AI is reshaping industries, economies, and societies at an unprecedented pace. From powering everyday digital assistants to revolutionizing research and decision making, AI’s reach is expanding. However, as technology evolves, our understanding of what it means for AI to be truly intelligent also evolves. To build robust, adaptable, and trustworthy AI, we must look beyond technical achievements and draw from the insights of behavioural science.

Why Now?

Tasks that require more than just speed and size are now being assigned to AI. These tasks demand reasoning, flexibility, and judgment qualities traditionally associated with human cognition. As we uncover glitches, biases, and inefficiencies in even the most advanced AI, it’s clear that we need to learn from human thinking and feeling.

From Quick Responses to Wise Intelligence

Top-notch AI, particularly the large language models (LLMs), excel at delivering instant responses. But when it comes to the slower, reflective kind of thinking? They’re not so hot. That’s where we see the usual suspects like making things up, choking on the unfamiliar, and burning through resources like there’s no tomorrow.

For organizations deploying AI, these challenges have real implications:

• Reliability: Inconsistent results can erode trust and impede progress.
• Efficiency: Sometimes, we overthink the simple stuff or bail too soon on the tough jobs, and that’s just resources down the drain and chances missed.
• Risk Management: Without human oversight, AI can produce suboptimal results, biases, or damage to reputation.

To be a trusty sidekick, AI needs to level up and, at times, safely automate decisions in high-stakes areas, hitting the sweet spot of human cognition fast and slow thinking.

The Behavioural Science Advantage

Through the application of behavioural science, we can create AI that’s not only fast but also wise.

1. Human-Like Reasoning Requires Metacognition

Relying on fast, automatic, and intuitive processing, most AI models today mirror the human brain’s “System 1.” However, robust decision-making also requires “System 2″reflective, deliberate, and analytical reasoning. The true advantage for AI lies in metacognition, the ability to think about its own thinking and choose the right mode for the task.

Consider the surgeon riddle, for example. LLMs can spot the punchline when it’s there, but metacognitive controls could help AI know when to take a shortcut and when to dig deeper.

2. Building a Metacognitive Controller

Envision a metacognitive controller as a savvy companion that always selects the perfect tool for the task. With a nod to behavioral science, we can craft AI that sizes up a problem, spots what it doesn’t know, and opts for the best strategy.

• Quick Fact Check: The controller sends simple queries to speedy, heuristic processors.
• Complex Tasks: It uses structured reasoning and formal checks for more challenging queries.
• Uncertainty: If it’s not sure, it’ll ask for more details or check with external sources.

This clever routing not only boosts accuracy but also saves us from knee-jerk errors and senseless waiting.

3. Resource Rationality: Smarter, Not Just Harder

Efficiency is key, especially when computing resources are limited. AI should focus on smart work, not just hard work.

A recent study, for example, showed that LLMs can sometimes “overthink” simple classification tasks, resulting in less human-like decisions and extended processing times. On the flip side, they may not invest enough effort in more demanding tasks. By embedding resource rationality, an explicit trade-off between expected accuracy and computational cost is made. AI can become more efficient and trustworthy.

4. Rewarding Wisdom, Not Just Outputs

Thanks to extensive human input, AI is trained to produce what we desire. But hey, behavioural science tells us to shake things up. We should be schooling AI in the art of wisdom, being humble, dealing with the unknown, listening to different voices, and knowing when to say, “You know what? You’re the expert here.”

Methods like Meta-Reinforcement Learning (MRL) or Process Reward Models (PRM) can reward these metacognitive skills, encouraging AI to reason wisely—expressing uncertainty when justified, seeking other viewpoints, and challenging its own conclusions.

5. Neurosymbolic AI: Integrating Fast and Slow Thinking

The future of AI may lie in hybrid architectures that combine pattern-matching neural networks (System 1) with rule-based, symbolic systems (System 2). Behavioral science provides a blueprint for how these systems should work together, not as separate entities but as a spectrum with learning flowing both ways.

For example, human expertise involves refining slow, deliberate analyses into fast, intuitive responses. Neurosymbolic AI can use formal models to refine neural “hunches” and, conversely, guide symbolic engines toward promising paths, reducing search burdens and making logic-based reasoning more practical at scale.

As AI’s influence grows, it’s clear that we need to pair it with the wisdom of behavioural science. We must move

Based on the Augment article from BIT.

Explore our AI services and capabilities at Perficient

Moments of currency change are where fortunes are made and lost. In January 2026, Bulgaria will enter one of those moments. The country will adopt the euro and officially retire the Bulgarian lev, marking a major euro adoption milestone and reshaping how investors, banks, and global firms manage currency risk in the region. The shift represents one of the most significant macroeconomic transitions in Bulgaria’s modern history and is already drawing attention across FX markets.

To understand how dramatically foreign exchange movements can shift value, consider one of the most famous examples in modern financial history. In September 1992, investor George Soros, “the man who broke the British Bank,” bet against the British pound, anticipating that the UK’s exchange rate policy would collapse. The resulting exchange rate crisis, now known as Black Wednesday, became a defining moment in forex trading and demonstrated how quickly policy decisions can trigger massive market dislocations.

By selling roughly $10 billion worth of pounds, his Quantum Fund earned ~$1 billion in profit when the currency was forced to devalue. The trade earned Soros the nickname “the man who broke the Bank of England” and remains a lasting example of how quickly confidence and capital flows can move entire currency systems.

GBP/USD exchange rate from May 1992 to April 1993, highlighting the dramatic plunge during Black Wednesday. When George Soros famously shorted the pound, forcing the UK out of the ERM and triggering one of the most significant currency crises in modern history

To be clear, Bulgaria is not in crisis. The Soros example simply underscores how consequential currency decisions can be. Even when they unfold calmly and by design, currency transitions reshape the texture of daily life. The significance of Bulgaria’s transition becomes more clear when you consider what the lev has long represented. Safety. Families relied on it through political uncertainty and economic swings, saved it for holidays, passed it down during milestones, and trusted it in moments when little else felt predictable. Over time, the lev became a source of stability as Bulgaria navigated decades of change and gradually aligned itself with the European Union..

Its retirement feels both symbolic and historic. But for global markets, currency traders, banks, and companies engaged in cross border business, the transition is not just symbolic. It introduces real operational changes that require early attention. This article explains what is happening, why it matters, and how organizations can prepare.

Some quick facts help frame the scale of this shift.

Map of Bulgaria

Bulgaria has a population of roughly 6.5 million.

The country’s GDP is about 90 billion U.S. dollars (World Bank, 2024)

Its largest trade partners are EU member states, Turkey, and China.

Why Bulgaria Is Adopting the Euro

​​Although the move from the Lev to the Euro is monumental, many Bulgarians also see it as a natural progression. ​​When Bulgaria joined the European Union in 2007, Euro adoption was always part of the long-term plan. Adopting the Euro gives Bulgaria a stronger foundation for investment, more predictable trade relationships, and smoother participation in Europe’s financial systems. It is the natural next step in a journey the country has been moving toward slowly, intentionally, and with growing confidence. That measured approach fostered public and institutional trust, leading European authorities to approve Bulgaria’s entry into the Eurozone on January 1, 2026 (European Commission, 2023; European Central Bank, 2023).

How Euro Adoption Affects Currency Markets

Bulgaria’s economy includes manufacturing, agriculture, energy, and service sectors. Its exports include refined petroleum, machinery, copper products, and apparel. It imports machinery, fuels, vehicles, and pharmaceuticals (OECD, 2024). The Euro supports smoother trade relationships within these sectors and reduces barriers for European partners.

Once Bulgaria switches to the Euro, the Lev will quietly disappear from global currency screens. Traders will no longer see familiar pairs like USD to BGN or GBP to BGN. Anything involving Bulgaria will now flow through euro-based pairs instead. In practical terms, the Lev simply stops being part of the conversation.

For people working on trading desks or in treasury teams, this creates a shift in how risk is measured day to day. Hedging strategies built around the Lev will transition to euro-based approaches. Models that once accounted for Lev-specific volatility will have to be rewritten. Automated trading programs that reference BGN pricing will need to be updated or retired. Even the market data providers that feed information into these systems will phase out Lev pricing entirely.

And while Bulgaria may be a smaller player in the global economy, the retirement of a national currency is never insignificant. It ripples through the internal workings of trading floors, risk management teams, and the systems that support them . It is a reminder that even quiet changes in one part of the world can require thoughtful adjustments across the financial landscape.

Combined with industry standard year-end code-freezes, Perficient has seen and helped clients stop their Lev trading weeks before year-end.

The Infrastructure Work Behind Adopting the Euro

Adopting the Euro is not just a change people feel sentimental about. Behind the scenes, it touches almost every system that moves money. Every financial institution uses internal currency tables to keep track of existing currencies, conversion rules, and payment routing. When a currency is retired, every system that touches money must be updated to reflect the change.

This includes:

• Core banking and treasury platforms
• Trading systems
• Accounting and ERP software
• Payment networks, including SWIFT and ISO 20022
• Internal data warehouses and regulatory reporting systems

Why Global Firms Should Pay Attention

If the Lev remains active anywhere after the transition, payments can fail, transactions can be misrouted, and reconciliation issues can occur. The Bank for International Settlements notes that currency changes require “significant operational coordination,” because risk moves across systems faster than many institutions expect. 

Beyond the technical updates, the disappearance of the Lev also carries strategic implications for multinational firms. Any organization that operates across borders, whether through supply chains, treasury centers, or shared service hubs, relies on consistent currency identifiers to keep financial data aligned. If even one system, vendor, or regional partner continues using the old code, firms can face cascading issues such as misaligned ledgers, failed hedging positions, delayed settlements, and compliance flags triggered by mismatched reporting. In a world where financial operations are deeply interconnected, a seemingly local currency change can ripple outward and affect global liquidity management and operational continuity.

Many firms have already started their transition work well in advance of the official date in order to minimize risk. In practice, this means reviewing currency tables, updating payment logic, testing cross-border workflows, and making sure SWIFT and ISO 20022 messages recognize the new structure. 

Trade Finance Will Feel the Change

For people working in finance, this shift will change the work they do every day. Tools like Letters of Credit and Banker’s Acceptances are the mechanisms that keep international trade moving, and they depend on accurate currency terms. If any of these agreements are written to settle in Lev, they will need to be updated before January 2026.

That means revising contracts, invoices, shipping documents, and long-term payment schedules. Preparing early gives exporters, importers, and the teams supporting them the chance to keep business running smoothly through the transition.

What Euro Adoption Means for Businesses

Switching to the Euro unlocks several practical benefits that go beyond finance departments.

• Lower currency conversion costs
• More consistent pricing for long-term agreements
• Faster cross-border payments within the European Union
• Improved financial reporting and reduced foreign exchange risk
• Increased investor confidence in a more stable currency environment

Because so much of Bulgaria’s trade already occurs with Eurozone countries, using the Euro simplifies business operations and strengthens economic integration.

How Organizations Can Prepare

The most important steps for institutions include:

1. Auditing systems and documents for references to BGN
2. Updating currency tables and payment rules
3. Revising Letters of Credit and other agreements that list the Lev
4. Communicating the transition timeline to partners and clients
5. Testing updated systems well before January 1, 2026

Early preparation ensures a smooth transition when Bulgaria officially adopts the Euro. Ensure that operationally you’re prepared to accept Lev payments through December 31, 2025, but given settlement timeframes, prepared to reconcile and settle Lev transactions into 2026.a

Final Thoughts

The Bulgarian Lev has accompanied the country through a century of profound change. Its retirement marks the end of an era and the beginning of a new chapter in Bulgaria’s economic story. For the global financial community, Bulgaria’s adoption of the Euro is not only symbolic but operationally significant.

Handled thoughtfully, the transition strengthens financial infrastructure, reduces friction in global business, and supports a more unified European economy.

References 

Bank for International Settlements. (2024). Foreign exchange market developments and global liquidity trends. https://www.bis.org

Eichengreen, B. (1993). European monetary unification. Journal of Economic Literature, 31(3), 1321–1357.

European Central Bank. (2023). Convergence report. https://www.ecb.europa.eu

European Commission. (2023). Economic and monetary union: Euro adoption process. https://ec.europa.eu

Henriques, D. B. (2011). The billionaire was not always so bold. The New York Times.

Organisation for Economic Co-operation and Development. (2024). Economic surveys: Bulgaria. https://www.oecd.org

World Bank. (2024). Bulgaria: Country data and economic indicators. https://data.worldbank.org/country/bulgaria

“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.

Healthcare leaders face a critical mandate: deliver seamless, patient-centered experiences while boosting efficiency and measurable outcomes. Lasting transformation happens when strategy, data, technology, and experience converge—and Rochester Regional Health’s recent recognition proves what’s possible.

We’re proud to share that our work with Rochester Regional Health earned two 2025 eHealthcare Leadership Awards and the Sitecore Digital Impact Award for Business Impact, underscoring the power of strategic digital investments in healthcare.

Why This Matters for Healthcare Leaders

Patients expect frictionless access to care, personalized experiences, and real-time engagement. Our recent Access to Care research outlines how these priorities drive competitive advantage for healthcare organizations. More than 50% of respondents who encountered friction when scheduling an appointment took their care elsewhere. That’s not just lost revenue—it’s lost continuity, lost data, and lost trust. To deliver on consumers’ expectations, leaders need a unified digital strategy that connects systems, streamlines workflows, and gives consumers simple, reliable ways to find and schedule care.

Rochester Regional Health and Perficient embraced this challenge, consolidating dozens of disparate websites into one seamless experience and implementing a mobile-first design that mirrors the simplicity of modern commerce. The results speak volumes.

Business Impact

Sitecore Digital Impact Award: Business Impact | Recognized for removing friction and focusing on experience, this award demonstrates how digital transformation accelerates growth and improves care access. Sitecore shares, “The Business Impact winners remind us that digital transformation only matters when it delivers real results for people and the business. Rochester Regional Health and Perficient turned 24 disconnected websites into one seamless experience, helping patients get to the care they need faster.… These stories show what happens when great brands remove friction, focus on experience, and grow because of it.” | Learn more about this award

Best Mobile Experience

eHealthcare Leadership Award, Gold, Healthcare System | A mobile-first redesign delivers intuitive navigation, regional personalization, and real-time appointment scheduling, boosting accessibility, engagement, and conversions. This award recognizes the best examples of healthcare mobile experience, whether via installed app or mobile website via a browser. Judges evaluated usability, design, branding, quality of content, clarity of purpose and consumer ratings. | Learn more about this award

Best Use of Artificial Intelligence in Healthcare Marketing

eHealthcare Leadership Award, Distinction, Healthcare System | Rochester Regional’s new site offers smart search, dynamic filters, and real-time booking making it easy for patients and their caregivers to discover and schedule care that best supports patient needs. It drove a 26% boost in appointment scheduling and $79K+ monthly saving in call center costs. This category awarded the successful application of AI and Machine Learning (ML) to achieve marketing goals, including customer acquisition and retention, online content personalization, digital experience, understanding user intent, physician search, call center optimization, and more. | Learn more about this award

What This Signals for 2026

The next phase of digital priorities will focus on scalable personalization, AI-driven operational efficiency, and connected ecosystems that extend beyond the hospital walls. Leaders are investing in platforms that integrate clinical, financial, and consumer data to deliver proactive care and predictive insights. Digital-first models, intelligent scheduling, and automation will become standard. Organizations that build flexible, cloud-based architectures now and leverage AI for personalization and resource optimization position themselves to improve access, reduce costs, and strengthen patient loyalty in a competitive market.

Explore the full case study to see how Rochester Regional Health partnered with Perficient to make this vision a reality.

Reimagine Access to Care with Confidence

These awards validate the impact of our approach and reinforce the urgency of digital innovation as a strategic imperative for healthcare leaders.

More importantly, it reflects what we’re hearing across the industry: the need to prioritize consumer-centric transformation is accelerating. Leaders are looking for solutions that improve access, personalize engagement, and deliver measurable outcomes for both patients and the business.

From insight to impact, our healthcare expertise equips leaders to modernize, personalize, and scale care. We drive resilient, AI-powered transformation to shape the experiences and engagement of health care consumers, streamline operations, and improve the cost, quality, and equity of care.

• 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.

We are trusted by leading technology partners, mentioned by analysts, and Modern Healthcare consistently ranks us as one of the largest healthcare consulting firms.

Discover why we’ve 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.

A second recognition underscores the urgency of consumer-centric transformation in healthcare.

Healthcare providers and insurers face mounting pressure to deliver affordable, convenient, and digitally enabled care. Our award-winning research reveals that digital front door strategies are now essential for meeting healthcare consumer experience expectations. Organizations that fail to adapt risk losing trust, loyalty, and revenue.

About the Award

The MarCom Awards honor excellence in marketing and communication, attracting more than 7,000 entries annually from dozens of countries. Winning a Platinum Award—the competition’s highest honor—signals global recognition for strategic insight and creative execution.

This achievement follows our earlier dotCOMM Award, further reinforcing the study’s impact and credibility.

Key Findings from Our Access to Care Research Study

Our proprietary study analyzed responses from 1,000+ U.S. healthcare consumers to uncover how digital experiences, affordability, and convenience shape patient expectations and influence provider choice.

Critical insights emerged for healthcare organizations (HCOs):

1. Digital-first access is non-negotiable. Health care consumers increasingly expect commerce-like experiences when traditional care options fall short.
2. Access is a journey, not a touchpoint. Interconnected decisions and barriers affect patient satisfaction, caregiver burnout, and Medicare star ratings.
3. Consumer-centric transformation drives outcomes. Organizations that prioritize digital access improve health outcomes, loyalty, and financial performance.

That means healthcare leaders must act now to:

• Reduce friction in care delivery through integrated digital experiences.
• Align investments with consumer expectations to protect market share.
• Improve quality metrics that directly impact reimbursement and reputation.

“With healthcare consumers setting higher expectations and becoming increasingly active in their care journeys, our research reveals a clear need for healthcare providers and insurers alike to embrace and adopt a digital-first mindset. Organizations that invest in technologies like AI and intuitive digital experiences that ease access to care are not only meeting patient and member demands but also positioning themselves for long-term growth and competitive advantage.” — Hari Madamalla, Senior Vice President, Perficient

Bottom line: Digital access must be viewed as a core business strategy that drives better health outcomes and ensures sustainable growth.

Reimagine Access to Care with Confidence

This dual recognition from MarCom and dotCOMM amplifies the visibility and credibility of our research on digital access to care. These awards validate the strength of our communication strategy and reinforce the relevance of our insights—helping elevate the conversation around digital access as a strategic imperative.

More importantly, it aligns with what we’re hearing directly from healthcare leaders: the urgency to prioritize consumer-centric transformation is real and growing.

From insight to impact, our healthcare expertise equips leaders to modernize, personalize, and scale care. We drive resilient, AI-powered transformation to shape the experiences and engagement of health care consumers, streamline operations, and improve the cost, quality, and equity of care.

• 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.

We are trusted by leading technology partners, mentioned by analysts, and Modern Healthcare consistently ranks us as one of the largest healthcare consulting firms.

Discover why we’ve 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.

In any long-term enterprise AI deployment, you eventually hit a critical trade-off between model capability and operational performance. This was precisely the challenge we faced with Scarlett, Perficient ‘s internal chatbot that has been operating 24×7 for the better part of a decade. While leveraging a powerful model like GPT-5-mini provided the quality of responses we needed, the associated inference latency (peaking at nearly 20 seconds) was becoming a significant friction point for our users.

When you’re architecting a solution meant to increase productivity, a 20-second delay is more than an inconvenience; it’s a barrier to adoption. We needed a solution that could maintain response quality while drastically improving performance. This led us to test the preview of Microsoft Azure ‘s AI Foundry Model Router. Our goal was to see if this new orchestration layer could intelligently route traffic to mitigate our latency problem.

The Architectural Shift: From Single Endpoint to Intelligent Orchestration

Our previous architecture was straightforward: Scarlett’s application logic made calls directly to a provisioned Azure OpenAI gpt-5-mini endpoint. This is a standard pattern, but it means every single query, simple or complex, bears the full performance and cost overhead of that one large model.

The Model Router introduces a new abstraction layer. Instead of calling a specific model, we now direct all traffic to the Model Router’s single endpoint. Under the hood, the router analyzes the incoming prompt and routes it to the most appropriate model from a predefined pool.

The implementation was a simple change of endpoint, but the result was a fundamental shift in how our requests were processed. The data from the first few days of operation was compelling. We saw a dramatic and immediate reduction in end-user latency, from a volatile 10-20 seconds down to a stable 3-5 seconds.

This wasn’t just a marginal improvement; it was a greater than 50% reduction in average response time, which directly translates to a better user experience.

Analyzing the Routing Logic and Business Impact

Digging into the metrics, we could see the “how” behind this performance gain. Over thousands requests, the router distributed the load as follows:

• 85% of requests were routed to gpt-5-mini, confirming that most of our user queries still required a capable model.

• 15% of requests were offloaded to gpt-5-nano, a smaller and faster model, for simpler tasks that didn’t require the overhead of its larger counterpart.

This dynamic allocation is the key. By intelligently offloading 15% of the queries, the router frees up resources and reduces the overall system latency. This technical efficiency produced a tangible business outcome. We track a user satisfaction score for Scarlett, and in the days following the implementation, we saw a clear uptick in satisfaction and overall message volume. Employees were not only happier with the tool, they were using it more.

An Architect’s Perspective and Next Steps

The most significant takeaway from this test was that the primary benefit of the Model Router, in our case, was the dramatic improvement in performance, even more so than the potential for cost optimization. It effectively solved our latency problem without requiring complex, custom-built orchestration logic in our application.

However, its “black box” nature presents new questions for architects. While the results are excellent, we lack direct control or detailed documentation on the internal routing logic. Looking ahead, I am particularly interested in stress-testing its behavior with more complex scenarios, such as prompts containing very large system messages (+2,000 tokens) or those with extensive conversation histories (+10,000 tokens). Understanding how context size impacts the router’s model selection will be crucial for architecting the next generation of complex AI agents.

This implementation has proven to be a highly effective solution for a common enterprise challenge.

Acquia is known for its open-source roots and marketer-friendly tools, and its approach to AI reflects that ethos. As digital experience platforms evolve, Acquia is embedding agentic and Generative AI to make experience creation more accessible, intuitive, and scalable, especially for teams without deep technical resources. 

Forrester’s report, The Impact of AI on Digital Experience Platforms, discusses how vendors like Acquia are using AI to simplify workflows, automate content generation, and empower practitioners across roles — and Acquia’s recent innovations reflect its commitment to democratizing digital experience delivery. 

Supporting Marketers Behind the Scenes With Agentic AI

Launched earlier this year, Acquia Source allows marketers to build web pages using AI prompts that generate code behind the scenes and eliminate the need for developer intervention. These agentic capabilities are designed to reduce friction and accelerate time to market for digital experiences. 

Perficient DXP Practice Director Josh Hover and his team have been helping clients take advantage of these capabilities to streamline experience creation.  

Josh said, “Acquia’s agentic AI tools are helping marketers take control of experience creation. We’re working with clients to integrate these capabilities into their workflows so they can move faster without sacrificing quality.” 

Simplifying Experience Creation with GenAI

Acquia has been embedding GenAI into its platform for years. For example, it partnered with Clarifai to provide image auto-tagging. Today, however, GenAI is powering more advanced use cases. From generating page layouts to summarizing customer interactions, teams can deliver relevant experiences with less manual effort. 

Reflecting on these innovations, Josh notes how GenAI is helping teams scale without adding complexity.  

“Acquia’s GenAI features are especially valuable for teams with limited resources. We’re helping clients use these tools to scale content and streamline operations without being complicated,” he said.  

Perficient Named 2025 Gold Globe® Winner for Best Artificial Intelligence Service Provider | Learn More 

Enabling Responsible AI Adoption Across Teams

Successful AI adoption requires strong data foundations, human oversight, and change management. Acquia’s focus on accessibility makes these considerations even more critical. That’s why we partner with clients to ensure AI tools are deployed responsibly and aligned with business goals. 

We’re honored to have contributed to the research in an interview for Forrester’s report , The Impact of AI on Digital Experience Platforms. We believe it reflects our deep expertise in helping enterprise clients implement and optimize DXPs like Acquia. Whether it’s orchestrating personalized journeys or deploying intelligent agents, we’re helping brands embrace AI responsibly and effectively. 

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).  

Learn more about our Acquia expertise here.  

Contact us to learn more. 

Optimizely has always been synonymous with experimentation. Its platform empowers organizations to test, learn, and optimize digital experiences with precision. Now, with the integration of agentic and generative AI, Optimizely is taking that mission further — helping teams not only run smarter experiments but also act on insights faster and more confidently. 

Forrester’s report, The Impact of AI on Digital Experience Platforms, discusses how vendors like Optimizely are embedding AI to support content creation, campaign testing, and practitioner enablement. Optimizely’s approach stands out for its focus on agility and continuous improvement. 

Agentic AI Turns Data Into Decisions

Optimizely’s Opal provides a unified view of customer interactions, enabling marketers to make informed decisions based on real-time data. Agentic AI capabilities are layered into this experience, offering recommendations, automating workflows, and guiding practitioners through complex optimization paths. 

Perficient Optimizely Director David Lewis explains how Perficient is helping clients unlock the full potential of these tools.  

Lewis said, “We’re seeing Optimizely’s AI capabilities accelerate how teams work—whether it’s Opal generating brand-consistent content in CMP, summarizing experiment results in Web Experimentation, or surfacing personalization ideas directly in Feature Experimentation. The real advantage is that these tools are connected to the workflows practitioners already use every day, so optimization happens naturally and at scale.” 

Accelerating Content and Campaign Innovation With GenAI

Optimizely’s GenAI features are designed to support rapid iteration. From generating brand-consistent content to summarizing customer behavior and simulating campaign outcomes, GenAI is helping teams move faster without compromising quality. 

Lewis shares how Perficient is guiding clients through this transformation. 

“We’re seeing GenAI help Optimizely users move faster, whether it’s writing audience-specific content or simulating campaign outcomes. The key is integrating these tools into workflows that already drive business value,” he said.  

Perficient Named 2025 Gold Globee® Winner for Best Artificial Intelligence Service Provider | Learn More 

Building a Smarter, Safer AI Strategy

Successful AI adoption requires strong data foundations, human oversight, and change management. That’s why we work with clients to ensure these elements are in place, especially when integrating AI into experimentation and optimization workflows. 

We’re honored to have contributed to the research in an interview for Forrester’s report , The Impact of AI on Digital Experience Platforms. We believe it reflects our deep expertise in helping enterprise clients implement and optimize DXPs like Optimizely. Whether it’s orchestrating personalized journeys or deploying intelligent agents, we’re helping brands embrace AI responsibly and effectively. 

If you’re exploring how to bring Optimizely’s 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).  

Learn more about our Optimizely expertise here.  

Contact us to learn more.

Sitecore has long positioned itself as a leader in composable digital experience platforms, offering flexibility and scalability for organizations looking to tailor their tech stacks. As AI becomes a central force in experience delivery, Sitecore is embedding agentic and Generative AI capabilities to help brands personalize at scale, streamline operations, and empower practitioners. 

Forrester’s report, The Impact of AI on Digital Experience Platforms, discusses how vendors like Sitecore are evolving their platforms to support intelligent orchestration and content creation. Sitecore’s modular architecture makes it uniquely suited to adopt these innovations. 

Empowering Practitioners With Agentic AI

Sitecore’s copilot launched in late 2024 and is designed to help marketers build hyper-personalized campaigns with less manual effort. These intelligent agents assist with segmentation, content recommendations, and campaign optimization all while integrating seamlessly into Sitecore’s composable stack. 

Perficient Sitecore Consulting Principal Mark Ursino has seen growing interest in Sitecore’s agentic AI capabilities, especially among clients looking to unify customer data and reduce friction in campaign execution. 

Ursino said, ” Sitecore’s AI orchestration capability, Stream, launched in late 2024 and is helping marketers move faster and smarter. We’re working with clients to integrate these tools into their workflows so they can focus on strategy while AI handles the complexity.” 

GenAI in a Composable World

Sitecore’s GenAI features are designed to complement its modular approach. Whether generating brand-consistent content, summarizing customer interactions, or supporting A/B testing, GenAI is helping teams deliver more relevant experiences without overhauling their existing infrastructure. 

“Sitecore’s composable architecture makes it easier to plug in GenAI capabilities where they’ll have the most impact. We’re helping clients identify those opportunities and align them with measurable goals,” said Ursino.  

Perficient Named 2025 Gold Globee® Winner for Best Artificial Intelligence Service Provider | Learn More 

Building a Responsible AI Foundation

Successful AI adoption requires more than just new features. Organizations must invest in strong data foundations, human oversight, and change management. That’s why we partner with clients to ensure these pillars are in place, especially when integrating AI into modular environments like Sitecore. 

We’re honored to have contributed to the research in an interview for Forrester’s report , The Impact of AI on Digital Experience Platforms. We believe it reflects our deep expertise in helping enterprise clients implement and optimize DXPs like Sitecore. Whether it’s orchestrating personalized journeys or deploying intelligent agents, we’re helping brands embrace AI responsibly and effectively. 

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).  

Learn more about our Sitecore expertise here.  

Contact us to learn more. 

Salesforce continues to evolve its digital experience capabilities by embedding artificial intelligence across its platform. Forrester’s report, The Impact of AI on Digital Experience Platforms, discusses how vendors like Salesforce are integrating agentic AI and Generative AI (GenAI) to streamline experience operations and empower practitioners. 

With tools like  Agentforce, Salesforce is moving beyond traditional automation to deliver intelligent, connected experiences. These innovations are designed not only to enhance customer engagement but also to support the practitioners behind the scenes — enabling smarter orchestration, faster decision-making, and more efficient workflows. 

Agentic AI Unifies Data and Drives Action

Salesforce’s agentic AI capabilities are designed to simplify complex orchestration across its ecosystem. These intelligent agents help practitioners navigate vast feature sets, automate routine tasks, and unlock insights from unified customer data. 

Perficient’s Salesforce Consulting Managing Director Megan Glasow has seen growing interest in these capabilities, especially as organizations look to consolidate their tech stacks and reduce friction in experience delivery. 

“Salesforce’s consolidation of DXP components paired with tools like Agentforce is helping clients streamline orchestration and unlock hidden data. We’re guiding them through this transformation with a focus on integration and measurable outcomes,” said Glasow.  

Unlocking Business Potential With Perficient and Agentforce. Get the Guide

Using GenAI From Content to Code

Salesforce’s GenAI tools are also enabling marketers to generate personalized content, optimize campaign strategies, and even build applications with minimal coding. These capabilities help teams move faster and deliver more relevant experiences across customer journeys, but speed alone isn’t the goal. As Glasow explains, success with GenAI depends on thoughtful implementation and alignment with business strategy. 

She said, “We’re helping clients explore how GenAI can reduce manual effort and accelerate personalization. But it’s not just about speed. It’s about aligning AI with business goals and ensuring responsible use.” 

Driving Innovation with Responsible AI Adoption

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 we continue to guide clients with strategic consulting and hands-on implementation. 

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

If you’re exploring how to bring AI into your DXP strategy, we invite you to connect with us and keep the conversation going. 

See How AI Drives Business Outcomes at Dreamforce 2025

At Dreamforce 2025, teams will demonstrate how AI-powered agents and unified data accelerate decision-making, improve adaptability, and deliver measurable results. Attendees can explore how organizations turn strategy into execution through exclusive experiences, including a happy hour, a keynote watch party, a tasting event, and personalized strategy sessions. 

These events highlight how AI enables leaders to streamline operations, reduce inefficiencies, and maximize return on investment. Visit the event landing page for full details and discover how to lead with confidence in an AI-first business environment. 

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

Learn more about our Salesforce expertise here.  

Contact us to learn more. 

As digital experience platforms (DXPs) evolve, Adobe continues to lead the way in embedding artificial intelligence to help organizations deliver more personalized, efficient, and scalable customer experiences. Forrester’s report, The Impact of AI on Digital Experience Platforms, explores how vendors like Adobe are integrating agentic AI and generative AI (GenAI) to streamline experience operations and empower practitioners. 

Adobe is among the leaders in this space, offering tools that help teams simulate ideas, analyze impact, and activate personalized experiences across channels, all within a unified platform. 

Agentic AI Turns Insights Into Action

Adobe’s DXP ecosystem, including Adobe Experience Platform and Adobe Sensei, is increasingly powered by agentic AI — intelligent agents that support marketers and developers in orchestrating and optimizing customer journeys. These agents are designed to reduce manual effort, surface actionable insights, and automate tasks across the experience lifecycle. 

Perficient Adobe Consulting Principal Robert Sumner has seen firsthand how Adobe’s orchestration capabilities can be extended to deliver more seamless digital experiences across platforms. 

“Adobe’s suite enables orchestration to happen naturally across its tools, but we often help clients go further by integrating these capabilities across platforms to create a seamless journey,” said Sumner.  

Creativity Meets Efficiency With GenAI

Adobe is also at the forefront of GenAI innovation. From generating brand-consistent content to enabling marketers to build data-driven briefs and presentations within Microsoft 365 via Adobe’s copilot, GenAI is transforming how teams create and collaborate. 

Sumner mentioned that Adobe’s GenAI tools are already delivering measurable impact for clients. He said, “We’re seeing Adobe’s GenAI tools help clients scale content creation and experimentation. But success depends on aligning these capabilities with business goals and ensuring strong governance.”  

Perficient Named 2025 Gold Globee® Winner for Best Artificial Intelligence Service Provider | Learn More

Responsible AI Adoption

Adobe’s AI capabilities are powerful, but success depends on strong data foundations, human oversight, and change management. That’s why we work closely with clients to ensure these pillars are in place, enabling responsible and effective adoption of AI. 

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

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).  

Learn more about our Adobe expertise here.  

Contact us to learn more. 

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

Why This Recognition Matters

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

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

We were also listed with a focus on three industries:

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

What This Means for Perficient and Our Clients

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

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

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

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

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

Embrace the New Era of AI-First Commerce

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

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

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

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