexico City to Cancun—a long, peaceful journey where I truly felt that freedom.”

Perficient’s culture of empowering its people has allowed Juan to thrive, take ownership of his growth, and lead with confidence. His pride shines through in his pursuit of excellence, client-focused mindset, and dedication to growing with the company.

Looking ahead, Juan aspires to grow into a director-level leadership role, continuing to represent Perficient with pride, vision, and a commitment to excellence. 

“I feel proud to be recognized, inside and outside, as someone who represents Perficient,” said Juan. “I even carry the logo on my laptop and in my personal space.” 

 _______________________________________ 

 Juan exemplifies how technical talent, authentic leadership, and a strong sense of purpose can transform careers, teams, and organizations. His story inspires those starting their journey in technology and reflects Perficient’s values: integrity, innovation, people-first culture, collaboration, pride, and unwavering client commitment. With every step, Juan turns passion into impact and vision into real, lasting growth. 

SEE MORE PEOPLE OF PERFICIENT 

It’s no secret our success is because of our people. No matter the technology or time zone, our colleagues are committed to delivering innovative, end-to-end digital solutions for the world’s biggest brands, and we bring a collaborative spirit to every interaction. We’re always seeking the best and brightest to work with us. Join our team and experience a culture that challenges, champions, and celebrates our people. 

Visit our Careers page to see career opportunities and more! 

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

Cloud is easy—until it isn’t.

Perficient’s Edge: A Strategic Cloud Partner Focused on Business Outcomes

Cloud adoption has skyrocketed. Multi-cloud. Hybrid cloud. AI-optimized workloads. Clients are moving fast, but many are moving blindly. The result? High costs, low returns, and strategies that stall before they scale.

That’s why this moment matters. Now, more than ever, your clients need a partner who brings more than just cloud expertise—they need business insight, strategic clarity, and real results.

In our latest We Are Perficient episode, we sat down with Kiran Dandu, Perficient’s Managing Director, to uncover exactly how we’re helping clients not just adopt cloud, but win with it.

If you’re in sales, this conversation is your cheat sheet for leading smarter cloud conversations with confidence.

Key #1: Start with Business Outcomes, Not Infrastructure

Kiran makes one thing clear from the start: “We don’t start with cloud. We start with what our clients want to achieve.”

At Perficient, cloud is a means to a business end. That’s why we begin every engagement by aligning cloud architecture with long-term business objectives—not just technical requirements.

Perficient’s Envision Framework: Aligning Cloud with Business Objectives

• Define their ideal outcomes

• Assess their existing workloads

• Select the right blend of public, private, hybrid, or multi-cloud models

• Optimize performance and cost every step of the way

This outcome-first mindset isn’t just smarter—it’s what sets Perficient apart from traditional cloud vendors.

Key #2: AI in the Cloud – Delivering Millions in Savings Today

Forget the hype—AI is already transforming how we operate in the cloud. Kiran breaks down the four key areas where Perficient is integrating AI to drive real value:

• DevOps automation: AI accelerates code testing and deployment, reducing errors and speeding up time-to-market.

• Performance monitoring: Intelligent tools predict and prevent downtime before it happens.

• Cost optimization: AI identifies underused resources, helping clients cut waste and invest smarter.

• Security and compliance: With real-time threat detection and automated incident response, clients stay protected 24/7.

The result? A cloud strategy that’s not just scalable, but self-improving.

Key #3: Beyond Cloud Migration to Continuous Innovation

Moving to the cloud isn’t the end goal—it’s just the beginning.

Kiran emphasizes how Perficient’s global delivery model and agile methodology empower clients to not only migrate, but to evolve and innovate faster. Our teams help organizations:

• Integrate complex systems seamlessly

• Continuously improve infrastructure as business needs change

• Foster agility across every department—not just IT

And it’s not just theory. Our global consultants, including the growing talent across LATAM, are delivering on this promise every day.

“The success of our cloud group is really going to drive the success of the organization.”
— Kiran Dandu

Global Talent, Local Impact: The Power of a Diverse Strategic Cloud Partner

While visiting our offices in Medellín, Colombia, Kiran highlighted the value of diversity in driving cloud success:

“This reminds me of India in many ways—there’s talent, warmth, and incredible potential here.”

That’s why Perficient is investing in uniting its global cloud teams. The cross-cultural collaboration between North America, LATAM, Europe, and India isn’t just a feel-good story—it’s the engine behind our delivery speed, technical excellence, and customer success.

Key Takeaways for Sales: Lead Smarter Cloud Conversations

If your client is talking about the cloud—and trust us, they are—this interview is part of your toolkit.
You’ll walk away understanding:

• Why Perficient doesn’t just build cloud platforms—we build cloud strategies that deliver

• How AI and automation are creating real-time ROI for our clients

• What makes our global model the best-kept secret in cloud consulting

• And how to speak the language of business outcomes, not just cloud buzzwords

Watch the Full Interview: Deep Dive with Kiran Dandu

Want to hear directly from the source? Don’t miss Kiran’s full interview, packed with strategic insights that will elevate your next sales conversation.

Watch now and discover how Perficient is transforming cloud into a competitive advantage.

Choose Perficient: Your Client’s Strategic Cloud Partner for a Competitive Edge

Perficient is not just another cloud partner—we’re your client’s competitive edge. Let’s start leading the cloud conversation like it.

Adobe is offering marketers the opportunity to experience the power of Adobe GenStudio for Performance Marketing firsthand via a product sandbox.

This interactive space will allow users to see the product in action and is designed to simulate the GenStudio for Performance Marketing workflow.

Luckily for you, Perficient is part of a select group of Adobe consulting partners with access to a fully functional sandbox. We’ve learned a few things since using the app that you can benefit from as you experiment with this new product.

In the following blog, our Adobe experts Ross Monaghan, Principal, and Raf Winterpacht, Director, share their initial impressions of GenStudio for Performance Marketing, tips and tricks, and some pitfalls to avoid when using the product for the first time.

Initial Impressions of GenStudio for Performance Marketing

When marketers first dive into Adobe GenStudio for Performance Marketing, they can expect a mix of familiarity and growing pains. Raf found the platform intuitive, noting, “It feels like Adobe. So, if you’ve worked with some of the other products like Adobe Experience Platform or AEM Assets, it’ll feel familiar to you. There wasn’t a huge learning curve when I started using it.”

From Ross’s perspective, he shared that the app initially felt early in its development. He explained, “When we first gained access to our sandbox, there weren’t a lot of channels where we could activate our assets. For example, Meta wasn’t even an option yet. So, you could create an asset, but you had no place to activate it. Everything was labeled as coming soon.”

However, he acknowledged that the platform has since evolved, with users now having access to Meta and Google Campaign Manager 360, while Microsoft Advertising, Snap, and TikTok are listed as coming soon.

Powerful Key Features

When using the application for the first time, marketers can expect a powerful tool that significantly enhances the ability to personalize and scale content creation while maintaining brand integrity.

Ross emphasized the importance of personalization at scale, noting that GenStudio allows marketers to quickly create, resize, and reuse assets to produce numerous variations that can be leveraged across various channels and campaigns. He said, “It’s practically impossible to create all the asset variations you need without something like GenStudio for Performance Marketing. You could be looking at hundreds of thousands of asset variations for just a single campaign across 25 countries and several languages.”

Raf added that the ability to rapidly generate high-quality, on-brand content is another powerful feature. One of the first things you do within GenStudio is set up your brand and provide guidelines for brand voice, imagery, logos, colors, and channel usage. Raf said, “Doing this allows marketers to use the application’s generative AI models to create high-quality content that not only adheres to your brand guidelines but also allows for rapid generation of personalized content variations that can be leveraged in various channels and campaigns.”

GenStudio for Performance Marketing Tips and Tricks

To maximize the potential of Adobe GenStudio for Performance Marketing, our experts recommend focusing on two key areas: brand completeness and prompt engineering.

Raf suggested thoroughly completing the brand section in GenStudio, as well as including as much information about your products and personas. This may take some time and effort, but doing so will ensure a better asset creation experience in the app.

He said, “We had to ask Adobe what the best, most efficient way to upload our brand guidelines was, and we were told to use a PDF. Our brand guidelines live on a SharePoint site, so we had to do some trial and error to get our brand content into a PDF that lists out all the different guidelines as much as possible. From there, the app does a good job of picking up those details from the PDF. You could do this manually if you needed to, though.”

Ross called attention to prompt engineering when using generative AI to create assets. He said, “Prompt engineering is critical when generating content in GenStudio. We found it difficult at first to figure out what the best prompts were to get the desired assets. Unfortunately, prompting is an iterative process and takes a bit of trial and error.”

Hopefully, Adobe Experience League will host a list of prompts marketers can use as a starting point one day, but until then, you can take a look at Adobe’s user guide to writing effective prompts.

Common Pitfalls to Avoid When Using GenStudio for the First Time

As with any powerful tool, success with Adobe GenStudio for Performance Marketing depends on understanding its limitations and setting it up thoughtfully from the start. Both Ross and Raf shared that while GenStudio can accelerate content creation, it’s not a replacement for human oversight.

“Users should think of it as a highly efficient way to get dynamic content to review,” Ross said. “Users still need to be the editor and verify that the content is still on brand. I worry that people want these tools to do the entire job.”

He also cautioned against underestimating the need for UI engineering. “I think another pitfall that users should avoid is thinking that they don’t need UI engineering. That is something that you definitely need to set up your templates appropriately,” he said, noting that it took significant back-and-forth with Adobe to get templates just right.

Raf echoed the importance of thoughtful setup, particularly around permissions and workflows. “You’re going to need to have certain permissions and users and groups set up in there ahead of time,” he said. “There’s a little bit of upfront work and configurations and things that have to be done before you can actually get in there and really start using the tool in a good cadence.”

Making GenStudio a Powerful Tool in Your Marketing Toolbox

Whether you’re just beginning to explore GenStudio or are already experimenting in the sandbox, the key takeaway is clear: success lies in preparation, experimentation, and a willingness to learn. With the right approach, GenStudio can become a powerful ally in your performance marketing toolkit.

Are you looking for a partner to help with your GenStudio for Performance Marketing implementation? Connect with us.

And if you’re looking for more Adobe expert insights, check out our Adobe blog site!

In the ever-evolving landscape of software development, new terms and methodologies constantly emerge, reshaping how we think about and create technology. Recently, a phrase has been buzzing through the tech world, sparking both excitement and debate: “vibe coding.” While the idea of coding based on intuition or a “feel” isn’t entirely new, the term has gained significant traction and a more specific meaning in early 2025, largely thanks to influential figures in the AI space.

This article will delve into what “vibe coding” means today, explore its origins and core tenets, describe a typical workflow in this new paradigm, and discuss its potential benefits and inherent challenges. Prepare to look beyond the strictures of traditional development and into a more fluid, intuitive, and AI-augmented future.

What Exactly Is Vibe Coding? The Modern Definition

The recent popularization of “vibe coding” is strongly associated with Andrej Karpathy, a co-founder of OpenAI and former AI leader at Tesla. In early 2025, Karpathy described “vibe coding” as an approach that heavily leverages Large Language Models (LLMs). In this model, the developer’s role shifts from meticulously writing every line of code to guiding an AI with natural language prompts, descriptions, and desired outcomes—essentially, conveying the “vibe” of what they want to achieve. The AI then generates the corresponding code.

As Karpathy put it (paraphrasing common interpretations from early 2025 discussions), it’s less about traditional coding and more about a conversational dance with the AI:

“You see things, say things, run things, and copy-paste things, and it mostly works.”

This points to a future where the barrier between idea and functional code becomes increasingly permeable, with the developer acting more as a conductor or a curator of AI-generated software components.

So, is this entirely new? Yes and no.

• The “New”: The specific definition tying “vibe coding” to the direct, extensive use of advanced LLMs like GitHub Copilot’s agent mode or similar tools is a recent development (as of early 2025). It’s about a human-AI symbiosis where the AI handles much of the syntactical heavy lifting.
• The “Not So New”: The underlying desire for a more intuitive, less rigidly structured coding experience—coding by “feel” or “flow”—has always been a part of developer culture. Programmers have long talked about being “in the zone,” rapidly prototyping, or using their deep-seated intuition to solve problems, especially in creative coding, game development, or initial exploratory phases. This older, more informal notion of “vibe coding” can be seen as a spiritual precursor. Today’s “vibe coding” takes that innate human approach and supercharges it with powerful AI tools.

Therefore, when we talk about “vibe coding” today (in mid-2025), we’re primarily referring to this AI-assisted paradigm. It’s about effectively communicating your intent—the “vibe”—to an AI, which then translates that intent into code. The focus shifts from syntax to semantics, from meticulous construction to intuitive direction.

The Core Tenets of (AI-Augmented) Vibe Coding

Given this AI-centric understanding, the principles of vibe coding look something like this:

1. Intuition and Intent as the Primary Driver

   The developer’s main input is their understanding of the problem and the desired “feel” or functionality of the solution. They translate this into natural language prompts or high-level descriptions for the AI. The “how” of the code generation is largely delegated.

2. Prompt Engineering is Key

   Your ability to “vibe” effectively with the AI depends heavily on how well you can articulate your needs. Crafting clear, concise, and effective prompts becomes a critical skill, replacing some traditional coding skills.

3. Rapid Iteration and AI-Feedback Loop

   The cycle is: prompt -> AI generates code -> test/review -> refine prompt -> repeat. This loop is incredibly fast. You can see your ideas (or the AI’s interpretation of them) come to life almost instantly, allowing for quick validation or correction of the “vibe.”

4. Focus on the “What” and “Why,” Less on the “How”

   Developers concentrate on defining the problem, the user experience, and the desired outcome. The AI handles much of the underlying implementation details. The “vibe” is about the end result and its characteristics, not necessarily the elegance of every single line of generated code (though that can also be a goal).

5. Embracing the “Black Box” (to a degree)

   While reviewing AI-generated code is crucial, there’s an implicit trust in the AI’s capability to handle complex boilerplate or even entire functions. The developer might not always delve into the deepest intricacies of every generated snippet, especially if it “just works” and fits the vibe. This is also a point of contention and risk.

6. Minimal Upfront Specification, Maximum Exploration

   Detailed, exhaustive spec documents become less critical for the initial generation. You can start with a fuzzy idea, prompt the AI, see what it produces, and iteratively refine the “vibe” and the specifics as you go. It’s inherently exploratory.

7. Orchestration Over Manual Construction

   The developer acts more like an orchestrator, piecing together AI-generated components, guiding the overall architecture through prompts, and ensuring the different parts harmonize to achieve the intended “vibe.”

A Typical AI-Driven Vibe Coding Workflow

Let’s walk through what a vibe coding session in this AI-augmented era might look like:

1. The Conceptual Spark

   An idea for an application, feature, or fix emerges. The developer has a general “vibe” of what’s needed – “I need a simple web app to track my reading list, and it should feel clean and modern.”

2. Choosing the Right AI Tool

   The developer selects their preferred LLM-based coding assistant (e.g., an advanced mode of GitHub Copilot, Cursor Composer, or other emerging tools).

3. The Initial Prompt & Generation

   The developer crafts an initial prompt.

   Developer:

   Generate a Python Flask backend for a reading list app. It needs a PostgreSQL database with a 'books' table (title, author, status, rating). Create API endpoints for adding a book, listing all books, and updating a book's status.

   The AI generates a significant chunk of code.

4. Review, Test, and “Vibe Check”

   The developer reviews the generated code. Does it look reasonable? Do the core structures align with the intended vibe? They might run it, test the endpoints (perhaps by asking the AI to generate test scripts too).

   Developer (to self): “Okay, this is a good start, but the ‘status’ should be an enum: ‘to-read’, ‘reading’, ‘read’. And I want a ‘date_added’ field.”

5. Refinement through Iterative Prompting

   The developer provides feedback and further instructions to the AI.

   Developer:

   Refactor the 'books' model. Change 'status' to an enum with values 'to-read', 'reading', 'read'. Add a 'date_added' field that defaults to the current timestamp. Also, generate a simple HTML frontend using Bootstrap for listing and adding books that calls these APIs.

   The AI revises the code and generates the new parts.

6. Integration and Manual Tweaks (if necessary)

   The developer might still need to do some light manual coding to connect pieces, adjust styles, or fix minor issues the AI missed. The goal is for the AI to do the bulk of the work.

7. Achieving the “Vibe” or Reaching a Milestone

   This iterative process continues until the application meets the desired “vibe” and functionality, or a significant milestone is reached. The developer has guided the AI to create something that aligns with their initial, perhaps fuzzy, vision.

This workflow is highly dynamic. The developer is in a constant dialogue with the AI, shaping the output by refining their “vibe” into increasingly specific prompts.

Where AI-Driven Vibe Coding Shines (The Pros)

This new approach to coding offers several compelling advantages:

• Accelerated Development & Prototyping: Generating boilerplate, standard functions, and even complex algorithms can be drastically faster, allowing for rapid prototyping and quicker MVP releases.
• Reduced Cognitive Load for Routine Tasks: Developers can offload tedious and repetitive coding tasks to the AI, freeing up mental energy for higher-level architectural thinking, creative problem-solving, and refining the core “vibe.”
• Lowering Barriers (Potentially): For some, it might lower the barrier to creating software, as deep expertise in a specific syntax might become less critical than the ability to clearly articulate intent.
• Enhanced Learning and Exploration: Developers can quickly see how different approaches or technologies could be implemented by asking the AI, making it a powerful learning tool.
• Focus on Creativity and Product Vision: By automating much of the rote coding, developers can spend more time focusing on the user experience, the product’s unique value, and its overall “vibe.”

The Other Side of the Vibe: Challenges and Caveats in the AI Era

Despite its promise, AI-driven vibe coding is not without its significant challenges and concerns:

• Quality and Reliability of AI-Generated Code: LLMs can still produce code that is subtly flawed, inefficient, insecure, or simply incorrect. Thorough review and testing are paramount.
• The “Black Box” Problem: Relying heavily on AI-generated code without fully understanding it can lead to maintenance nightmares and difficulty in debugging when things go wrong.
• Security Vulnerabilities: AI models are trained on vast datasets, which may include insecure code patterns. Generated code could inadvertently introduce vulnerabilities. The “Bad Vibes Only” concern noted in some discussions highlights this risk.
• Skill Atrophy and the Future of Developer Skills: Over-reliance on AI for core coding tasks could lead to an atrophy of fundamental programming skills. The skill set may shift towards prompt engineering and systems integration.
• Bias and Homogenization: AI models can perpetuate biases present in their training data, potentially leading to less diverse or innovative solutions if not carefully guided.
• Intellectual Property and Originality: Questions around the ownership and originality of AI-generated code are still being navigated legally and ethically.
• Debugging “Vibes”: When the AI consistently misunderstands a complex “vibe” or prompt, debugging the interaction itself can become a new kind of challenge.
• Not a Silver Bullet: For highly novel, complex, or performance-critical systems, the nuanced understanding and control offered by traditional, human-driven coding remain indispensable. Vibe coding may not be suitable for all types of software development.

Finding the Balance: Integrating Vibes into a Robust Workflow

The rise of AI-driven “vibe coding” doesn’t necessarily mean the end of traditional software development. Instead, it’s more likely to become another powerful tool in the developer’s arsenal. The most effective approaches will likely integrate the strengths of vibe coding—its speed, intuitiveness, and focus on intent—with the rigor, discipline, and deep understanding of established software engineering practices.

Perhaps “vibe coding” will be most potent in the initial phases of development: for brainstorming, rapid prototyping, generating initial structures, and handling common patterns. This AI-generated foundation can then be taken over by developers for refinement, security hardening, performance optimization, and integration into larger, more complex systems, applying critical thinking and deep expertise.

The future isn’t about replacing human developers with AI, but about augmenting them. The “vibe” is the creative human intent, and AI is becoming an increasingly powerful means of translating that vibe into reality. Learning to “vibe” effectively with AI—to communicate intent clearly, critically evaluate AI output, and seamlessly integrate it into robust engineering practices—will likely become a defining skill for the next generation of software creators.

So, as you navigate your coding journey, consider how you can harness this evolving concept. Whether you’re guiding an LLM or simply tapping into your own deep intuition, embracing the “vibe” might just unlock new levels of creativity and productivity. But always remember to pair that vibe with critical thinking and sound engineering judgment.

We are thrilled to announce that Perficient has been recognized in Forrester’s recent report, “The Connected Product Engineering Services Landscape, Q2 2025.” Forrester defines connected product engineering services providers as:  

“Firms that conceive, design, develop, launch, and scale new connected (or embodied) products that combine a physical product with digital applications to directly deliver new revenue for their clients.” 

We believe this acknowledgment highlights our commitment to driving innovation and delivering exceptional value to our clients through connected product engineering services. 

Access The Connected Product Engineering Services Landscape, Q2 2025 to find out more. 

Driving Connected Product Innovation Across Key Industries 

Whether it’s enabling a shift to product-as-a-service models, managing the ongoing support and monetization of field-deployed connected products, or improving workforce productivity through modern workplace technologies, we believe our strategic and management consulting expertise empowers organizations to navigate complexity and deliver meaningful outcomes. Notably, we’ve achieved success for clients in the life sciences, manufacturing, and utilities industries when it comes to connected product innovation. Our clients rely on us not only for engineering and implementation, but also for the high-value strategic work that drives connected product success. 

What Are Connected Product Engineering Services? 

From Perficient’s perspective, Connected Product Engineering Services are a comprehensive suite of offerings designed to create products that blend physical components with digital applications. These services cover the entire life cycle of product development, including: 

Conception: Ideating new connected products that meet market needs and client requirements. 

Design: Crafting designs that integrate both physical and digital elements to ensure seamless functionality and user experience. 

Development: Building and programming the product, including hardware and software integration. 

Launch: Bringing the product to market, including strategies for deployment and initial user adoption. 

Scaling: Expanding the product’s reach and capabilities to grow user bases and evolving market demands. 

The goal of connected product engineering services is to deliver products that not only function effectively but also generate new revenue streams for clients by leveraging the synergy between physical and digital technologies. Perficient’s expertise in this area runs deep and provides clients with improved data strategy, monetization, and user interfaces that ultimately instill customer trust and loyalty. 

Common Disrupters and Challenges  

With the results from Perficient’s own research, we have found that as the connected product landscape evolves, so do the challenges and disruptions organizations must navigate. One disruptor we’re seeing in the marketplace is the growing customer expectation for seamless interoperability between connected products. Namely, 50% of commercial users responded that their connected products integrated only “somewhat well” with their existing systems and infrastructure. 

Buyers are increasingly making purchasing decisions based on how well new products integrate with their existing connected ecosystems. This shift is creating a strong push for increased collaboration and partnerships between OEMs to enable cross-product connectivity, such as linking garage door openers with vehicles or syncing household appliances with mobile devices. 

Another challenge is overcoming negative customer sentiment toward connected features. Some consumers view these features as unnecessary luxuries or express concerns about privacy and data security. Only 19% of consumers feel aware of data collection practices. In industrial settings like manufacturing and supply chain, connected products are sometimes perceived as intrusive or overly surveillance-focused. 

Additionally, there’s often a gap in user education. Many OEMs struggle to implement the right structures for ongoing support and training, making it difficult for customers to fully understand and leverage all available product features. Addressing these concerns through thoughtful design, transparent data practices, and strong customer enablement programs is essential for long-term success in the connected product space. 

Perficient’s Approach to Connected Product Engineering 

At Perficient, we take a comprehensive, end-to-end approach to connected product delivery, combining strategy, engineering, prototyping, and testing to bring innovative ideas to life. Especially when it comes to connected products, we understand that it starts with a strong data foundation. That’s why we prioritize helping clients define a robust data strategy from the start.  

When the foundation is solid, identifying how to utilize that data and create new revenue streams is the next step. Subscription models are becoming a key driver of connected product monetization, and we guide clients in building scalable ecosystems that support recurring revenue. Additionally, we recognize that customer experience is a critical differentiator, often enabled through companion apps that provide seamless access to product features and functionality. These strategic considerations—data, subscriptions, and experience—are essential components of a successful connected product strategy, and they remain central to how Perficient delivers value to our clients. 

Real and actionable insights drive our strategy. We’ve based our approach for connected product manufacturers on our own research – a study on the sentiments of consumers, commercial users, and manufacturers of connected products – which you can explore here. 

Learn more about our manufacturing industry expertise. 

Asking questions about data has been part of Oracle Analytics through the homepage search bar for several years now. It did that with Natural Language Processing (NLP) to respond to questions with various automatically generated visualizations. What has been introduced since late 2024 is the capability to leverage Large Language Models (LLM) to respond to user questions and commands from within a Workbook. This brings a much-enhanced experience, thanks to the evolution of language processing from classic NLP models to LLMs. The newer feature is the AI Assistant, and while it was earlier only available to larger OAC deployments, with the May 2025 update, it has now been made available to all OAC instances!

If you’re considering a solution that leverages Gen AI for data analytics, the AI Assistant is a good fit for enterprise-wide deployments. I will explain why.

• Leverages an enterprise semantic layer: What I like most about how AI Assistant works is that it reuses the same data model and metadata that are already in place and caters for various types of reporting and analytical needs. AI Assistant adds another channel for user interaction with data, without the risks of data and metadata redundancy. As a result, no matter whether creating reports manually or leveraging AI, everyone across the organization remains consistent in using the same KPI definitions, the same entity relationships and the same dimensional rollup structures for reporting.
• Data Governance: This is along the same lines as my first point, but I want to stress the importance of controls when it comes to bringing the power of LLMs to data. There are many ways of leveraging Gen AI with data and some are native to the data management platforms themselves. However, implementing Gen AI data querying solutions directly within the data layer requires a closer look at security aspects of the implementation. Who will be able to get answers on certain topics? And if the topic is applicable to the one asking, how much information are they allowed to know?

The AI Assistant simply follows the same object and row level security controls that are enforced by the semantic data model.

• What about agility? Yes, governed analytics is very important. But how can people innovate and explore more effective solutions to business challenges without the ability to interact with the data that comes along with these challenges. The AI Assistant works not only with the common enterprise data model, but with individually prepared data sets as well. As a result, the same AI interface caters to questions asked about both enterprise data as well as departmental or individualized data sets.
• Tunability and Flexibility: Enabling the AI Assistant for organizational data, while relatively an easy task, does allow for a tailored setup. The purpose of tuning the setup is to increase the levels of reliability and accuracy. The flexibility comes into play when directing the LLM on what information to take into consideration when generating responses. And this can be done through a fine-tuning mechanism of designating which data entities and/or fields of data within these entities, can be considered.
• Support for data indexing, in addition to metadata: When tuning the AI Assistant setup, three options are available to pick from, down to the field level: Don’t Index, Index Metadata Only, and Index. With the Index option, we can include information about the actual data in a particular field so the AI Assistant is aware of that information. This can be useful, for example, for a Project Type field so the LLM is informed of the various possible values for Project Type. Consequently, the AI Assistant provides more relevant responses to questions that include specific project types as part of the prompt.
• Which LLM to use? LLMs continue to evolve, and it seems that there will always be a better, more efficient and more accurate LLM to switch to. Oracle has made the setup for the AI Assistant open, to an extent, in that it can accommodate external LLMs, besides the built-in LLM that is deployed and managed by Oracle. At this time, if not using the built-in LLM, we have the option of using an Open AI model via the Open AI API. Why may you want to use the built-in LLM vs an Open AI model?
  • The embedded LLM is focused on the analytical data that is part of your environment. So it’s more accurate in that it is less prone to hallucinations. However, this approach doesn’t provide flexibility in terms of access to external knowledge.
  • External LLMs include public knowledge (depending on what knowledge an LLM is trained on) in addition to the analytical data that is specific to your environment. This normally allows AI Assistant to have better responses when the questions asked are broad and require public knowledge to tie into the specific data elements housed in one system. Think for example about geographical facts, statistics, weather, business corporations’ information, etc. These are public information and can help in responding to analytical questions within the context of an organization’s data.
  • If the intent is to use an LLM but avoid the inclusion of external knowledge when generating responses, there is the option to restrict the LLM so it limits responses based on organizational data only. This approach leverages the reasoning capabilities of models without compromising the source of information for the responses.
• The Human Factor: AI Assistant factors in the human aspect of leveraging LLMs for analytics. Having a conversation with data through natural language is to the most part straight forward when dealing with less complex data sets. This is because, in the case, the responses are more deterministic. As the data model gets more complex, there will be more opportunities for misunderstanding and missed connections between what’s on one’s mind versus an AI generated response, let alone a visual one. This is why the AI Assistant has the capability for an end user to adjust the responses to better align with their preferences, without reiterating prompts and elongated back and forth conversations. These adjustments can be easily applied with button clicks, for example to change a visual appearance or change/add a filter or column, all within a chat window. And whatever visualizations the AI Assistant produces, can be added to a dashboard for further adjustments and future reference.

In the next post, I will mention a few things to watch out for when implementing AI Assistant. I will also demo what it looks like to use AI Assistant for project management.

Can a Product Information Management (PIM) System Make Your Data AI-Ready?

Yes, a PIM system can help get your data ready for AI—but only if it’s set up the right way.

If you’re managing product info across channels, you already know that bad data means bad results. And if you’re thinking of adding AI—like auto-tagging, personalization, or predictive tools—your product data needs to be spot-on.

This post breaks down what “AI-ready data” actually means, why messy product data kills your AI plans, and how a PIM system fits into fixing it.

What Does “AI-Ready” Data Mean?

AI-ready data is clean, complete, consistent, and structured to match what the AI needs to do. If any part of that is missing, the results from your AI model will be wrong or useless.

Gartner outlines five key steps to make data AI-ready:

1. Assess the data needed for each AI use case. You can’t just throw all your product data into an AI tool and expect magic. You need to know what the AI is supposed to do—recommend products, tag images, write descriptions—and check if the data supports that.

2. Align your data with the AI’s goals. Let’s say your goal is to personalize search results. That means every product needs the right tags, images, and categories. If that info’s missing or inconsistent, AI can’t deliver what you want.

3. Set clear rules for data governance. This includes naming standards, formatting rules, and tracking changes. AI systems rely on patterns. Without strong data governance, the AI can’t recognize patterns well enough to learn or predict accurately.

4. Use metadata to give your data context. Metadata helps AI understand what each piece of data means. It’s how you tell a machine the difference between a color and a size, or between an image and a feature.

5. Make data everyone’s job. If only IT or product teams handle data cleanup, you’ll never scale. You need marketing, content, and sales to be part of the process. That cross-team input helps AI models learn faster and smarter.

Without these steps, AI tools waste time trying to clean or guess data—and that leads to mistakes.

Common Product Data Problems That Hurt AI Outcomes

AI depends on structured, reliable data. When product data is messy or incomplete, AI tools can’t learn correctly or make accurate decisions.

Here are the most common issues that mess up AI results:

1. Missing values. If your product descriptions don’t always include size, color, or materials, the AI can’t group or recommend items correctly.

2. Inconsistent formats. “Red”, “RED”, and “#FF0000” might mean the same thing to people—but not to machines. AI models treat each format as different unless the data is standardized.

3. Duplicate entries. Two versions of the same product can confuse the AI. It might see them as separate products and deliver incorrect suggestions or analytics.

4. Unstructured content. If your product titles are crammed with keywords but no pattern, AI can’t extract useful meaning. Structured data is easier for models to work with.

5. Lack of metadata. AI models need more than just the product image or title. Without tags, category labels, and usage context, the model can’t learn how to connect products.

6. Outdated info. AI training requires current, real-world data. If product details change often but don’t get updated fast enough, the AI works off bad inputs and gives wrong outputs.

Each of these issues reduces the accuracy of your AI’s predictions, recommendations, or automations.

How PIM Systems Solve These Problems

A PIM system helps fix the data issues that stop AI from working well. It brings structure, control, and context to your product data—all of which AI needs to deliver value.

Here’s how PIM lines up with the five AI-readiness steps from Gartner:

1. Data aligned with use case: In a PIM, you define which attributes are required for each product category. If your AI needs color, size, and material to personalize product recommendations, PIM ensures that data is there—before the product is published.

2. Data normalization: PIM tools standardize formats. “Blue” won’t show up as “BLU” or “navy blueish” in different listings. The system enforces data rules, so your AI can trust the inputs.

3. Data governance: PIM systems let you set validation rules, version tracking, and user permissions. This means every change is tracked, and only approved data moves forward—key for AI systems that depend on clean histories.

4. Metadata management: PIM systems store and manage metadata like categories, usage tags, and even SEO terms. This extra layer helps AI models understand context—whether it’s matching a product to a search or choosing the best image.

5. Cross-team collaboration: With a PIM, marketing, product, and eCommerce teams work from the same source. This reduces errors, speeds up updates, and gives AI a steady flow of reliable product information.

By solving these issues at the source, a PIM platform creates the clean, structured, and well-governed data foundation that AI tools need to do their job right.

Can PIM Alone Get You There?

A PIM system solves the data problems—but it doesn’t replace the AI stack. Think of PIM as the prep kitchen. It gets everything clean, sorted, and ready to go. But you still need the right tools to cook.

Here’s what PIM does well:

• Cleans up product attributes

• Standardizes formats and values

• Adds missing metadata

• Makes data accessible across teams

But once the data is ready, you still need AI platforms to do the heavy lifting. That includes:

• Machine learning models to drive personalization

• Predictive tools to forecast demand or returns

• Agentic AI tools that take action (like re-tagging or alerting on gaps)

• Analytics platforms to visualize outcomes

So no, a PIM alone won’t give you full AI capabilities. But without a PIM, your AI tools will spend most of their time cleaning up your mess instead of giving you results.

PIM Is Your First Step to Smarter AI

AI can only work well when the data behind it is complete, consistent, and structured. A PIM system lays that foundation. It organizes your product information, enforces data standards, and adds the context that AI tools need to operate accurately.

Without clean data, AI models deliver flawed results. But with a strong PIM in place, you give AI the best chance to succeed—whether it’s automating product tagging, powering recommendations, or optimizing digital experiences.

Need help setting up a PIM or making your product data AI-ready?
Connect with us today—We help businesses use the right mix of PIM and AI to get real results faster. Whether you’re starting fresh or upgrading what you’ve got, we’ll make sure your data is ready for the next step.

 “The strength of a nation derives from the integrity of the home.” – Confucius.

 A room full of smart people, eyes glinting with the thrill of the future. Words like predictive models, AI-driven insights, and automated decisioning fly across the table like a Wimbledon final. Budgets, approved. Deadlines, drawn. Headlines, dreamed about.

But no one talks to or notices the quiet, slightly awkward one in the room, “it’s Data Governance”. The one who isn’t flashy … The one who shows up early with spreadsheets. The one who asks annoying questions, such as, “Where did this data come from?” and “Can we really trust this source?”

And yet, in almost every great technology story and every technology failure, Data Governance is the silent architect, whether you call it that or not. It was present, building the foundation… or sometimes silently watching as the castle falls.

The Illusion of Data-Driven Greatness

Some time back, I was working on a project where a major trading platform launched a new engine to automate trade surveillance and compliance monitoring.

Dollars were invested. The system promised to detect insider trading, front-running, and wash trade patterns too subtle for human eyes to catch. At first, everyone celebrated… until the false positives began to roll in. Legitimate trades were flagged as suspicious!!! Compliance officers were drowning in noise!!! Clients grew agitated, and regulatory auditors began asking uncomfortable questions.

When traced back, the root cause wasn’t the model itself. It was the data feeding it!

• Trade timestamps were off by milliseconds across systems.
• Reference data on instrument types was incomplete.
• Entity mappings between clients and brokers were outdated by over 9%.
• Historical compliance notes were inconsistently formatted and misclassified.

The model learned from incorrect data… and produced inaccuracy at an exponential scale. The organization had to suspend the AI engine and return to manual reviews in parallel, a massive operational setback.

The real problem wasn’t a technology failure. It was a data governance failure.

Why Data Governance is the New Competitive Edge

In the coming decade, success won’t be determined by who has the flashiest algorithms. Algorithms are cheap, open-source, and are increasingly commoditized. Success will hinge on who has better data, the companies that:

• Know where their data comes from.
• Know how it has been transformed.
• Know its limitations, its biases, and its gaps.
• Know how to course-correct in real time when something goes wrong.

Data governance used to be framed as a compliance tax… a necessary evil. But in the AI economy? It has become the operating system. Companies that treat governance like a strategic weapon, like a competitive differentiator, will build systems that are faster, smarter, safer, and more trusted. Everyone else will just be building very expensive sandcastles at low tide and praying tides don’t change.

The Risks Few Are Talking About

People love to talk about risks in the AI-driven world in sci-fi terms: rogue robots, existential threats, AI Models running for president

The real risk, one that is already unfolding in boardrooms and regulatory filings today, is much simpler: bad data feeding powerful systems.

• False alerts triggering unnecessary audits.
• Missed detection of real financial crimes.
• Market surveillance breakdowns causing regulatory breaches.
• Systemic compliance failures due to unseen data quality gaps.

All because governance was an afterthought.

The New Playbook for the AI Economy

If you’re a business leader, here’s the shift you need to make:

Smart organizations are now embedding governance into the very DNA of how they build, deploy, and manage AI systems. They’re asking:

• Who owns this dataset?
• How do we know it’s complete?
• What biases are hiding here?
• How do we certify and monitor trustworthiness over time?

And they’re investing accordingly — not reactively, but proactively.

Respect the Architect

Here’s the thing about architects. If they do their jobs right, no one notices them. The building just stands tall, sturdy, unshakable against storms. But what about when the foundation is weak? When the beams are poorly set? When the wiring is rushed? Well, then everyone notices. Usually, it’s too late. Data Governance is the silent architect of the AI structures. It’s time we gave it the respect and the investment it deserves. Because in the end, it’s not the flashiest ideas that win.
It’s the ones built on unshakable foundations.

Remember: “It is not the beauty of a building you should look at; it is the construction of the foundation that will stand the test of time.” – David Allan Coe.

Healthcare organizations (HCOs) face mounting pressure to boost operational efficiency, improve health and wellness, and enhance experiences. To drive these outcomes, leaders are aligning enterprise and business goals with digital investments that intelligently automate processes and optimize the health journey. 

Clinical intelligence plays a pivotal role in this transformation. It unlocks advanced data-driven insights that enable intelligent healthcare organizations to drive health innovation and elevate impactful health experiences. This approach aligns with the healthcare industry’s quintuple aim to enhance health outcomes, reduce costs, improve patient/member experiences, advance health equity, and improve the work life of healthcare teams. 

Intelligent Healthcare Organizations: Driven By Clinical Intelligence  

Our industry experts were recently interviewed by Forrester for their April 2025 report, Clinical Intelligence Will Power The Intelligent Healthcare Organization, which explores ways healthcare and business leaders can transform workflows to propel the enterprise toward next-gen operations and experiences. 

We believe the fact that we were interviewed for this report highlights our commitment to optimize technology, interoperability, and digital experiences in ways that build consumer trust, drive innovation, and support more-personalized care.  

We combine strategy, industry best practices, and technology expertise to deliver award-winning results for leading health plans and providers: 

• Business Transformation: Activate strategy for transformative outcomes and health experiences. 

• Modernization: Maximize technology to drive health innovation, efficiency, and interoperability. 

• Data Analytics: Power enterprise agility and accelerate healthcare insights. 

• Consumer Experience: Connect, ease, and elevate impactful health journeys. 

Understand and Deliver On Consumer Needs and Expectations 

Every individual brings with them an ever-changing set of needs, preferences, and health conditions. Now more than ever, consumers are flat out demanding a more tailored approach to their health care. This means it is imperative to know your audience. If you do not approach people as individuals with unique, personal needs, you risk losing them to another organization that does.  

Becoming an intelligent healthcare organization (IHO) takes more than just a technology investment; it is a complete restructuring of the enterprise to infuse and securely utilize clinical intelligence in every area and interaction.

In its report, Forrester defines an IHO as, “A healthcare organization that perpetually captures, transforms, and delivers data at scale and creates and seamlessly disseminates clinical intelligence, maximizing clinical workflows and operations and the experience of employees and customers. IHOs operate in one connected system that empowers engagement among all stakeholders.”

Ultimately, consumers – as a patient receiving care, a member engaging in their plan’s coverage, or a caregiver supporting this process – want to make and support informed health care decisions that cost-effectively drive better health outcomes. IHOs focus on delivering high-quality, personalized insights and support to the business, care teams, and consumers when it matters most and in ways that are accessible and actionable.

Orchestrate Better Health Access 

Digital-first care stands at the forefront of transformation, providing more options than ever before as individuals search for and choose care. When digital experiences are orchestrated with consumers’ expectations and options in mind, care solutions like telehealth services, find-care experiences, and mobile health apps can help HCOs deliver the right care at the right time, through the right channel, and with guidance that eases complex decisions, supports proactive health, and activates conversions. 

The shift toward digital-first care solutions means it is even more crucial for HCOs to understand real-time consumer expectations to help shape business priorities and form empathetic, personalized experiences that build trust and loyalty. 

In its report, Forrester states, “And as consumer trust has taken a hit over the past three years, it is encouraging that 72% of healthcare business and technology professionals expect their organization to increase its investment in customer management technologies.”  

Clinical intelligence, leveraged well, can transform the ways that consumers interact and engage across the healthcare ecosystem. IHOs see clinical intelligence as a way to innovate beyond mandated goals to add business value, meet consumers’ evolving expectations, and deliver equitable care and services.  

Interoperability plays a crucial role in this process, as it enables more seamless, integrated experiences across all digital platforms and systems. This interconnectedness ensures that consumers receive consistent, coordinated care, regardless of where they are seeking treatment and are supported by informed business and clinical teams. 

Mandates such as Health Level 7 (HL7) standards, Fast Healthcare Interoperability Resources (FHIR), and Centers for Medicare & Medicaid Services (CMS) Interoperability and Patient Access Final Rule are creating a more connected and data-driven healthcare ecosystem. Additionally, CMS price transparency regulations are empowering consumers to become more informed, active, and engaged patients. Price transparency and cost estimator tools have the potential to give organizations a competitive edge and drive brand loyalty by providing a transparent, proactive, personalized, and timely experience. 

The most successful organizations will build a proper foundation that scales and supports successive mandates. Composable architecture offers a powerful, flexible approach that balances “best in breed,” fit-for-purpose solutions while bypassing unneeded, costly features or services. It’s vital to build trust in data and with consumers, paving the way for ubiquitous, fact-based decision making that supports health and enables relationships across the care continuum. 

Success in Action: Empowering Healthcare Consumers and Their Care Ecosystems With Interoperable Data

Enable Caregivers and Care Teams 

As the population ages, caregivers play an increasingly important role in the healthcare journey, and their experience is distinct. They may continually move in and out of the caregiver role. It’s essential to understand and engage these vital partners, providing them with important tools and resources to support quality care.  

Clinical intelligence can provide HCOs with advanced insights into the needs of caregivers and care teams, helping clinical, operational, IT, digital, and marketing leaders design systems that support the health and efficacy of these important care providers.  

Integrated telehealth and remote monitoring have become essential to managing chronic conditions and an aging population. Intuitive, integrated digital tools and personalized messaging can help mitigate potential health barriers by proactively addressing concerns around transportation, costs, medication adherence, appointment scheduling, and more.  

A well-planned, well-executed strategy ideally supports access to care for all, creating a healthier and more-welcoming environment for team members to build trust, elevate consumer satisfaction, and drive higher-quality care.  

Success in Action: A Digital Approach to Addressing Health Equity 

Improve Operational Efficiencies for Care Teams 

HCO leaders are investing in advanced technologies and automations to modernize operations, streamline experiences, and unlock reliable insights.  

Clinical intelligence paired with intelligent automations can accelerate patient and member care for clinical and customer care teams, helping to alleviate stress on a workforce burdened with high rates of burnout.  

In its report, Forrester shares, “In Forrester’s Priorities Survey, 2024, 65% or more of healthcare business and technology professionals said that they expect their organization to significantly increase its investments in business insights and analytics, data and information management, AI, and business automation and robotics in the next 12 months.”  

It’s clear the U.S. healthcare industry stands on the cusp of a transformative era powered by advanced analytics and holistic business transformation. AI-driven automations can reduce administrative costs, while AI-enabled treatment plans offer hyper-personalized precision medicine. As technology continues to shape healthcare experiences, Felix Bradbury, Perficient senior solutions architect, shares his thoughts on the topic: 

“Trust is crucial in healthcare. Understanding how to make AI algorithms interpretable and ensuring they can provide transparent explanations of their decisions will be key to fostering trust among clinicians and patients.” 

AI can be a powerful enabler of business priorities. To power and scale effective use cases, HCOs are investing in core building blocks: a modern and secure infrastructure, well-governed data, and team training and enablement. A well-formed strategy that aligns key business needs with people, technology, and processes can turn data into a powerful tool that accelerates operational efficiency and business success, positioning you as an intelligent healthcare organization.  

Success in Action: Engaging Diverse Audiences As They Navigate Cancer Care 

Healthcare Leaders Turn To Us

Discover why we have been trusted by the 10 largest health systems and the 10 largest health insurers in the U.S. Explore our healthcare expertise and contact us to learn more. 

As my clients start to harness the power of AI to drive innovation and improve operational efficiency, the journey to production is fraught with challenges, including ethical considerations, risk management, and regulatory compliance. Perficient’s PACE framework offers a holistic approach to AI governance, ensuring responsible and effective AI integration. By leveraging IBM watsonx.governance, enterprises can streamline this process, ensuring robust governance and scalability.

Starting Point

The implementation of the PACE framework using IBM watsonx.governance begins with a clear understanding of the enterprise’s AI goals and readiness. This involves:

1. Assessment of AI Readiness: Evaluating the current state of AI within the organization, including existing capabilities, infrastructure, and stakeholder buy-in.
2. Defining Objectives: Establishing clear, measurable goals for AI integration that align with business objectives and ethical standards.
3. Stakeholder Engagement: Ensuring that all relevant stakeholders, from executives to technical teams, are engaged and informed about the AI governance strategy.

Challenges

Several challenges may be encountered during the implementation process:

1. Ethical and Regulatory Compliance: Navigating the complex landscape of AI ethics and regulatory requirements can be daunting. IBM watsonx.governance provides tools to automate compliance management, but continuous monitoring and adaptation are necessary.
2. Risk Management: Identifying and mitigating risks associated with AI systems, such as biases and security vulnerabilities, requires robust oversight and auditing mechanisms. IBM watsonx.governance’s risk management capabilities can help address these challenges.
3. Cultural Resistance: Promoting advocacy and adoption of AI within the organization may face resistance. Continuous education and collaboration are essential to overcome this barrier.
4. Scalability: Ensuring that AI governance processes can scale with the growth of AI initiatives is crucial. IBM watsonx.governance offers lifecycle governance tools to manage this scalability effectively.

Connecting IBM watsonx.governance to the PACE Framework

IBM watsonx.governance offers several features that align perfectly with the principles of the PACE framework:

1. Policies: IBM watsonx.governance helps define and enforce corporate guidelines for AI usage through automated compliance management tools. These tools simplify the identification of regulatory changes and translate them into enforceable policies, ensuring that AI systems adhere to established standards.
2. Advocacy: The platform supports continuous education and collaboration by providing insights and metrics that can be shared across the organization. This fosters a culture of understanding and adoption of AI, aligning with the advocacy component of the PACE framework.
3. Controls: IBM watsonx.governance offers robust risk management capabilities, including automated risk metrics and bias detection tools. These features enable enterprises to conduct thorough audits and maintain oversight of AI systems, ensuring they operate within acceptable risk parameters.
4. Enablement: The platform provides lifecycle governance tools that monitor and manage the complete AI lifecycle, from model selection to deployment, monitoring, and replacement. This ensures that technology teams have the necessary resources and support to innovate responsibly.

Measuring Success with the PACE Framework

Success in implementing the PACE framework with IBM watsonx.governance can be measured through several key indicators:

1. Compliance and Risk Metrics: Monitoring compliance with ethical standards and regulatory requirements, as well as tracking risk metrics to ensure AI systems are secure and reliable.
2. Stakeholder Engagement: Assessing the level of engagement and understanding among stakeholders, including feedback from continuous education initiatives.
3. Operational Efficiency: Evaluating improvements in operational efficiency and innovation resulting from AI integration.
4. Business Impact: Measuring the tangible business impact, such as revenue growth, cost savings, and customer satisfaction, resulting from AI initiatives.

By integrating Perficient’s PACE framework with IBM watsonx.governance, large enterprises can confidently embrace AI, driving innovation while ensuring responsible and ethical AI usage. This combined approach not only mitigates risks but also accelerates the adoption of AI, paving the way for a transformative impact on business operations and customer experiences.