Perficient is proud to be named a Major Player in the IDC MarketScape: Worldwide Experience Build Services 2025 Vendor Assessment (Doc #US52973125, October 2025) and IDC MarketScape: Worldwide Experience Design Services 2025 Vendor Assessment (Doc #US52973225, October 2025). These IDC MarketScapes assessed providers, offering a comprehensive framework including product and service offerings, capabilities and strategies, and current/future market success factors.

“We believe being recognized by IDC for Experience Design and Experience Build reinforces the impact we have on behalf of clients creating personalized, seamless interactions that accelerate growth. In today’s experience-driven economy, that’s the competitive advantage that matters,” says Erin Rushman, general manager of digital marketing and experience design operations at Perficient.

What This Inclusion Means for Perficient

Being named a Major Player, we believe, underscores our dedication to transforming customer experiences and empowering businesses through personalized, seamless, and impactful interactions. Perficient combines strategy and research with human-centered design to help organizations craft agile, customer-focused solutions that thrive in dynamic markets. By leveraging data-driven insights, personalization, AI, and more, we deliver end-to-end experiences that deepen engagement and drive measurable business impact.

According to the IDC MarketScape for Experience Design Services, “Perficient has strong capabilities in digital offering design and offers leading-edge experience design services backed by a global innovation network.” The report also notes, “In conversations with Perficient’s reference clients, the three areas where experience design services buyers commended the vendor highly were for the quality of its professionals, for its industry specific capabilities, and differentiation as a vendor.”

The IDC MarketScape for Experience Build Services states, “As an independent digital experience agency, Perficient combines business and technology transformation capabilities, including a robust collection of supporting assets and tools, with a focus on the design and build of customer experiences. Perficient has strong personalization capabilities.”

Additionally, Perficient was named a Major Player in the IDC MarketScape for Customer Experience Strategy Consulting Services 2025 Vendor Assessment (Doc #US52973025, September 2025). We believe this inclusion reflects our commitment to delivering AI-first solutions that transform customer experiences through scalable, high-impact innovations. It establishes Perficient as a trusted partner, driving unmatched success in the experience-driven market of tomorrow.

Read the News Release: Perficient Named a Major Player in Three IDC MarketScapes For AI-First Approach to Customer Experience

What This Inclusion Means for Our Clients

Perficient continues to be a leader in experience strategy and design, helping clients align vision, accelerate innovation, and achieve lasting transformation. We enable businesses to embed AI into processes and deliver personalized customer experiences at scale. By expanding and strengthening alliances with partners, we ensure our solutions remain innovative and leading-edge, empowering clients to stay ahead in a dynamic market.

Exceptional CX is essential for growth and loyalty. Our expertise across platforms and global delivery ensures brands can quickly adapt, innovate, and meet rising customer expectations. Explore our expertise to see how we can be a partner in your experience journey.

“Powerful technologies require equally powerful ethical guidance.” (Bostrom, N. Superintelligence: Paths, Dangers, Strategies. Oxford University Press, 2014).

The ethics of using artificial intelligence depend on how we apply its capabilities—either to enhance learning or to prevent irresponsible practices that may compromise academic integrity. In this blog, I share reflections, experiences, and insights about the impact of AI in our environment, analyzing its role as a creative tool in the hands of developers and as a challenge within the academic context.

Between industry and the classroom

As a Senior Developer, my professional trajectory has led me to delve deeply into the fascinating discipline of software architecture. Currently, I work as a Backend Developer specializing in Microsoft technologies, facing daily the challenges of building robust, scalable, and well-structured systems in the business world.

Alongside my role in the industry, I am privileged to serve as a university professor, teaching four courses. Three of them are fundamental parts of the software development lifecycle: Software Analysis and Design, Software Architecture, and Programming Techniques. This dual perspective—as both a professional and a teacher—has allowed me to observe the rapid changes that technology is generating both in daily development practice and in the formation of future engineers.

Exploring AI as an Accelerator in Software Development

One of the greatest challenges for those studying the software development lifecycle is transforming ideas and diagrams into functional, well-structured projects. I always encourage my students to use Artificial Intelligence as a tool for acceleration, not as a substitute.

For example, in the Software Analysis and Design course, we demonstrate how a BPMN 2.0 process diagram can serve as a starting point for modeling a system. We also work with class diagrams that reflect compositions and various design patterns. AI can intervene in this process in several ways:

• Code Generation from Models: With AI-based tools, it’s possible to automatically turn a well-built class diagram into the source code foundation needed to start a project, respecting the relationships and patterns defined during modeling.
• Rapid Project Architecture Setup: Using AI assistants, we can streamline the initial setup of a project by selecting the technology stack, creating folder structures, base files, and configurations according to best practices.
• Early Validation and Correction: AI can suggest improvements to proposed models, detect inconsistencies, foresee integration issues, and help adapt the design context even before coding begins.

This approach allows students to dedicate more time to understanding the logic behind each component and design principle, instead of spending hours on repetitive setup and basic coding tasks. The conscious and critical use of artificial intelligence strengthens their learning, provides them with more time to innovate, and helps prepare them for real-world industry challenges.

But Not Everything Is Perfect: The Challenges in Programming Techniques

However, not everything is as positive as it seems. In “Programming Techniques,” a course that represents students’ first real contact with application development, the impact of AI is different compared to more advanced subjects. In the past, the repetitive process of writing code—such as creating a simple constructor public Person(), a function public void printFullName() or practicing encapsulation in Java with methods like public void setName(String name) and public String getName()—kept the fundamental programming concepts fresh and clear while coding.

This repetition was not just mechanical; it reinforced their understanding of concepts like object construction, data encapsulation, and procedural logic. It also played a crucial role in developing a solid foundation that made it easier to understand more complex topics, such as design patterns, in future courses.

Nowadays, with the widespread availability and use of AI-based tools and code generators, students tend to skip these fundamental steps. Instead of internalizing these concepts through practice, they quickly generate code snippets without fully understanding their structure or purpose. As a result, the pillars of programming—such as abstraction, encapsulation, inheritance, and polymorphism—are not deeply absorbed, which can lead to confusion and mistakes later on.

Although AI offers the promise of accelerating development and reducing manual labor, it is important to remember that certain repetition and manual coding are essential for establishing a solid understanding of fundamental principles. Without this foundation, it becomes difficult for students to recognize bad practices, avoid common errors, and truly appreciate the architecture and design of robust software systems.

Reflection and Ethical Challenges in Using AI

Recently, I explained the concept of reflection in microservices to my Software Architecture students. To illustrate this, I used the following example: when implementing the Abstract Factory design pattern within a microservices architecture, the Reflection technique can be used to dynamically instantiate concrete classes at runtime. This allows the factory to decide which object to create based on external parameters, such as a message type or specific configuration received from another service. I consider this concept fundamental if we aim to design an architecture suitable for business models that require this level of flexibility.

However, during a classroom exercise where I provided a base code, I asked the students to correct an error that I had deliberately injected. The error consisted of an additional parameter in a constructor—a detail that did not cause compilation failures, but at runtime, it caused 2 out of 5 microservices that consumed the abstract factory via reflection to fail. From their perspective, this exercise may have seemed unnecessary, which led many to ask AI to fix the error.

As expected, the AI efficiently eliminated the error but overlooked a fundamental acceptance criterion: that parameter was necessary for the correct functioning of the solution. The task was not to remove the parameter but to add it in the Factory classes where it was missing. Out of 36 students, only 3 were able to explain and justify the changes they made. The rest did not even know what modifications the AI had implemented.

This experience highlights the double-edged nature of artificial intelligence in learning: it can provide quick solutions, but if the context or the criteria behind a problem are not understood, the correction can be superficial and jeopardize both the quality and the deep understanding of the code.

I haven’t limited this exercise to architecture examples alone. I have also conducted mock interviews, asking basic programming concepts. Surprisingly, even among final-year students who are already doing their internships, the success rate is alarmingly low: approximately 65% to 70% of the questions are answered incorrectly, which would automatically disqualify them in a real technical interview.

Conclusion

Artificial intelligence has become increasingly integrated into academia, yet its use does not always reflect a genuine desire to learn. For many students, AI has turned into a tool for simply getting through academic commitments, rather than an ally that fosters knowledge, creativity, and critical thinking. This trend presents clear risks: a loss of deep understanding, unreflective automation of tasks, and a lack of internalization of fundamental concepts—all crucial for professional growth in technological fields.

Various authors have analyzed the impact of AI on educational processes and emphasize the importance of promoting its ethical and constructive use. As Luckin et al. (2016) suggest, the key lies in integrating artificial intelligence as support for skill development rather than as a shortcut to avoid intellectual effort. Similarly, Selwyn (2019) explores the ethical and pedagogical challenges that arise when technology becomes a quick fix instead of a resource for deep learning.

References:

• Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson Education. [Available at: https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/about-pearson/innovation/open-ideas/Intelligence-Unleashed-Publication.pdf]
• Selwyn, N. (2019). Should Robots Replace Teachers? AI and the Future of Education. Polity Press.

The headlines are troubling but predictable. The Trump administration will launch a program next year to find out how much money an artificial intelligence algorithm could save the federal government by denying care to Medicare patients. Meanwhile, a survey of physicians published by the American Medical Association in February found that 61% think AI is “increasing prior authorization denials, exacerbating avoidable patient harms and escalating unnecessary waste now and into the future.”

We’re witnessing the healthcare industry’s narrow vision of AI in action: algorithms designed to say “no” faster and more efficiently than ever before. But what if we’re missing the bigger opportunity?

The Current AI Problem: Built to Deny, Not to Help

The recent expansion of AI-powered prior authorization reveals a fundamental flaw in how we’re approaching healthcare technology. “The more expensive it is, the more likely it is to be denied,” said Jennifer Oliva, a professor at the Maurer School of Law at Indiana University-Bloomington, whose work focuses on AI regulation and health coverage.

This approach creates a vicious cycle: patients don’t understand their benefits, seek inappropriate or unnecessary care, trigger costly prior authorization processes, face denials, appeal those denials, and ultimately either give up or create even more administrative burden for everyone involved.

The human cost is real. Nearly three-quarters of respondents thought prior authorization was a “major” problem in a July poll published by KFF, and we’ve seen how public displeasure with insurance denials dominated the news in December, when the shooting death of UnitedHealthcare’s CEO led many to anoint his alleged killer as a folk hero.

A Better Vision: The AI Concierge Approach

What if instead of using AI to deny care more efficiently, we used it to help patients access the right care more effectively? This is where the AI Concierge concept transforms the entire equation.

An AI Concierge doesn’t wait for a claim to be submitted to make a decision. Instead, it proactively:

• Educates patients about their benefits before they need care
• Guides them to appropriate providers within their network
• Explains coverage limitations in plain language before appointments
• Suggests preventive alternatives that could avoid more expensive interventions
• Streamlines pre-authorization by ensuring patients have the right documentation upfront

The Quantified Business Case

The financial argument for AI Concierge services is compelling:

Star Ratings Revenue Impact: A half-star increase in Medicare Star Ratings is valued at approximately $500 per member. For a 75,000-member plan, that translates to $37.5 million in additional funding. An AI Concierge directly improves patient satisfaction scores that drive these ratings.

Operational Efficiency Gains: Healthcare providers implementing AI-powered patient engagement systems report 15-20% boosts in clinic revenue and 10-20% reductions in overall operational costs. Clinics using AI tools see 15-25% increases in patient retention rates.

Cost Avoidance Through Prevention: Utilizing AI to help patients access appropriate care could save up to 50% on treatment costs while improving health outcomes by up to 40%. This happens by preventing more expensive interventions through proper preventive care utilization.

The HEDIS Connection

HEDIS measures provide the perfect framework for demonstrating AI Concierge value. With 235 million people enrolled in plans that report HEDIS results, improving these scores directly impacts revenue through bonus payments and competitive positioning.

An AI Concierge naturally improves HEDIS performance in:

• Preventive Care Measures: Proactive guidance increases screening and immunization rates
• Care Gap Closure: Identifies and addresses gaps before they become expensive problems
• Patient Engagement: Improves medication adherence and chronic disease management

Beyond the Pilot Programs

While government initiatives like the WISeR pilot program focus on “Wasteful and Inappropriate Service Reduction” through AI-powered denials, forward-thinking healthcare organizations have an opportunity to differentiate themselves with AI-powered patient empowerment.

The math is simple: preventing a $50,000 hospitalization through proactive care coordination delivers better ROI than efficiently denying the claim after it’s submitted.

AI Healthcare Concierge Implementation Strategy

For healthcare leaders considering AI Concierge implementation:

• Phase 1: Deploy AI-powered benefit explanation tools that reduce call center volume and improve patient understanding
• Phase 2: Integrate predictive analytics to identify patients at risk for expensive interventions and guide them to preventive alternatives
• Phase 3: Expand to comprehensive care navigation that optimizes both patient outcomes and organizational performance

The Competitive Advantage

While competitors invest in AI to process denials faster, organizations implementing AI Concierge services are investing in:

• Member satisfaction and retention (15-25% improvement rates)
• Star rating improvements ($500 per member value per half-star)
• Operational cost reduction (10-20% typical savings)
• Revenue protection through better member experience

Conclusion: Choose Your AI Future

The current trajectory of AI in healthcare—focused on denial optimization—represents a massive missed opportunity. As one physician noted about the Medicare pilot: “I will always, always err on the side that doctors know what’s best for their patients.”

AI Healthcare Concierge services align with this principle by empowering both patients and providers with better information, earlier intervention, and more effective care coordination. The technology exists. The business case is proven. The patient need is urgent.

The question isn’t whether AI will transform healthcare—it’s whether we’ll use it to build walls or bridges between patients and the care they need.

The choice is ours. Let’s choose wisely.

My 30-year technology career has taught me many things…and one big thing: the companies that treat technology as a cost center are the ones that get blindsided. In manufacturing, that blindside is already here — and it’s wearing the name tag “AI.”

For decades, manufacturers have been locked into rigid systems, long upgrade cycles, and siloed data. The result? Operations that run on yesterday’s insights while competitors are making tomorrow’s moves. Sound familiar? It’s the same trap traditional IT outsourcing fell into — and it’s just as deadly in the age of smart manufacturing.

The AI Advantage in Manufacturing

Oracle Fusion AI for Manufacturing Smart Operations isn’t just another software upgrade. It’s a shift from reactive to predictive, from siloed to synchronized. Think:

• Real-time anomaly detection that flags quality issues before they hit the line.
• Predictive maintenance that slashes downtime and extends asset life.
• Intelligent scheduling that adapts to supply chain disruptions in minutes, not weeks.
• Embedded analytics that turn every operator, planner, and manager into a decision-maker armed with live data.

This isn’t about replacing people — it’s about giving them superpowers. Read more from Oracle here.

Proof in Action: Roeslein & Associates

If you want to see what this looks like in the wild, look at Roeslein & Associates. They were running on disparate, outdated legacy systems — the kind that make global process consistency a pipe dream. Perficient stepped in and implemented Oracle Fusion Cloud Manufacturing with Project Driven Supply Chain, plus full Financial and Supply Chain Management suites. The result?

• A global solution template that can be rolled out anywhere in the business.
• A redesigned enterprise structure to track profits across business units.
• Standardized manufacturing processes that still flex for highly customized demand.
• Integrated aftermarket parts ordering and manufacturing flows.
• Seamless connections between Fusion, labor capture systems, and eCommerce.

That’s not just “going live” — that’s rewiring the operational nervous system for speed, visibility, and scale.

Why Standing Still is Riskier Than Moving Fast

In my words, “true innovation is darn near impossible” when you’re chained to legacy thinking. The same applies here: if your manufacturing ops are running on static ERP data and manual interventions, you’re already losing ground to AI‑driven competitors who can pivot in real time.

Oracle Fusion Cloud with embedded AI is the equalizer. A mid‑sized manufacturer with the right AI tools can outmaneuver industry giants still stuck in quarterly planning cycles.

Where Perficient Comes In

Perficient’s Oracle team doesn’t just implement software — they architect transformation. With deep expertise in Oracle Manufacturing Cloud, Supply Chain Management, and embedded Fusion AI solutions, they help you:

• Integrate AI into existing workflows without blowing up your operations.
• Optimize supply chain visibility from raw materials to customer delivery.
• Leverage IoT and machine learning for continuous process improvement.
• Scale securely in the cloud while keeping compliance and governance in check.

They’ve done it for global manufacturers, and they can do it for you — faster than you think.

The Call to Action

If you believe your manufacturing operations are immune to disruption, history says otherwise. The companies that win will be the ones that treat AI not as a pilot project, but as the new operating system for their business.

Rather than letting new entrants disrupt your position, take initiative and lead the charge—make them play catch-up.

As artificial intelligence continues to reshape the digital landscape, organizations are seeking clarity on how to strategically integrate AI into their digital experience platforms (DXPs). In its latest report, The Impact of AI on Digital Experience Platforms, Forrester explores how DXP vendors are embedding AI agents to streamline experience operations and expand toolsets — from copilots to code generators. Perficient was proud to contribute its DXP and AI expertise to this research, joining the group of vendors and service providers interviewed for the report. 

“Generative AI (GenAI) is reshaping digital experience platforms (DXPs) by automating content and campaign creation, powering intelligent copilots for marketers and developers, and boosting customer engagement through predictive insights and personalization.” —Forrester, The Impact of AI on Digital Experience Platforms

Why AI Belongs in the DXP Conversation

Today’s tech leaders face a growing challenge: delivering consistent, personalized experiences across a fragmented landscape of devices, channels, and customer expectations. DXPs are uniquely positioned to address this complexity. As Forrester notes, DXPs are rich in customer interaction data, support high-value business outcomes, and serve a diverse set of practitioners — all of which make them fertile ground for AI innovation. 

“DXP is a trove of trusted customer interaction data… Unleashing AI on this data enables organizations to understand audience intent and respond to that intent with irresistible offers.” — Forrester, The Impact of AI on Digital Experience Platforms

And Perficient’s experts agree that DXPs are a great place to start when integrating AI into experience delivery. 

“Marketing and IT leaders are under pressure to deliver personalized, omnichannel experiences at scale, and that’s exactly where DXPs shine. By embedding AI into the core of these platforms, clients can unlock the full value of their customer data and streamline operations across content, commerce, and campaigns,” said Mark Polly, Perficient Principal, Customer Experience Platforms.  

Agentic AI Ushers in a New Era of Experience Operations

The report highlights the rise of agentic AI and how these intelligent agents operate within DXPs to automate tasks, answer questions, and optimize workflows. These agents are transforming how marketers, developers, and content creators interact with their platforms, reducing friction and accelerating time to value. 

Perficient’s experience with orchestration tools and AI agents reinforces this trend. While many vendors offer orchestration capabilities, Perficient often helps clients integrate these tools across their broader tech ecosystem. 

” For marketing and IT leaders, true orchestration moves beyond linear workflows; it’s about integrating platforms like AEM and Optimizely to create a dynamic, responsive system. This integration is the key to driving operational efficiency and gaining the unified insights needed for deeper customer engagement. AI agents play a critical role here, transforming those rigid workflows into the real-time orchestration that a modern customer journey demands. Adoption is still in its early stages, which is where we help clients build a strategic advantage,” said Perficient digital strategy principal Grant Davies. 

Emerging GenAI Use Cases in DXPs

From brand-aware content creation to code generation and experimentation, GenAI is also rapidly expanding its footprint in DXP environments. Vendors like Adobe, Sitecore, and Salesforce are launching copilots and agents that empower users to create, test, and optimize experiences with unprecedented speed and precision. 

Perficient is already helping clients explore these capabilities and align them with business goals. 

Polly said, “We’re seeing real momentum around GenAI use cases in DXP from brand-consistent content creation to intelligent experimentation. These capabilities aren’t just flashy features; they’re solving real business problems like reducing time to market and improving conversion rates. The key is aligning AI with measurable outcomes and ensuring strong governance.” 

Perficient Wins 2025 Artificial Intelligence Excellence Award for GenAI Integrity Accelerator   Learn More

Strategic Adoption Requires Strong Foundations 

Successful AI adoption in DXPs requires more than just technology. Organizations must invest in strong data foundations, human oversight, and change management to ensure responsible and effective use of AI. These are areas where Perficient continues to guide clients with strategic consulting and hands-on implementation. 

We believe our inclusion in the research for Forrester’s report, The Impact of AI on Digital Experience Platforms, reflects our deep expertise in helping enterprise clients implement and optimize DXPs. Whether it’s integrating predictive analytics, deploying cognitive copilots, or improving data governance, our teams are leading the way in enabling AI-powered transformation. 

We’re honored to be interviewed for this research and proud to contribute to the evolving conversation around AI in digital experience. If you’re exploring how to bring AI into your DXP strategy, we invite you to connect with us and keep the conversation going. 

Access the report here (available to Forrester subscribers or for purchase).

Contact us  to learn more. 

Meet Acquia Source

Acquia Source powered by Drupal is Acquia’s SaaS solution to streamline building, managing, and deploying websites at scale, representing a fundamental shift in how organizations approach digital experience creation. This innovative platform combines the power of Drupal with a modern component-based architecture, delivering a unique hybrid approach that bridges traditional CMS capabilities with contemporary development practices.

At its core, Acquia Source is a SaaS offering that provides Drupal functionality enhanced with a custom component architecture built on React and Tailwind CSS. Components can be created through React, Tailwind 4, and CSS, allowing developers to write CSS and React directly within the platform without need for complex dev workflows. This approach eliminates the need for custom modules or PHP code, streamlining the development process while maintaining the robust content management capabilities that Drupal is known for.

Unlike traditional Drupal implementations that require extensive backend development, Acquia Source focuses on frontend component creation and content architecture. This makes it accessible to a broader range of developers while still leveraging Drupal’s proven content management foundation. For detailed technical specifications and implementation guides, explore the comprehensive documentation and learn more about the platform on Acquia’s official pages.

Why Acquia Source is a Game-Changer

The React-based component architecture at the heart of Acquia Source offers several compelling advantages that address common pain points in digital experience development. It provides a user-friendly Experience Builder to help you create and edit pages, robust user management features to control permissions and collaboration, and a design system approach that enables teams to define and enforce style and interaction patterns across pages.

One of the most significant benefits is the demoable, out-of-the-box feature set that allows teams to showcase functionality immediately without extensive development work. Since Acquia Source operates as a SaaS solution, updates and platform management are completely offloaded from your team, eliminating the traditional burden of infrastructure maintenance, security patching, and version upgrades that typically consume resources in custom Drupal implementations.

The platform maintains Drupal’s standard content type architecture, ensuring that content creators and administrators can leverage familiar workflows and structures. This consistency reduces training requirements and maintains efficiency while introducing modern frontend capabilities.

Perhaps most importantly for development teams, Acquia Source uses React and CSS technologies that frontend developers already understand. Unlike proprietary low-code solutions that require learning platform-specific languages or architectures, developers can immediately apply their existing React and Tailwind CSS knowledge. This eliminates the typical learning curve associated with new platforms and enables faster team onboarding and development.

Changing the Playbook for Smaller companies

Acquia Source fundamentally changes the accessibility of high-end digital Drupal experiences, particularly for smaller companies and businesses that previously couldn’t justify the cost or complexity of enterprise-level implementations. The platform’s quick spin-up capability means organizations can have a sophisticated digital presence operational in weeks/months rather than months/years.

With updates handled entirely by the SaaS solution, businesses no longer need to budget for ongoing maintenance, security updates, or platform upgrades. This predictable cost model makes enterprise-level functionality accessible to organizations with limited technical resources or budget constraints.

The platform eliminates the need for complex strategy engagements or extensive architecture planning that typically precede major Drupal implementations. For many use cases, the offering can be as simple as skinning out-of-the-box components to match brand requirements, dramatically reducing both time-to-market and project complexity. Gone are the days of extensive discussions about which address module or maps integration are required for a specific implementation.

The content-type-only architecture approach allows smaller development teams to deliver sophisticated results without deep Drupal expertise. This lower barrier of entry enables smaller firms to confidently engage with top-tier Acquia partners such as Perficient, providing access to extensive libraries of industry and technology-specific experts without requiring large internal development teams. This ease of access means that businesses can leverage enterprise-grade expertise and proven methodologies regardless of their size or internal technical capabilities.

Conclusion: Your Next Learning Priority

Acquia Source represents the future of accessible, scalable digital experience development. By combining the proven content management capabilities of Drupal with modern React-based component architecture, it offers a compelling solution for organizations seeking to deliver sophisticated digital experiences without the traditional complexity and resource requirements.

For marketing professionals, Acquia Source offers unprecedented speed-to-market, creative flexibility and ability to leverage existing frontend resources. For architects and developers, it provides a platform that leverages existing skills while eliminating infrastructure concerns and reducing project complexity.

The platform’s unique position in the market, providing advanced Drupal capabilities through a SaaS model with familiar development technologies makes it an invaluable tool for any developer or agency to have in their toolbox.

Start your Acquia Source journey today by exploring the comprehensive documentation and registering for the Partner Master Class: Introducing Acquia Source powered by Drupal to gain hands-on experience with this transformative platform.

Whether you’re building embedded software for next-gen diagnostics, modernizing lab systems, or scaling user-facing platforms, the pressure to innovate is universal, and AI is becoming a key differentiator. When embedded into the software development lifecycle (SDLC), AI offers a path to reduce costs, accelerate timelines, and equip the enterprise to scale with confidence. 

But AI doesn’t implement itself. It requires a team that understands the nuance of regulated software, SDLC complexities, and the strategic levers that drive growth. Our experts are helping MedTech leaders move beyond experimentation and into execution, embedding AI into the core of product development, testing, and regulatory readiness. 

“AI is being used to reduce manual effort and improve accuracy in documentation, testing, and validation.” – Reuters MedTech Report, 2025 

Whether it’s generating test cases from requirements, automating hazard analysis, or accelerating documentation, we help clients turn AI into a strategic accelerator. 

AI-Accelerated Regulatory Documentation 

Outcome: Faster time to submission, reduced manual burden, improved compliance confidence 

Regulatory documentation remains one of the most resource-intensive phases of medical device development.  

• Risk classification automation: AI can analyze product attributes and applicable standards to suggest classification and required documentation. 
• Drafting and validation: Generative AI can produce up to 75% of required documentation, which is then refined and validated by human experts. 
• AI-assisted review: Post-editing, AI can re-analyze content to flag gaps or inconsistencies, acting as a second set of eyes before submission. 

AI won’t replace regulatory experts, but it will eliminate the grind. That’s where the value lies. 

For regulatory affairs leaders and product teams, this means faster submissions, reduced rework, and greater confidence in compliance, all while freeing up resources to focus on innovation. 

Agentic AI in the SDLC 

Outcome: Increased development velocity, reduced error rates, scalable automation 

Agentic AI—systems of multiple AI agents working in coordination—is emerging as a force multiplier in software development. 

• Task decomposition: Complex development tasks are broken into smaller units, each handled by specialized agents, reducing hallucinations and improving accuracy. 

• Peer review by AI: One agent can validate the output of another, creating a self-checking system that mirrors human code reviews. 

• Digital workforce augmentation: Repetitive, labor-intensive tasks (e.g., documentation scaffolding, test case generation) are offloaded to AI, freeing teams to focus on innovation. This is especially impactful for engineering and product teams looking to scale development without compromising quality or compliance. 

• Guardrails and oversight mechanisms: Our balanced implementation approach maintains security, compliance, and appropriate human supervision to deliver immediate operational gains and builds a foundation for continuous, iterative improvement. 

Agentic AI can surface vulnerabilities early and propose mitigations faster than traditional methods. This isn’t about replacing engineers. It’s about giving them a smarter co-pilot. 

AI-Enabled Quality Assurance and Testing 

Outcome: Higher product reliability, faster regression cycles, better user experiences 

AI is transforming QA from a bottleneck into a strategic advantage. 

• Smart regression testing: AI frameworks run automated test suites across releases, identifying regressions with minimal human input. 
• Synthetic test data generation: AI creates high-fidelity, privacy-safe test data in minutes—data that once took weeks to prepare. 
• GenAI-powered visual testing: AI evaluates UI consistency and accessibility, flagging issues that traditional automation often misses. 
• Chatbot validation: AI tools now test AI-powered support interfaces, ensuring they provide accurate, compliant responses. 

We’re not just testing functionality—we’re testing intelligence. That requires a new kind of QA.

Organizations managing complex software portfolios can unlock faster, safer releases. 

AI-Enabled, Scalable Talent Solutions 

Outcome: Scalable expertise without long onboarding cycles 

AI tools are only as effective as the teams that deploy them. We provide specialized talent—regulatory technologists, QA engineers, data scientists—that bring both domain knowledge and AI fluency. 

• Accelerate proof-of-concept execution: Our teams integrate quickly into existing workflows, leveraging Agile and SAFe methodologies to deliver iterative value and maintain velocity. 
• Reduce internal training burden: AI-fluent professionals bring immediate impact, minimizing ramp-up time and aligning with sprint-based development cycles. 
• Ensure compliance alignment from day one: Specialists understand regulated environments and embed quality and traceability into every phase of the SDLC, consistent with Agile governance models. 

Whether you’re a CIO scaling digital health initiatives or a VP of Software managing multiple product lines, our AI-fluent teams integrate seamlessly to accelerate delivery and reduce risk. 

Proof of Concept Today, Scalable Solution Tomorrow 

Outcome: Informed investment decisions, future-ready capabilities 

Many of the AI capabilities discussed are already in early deployment or active pilot phases. Others are in proof-of-concept, with clear paths to scale. 

We understand that every organization is on a unique AI journey. Whether you’re starting from scratch, experimenting with pilots, or scaling AI across your enterprise, we meet you where you are. Our structured approach delivers value at every stage, helping you turn AI from an idea into a business advantage. 

As you evaluate your innovation and investment priorities across the SDLC, consider these questions: 

1. Are we spending too much time on manual documentation?
2. Do we have visibility into risk classification and mitigation?
3. Can our QA processes scale with product complexity?
4. How are we building responsible AI governance?
5. Do we have the right partner to operationalize AI?

Final Thought: AI Demands a Partner, Not Just a Platform 

AI isn’t the new compliance partner. It’s the next competitive edge, but only when guided by the right strategy. For MedTech leaders, AI’s real opportunity comes by adopting and scaling it with precision, speed, and confidence. That kind of transformation can be accelerated by a partner who understands the regulatory terrain, the complexity of the SDLC, and the business outcomes that matter most. 

No matter where you sit — on the engineering team, in the lab, in business leadership, or in patient care — AI is reshaping how MedTech companies build, test, and deliver value. 

From insight to impact, our industry, platform, data, and AI expertise help organizations modernize systems, personalize engagement, and scale innovation. We deliver AI-powered transformation that drives engagement, efficiency, and loyalty throughout the lifecycle—from product development to commercial success. 

• Business Transformation: Deepen collaboration, integration, and support throughout the value chain, including channel sales, providers, and patients. 
• Modernization: Streamline legacy systems to drive greater connectivity, reduce duplication, and enhance employee and consumer experiences. 
• Data + Analytics: Harness real-time data to support business success and to impact health outcomes. 
• Consumer Experience: Support patient and consumer decision making, product usage, and outcomes through tailored digital experiences. 

Ready to move from AI potential to performance? Let’s talk about how we can accelerate your roadmap with the right talent, tools, and strategy. Contact us to get started. 

In 2025, MedTech leaders are redefining innovation by architecting intelligent ecosystems that span the entire product lifecycle. As AI in medical device development becomes a strategic imperative, forward-thinking organizations are aligning digital investments with business-critical outcomes:

• Faster time to market
• Stronger regulatory readiness
• Scalable clinical impact

This blog explores how AI-powered transformation is reshaping every stage of the device lifecycle—from concept to commercialization, integration to post-market support—and how MedTech executives can lead with intelligence to drive growth, efficiency, and trust.

Concept & Feasibility: Design for commercialization from day one

Successful MedTech products start with a clear understanding of unmet clinical needs, market dynamics, consumer expectations, and regulatory pathways. Today’s health care consumers demand intuitive, personalized, and transparent device experiences, and these expectations must shape product strategy from the outset.

By incorporating buyer insights, personas, and journey maps, teams can ground product direction in real-world behaviors and emotional drivers, not just clinical feasibility. Designing with commercialization, adoption, and lifecycle management in mind reduces risk and accelerates value creation while Agile and SAFe principles, such as iterative planning and cross-functional collaboration, help teams adapt quickly as new insights emerge.

Industry Insight:

The FDA’s Total Product Life Cycle (TPLC) Advisory Program is expanding to accelerate innovation through early, strategic communication between device developers and regulators. This initiative aims to reduce time to market and improve product-market fit by engaging stakeholders during the earliest phases of development. However, recent staffing cuts at the FDA’s device center, affecting over 200 employees, are already causing delays in regulatory reviews—especially for complex technologies like AI-enabled devices. These dynamics make early engagement and strategic planning more critical than ever.

Approach:

• Design for adoption (not just approval) with a user-centric mindset focused on usability, integration, and a roadmap grounded in real-world behavior
• Synthesize data with AI to create dynamic personas and journey maps that evolve with user behavior and help validate assumptions before they become launch risks
• Accelerate IP and regulatory pathway understanding with AI, and establish a QMS aligned with ISO 13485
• Align product strategy with go-to-market and lifecycle management
• Integrate AI into Agile practices to accelerate backlog prioritization and refinement, sprint planning, estimation, metrics, and retrospectives
• Use Product IQ to assess digital product readiness across seven dimensions of product excellence
• Apply AI AMP to identify where AI can accelerate planning, design, and operations

Outcome: Faster time to value, fewer post-launch surprises, and stronger cross-functional alignment.

Design & Development: Build for usability, scalability, and intelligence

This phase transforms ideas into prototypes that deliver long-term value through usability, scalability, and performance. Medical device manufacturers are increasingly embedding AI and machine learning into product design to enhance clinical outcomes and user experience. Journey maps and personas validate design decisions against real clinical workflows and emotional needs, ensuring relevance and ease of use. Agile ceremonies, such as sprint reviews and backlog grooming, incorporate feedback from these tools to prioritize the features that matter most to users.

Industry Insight:

According to the FDA, AI is transforming healthcare by enabling devices to learn from real-world use, adapt to user needs, and deliver more personalized care. The FDA emphasizes that integrating human factors and usability principles into AI-enabled device design is essential to ensure safety and effectiveness.

Approach:

• Design with the user, not just for the user
• Accelerate value with secure, compliant, and modern technology and data platforms and Agile DataOps
• Intelligently automate processes and streamline systems to accelerate product development and facilitate connections
• Let personas guide your backlog and use journey maps to prioritize the features and accessibility measures that matter most
• Leverage CX AMP to rapidly prototype and validate future-state digital experiences
• Use AI-driven development to accelerate delivery and improve quality
• Leverage natural language processing (NLP) to enhance user feedback analysis by analyzing user reviews, support tickets, and survey responses
• Embed Agentic AI to enable autonomous decision-making and adaptive functionality

Outcome: More personalized, usable, and scalable products delivered with greater speed and confidence.

Engineering & Integration: Ensure seamless ecosystem fit

As devices move toward production, seamless integration becomes critical. Hospitals are increasingly unwilling to adopt devices that require complex IT overhauls. Plug-and-play interoperability with secure, real-time data exchange across systems is now a baseline expectation, especially as devices become more connected. Without this capability, provider resistance increases—particularly when integration demands manual effort or disrupts existing workflows.

As software complexity grows, combining domain expertise with AI-driven automation enables scalable, compliant development that meets rising customer expectations. Applying AI to software hazard analysis and DFMEA helps teams anticipate risks earlier, enhance mitigation strategies, and strengthen product safety. Agile methodologies support continuous improvement and cross-functional alignment, even in technical phases like integration and validation.

Industry Insight:

The FDA emphasizes that medical device interoperability is essential for improving patient care, reducing errors, and enabling innovation. As of mid-2024, the agency has authorized more than 880 AI/ML-enabled medical devices, reflecting rapid adoption of intelligent technologies across the MedTech sector. To support this growth, the FDA is expanding regulatory science tools and issuing new guidance to help the industry improve transparency, safety, and speed to market for innovations like digital twins and AI-driven diagnostics.

Approach:

• Keep the user voice alive during scale-up
• Use AI to surface insights and rapidly align engineering with evolving development and market needs
• Intelligently automate repetitive development tasks
• Leverage AI-driven tools to generate boilerplate code, suggest improvements, and automate testing and test case generation
• Develop integration frameworks with flexible APIs and real-time analytics
• Build software that’s scalable, safe, and compliant
• Partner with hospital IT vendors to reduce friction and accelerate implementation
• Offer turnkey integration kits and in-house engineering support

Outcome: Higher provider adoption, reduced implementation costs, and stronger ecosystem trust.

Regulatory Approval & Commercialization: Accelerate go-to-market and build trust through transparency

Gaining regulatory clearance and launching successfully requires more than precision—it demands trust. As MedTech companies shift from traditional sales and marketing models to AI-powered precision, transparency becomes essential. Explainable AI (XAI) frameworks help demystify decision-making, making it easier for clinicians and patients to trust AI-generated insights. To build lasting confidence, health equity must be embedded from the start.

Industry Insight:

GenAI and digital twins are redefining how MedTech companies engage regulators, launch products, and educate stakeholders. But to realize their full potential, these tools must be built on inclusive, validated data. Without it, they risk reinforcing bias and producing unequal outcomes, especially for underrepresented populations.

Mapping the buyer journey, including procurement cycles and value-based care priorities, enables more tailored and equitable messaging. Meanwhile, simulated clinical scenarios and device performance visualizations, when validated against real-world data, can enhance regulatory submissions, clinician training, and stakeholder engagement. As AI adoption grows—86% of healthcare organizations already use it, according to a 2024 HIMSS survey—MedTech teams are channeling this momentum into smarter, more inclusive go-to-market strategies. They’re improving launch success rates, accelerating market access, and boosting commercial efficiency and ROI with predictive analytics and AI-enhanced sales forecasting and lead scoring. Still, consumer trust remains a barrier. Addressing concerns around privacy, bias, and transparency through ethical AI governance is essential to building lasting confidence.

Approach:

• Prioritize responsible, ethical AI governance
• Map the buyer journey to accelerate adoption and address equity concerns
• Personalize market strategies using claims data, EMRs, and competitive insights, being sure to ensure diverse representation
• Create digital twins for clinician training and patient education, grounded in real-world, inclusive data
• Simulate health economics models to support value-based pricing
• Use predictive analytics to optimize launch success and sales rep targeting while monitoring for bias
• Tailor messaging with AI-curated provider and payer personas that reflect diverse care settings
• Leverage genAI and secure, trusted data sources to accelerate the creation of compliant content aligned to your go-to-market priorities, target audience, and brand voice
• Build intuitive, guided selling experiences that foster transparency
• Enhance engagement with virtual demos and personalized product experiences that build trust

Outcome: Faster approvals, stronger engagement, and more equitable, trustworthy value propositions.

Post-Market Surveillance & Support: Secure and scale remote devices

After launch, continuous monitoring is essential to ensure safety, compliance, and customer satisfaction. As connected and remote devices proliferate, so do the risks. Usability, cybersecurity, and lifecycle management have become strategic imperatives. Journey sciences inform support and training strategies, especially for digital devices where user experience directly impacts satisfaction and safety. Agile Ops enables faster responses to post-market signals, supporting continuous improvement and building trust. Closing the loop with real-world insights strengthens long-term product performance.

Industry Insight:

Cybersecurity incidents have rendered medical devices and hospital networks inoperable, disrupting patient care across healthcare systems in the U.S. and globally. In response, the FDA has emphasized that cybersecurity is a core component of post-market safety and quality system regulation. However, recent staffing cuts at the FDA’s device center are straining the agency’s ability to respond to emerging threats and manage post-market oversight. These challenges raise concerns about delayed responses to vulnerabilities in remote and connected devices.

Approach:

• Deploy predictive maintenance and dynamic error resolution using AI-driven analytics to detect and address issues before they impact patient care
• Integrate cybersecurity-by-design principles into device architecture and post-market monitoring, aligned with FDA guidance and ISO27001 standards
• Ensure secure, scalable remote monitoring across hospital networks and cloud environments
• Enable end-of-life prediction and lifecycle management through real-time data insights and automated compliance tracking

Outcome: Safer, more reliable care and a stronger, trust-sustaining position in the connected health ecosystem.

Retirement & Disposal: Lead end-of-life planning amid shifting priorities

While less emphasized in FDA guidance, end-of-life planning is increasingly important in sustainability and healthcare technology management (HTM) context.

Industry Insight:

HTM encompasses the operational, maintenance, and end-of-life responsibilities managed by clinical engineering or biomedical departments within healthcare facilities. The FDA’s draft guidance on AI-enabled medical devices highlights the importance of lifecycle documentation—including end-of-life planning—as part of a comprehensive Total Product Life Cycle (TPLC) strategy.

However, the current administration’s rollback of ESG priorities has slowed federal momentum around healthcare sustainability. This shift places greater responsibility on MedTech firms and healthcare systems to lead end-of-life planning and environmental stewardship independently. At the same time, consumer expectations for sustainability are rising. Patients and providers increasingly demand that manufacturers adopt circular economy principles, reduce e-waste, and demonstrate environmental accountability.

Approach:

• Plan for product discontinuation and regulatory notifications using structured documentation workflows that ensure traceability and compliance
• Support HTM teams with digital documentation, training, and integration into asset and lifecycle management systems
• Partner with sustainability-certified vendors to enable responsible recycling, reuse, and upcycling of devices
• Embed sustainability into lifecycle strategy by aligning with internal sustainability goals
• Compensate for reduced federal ESG support by proactively tracking environmental impact and reporting sustainability metrics to stakeholders

Outcome: Stronger environmental stewardship, regulatory resilience, and consumer trust through independently led, data-driven end-of-life planning and sustainable device decommissioning.

Lead Across the Lifecycle With Intelligence

MedTech success in 2025 demands more than innovation. It requires intelligence. By aligning digital investments with the full lifecycle of your products, you can:

• Scale smarter with AI-driven product development
• Integrate faster with seamless platform readiness
• Lead with intelligence through Agentic AI and responsible governance

From insight to impact, our industry, platform, data, and AI expertise help organizations modernize systems, personalize engagement, and scale innovation. We deliver AI-first transformation that drives engagement, efficiency, and loyalty throughout the lifecycle – from product development to commercial success.

• Business Transformation: Deepen collaboration, integration, and support throughout the value chain, including channel sales, providers, and patients.
• Modernization: Streamline legacy systems to drive greater connectivity, reduce duplication, and enhance employee and consumer experiences.
• Data + Analytics: Harness real-time data to support business success and to impact health outcomes.
• Consumer Experience: Support patient and consumer decision making, product usage, and outcomes through tailored digital experiences.

Let’s build what’s next—together. Contact us to get started.

Customer experience (CX) continues to be a defining factor in business success. In a digital-first world, even a single poor interaction can drive customers to competitors, contributing to an estimated $1.6 trillion in annual losses in the U.S. alone. On the other hand, exceptional omnichannel experiences build trust, deepen loyalty, and turn customers into lifelong advocates.

Perficient included in IDC Market Glance: Customer Experience Services, 2025

We’re proud to share that Perficient has once again been included in the category of IT Services Providers in the IDC Market Glance: Customer Experience Services, 2Q25 report (doc #US52469525, June 2025).

According to IDC, “Agentic AI and GenAI are working their way into marketing and sales technologies and services, beginning with a pragmatic focus on automating, improving and scaling existing business processes and offerings. New AI-based business models have yet to emerge, but AI is already putting existing CX services under pressure to change.”

Embracing an AI-First Future

As part of our AI-first company mission, Perficient is committed to helping organizations harness the power of artificial intelligence to revolutionize customer experiences. From the use of generative AI in content creation and virtual agents, to intelligent automation and predictive analytics, we’re enabling businesses to unlock new levels of personalization, efficiency, and growth.

Strategy Meets Innovation

Our strategists use Journey Science, a core component of our Envision Framework, to help clients identify opportunities, define a customer-centric vision, and build a prioritized roadmap for transformation. This approach ensures that every touchpoint is optimized to deliver seamless, personalized, and measurable experiences.

Operationalizing CX with Data and AI

The future of CX is rooted in customer obsession—and we help you execute that vision. By combining deep customer insights with AI-powered tools and data-driven strategies, we enable organizations to deliver extraordinary value at every stage of the customer journey.

Ready to elevate your customer experience strategy? Explore how Perficient’s AI-first approach and CX expertise can help you drive measurable results: Customer Experience + Digital Marketing Services | Perficient

The New Expectations in Medical Device Marketing

Today’s providers and patients expect more than just innovative products—they want personalized, value-driven experiences. Much of this shift is influenced by consumer experiences in other industries. From online retail to financial services, B2C brands have redefined personalization, speed, and convenience—raising expectations across all sectors, including healthcare. 

Despite this shift, many medical device marketing efforts still rely on outdated tactics that no longer meet modern demands or regulatory requirements. To stay competitive, leading medical device companies are: 

• Connecting every touchpoint
• Building seamless digital journeys
• Delivering timely, relevant, and compliant communications 

They are modernizing field marketing, improving sales support, and fostering trusted engagement at scale—with the right digital marketing platform. 

Why Salesforce Marketing Cloud?

In an industry where personalized engagement, regulatory compliance, and speed-to-market are critical, Salesforce Marketing Cloud offers the flexibility and scalability medical device companies need to stay ahead. 

As consumer brands raise the bar for digital experiences, healthcare organizations must adapt to meet these expectations. Salesforce Marketing Cloud enables you to: 

• Unify and activate first-party data across your CRM
• Segment audiences for targeted, personalized campaigns
• Automate marketing across email, ads, mobile, and web
• Deliver real-time personalization at every touchpoint
• Power ABM and lead generation strategies
• Leverage AI-driven insights to optimize spend and performance
• Increase team productivity with built-in Agentforce and AI tools 

The platform helps create meaningful, compliant, and personalized interactions with both healthcare providers and patients. 

How Perficient’s Salesforce Marketing Cloud Solution for Medical Devices Helps

With over 15 years of experience building Salesforce solutions for medical device organizations, Perficient understands the industry’s unique challenges and opportunities. 

Our Salesforce Marketing Cloud for Medical Devices solution enables you to strengthen acquisition, conversion, and nurture campaigns through better data analysis, actionable insights, and strategic execution. You can: 

• Increase conversion and adoption by educating and building relationships with healthcare professionals and patients
• Boost product sales with optimized buying journeys and enhanced sales and marketing tools
• Reduce call center volume and increase satisfaction through self-service options
• Improve adherence and outcomes by educating patients on proper device usage 

If your marketing feels disconnected or you’re struggling to prove business value, Perficient can help. Let’s modernize your marketing, elevate your engagement strategy, and make every campaign smarter. 

Clinical trial data management is critical to pharmaceutical research, yet it remains a significant challenge for many organizations. The industry faces several persistent hurdles:

Success In Action

Our tailored solution for a top-five life sciences leader integrated data from 13 sources and included bi-directional EDC integration and multiple AI models. Our deep understanding of clinical trial processes, data management, and platforms proved instrumental in delivering a solution that met—and exceeded—expectations. 

Want to know more about our approach to clinical trial data collaboration? Check out our guide on the subject.

Transform Clinical Data Review With An Expert Partner

Discover why the largest life sciences organizations – including 14 of the top 20 pharma/biotech firms, 6 of the top 10 CROs, and 14 of the top 20 medical device organizations – have counted on our world-class industry capabilities and experience with leading technology innovators. Our deep expertise in life sciences and digital technologies, including artificial intelligence and machine learning, helps transform the R&D process and deliver meaningful value to patients and healthcare professionals.

Contact us to learn about our life sciences and healthcare expertise and capabilities, and how we can help you transform your business.

Empower Healthcare With AI-Driven Insights

As we move through 2025, artificial intelligence — especially generative AI — continues to redefine how enterprises operate and compete. What began as experimentation has rapidly evolved into large-scale adoption, with organizations embedding AI into core strategies, operations, and customer experiences. From intelligent automation and predictive insights to dynamic content generation, AI is now a driving force behind innovation, agility, and growth. Today, staying ahead means not just keeping up with AI but leading with it.

AI Technical Services Landscape

Forrester’s AI Technical Services Landscape, Q2 2025 report provides an overview of 36 notable providers in the rapidly evolving AI services market. It aims to help technology leaders understand the value various vendors bring, and how those offerings align with organizational needs across industries and use cases.

Forrester defines AI Technical Services as: “The delivery capability of repeatable and scalable AI solutions, encompassing AI and data infrastructure, governance, training, and innovation.” When executed with expert partners, AI initiatives can drive significant operational and competitive advantages. We’re proud to be recognized in the Forrester AI Technical Services Landscape as a consultancy with an industry focus in the sectors of financial services, manufacturing, and pharmaceuticals and medical equipment, and a geographic focus in North America, Asia Pacific, and Latin America.

AI Capabilities and Focus Areas

We believe this recognition highlights our commitment to helping clients harness the power of AI to solve complex business challenges, creating meaningful transformation.

One of the ways we accelerate AI adoption is through our AI AMP Jumpstart, a focused, five-week engagement designed to quickly uncover and activate AI opportunities. Using our proven modeling process, we help clients explore real-world applications of machine learning, natural language processing, and cognitive technologies.

Our services go beyond development. Through our PACE framework, which emphasizes governance, AI literacy, data readiness, and responsible innovation, we help organizations lay the foundation for scalable, ethical AI solutions that last.

We’re continually expanding our expertise across key domains, including generative AI, predictive analytics, and natural language understanding, empowering our clients to innovate faster, operate smarter, and deliver better outcomes.

Ready to Take the Next Step?

Whether you’re just beginning your AI journey or looking to scale existing AI initiatives, choosing the right partner is key to getting real results. At Perficient, our team of over 300 AI experts, spanning data scientists, engineers, architects, and developers, brings the hands-on experience needed to turn potential into performance.

Contact us to explore how our expertise can accelerate your transformation.

Download the Forrester report, The AI Technical Services Landscape, Q2 2025 to learn more (link to report available to Forrester subscribers and for purchase).

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 here .