Organizations are no longer moving to the cloud simply to update infrastructure, reduce technical debt, or improve scalability. They need modern cloud foundations that can support trusted data, responsible AI, and new ways of working across the business. 

That takes more than adding AI tools to an existing technology environment. AI requires an operating model built for continuous change, governed decision-making, and disciplined execution. Without those elements, even promising initiatives can remain stuck in experimentation. 

At Perficient, we believe cloud modernization should create a practical foundation for AI adoption. That means connecting platforms, data, engineering, governance, and customer experience so organizations can move confidently from an initial idea to production at scale. 

IDC Recognizes Perficient as a Major Player 

Perficient has been named a Major Player in the IDC MarketScape: Worldwide Cloud Professional Services in the AI Era 2026 Vendor Assessment (Doc #US54117426, September 2026). 

We believe this recognition reflects Perficient’s AI-native approach to cloud transformation, depth of proprietary cloud and AI intellectual property, industry expertise, and ability to help organizations move from strategy to production at scale. 

“Too many organizations are treating AI as a technology initiative when it’s actually an operating model shift,” said Joel Lindsey, Vice President of Cloud Modernization Platform Services, Perficient. “Without modern cloud foundations, trusted data, and disciplined governance, AI remains just an experiment. We believe being named a Major Player in this IDC MarketScape reflects our ability to help clients build AI-ready businesses through modern cloud foundations, responsible governance, and disciplined execution.” 

The point is straightforward: AI success depends on more than technology. Organizations need the cloud foundations, trusted data, governance, and operating model required to move AI beyond experimentation and into production. 

Moving Beyond Infrastructure and Modernization 

According to the IDC MarketScape, “Perficient’s key strength is the depth and operational specificity of its proprietary cloud and AI IP relative to its size, paired with a well-articulated delivery governance model.”.” 

The report also noted that “Perficient is particularly compelling for Americas-based organizations in financial services, healthcare, manufacturing, or retail that want a firm to embed senior practitioners directly in delivery rather than hand off after assessment, and for buyers that value customer-experience and product-engineering depth alongside cloud infrastructure capability.” 

That combination matters because an AI-ready business cannot be built through infrastructure alone. Organizations must also modernize the applications, experiences, data environments, governance structures, and delivery models surrounding that infrastructure. 

Perficient brings those capabilities together. We help organizations modernize technology, unlock the value of their data, and build AI-ready businesses through platform, engineering, and consulting expertise. 

Turning AI Strategy Into Production 

Moving AI into production requires organizations to make complex decisions about architecture, data, security, governance, cost, and business value. Committing to broader transformation without first establishing the right foundation creates unnecessary risk. 

Perficient helps clients establish clear priorities, validate ideas, and build confidence before expanding their investments. 

Our teams work across AWS, Microsoft Azure, Google Cloud, hybrid environments, and leading AI platforms, including Anthropic. We apply proprietary cloud and AI intellectual property to help clients move from strategy to production with greater speed, consistency, and confidence. 

Just as important, our senior practitioners remain engaged in delivery. We do not stop at assessment or hand over a roadmap without a practical path forward. We connect strategy to execution and build governance into the process from the beginning. 

Cloud That Supports an AI-Ready Business 

We view this recognition as evidence of Perficient’s sustained focus on helping clients create measurable value from cloud and AI investments. 

The next stage of modernization will not be defined by how many AI pilots an organization launches. It will be defined by how effectively the organization puts AI into production, governs it responsibly, and uses it to improve how the business operates. 

Modern cloud foundations make that possible. Trusted data, disciplined governance, and embedded execution make it sustainable. 

To learn more about Perficient’s cloud professional services, you can access the News Release for more details about Perficient’s recognition as a Major Player. 

The post Perficient Recognized as a Major Player in IDC MarketScape for Cloud Professional Services in the AI Era appeared first on Perficient Blogs.

AI agents are moving past the demo stage. Revenue leaders now face a harder test: proving these tools can improve pipeline, accelerate sales, and help teams serve customers at scale. 

Marketing teams need to turn engagement signals into growth. Sellers need more time for customer conversations. Service teams need to scale without weakening the customer experience. Another tool in an already crowded technology stack won’t solve these challenges. 

Results depend on how agents fit into the way teams make decisions and get work done. Agents deliver value when they are embedded in workflows where people make decisions and take action. That requires more than connecting an agent to CRM. It requires trusted data, clear processes, human oversight, defined performance measures, and a plan for adoption after deployment. 

Forrester Examines Where AI Agents Deliver Results in CRM 

Perficient was included in Forrester’s Real-World Use Cases for AI Agents in CRM report, which outlines common applications for AI agents across marketing, sales, and customer service. 

The report focuses on how organizations are using AI agents in CRM today.  

For leaders deciding where to invest, useful questions to consider include where agents work today, what conditions make them effective, and how organizations can turn early gains into sustained business performance. 

An SDR Agent Moves Qualified Leads Forward Faster  

A Perficient client example cited by Forrester shows how an AI agent can improve the path from lead to revenue. 

Our team worked with a technology provider serving businesses in specialized vertical markets to improve lead qualification and account research. A production-ready sales development representative agent helped their team prioritize the right leads, streamline research, and act on opportunities sooner. 

Within four weeks, the agent generated more pipeline and reduced lead-to-revenue time by 83%, from 56 days to nine days. 

The results came from improving the full lead process, from research and qualification through prioritization and follow-up. The agent gave the team the context needed to focus on qualified opportunities and move them forward sooner. Organizations should evaluate agentic AI investments the same way: start with the decision, workflow, or customer moment that needs to improve, then determine where an agent can make a meaningful difference. 

What Happens After an AI Agent Goes Live 

A strong use case can still stall without adoption and ongoing refinement. 

Launching an agent does not guarantee that teams will trust it, use it, or change how they work. Organizations need to monitor outputs, measure performance, gather feedback, refine agent behavior, and maintain clear guardrails. Human review remains essential, particularly when agents influence customer-facing decisions. 

Chelsea Monda, director of agentic transformation at Perficient, explained in an interview for the Forrester report why the work continues after launch: 

“We stay engaged with our clients over many weeks post-rollout to drive usage and make sure we are realizing the value promised.” 

That continued engagement helps teams build confidence in the agent, identify where adjustments are needed, and keep the technology aligned with business goals. It also creates a path for successful agents to expand into more connected workflows. 

This is where many AI programs lose momentum. Organizations treat launch as the finish line, then move to the next pilot. The result is a collection of isolated tools rather than a connected revenue system. 

Building an Agentic Front Office 

Our Agentic Front Office approach connects intelligence across CRM, collaboration platforms, business workflows, and customer-facing teams. It starts with processes, not platforms. 

We help organizations identify high-value workflows, prepare the right data and context, establish governance, define KPIs, and embed human oversight. We also support the adoption and optimization required after agents go live. 

We believe Perficient’s inclusion in the Forrester report reflects our sustained focus on making agentic AI work in real operating environments. We have tested the model internally as Customer Zero and applied those lessons with clients facing real data, process, and adoption challenges. 

 Progress in CRM AI will be reflected in better decisions, faster action, stronger customer relationships, and sustained business results. 

Explore Perficient’s Agentic Front Office approach to learn how connected intelligence, human judgment, and continuous optimization can turn AI agents into measurable front-office outcomes. 

The post Perficient Cited in Forrester Report on How AI Agents Are Delivering Results in CRM appeared first on Perficient Blogs.

In this post, I explain why evaluation and monitoring separate an agent pilot from an agent an enterprise can rely on, how most organizations handle this today, and how the Databricks approach differs. My focus is on evaluating agent behavior and output quality. Infrastructure concerns such as uptime, latency and token spend matter, but they are a separate discipline and not the subject of this post. 

What Makes Agents Hard to Judge 

Traditional software is tested against expected results. Agents do not work that way. Ask the same question twice and you may get two differently worded answers, both acceptable. Phrase it slightly differently and the agent may take an entirely different route to get there. 

Output-only testing cannot catch what goes wrong in between. An agent can be right for the wrong reason, having pulled from a stale source or from a document the user was not entitled to see. It can be right after several redundant tool calls. These checks pass but are not production quality. 

How Organizations Handle This Today 

Three categories of tooling have formed around this problem. 

Open-source evaluation frameworks: Teams run frameworks such as DeepEval and Arize Phoenix inside their test suites and CI pipelines, drawing on each library’s catalogue of scoring metrics. These are inexpensive, portable, and popular with engineering teams. They are also developer tools that produce developer artifacts. 

Commercial evaluation and observability platforms: LangSmith, Braintrust, Arize AX and Galileo combine execution tracing with automated scoring and dashboards, including scoring of live traffic. They are capable and well designed. They are also a separate platform to procure, secure and govern, holding a copy of your prompts, your responses and often your retrieved content. 

Cloud provider evaluation services: Microsoft Foundry observability, Amazon Bedrock AgentCore and Google’s Gen AI evaluation service, build evaluation into their respective stacks. These primarily suit organizations committed to a single cloud for AI workloads. 

Where the Databricks Approach Differs: Evaluation Lives with Your Data 

Databricks evaluates agents through Managed MLflow using scorers: code-based checks, and LLM judges where a language model grades the agent’s work. Built-in judges cover groundedness, relevance, safety, correctness against known answers, and whether tool calls were right and efficient. Quality rules can be written as plain sentences rather than code, so a compliance officer can author one and read the score with the reasoning attached. Domain experts label real traces in a web interface, and those judgments tune the judges toward your standards. 

These capabilities are comparable to what is available by commercial evaluation platforms and cloud service providers, but the difference is in where the evaluation lives. 

Evaluation artifacts are governed like data, because they are data. Traces, evaluation datasets, judges and expert feedback are Unity Catalog objects in Databricks sitting next to the tables the agent reads, under the same permissions, lineage and audit trail. When someone asks who was allowed to see the content an agent retrieved, there is always one answer. 

There is no new platform to clear. Every standalone evaluation platform holds a copy of your prompts, your responses, and often your retrieved documents. That copy needs its own security review, its own data processing agreement, and its own access recertification. For an organization already running on Databricks, evaluation is a capability of a platform that has been through all of that. 

The engine is open. MLflow is Apache-2.0 licensed and runs outside Databricks. MLflow also executes DeepEval, RAGAS and Phoenix judges through the same interface as its own and lets you choose which model powers a judge. Adopting it means neither abandoning metrics a team already trusts nor accepting one vendor’s idea of a grader. 

Judging the Route, Not Only the Answer 

MLflow records each agent run step by step, down to the individual model calls, retrievals and tool invocations. Evaluation can therefore examine how the agent reached its answer, scoring whether the right tools were called with the right arguments and whether the sequence was efficient. 

This is where the failures that output scoring misses become visible: the agent that quietly queried the wrong source, the one that looped on an ambiguous result, the one that reached a correct conclusion by a path nobody would have approved. It also improves the conversation with the business. Rather than debating whether an answer felt right, teams review what the agent did. 

What This Means for the Operating Model 

Agent evaluation is not a one-time activity. It’s an online feature. With Databricks Managed MLflow, production observability runs as background jobs. Live production metrics with custom LLM judge scoring for hallucination execute asynchronously and surface low-performing prompts from traces. Such edge cases can then go through a human-in-the-loop verification, agent adjustment and ultimately enriches the validation data set resulting in a continuous loop of improvement.

Some key questions to keep in mind when thinking through agent evaluation: who writes
the quality criteria, where do those criteria live, and how quickly it is possible to present the scores.

The post How Do You Know Your AI Agent Is Working? appeared first on Perficient Blogs.

Here are some of the biggest takeaways:

1. Great Careers Rarely Follow a Straight Line

One of the most refreshing parts of the conversation was the panelists’ honesty about how unpredictable career growth can be. 

Oxley shared that her path to becoming Chief Human Resources Officer was anything but planned. Beginning her career in technology services delivery, she eventually transitioned into HR after realizing her career needed to better align with her personal goals and family life. 

Reflecting on a pivotal career moment, she described returning from maternity leave only to face a reduction in force and having to make a difficult decision about her future. Looking back, she realized she carried the weight of that experience for longer than she needed to. 

That experience ultimately shaped how she thinks about leadership and personal branding today, she explained. 

Similarly, Rawat described a career fueled less by strategic planning and more by a willingness to see how things work. 

“I was driven by [a] curiosity to see how things work,” she said. “It’s been an unconventional career, but that has been driven by the fact that I’m a risk taker.” 

2. The Leadership Shift: Stop Solving Every Problem Yourself

One recurring theme was the evolution leaders must make as they advance in their careers. 

For Rawat, one of the hardest leadership lessons was learning that success wasn’t about solving every problem herself. 

“My role was no longer to fix systems, but to let go of the technical details,” she said. “[To] focus on the big picture, focus on building the culture, the trust, the alignment that actually allows the team to excel.” 

It’s a challenge many high performers face. The skills that make individuals successful contributors aren’t always the same skills required to lead teams effectively. 

Her advice? Prioritize relentlessly, delegate where appropriate, and focus energy on the few activities that will drive the greatest impact. 

“There are always 15 things that you have to address,” she said. “Figure out: These are the three things that I have to get out today.” 

3. Mentors Guide. Sponsors Advocate.

One of the most actionable discussions centered on the difference between mentors and sponsors. 

Grapenthin emphasized that while mentors and sponsors are both important, they serve different purposes. 

“Mentors are offering advice based on their own lived experiences,” she said. “Sponsors, on the other hand, are willing to use their own network and their own reputation to help champion on your behalf.” 

She encouraged attendees to think of mentors and sponsors as a “personal board of directors,” a group of trusted advisors who provide different perspectives and challenge your assumptions. 

“Everybody in the room has an equal voice,” she explained. “It’s up to you to take all of those inputs in and evaluate which things are most meaningful for you.” 

4. Change Isn’t Optional. It’s Necessary.

Rather than viewing organizational change as something to endure, the panelists challenged attendees to broaden their perspective. 

One message rang clear: “Change is constant and it’s part of survival in this industry right now.” 

Oxley encouraged attendees to zoom out and consider the broader economic, technological, and societal shifts happening around us. 

Her perspective was simple but powerful: change isn’t something organizations can avoid. It’s often what enables them to grow and remain relevant. 

Rawat shared a similar philosophy rooted in her years living and working in Silicon Valley: “Continuous beta.” 

She explained that instead of viewing change as disruption, professionals should view it as a new set of variables to optimize around. 

“We have to stop viewing sudden changes as system failures or something to fear,” she said. “We should start viewing them as new parameters to optimize around.” 

5. AI Is Accelerating Work, But Humans Still Drive Outcomes

The conversation naturally turned toward AI and its growing influence on the workplace. 

The panelists shared practical examples of how they use AI to support writing, research, analysis, presentations, HR processes, and decision-making. Rather than replacing expertise, AI is helping teams move faster and focus on higher-value work. 

One insight from Grapenthin stood out: “Content on page is nothing until it comes to life in some sort of business outcome.” 

As marketers, strategists, and business leaders generate work more efficiently, the real differentiator becomes execution and impact. 

The panelists also emphasized maintaining a human-in-the-loop mindset, ensuring outputs are reviewed, refined, and connected to meaningful business outcomes. 

Running Your Own Race 

To close the conversation, the panelists reflected on the advice they would give their younger selves. 

Rawat’s answer was simple, but powerful. 

“Run your own race.” 

In an era where comparison is constant and social media makes it easy to focus on what others are doing, her message was a reminder that everyone’s path is different. 

“You are a unique individual,” she said. “You were brought onto this planet with a different plan. You have the agency to go set that plan and that course of action.” 

Final Thoughts 

The conversation reinforced something many professionals need to hear: leadership is not about having all the answers. It’s about staying curious, embracing change, building relationships, learning from setbacks, and continuing to grow. 

Perhaps Oxley summarized it best when discussing what future leaders will need most: 

“If something’s hard and it’s worth doing, it’s supposed to be hard.” 

Whether the topic was career growth, mentorship, AI adoption, or leading through uncertainty, the panel’s message was consistent: meaningful growth rarely happens in comfort zones. Success isn’t defined by a perfect path. It’s defined by how you navigate the challenges, opportunities, and changes along the way. 

The post Run Your Own Race: Leadership Lessons From Perficient’s Women in Technology Panel appeared first on Perficient Blogs.

These channels open new paths to purchase, but each one changes the cost and level of control involved in reaching customers. 

Many organizations are responding by testing new channels as quickly as possible. But showing up everywhere is not a strategy. Each new environment brings different costs, operating requirements, data dependencies, and levels of customer control. Without a clear economic framework, channel expansion can increase visibility while reducing margins. 

The strategic question is which channels can produce profitable growth.  

New Channels Bring New Operating Requirements 

For years, expanding into a new channel followed a familiar logic: reach more customers and generate more revenue.  But the equation changes as algorithms and large language models influence which products buyers discover, they compare their options, and which brands make the shortlist. 

Every distributed commerce environment plays by different rules. Answer engines reward authoritative content and structured product data. Social platforms reward engagement and algorithmic relevance. Marketplaces favor competitive pricing, fulfillment performance, and positive ratings. Connected devices prioritize frictionless transactions, often with less direct brand visibility. 

The economics and capabilities that drive growth in one environment may not work in another. 

Organizations need to look beyond  new storefronts or technical integrations.  Sustained growth depends on  strong product data, adaptable content operations, clear governance, reliable measurement, and an operating model capable of supporting each channel after launch. Modernization means strengthening these foundations, then investing in the channels where they can create lasting value. 

Perficient Participated in Forrester’s Research 

Against this backdrop, Perficient was among the organizations interviewed for Forrester’s report, Distributed Commerce Strategy: Algorithms And LLMs Become Sellers, published in June 2026. 

The report examines how social platforms, answer engines, and connected devices are creating new paths to purchase outside brand-owned sites.  

It also explores how algorithms and AI agents shape product discovery and turn customer intent into purchases  with fewer steps than traditional buying journeys. 

Participating in these environments can require expanded content operations, make attribution harder to track, and give third-party systems more influence over how a brand and its products are represented. 

 These tradeoffs need to be addressed before launching, not after a channel is already in market. 

Treat Channels as an Investment Portfolio 

Perficient’s participation in the research reflects the questions we  help organizations address: 

• Where should the business compete?

• What capabilities will each environment require?  

• How much control will the organization retain?

• Do the economics justify continued investment?

The answers will differ by channel. 

A marketplace initiative may increase revenue while compressing margins and limiting access to customer data. An answer-engine strategy may improve product visibility while requiring sustained investment in structured data, content quality, and governance. Social commerce may create demand while increasing dependence on algorithms the organization does not control. 

Distributed commerce should be managed as an investment portfolio, not a channel checklist. 

Leaders should evaluate each opportunity based on revenue potential, margin impact, operational complexity, customer relevance, governance requirements, and ownership of the customer relationship. Before launching a pilot, they should establish success criteria, decision points, and exit conditions. 

A disciplined strategy defines where to invest, when to scale, and how each channel can support profitable growth. 

Turning Distributed Commerce Strategy into Action 

Perficient helps organizations connect commerce strategy to the capabilities required to execute it. We modernize product and customer data and structure content for AI-powered discovery. We also integrate commerce platforms with enterprise systems and establish governance and measurement models that guide investment decisions. 

We also help teams pressure-test new opportunities before committing to broader investment. The result is a model that can generate measurable value, operate efficiently, and adapt as platforms, algorithms, and customer behavior change. 

Our participation in Forrester’s research reflects a sustained focus on how AI, data, content, and platform ecosystems are reshaping commerce. It also reinforces a practical reality: more distribution does not automatically create more value. 

Organizations that build a sustainable distributed commerce model will be positioned to respond as new channels emerge and customer expectations evolve.  

For a deeper look at how algorithms and LLMs are changing product discovery and buying, access, Forrester’s Distributed Commerce Strategy: Algorithms And LLMs Become Sellers. 

The post Perficient Included in Forrester Research on the Rise of Profitable Distributed Commerce  appeared first on Perficient Blogs.

Migrating dashboards, reports, and calculations may satisfy a short-term platform requirement, but it rarely solves the challenges that create long-term friction: inconsistent metrics, fragmented business definitions, limited data lineage, and duplicated logic across tools. 

True modernization requires a different approach. Instead of treating migration as a conversion exercise, organizations should view it as an opportunity to establish a common semantic foundation that supports analytics, governance, and AI innovation across the enterprise.

The Challenge: Migration Doesn’t Eliminate Fragmentation 

Many organizations operate multiple BI platforms that have evolved over years of growth, acquisitions, and technology changes. Business logic often becomes embedded within reports, dashboards, and calculations across numerous systems. 

As a result, organizations frequently encounter challenges such as: 

• Multiple definitions of the same KPI 

• Inconsistent calculations across reporting environments 

• Limited visibility into data lineage 

• Difficulty adopting new analytics platforms 

• Increased risk during modernization initiatives 

Simply moving content from one BI tool to another transfers much of this complexity to a new environment. 

Without addressing the underlying semantic layer, fragmentation persists and organizations may find themselves repeating the same migration effort during the next technology transition. 

A Better Approach: Build a Common Semantic Model 

A more sustainable modernization strategy starts with creating a platform-agnostic semantic model that captures the structure, business logic, and relationships that underpin reporting assets. 

Rather than focusing solely on report conversion, this approach extracts and standardizes critical metadata, including: 

• Data tables and relationships 

• Business logic and metric definitions 

• Calculated columns and measures 

• Business terminology 

• Field-level lineage from source to report 

By consolidating these elements into a common semantic foundation, organizations create a reusable asset that can support multiple analytics platforms over time. 

Supporting Today’s Platforms and Tomorrow’s Strategy 

Modern analytics ecosystems increasingly rely on cloud data platforms such as Snowflake, Databricks, Microsoft Fabric, AWS Redshift, and Google BigQuery to drive innovation and scale. 

However, technology platforms alone do not solve data consistency challenges. 

Successful analytics programs depend on trusted business definitions, governed metrics, and traceable lineage. Without these elements, organizations risk recreating the same reporting inconsistencies that existed before modernization. 

A common semantic model helps address this challenge by providing: 

• Standardized business definitions 

• Consistent metric calculations 

• Reusable governance structures 

• Improved data lineage and transparency 

• A foundation for future platform adoption 

This enables organizations to modernize their analytics environments while maintaining flexibility to support future technology investments. 

Migration vs. Modernization 

The distinction between migration and modernization is significant. 

Migration: 

• Moves reports from one platform to another 

• Replicates existing calculations and logic 

• Often preserves fragmented definitions 

• Creates dependency on a specific platform 

Modernization: 

• Consolidates business definitions into a governed framework 

• Standardizes semantic logic across environments 

• Maintains lineage and traceability 

• Enables flexibility across future platforms and tools 

Organizations that focus on modernization gain more than a successful technology transition. They establish a foundation that supports long-term business agility. 

Accelerating Modernization Through Intelligent Automation 

Modern BI estates can contain thousands of reports, dashboards, and data assets, making manual migration both costly and time-consuming. 

To accelerate modernization efforts while maintaining quality, organizations are increasingly combining deterministic automation, AI-assisted conversion, and subject matter expertise. 

This blended approach enables teams to: 

• Automate rules-based conversion activities 

• Address complex business logic with AI-assisted analysis 

• Validate edge cases through expert review 

• Improve visibility into migration complexity and risk 

The result is a more scalable and efficient modernization process that allows organizations to focus resources on strategic outcomes rather than manual conversion tasks. 

Building the Foundation for AI-Driven Analytics 

One of the most compelling benefits of a common semantic model is its ability to support emerging AI capabilities. 

As organizations invest in generative AI, natural language interfaces, and intelligent automation, trusted business context becomes increasingly important. 

A governed semantic foundation can help enable: 

Natural Language Querying: Business users can ask questions in plain language and receive responses grounded in approved business definitions and metric logic. 

Agentic AI Workflows: Intelligent agents can leverage standardized business context to identify anomalies, investigate root causes, and support downstream business processes. 

Knowledge-Driven Analytics: Clearly defined entities, relationships, and business concepts establish the foundation for more advanced analytics, governance initiatives, and AI-powered discovery experiences. 

Stronger Governance: By centralizing business definitions and lineage, organizations improve transparency, compliance, and confidence in analytics outcomes. 

The Strategic Value of Modernization 

BI modernization should deliver more than a successful migration project. 

It should create a lasting foundation that supports governance, analytics innovation, and future technology adoption. 

Organizations that establish a common semantic model gain the ability to standardize business definitions, improve trust in data, accelerate platform adoption, and better prepare for AI-driven initiatives. 

The question is no longer whether organizations will modernize their BI environments. The question is whether modernization will simply relocate existing complexity or create a strategic foundation for what’s next. 

By treating the semantic model as the destination rather than the migration itself, organizations can modernize once and continue evolving as business and technology needs change. Perficient’s BI Modernization Accelerator is built on this approach, helping organizations establish a scalable semantic foundation while accelerating modernization efforts.  

Perficient helps organizations modernize their data and analytics ecosystems with strategies that improve governance, accelerate platform adoption, and prepare businesses for what’s next. Contact our team to explore your BI modernization roadmap. 

The post BI Modernization Is More Than Migration: Why a Common Semantic Model Is the Real Destination appeared first on Perficient Blogs.

As organizations move beyond AI experimentation and focus on scaling transformation across the enterprise, the need for proven strategies, governance, and measurable business outcomes has never been greater. The Copilot Frontier Club creates opportunities for Microsoft and participating partners to collaborate, share insights, and advance best practices that help organizations realize greater value from their AI investments.

At Perficient, we’re helping clients unlock the full potential of AI through Microsoft Copilot, custom agents, modern data foundations, and responsible AI adoption strategies. Participation in the Copilot Frontier Club aligns with our commitment to helping clients innovate faster, improve productivity, enhance decision-making, and transform customer and employee experiences.

“Organizations are looking for practical ways to scale AI and generate measurable business value,” said Joel Lindsey, VP of Cloud Modernization and Microsoft Services at Perficient. “We’re excited to collaborate with Microsoft and fellow partners as we continue helping clients accelerate adoption of Copilot and AI-powered solutions.”

As AI continues to reshape the future of work, Perficient remains focused on helping organizations move from experimentation to enterprise-wide impact with Microsoft’s AI ecosystem.

Turning AI Innovation into Business Value

Our clients are already seeing meaningful results from AI-powered transformation initiatives:

• A Top-Tier Global Financial Institution: Discover how a global financial services leader improved employee productivity and streamlined access to internal knowledge through an AI-powered digital assistant experience.
• A Leading Global Insurance Organization: Learn how a global insurance provider transformed the employee experience with generative AI, making information more accessible while improving operational efficiency and service delivery.

Explore our Microsoft AI and Copilot practice to learn how Perficient can help your organization accelerate AI adoption and business transformation.

The post Perficient Invited to Microsoft’s Copilot Frontier Club appeared first on Perficient Blogs.

As vehicles, equipment, cameras, sensors, and connected devices generate larger volumes of data, centralized processing can increase latency, consume bandwidth, raise infrastructure costs, and create privacy concerns.  

In many environments, the value of AI depends on whether a decision can happen in the moment, not minutes later. 

That’s why Physical AI is gaining attention. Perficient’s transportation enforcement deployment shows what this approach can deliver in production and was included in the IDC Digital Engineering and Operational Technology Services Case Studies Showcase Report, Part 9: Physical AI Services (Doc #US54579026, July 2026). The IDC Case Study noted, “As a leading provider of safety solutions for public transportation and fleet operators across North America, the company works closely with transit authorities, municipalities, and school districts to improve passenger safety, roadway compliance, and operational accountability.”  

The deployment also points to a broader shift in where and how enterprise intelligence operates. 

Physical AI Is Moving Beyond the Lab 

Physical AI is often described as the combination of AI, sensors, and physical systems. That’s accurate, but incomplete. The real shift is operational. 

Organizations are moving from AI that analyzes information after the fact to AI that participates in decision-making while events are happening. The difference may sound subtle, but that shift changes how organizations design, deploy, and manage AI systems.  

Consider transportation environments. A violation detected five minutes later has analytical value. Detecting it as it occurs creates operational value. The most successful Physical AI deployments aren’t built around the intelligence itself; they’re built around the outcome that intelligence needs to deliver.  

Why Edge Intelligence Is Essential 

The IDC Case Study noted, “The company made a strategic decision to move intelligence directly onto the vehicle and partnered with Perficient to define and execute this approach at scale. The organization deployed edge-based computer vision systems that process video in real time, allowing buses to detect violations and external threats as they occur.” 

The challenge wasn’t simply identifying an event. The system needed to generate evidence-quality results, support law enforcement workflows, and maintain privacy protections under changing operating conditions. 

A cloud-centric architecture wasn’t enough. Video data volumes, connectivity constraints, response-time requirements, and privacy obligations all pointed to the same conclusion: intelligence had to operate directly on the vehicle. 

We deployed edge-based computer vision on NVIDIA Jetson devices, processing video locally and applying privacy protections before information left the vehicle. This wasn’t a technology preference. It was necesssarynecessary for the system to work as intended.  

That’s an important lesson for organizations exploring Physical AI. The most effective architectures are rarely determined by technology trends. They’re determined by what the business needs the system to accomplish. 

What Physical AI Means for Software-Defined Vehicles 

It’s easy to view this as a transportation story. We see it differently. This is an architectural preview of where software-defined vehicles are heading. 

Much of the conversation around software-defined vehicles has focused on digital experiences, connected services, and new software capabilities. Those opportunities matter. At the same time, vehicles are evolving from data generators into intelligent decision-making systems. They can sense their surroundings, interpret events, and act in the moment.  

That architectural model is becoming increasingly relevant as automotive manufacturers and suppliers invest in real-time inference, distributed intelligence, privacy-by-design architectures, predictive maintenance, fleet optimization, and advanced driver systems.  

Some decisions cannot wait for a round trip to the cloud. The closer intelligence moves to where data is created, the faster vehicles can respond to changing conditions.  

Physical AI Requires More Than Model Performance 

One of the biggest misconceptions about Physical AI is that success depends primarily on AI performance. Experience tells a different story. 

Real-world deployments introduce variables that may not appear in controlled environments. Connectivity fluctuates. Weather changes. Hardware degrades. Physical operating conditions create challenges that models alone can’t solve. In the transportation deployment, environmental conditions created visibility issues that required modifications to the physical system itself. 

The physical environment becomes part of the architecture. Organizations that succeed in this space understand that deploying AI is only one part of the challenge. They must also build systems that perform reliably as operating conditions change.  

Recognition Reflects a Broader Industry Shift 

We believe IDC’s inclusion of the deployment reflects growing industry attention to Physical AI in production. The industry is moving beyond AI experimentation and toward operational intelligence. 

Across automotive, manufacturing, transportation, and industrial environments, organizations are looking for ways to generate value at the point where data is created. They’re investing in systems that make decisions locally, respond in real time, reduce dependency on centralized infrastructure, and perform reliably under production conditions.  

That’s where Perficient continues to focus. We help organizations move from pilots to production by combining AI engineering, modern delivery, edge computing, data platforms, and industry expertise to solve business problems under demanding operating conditions. 

The future of AI isn’t defined by how much data organizations collect. It’s defined by how effectively they turn that data into action. We believe Perficient’s inclusion in the IDC Case Study report reinforces what we’re seeing across industries: intelligence is moving closer to the edge because that’s where outcomes are produced.  

Learn more about Perficient’s Automotive & Industrials expertise and how we help organizations deploy AI systems built for production. 

The post IDC Case Study on Physical AI in Production and the Future of Enterprise Intelligence appeared first on Perficient Blogs.

When someone asks an AI assistant what to buy, which company to trust, or where to start, the answer can determine which brands enter consideration and which ones don’t. That makes visibility in AI-driven discovery a business priority, not another marketing channel to manage. 

AI is influencing choice. We can make your brand part of the answer. 

At Sitecore Symposium 2026, November 10-12 in Orlando, we’ll show what that takes. As a Titanium Sponsor, we’re bringing practical ways to strengthen how your brand appears in AI-driven discovery, build content operations that can keep pace, and create digital experiences that give customers a reason to keep engaging. 

The opportunity for digital leaders is clear: earn visibility earlier, move with greater speed, and make every interaction count. 

Elevate Your Brand Visibility 

Search is no longer the only place where brands compete to be discovered. 

Customers are turning to AI assistants and answer engines to research products, compare companies, and narrow their choices. That puts a new question in front of marketing teams: What do these systems know about your brand, and how accurately do they represent it? 

At the AI Visibility Expert Hub in the Solutions Pavilion on November 11 and 12, you can sit down with our experts for a one-on-one AEO and GEO consultation to discuss your personalized Scrunch report. We’ll look at how your brand appears across AI-driven discovery, how that visibility compares with competitors, and where you have opportunities to improve. 

You’ll leave with a clearer picture of where your brand stands and specific areas your team can address. 

At our booth, you can also explore solutions built around specific business needs, including an AI Concierge for healthcare, our Search Visibility Advisory offerings, and AI accelerators that help teams put AI into working processes. 

The goal is to make your brand easier to find, understand, and choose wherever customers begin. 

Build the Content Operation to Keep Up 

Being part of the answer requires the content and infrastructure behind it to keep pace. 

Marketing teams produce content for more channels and experiences while still managing accessibility, governance, brand standards, and quality. Increasing output without addressing those operational demands only adds complexity. 

Our client, BlueCross BlueShield of Tennessee, is a finalist in Sitecore’s 2026 Digital Impact Awards in the Intelligent Tech Stack category, recognizing organizations that connect technology to improve digital experiences and business results. Stop by our booth to learn how the organization modernized content operations across 42 digital properties, increasing publishing velocity while strengthening accessibility and governance. 

Connect With the People Shaping What Comes Next 

The decisions behind better digital experiences rarely happen in isolation. 

Our Women in Digital Breakfast returns November 12 for its third year, bringing women and allies together for candid conversation about AI, leadership, career growth, and how digital work is changing. 

Megan Mueller Jensen will moderate this year’s panel, with the conversation centered on experiences and lessons people can take back to their teams and careers. It’s also a chance to build relationships with others working through many of the same leadership and technology decisions. 

On November 11, we’re teaming up with Vercel for an invite-only evening at EPCOT, building on our recognition as Vercel’s Solution Partner of the Year for the Americas. After a day of sessions, attendees will have a more relaxed setting for dinner and conversation with peers across the Sitecore community. 

Make Your Brand Part of the Answer 

If you’re headed to Orlando, come see what AI-influenced discovery means for your brand. 

Visit the AI Visibility Expert Hub to understand where you appear today. Join us on stage to hear how organizations are modernizing content operations at scale. Talk with our experts about agentic marketing, Sitecore, and the digital priorities shaping your 2027 plans. 

You’ll find us in the Solutions Pavilion, in our sessions, at the Women in Digital Breakfast, and throughout Symposium. 

AI is influencing what customers discover and consider. Let’s make sure your brand earns its place in the answer. Visit our resource center for more information: Perficient at Sitecore Symposium 2026.  

The post Sitecore Symposium 2026: AI Is Influencing Choice. Make Your Brand Part of the Answer appeared first on Perficient Blogs.

Claude Skills for business analysis and automation. ElevenLabs voice agents for sales and customer engagement.

How to Read This

The two tools here solve opposite halves of the same problem. Claude Skills is about consistency on internal work: teaching AI your team’s process once so it follows it every time, instead of re-explaining the format in every conversation. ElevenLabs voice agents are about scale on external conversations: handling customer calls in Indian languages at a volume no call centre could staff.

As with the earlier briefings, each section shows what the tool looks like in use, explains what it does, and walks through two concrete scenarios. The visuals are illustrative diagrams of how each tool works, drawn for this document.

1. Claude Skills — Teaching AI How Your Team Works

Most people using AI at work hit the same wall: the output is good, but it’s never quite in the shape their organisation needs. So, every conversation starts with the same paragraph of instructions — use this template, follow this naming convention, include these sections, match this tone. Claude Skills removes that repetition.

A Skill is a folder containing instructions, reference material, and optionally executable scripts, that Claude loads automatically when a task matches. Rather than a prompt you paste each time, it’s a reusable package of how a particular job gets done. Anthropic ships pre-built Skills for Word, Excel, PowerPoint and PDF, and you can create your own — no coding required for simple ones, just a Markdown file describing the process.

The design detail that makes this practical is progressive disclosure. Claude only holds each Skill’s name and description in memory (roughly 100 tokens each), so you can install many without slowing anything down. The full instructions load only when a Skill is triggered, and bundled files and scripts load only if that specific task needs them.

What a Skill Is and How It Loads

Key Features

• Reusable: write the process once; Claude applies it automatically in every future conversation that matches.
• Self-selecting: you don’t invoke a Skill by name — Claude matches your request against each Skill’s description and loads what fits.
• Composable: multiple Skills stack on one task, so a “BRD template” Skill and a “data dictionary” Skill can work together.
• Can run real code: a Skill can bundle scripts, so validation and calculation are done deterministically rather than generated as text.
• Portable across surfaces: the same SKILL.md folder works in claude.ai, Claude Code, and the API — though it must be uploaded to each surface separately.
• Pre-built document Skills: Word, Excel, PowerPoint, and PDF Skills are available with no setup.

Use Case in Practice

Worth knowing: on claude.ai, custom Skills are uploaded per user and aren’t centrally managed by admins, so team-wide rollout means sharing the folder rather than pushing it out. And because a Skill can run code, only install Skills you wrote or got from a trusted source — treat it like installing software, not like pasting a prompt.

2. ElevenLabs Voice Agents — Customer Conversations at Scale

ElevenLabs positions itself as a full-stack voice AI platform for Indian enterprises, with the pitch being the ability to run millions of real-time customer conversations across Indian languages at very low latency without giving up trust or compliance. The relevant claim for a sales or support team is not that AI can speak — it’s that it can hold a natural phone conversation, in the customer’s language, at a volume that headcount can’t match.

What makes it work in the Indian market specifically is handling how people speak. The voice models are tuned for Indian accents and code-switched speech, so a caller who starts in Hindi and slips into English mid-sentence is still understood — and the models are built to hold up in noisy, real-world call conditions rather than clean studio audio. The voice library runs to 10,000+ voices, including 12 Indian languages and accents.

The Two Platforms

On signing in you choose a platform, and you can switch at any time. ElevenCreative covers content production — text to speech, dubbing, voice cloning, music, and video. ElevenAgents is the one relevant to sales and customer engagement: it’s where conversational agents are built, connected to a knowledge base and tools, given phone numbers, and monitored.

Key Features

• Conversational agent orchestration: low-latency agents across voice and chat, with guardrails, escalation logic, and handoff to a human when needed.
• Indian telephony integration: connects to providers including Ozonetel, Exotel and Plivo for real-time inbound and outbound calling and routing.
• Knowledge base grounding: agents answer from your own documented policies and product information rather than improvising.
• Voice library and cloning: choose from a large library or create a consistent brand voice for every customer touchpoint.
• Compliance and residency: India data residency options and enterprise governance controls, aimed at regulated industries.
• Deployment support: forward-deployed engineering support to get agents working in live production rather than just in a demo.

Use Case in Practice

Worth knowing: voice agents touch regulated ground. Disclosure that the caller is speaking to an AI, consent and call-recording rules, and data residency all need answering before a pilot goes live, not after. These are also exactly the areas ElevenLabs markets its enterprise controls around, so they’re a fair thing to press on during evaluation.

Where Each Tool Fits

A quick reference for deciding which of the two a given problem belongs to.

Closing Thought

These two tools point at the same underlying shift from different directions. Claude Skills captures how work is done so it can be repeated without the person who knows it.

Voice agents capture a conversation pattern so it can be held without the person who usually holds it.

In both cases the value isn’t replacing people — it’s that the parts of the job that were purely procedural stop consuming the hours that should go to the parts that aren’t.

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