As users increasingly turn to AI platforms to answer questions, brands are competing for something entirely different: AI citations, mentions, recommendations, and share of voice. 

The most important question is no longer: “How do I rank #1?” 

Instead, it’s: “Why would an AI mention my brand instead of a competitor’s?” 

The brands winning in AI search and discovery understand a critical shift: AI systems aren’t selecting webpages. They’re selecting trusted sources, recognized experts, and authoritative brands. 

Here’s how leading organizations are building competitive advantage in the AI era. 

How AI Decides Which Brands to Mention 

Before discussing tactics, it’s important to understand how AI systems think. 

Whether it’s ChatGPT, Gemini, Claude, Copilot, or Perplexity, most platforms are evaluating some variation of three key questions:

1.Do I Know This Brand Exists?

This is entity recognition. 

Can the AI confidently identify: 

• Who you are 

• What you do 

• Which products you offer 

• What topics you’re associated with

2.Do I Trust This Brand?

This is authority. Have reputable sources validated your expertise? Have analysts, journalists, customers, and industry experts referenced your work?

3. Should This Brand Be Included in the Answer?

This is topical relevance. When users ask questions about a specific topic, does your organization consistently appear among the most credible sources? The brands that succeed at all three questions earn significantly more AI visibility. 

Strategy #1: Own a Topic, Not a Product 

Many organizations still make the mistake of trying to dominate conversations about their products. AI systems care more about expertise than product features. The strongest brands become synonymous with specific topics. 

Consider the difference: 

When users ask questions about those topics, these brands frequently appear regardless of whether someone specifically searched for them. The goal is to create a mental and algorithmic association between your brand and a specific category. 

Eventually, AI systems begin connecting those concepts directly with the brand. 

Strategy #2: Win Mentions Outside Your Website 

One of the biggest differences between traditional SEO and modern GEO is where authority is built. Historically, marketers focused almost exclusively on their own websites. AI platforms pay far more attention to what other people say about you. 

A mention in: 

• Gartner 

• Forrester 

• IDC 

• CMSWire 

• MarTech 

• Digiday 

• Search Engine Journal 

• Industry communities 

may contribute more to AI visibility than another hundred pages on your corporate website. 

Why? Because third-party validation is a powerful trust signal. It’s your reputation. It’s what others in your industry say about you. 

Compare these statements: 

Weak Signal: “Our company is a leader in digital experience.” 

Strong Signal: “Gartner named the company a Leader in Digital Experience Platforms.” 

AI systems generally trust the second statement much more because it originates from an independent source. 

Strategy #3: Publish Original Research 

One of the fastest paths to increasing AI citations is becoming the creator of original data. 

AI platforms consistently prefer content that provides: 

• Statistics 

• Research findings 

• Benchmarks 

• Surveys 

• Industry studies 

Commodity Content: “Personalization improves customer engagement.” 

Citation-Friendly Content: “Our analysis of 5 million website sessions found personalized experiences increased conversions by 17%.” 

The first is an opinion. The second is evidence. Evidence gets cited. 

This is one reason organizations like Forrester, Gartner, Bain, Deloitte, and McKinsey appear so frequently in AI-generated responses. They continuously generate proprietary research that other publishers reference. 

Strategy #4: Strengthen Your Brand Entity 

Increasingly, AI systems do not think in webpages. They think in entities. An entity is the collection of relationships surrounding a brand. 

For example, AI may understand that a company is associated with: 

• Specific products 

• Industries 

• Technologies 

• Executives 

• Partners 

• Customers 

• Awards 

• Certifications 

The more consistently those associations appear across the internet, the stronger the brand entity becomes. 

For example, if a company’s name frequently appears alongside: 

• Sitecore 

• Scrunch 

• Vercel 

• Coveo 

• Gradial 

those relationships become embedded into the AI’s understanding of the organization. 

Strategy #5: Focus on Questions, Not Brochures 

AI systems exist to answer questions. Yet most marketing content still resembles product brochures. This creates a major opportunity. 

Instead of creating content like: 

• Product features 

• Solution overviews 

• Corporate messaging 

Create content around the actual questions users ask: 

• How does Sitecore compare to Salesforce? 

• What is a composable DXP? 

• How should organizations structure content for AI? 

• What drives successful personalization programs? 

• How long does an XM Cloud migration typically take? 

The brands answering these questions become the brands AI cites. 

Strategy #6: Make Content Easy for AI to Reuse 

The highest-performing AI content is surprisingly structured. 

AI models frequently pull information from: 

• Definitions 

• Checklists 

• Frameworks 

• Comparison tables 

• FAQs 

• Statistics 

• Step-by-step guidance 

In contrast, they struggle to extract value from lengthy marketing language. 

Difficult to Reuse “Our platform enables businesses to unlock transformational customer-centric innovation.” 

Easy to Reuse “A composable DXP separates content, presentation, search, analytics, and personalization into independent services that can evolve independently.” 

The second statement is specific, educational, and reusable. AI systems love reusable content. 

Strategy #7: Increase Expert Visibility 

AI doesn’t just recognize brands. It increasingly recognizes people. 

Consider how often these names appear in AI-generated responses: 

• Jensen Huang 

• Satya Nadella 

• Scott Brinker 

• Rand Fishkin 

Their visibility strengthens their companies’ visibility. The same principle applies to every industry. 

Brands benefit when their experts become visible through: 

• Conferences 

• Webinars 

• Podcasts 

• Industry publications 

• LinkedIn articles 

• Research reports 

Organizations that invest in expert visibility often outperform organizations that publish only under a corporate brand. 

Strategy #8: Own the Comparison Conversation 

Some of the most valuable AI prompts involve comparisons. 

Examples include: 

• Sitecore vs Adobe 

• Adobe vs Optimizely 

• Contentful vs Contentstack 

• Headless CMS vs DXP 

• Personalization vs recommendation engines 

These are often high-intent buying questions. Yet many companies avoid comparison content because they fear helping competitors. That’s usually a mistake. If your organization doesn’t participate in those comparisons, AI systems will learn from competitors that do. The brands that shape the comparison often shape the buying decision. 

Strategy #9: Build Authority Beyond Your Website 

Modern AI systems learn from far more than websites. 

Increasingly important sources include: 

• LinkedIn 

• Podcasts 

• YouTube 

• Reddit 

• Industry communities 

• Conferences 

• Webinars 

• Public discussions 

This is particularly important for platforms like: 

• ChatGPT 

• Grok 

• Meta AI 

The strongest AI visibility programs create an ecosystem of authority across multiple channels rather than relying solely on a corporate website. 

Strategy #10: Write Content AI Can Quote 

One overlooked tactic is creating content that can be quoted directly. 

Ask: “Could an AI model lift this sentence and include it in an answer?” 

If the answer is yes, you’ve likely created citation-friendly content. 

Strong examples include: 

• Definitions 

• Frameworks 

• Methodologies 

• Statistics 

• Industry explanations 

Weak examples include generic marketing language that says little and teaches nothing. The easier it is for an AI to understand and reuse your content, the more likely it is to become part of future responses. 

From SEO Rankings to AI Recommendations 

The most successful brands are shifting their mindset.  

Traditional SEO focused on: Ranking → Traffic → Conversions 

Modern GEO increasingly focuses on: Authority → Citations → Mentions → Recommendations 

That’s because AI systems aren’t asking: “Which page ranks highest?” 

They’re asking: “Which organization would a knowledgeable expert reference when answering this question?” 

The companies that become recognized experts, generate original research, earn third-party validation, cultivate visible thought leaders, and consistently answer important industry questions will earn a growing share of AI citations and mentions. 

And in the age of AI-powered discovery, those mentions increasingly determine which brands make the shortlist, enter the consideration set, and ultimately win customers. 

The post Battle of the Brands: Ten Ways You Can Outperform Your Competitors in the War for AEO/GEO Dominance appeared first on Perficient Blogs.

But there’s a bigger and often overlooked reality: 

AI platforms rarely decide who is authoritative based solely on what a company says about itself. They decide based on what everyone else says about that company. As AI-powered search and answer engines become more influential, brands need to think beyond publishing content and start building what can be called an authority ecosystem. 

The brands earning the most mentions in ChatGPT, Copilot, Perplexity, Gemini, Claude, Google AI Overview, and Google AI Mode are often the brands that appear repeatedly in analyst reports, industry publications, independent research, conference presentations, customer reviews, and expert commentary. 

The question isn’t simply: “How do we create content?” 

It’s: “How do we become one of the sources AI sees everywhere?” 

Why Third-Party Citations Matter More Than Ever 

AI systems are designed to identify trustworthy information. When multiple independent sources repeatedly connect a company to a particular topic, AI begins to develop confidence in that association. For example, if analysts, journalists, customers, conference speakers, and industry experts consistently reference a company in discussions about: 

• Digital experience platforms 

• AI governance 

• Commerce transformation 

• Customer experience 

• Sitecore implementation 

the company becomes increasingly associated with those topics inside AI-generated responses. 

In many cases, a mention in a trusted third-party publication has more GEO impact than another article published on a corporate website. 

Why? Because independent validation is one of the strongest signals of authority available. 

Build an Analyst Relations Program, Not an Analyst Event 

Many organizations engage analysts only when a major report is approaching. They scramble to brief analysts before a Magic Quadrant, Wave, or MarketScape evaluation and then disappear for the rest of the year. That approach limits visibility. 

The most influential brands maintain ongoing relationships with analyst firms such as: 

• Gartner 

• Forrester 

• IDC 

• ISG 

• Everest Group 

• Constellation Research 

These organizations brief analysts regularly on: 

• Product innovation 

• Market trends 

• Customer success stories 

• Industry observations 

• Emerging technologies 

The goal isn’t simply earning placement in a report. The goal is becoming top of mind whenever analysts discuss your category. Over time, analyst firms begin associating your organization with key topics, and those associations often find their way into the broader information ecosystem that AI platforms consume and reference. 

Publish Research That Everyone Else Wants to Cite 

One of the fastest ways to increase AI citations is to stop publishing opinions and start publishing data. Most organizations create thought leadership articles. Far fewer produce original research. That’s a missed opportunity because AI systems and industry publications both love statistics. 

Typical Thought Leadership “Personalization improves customer engagement.” 

Research-Based Insight “Our analysis of 5 million customer sessions across 40 enterprise websites found that personalized experiences improved conversion rates by 17%.” 

The first statement resembles countless others published online. The second becomes a source. And sources get cited. 

This is why reports from organizations such as: 

• Salesforce 

• HubSpot 

• Deloitte 

• Bain 

• McKinsey 

• Adobe 

• Gartner 

are referenced repeatedly year after year. The data becomes part of the industry’s collective knowledge. 

Give Publications What They Actually Want 

A common mistake in media outreach is pitching products when editors want insights. 

Industry publications are generally looking for four things:

1. Statistics: Data-backed findings immediately increase the likelihood of coverage.

• Budget trends 

• Platform adoption rates 

• Customer expectations 

• Implementation benchmarks

2.Contrarian Perspectives: Publications are drawn to ideas that challenge conventional wisdom.

“Most personalization projects fail because organizations focus on technology before fixing content operations.” 

Strong opinions often generate strong engagement.

3.Benchmarks: Editors love helping their audiences understand what good performance looks like.

• Migration timelines 

• Conversion rates 

• Adoption rates 

• Implementation costs

4.Frameworks: Frameworks simplify complex ideas. They are frequently cited, referenced, and repeated.

• Maturity models 

• Readiness assessments 

• Governance frameworks 

• Organizational operating models 

Frameworks are especially powerful because AI systems can easily summarize and reuse them. 

Turn Subject Matter Experts into Industry Sources 

Journalists quote people, not logos. That’s why many of the most visible organizations have recognizable experts attached to them. 

Think about how frequently AI surfaces names such as: 

• Rand Fishkin 

• Scott Brinker 

• Brian Solis 

• Ann Handley 

• Byron Sharp 

Their visibility reinforces the visibility of their organizations and ideas. The same principle applies to every industry. 

Instead of solely promoting corporate brand content, invest in elevating: 

• Architects 

• Strategists 

• Consultants 

• Product leaders 

• Industry specialists 

Encourage them to contribute: 

• Guest articles 

• LinkedIn thought leadership 

• Podcasts 

• Webinars 

• Industry panels 

• Conference sessions 

Every time an expert is quoted, their organization gains another trust signal. 

Prioritize Authority Instead of Volume 

Not all mentions are equal. A mention in a highly respected publication may carry more influence than dozens of mentions in low-authority blogs. 

For B2B technology brands, high-value placements often include: 

Analyst Sources 

• Gartner 

• Forrester 

• IDC 

• Everest Group 

• ISG 

Industry Publications 

• CMSWire 

• MarTech 

• Digiday 

• Adweek 

• Search Engine Journal 

• Search Engine Land 

Community Sources 

• Sitecore community publications 

• Adobe Experience League contributors 

• Microsoft ecosystem publications 

• Developer and architect communities 

The objective should be strategic authority accumulation, not raw mention volume. 

Participate in Industry Predictions and Expert Roundups 

One of the most efficient ways to increase third-party mentions is through industry prediction content. 

Editors publish these articles constantly: 

• Marketing trends for next year 

• AI predictions 

• Customer experience forecasts 

• Technology outlooks 

• Industry transformation trends 

Contributing a concise expert quote can place your organization in dozens of reputable publications annually. These mentions may be small individually, but collectively they create a powerful network of authority signals for AI systems. 

Create Proprietary Frameworks and Indexes 

Named research and frameworks are significantly more memorable than generic thought leadership. 

     Generic: AI Maturity Assessment 

     Branded: AI Readiness Index 

     Generic: Customer Experience Framework 

     Branded: Digital Experience Transformation Framework

Named models often become reference points that others cite. The more a framework is discussed externally, the greater the opportunity for AI systems to encounter and repeat it. Organizations like Gartner, McKinsey, Forrester, Deloitte, and Bain have leveraged this strategy for decades. 

Make It Easy for Others to Quote You 

The most cited brands actively support journalists, analysts, researchers, and creators. 

Consider building a media and research hub that includes: 

• Proprietary statistics 

• Industry benchmark data 

• Executive biographies 

• Downloadable charts 

• Survey findings 

• Expert contact information 

• Key industry trends 

Think of this less as a press room and more as a citation engine. Every chart reused, statistic referenced, or framework quoted creates another authority signal. 

Let Customers Tell the Story 

One of the most powerful authority signals comes from customers. AI systems often place significant trust in customer validation because it represents independent experience. 

This makes sources such as: 

• Gartner Peer Insights 

• G2 

• TrustRadius 

• Capterra 

particularly valuable. 

Customer success stories, conference presentations, awards, and case studies can often generate more credibility than vendor-authored marketing. A customer who says: “The project reduced content production time by 40%.” typically creates a stronger trust signal than a company making the same claim about itself. 

The GEO Authority Flywheel 

The most successful organizations create a self-reinforcing cycle of visibility and authority: 

Original Research →
Analyst Recognition→
Industry Publication Coverage→
Conference Speaking→
Expert Visibility→
AI Citations and Mentions→
Additional Industry References→

More AI Visibility 

The key insight is that AI platforms rarely create authority from scratch. Instead, they discover authority that has already been established through analysts, journalists, customers, experts, communities, researchers, and industry publications. 

Brands that intentionally build those third-party relationships create a competitive advantage that extends far beyond SEO. 

The Bottom Line 

If your GEO strategy depends entirely on publishing more content to your website, you’re likely missing the biggest opportunity. The brands that earn the most mentions in AI-generated answers are rarely the loudest brands. They’re the most validated brands. 

They invest in: 

• Analyst relations 

• Original research 

• Industry publications 

• Thought leadership 

• Expert visibility 

• Conference speaking 

• Customer advocacy 

• Independent reviews 

In the AI era, success isn’t measured only by how often you publish. It’s increasingly measured by how often authoritative sources reference you. And when enough trusted sources talk about your brand, AI platforms eventually start doing the same. 

The post AI Cares About Your Reputation: Why The Work You Do OFF Your Website Matters More Than Ever appeared first on Perficient Blogs.

Why a dedicated evaluation process is needed: AI agents produce varied, non-exact answers, and traditional software tests that rely on exact matches are therefore inadequate. What is needed is an evaluation-driven loop: 

• Define what “good” looks like for your business. 

• Test against a representative set of examples. 

• Score the results automatically wherever possible. 

• Bring in human experts for calibration. 

• Improve the agent, re-test, and then monitor in production. 

Working through this loop reduces risk, builds business trust in the agent, and makes continuous improvement practical. Databricks supports offline evaluation during development as well as online monitoring in production, leveraging consistent scoring tools. 

How Databricks handles it: MLflow records what the agent did at every step of a request, applies automated graders to that record, routes the cases that require human judgment to business experts through a review interface, and then runs those same checks against sampled live traffic once the agent is deployed. Traces, evaluation datasets and expert feedback all reside in Unity Catalog, alongside the data the agent reads. As a result, the evidence of agent quality is governed like any other data asset rather than sitting in a separate tool. 

In the rest of this post, I give a phased task list for implementing agent evaluation in an enterprise. For each task I identify what the Business SME owns, what IT owns, and which Databricks/MLflow component does the work. The intent is something you can lift into a project plan. 

The Components 

Five building blocks are good to define upfront. 

1. Traces are detailed automatic logs of everything the agent did on a single request: the question it received, the documents it retrieved, the systems it queried, its intermediate reasoning, and the answer it produced. All other components are dependent on traces. 
2. Scorers attach a score and a written rationale to a trace. They come in four varieties. Built-in judges are ready-made graders for the dimensions most agents need, covering correctness against known answers, groundedness in retrieved sources, relevance to the question, retrieval quality, and safety. Guidelines judges turn plain-English business rules into automated checks. Custom judges handle domain-specific quality that no generic grader can assess. Code-based scorers are deterministic checks for rules that have known right answers, such as whether the correct system was queried or whether a figure matches the source record.
3. Evaluation datasets are curated collections of realistic questions and tasks, with expected answers or key facts attached where they exist. They live as governed, versioned tables in Unity Catalog, alongside the data the agent reads.
4. The Review App is a web interface where business experts read the agent’s answers, rate them, supply corrected expectations, and leave comments. No technical skill is required to use it.
5. Metrics and monitoring aggregate scores into quality, cost, and latency figures, enforce the thresholds the business has agreed to, and run the identical set of scorers against a sample of production traffic after launch. 

Who Owns What and Why It Matters 

Defining the right ownership of activities is key to avoiding unfavorable outcomes. Business SMEs own the definition of quality. They live the process and therefore know what a correct and useful answer looks like, which questions the agent must refrain from answering, and what the company is willing to say in writing. IT owns instrumentation, automation, scorer construction, release gates, and production monitoring. 

When IT writes the quality criteria, the agent scores well on generic tests and disappoints real users. When the business is handed raw evaluation output with no explanation, nobody trusts the numbers. Therefore, the task list below keeps both parties in their lane and defines the handoffs explicitly.

Phase 1 — Define What Good Looks Like

This phase produces written agreement on what is being measured and what score is good enough to deploy.

Phase 2 — Instrument the Agent 

IT owns this phase almost entirely. It is a pre-requisite for the rest of the phases. 

Phase 3 — Build the Evaluation Sets 

This phase has the most business commitment and is the phase that most often sets the schedule. Best practice is to build several focused suites rather than one consolidated set, because they have different owners, different pass thresholds, and different refresh cycles. 

Phase 4 — Score the Agent and Set a Baseline 

This is when the first real measurement happens. Initial scores are expected to require adjustment to address inaccuracies.

Task 4.5 deserves attention out of proportion to its size. It is where the business decides whether to trust the scoring at all.  

Phase 5 — Calibrate the Judges Against Expert Judgment 

Automated judges are capable but not automatically correct about your business. While deterministic checks don’t require calibration, this phase tunes the custom judge until its scores track what your experts would have said. 

It’s worth setting business expectations before this phase starts since the aligned judge will usually report lower scores than it did beforehand. The agent has not declined but the grading has simply become more accurate. 

Phase 6 — Improve, Launch, and Monitor 

Evaluation starts prior to agent deployment and continues beyond launch as it becomes part of how the agent is released and operated. 

What Actually Governs the Schedule 

The first implementation along these lines typically runs seven to eight weeks. The distribution is the part worth planning around. 

Phases 1 and 2 take about a week and are mostly IT. Phase 3 takes two to three weeks and is almost entirely business time. Phase 4 takes a week. Phase 5 takes a week, however long the experts need to finish labeling. Phase 6 takes about two weeks to stand up and continues as long as the agent is in use. 

The second thing worth budgeting for is expert attention after launch. An hour a week of informed review, fed back into the test set, is what keeps quality from drifting as information and user behavior change. 

If you are planning an agent deployment and want help setting up the evaluation framework around it, reach out to Perficient for a discussion with our AI and Databricks specialists. 

The post An Implementation Plan for Evaluating AI Agents on Databricks appeared first on Perficient Blogs.

According to Forrester, “healthcare providers and health insurers are investing aggressively in AI”.

But spending more on technology will not solve the industry’s hardest problems.

Care is moving beyond hospitals and clinics into a connected ecosystem of providers, insurers, digital platforms, community organizations, and local resources. At the same time, AI is changing how healthcare employees complete tasks, make decisions, and support patients and members.

Forrester’s Healthcare Workforce Reinvention report indicates that modernization now requires more than implementing new tools. Healthcare organizations must redesign how work gets done.

Perficient Contributes to Forrester’s Healthcare Workforce Research

Perficient is proud to be cited in Forrester’s Healthcare Workforce Reinvention report, which examines how AI is reshaping healthcare workforces and organizations. The research includes insights from Priyal Patel, associate vice president of healthcare at Perficient.

According to the report, healthcare providers and health insurers must prepare employees, redesign workflows, and address gaps in trust between decision-makers and the people expected to use and oversee AI.

We believe Perficient’s inclusion reflects the practical experience we bring to helping healthcare organizations connect AI adoption with workforce readiness and operating-model change.

AI Will Reshape Work, Not Simply Automate It

Healthcare’s AI conversation has focused heavily on automation. That focus is too narrow.

Adding AI to an outdated workflow may accelerate individual tasks, but it will not create a fundamentally better operating model. Organizations need to determine which activities AI should handle, where human judgment must remain central, and how accountability should work when people and intelligent systems make decisions together.

As Priyal Patel shared during her research interview with Forrester:

AI won’t shrink the workforce. It will reshape it, creating new opportunities for employees to advance into leadership and supervisory roles while amplifying the value of human expertise.

Forrester states, “AI is absorbing repetitive, rules-based, and administrative tasks, while humans retain responsibility for judgment, oversight, and complex decision-making.”

Healthcare organizations need defined responsibilities, practical training, trusted governance, and clear escalation paths. Employees must understand how AI reaches a recommendation, when they should intervene, and who remains accountable for the outcome.

Build the Foundations for Connected Care

Workforce reinvention is one part of a broader change. As care moves beyond the four walls of the hospital, healthcare organizations need four connected foundations:

• Interoperable data that gives AI and care teams reliable information across systems
• Human-AI operating models that align roles, workflows, and decision-making
• Accountable governance that defines oversight, performance standards, and escalation thresholds
• Ecosystem partnerships that connect clinical services with payers, technology providers, and community resources

Prior authorization shows why these foundations matter. Clinical notes, payer requirements, supporting documentation, and review activities often sit across separate systems. That fragmentation slows decisions and increases administrative work.

Perficient’s AI-driven Prior Authorization solution helps organizations extract clinical information, assess documentation readiness, strengthen submissions, and centralize review and prioritization. The goal is not automation for its own sake. It is a more connected workflow that supports faster decisions, stronger submission quality, and lower administrative burden.

Perficient’s Perspective: Measure What Changes, Not What Gets Deployed

Building on the report’s findings, we believe healthcare organizations should track whether AI reduces unnecessary work, strengthens decision quality, improves employee readiness, and helps patients or members move more quickly to the next step in their care. Trust also matters. AI cannot scale if employees do not understand it or feel equipped to question its outputs.

Perficient brings together healthcare expertise, data and interoperability capabilities, AI implementation, workflow redesign, governance, and organizational change. We help healthcare organizations build the foundations required to move from isolated AI experiments to responsible, scalable impact.

The healthcare workforce is already changing. Organizations that redesign work now will be better prepared to connect people, technology, and care across the expanding healthcare ecosystem.

Forrester clients can access Healthcare Workforce Reinvention (Forrester Research, Inc., September 2026) directly through Forrester here.

The post Perficient Cited in Forrester Report on AI and Healthcare Workforce Reinvention appeared first on Perficient Blogs.

A maintenance knowledge graph brings together asset relationships, telemetry, work orders, permits, and parts into a connected view of equipment health and maintenance activity. Combined with a natural language interface, operators can ask questions, investigate failures, and plan corrective action from a single environment rather than navigating multiple disconnected systems. 

A Realistic Maintenance Scenario 

To demonstrate these capabilities, we developed a maintenance knowledge graph demo for a nuclear energy power company. While the demo centers on a nuclear site, the same concepts apply across all asset-intensive industries. 

Nuclear sites move a lot of water. Water systems support everything from condensate and cooling to normal plumbing, which makes pumps critically important. In this example, we follow a developing issue involving Pump 301 as a user works to diagnose and address a failure.  

Finding the Bad Actors 

Knowing where to begin an investigation is the often first challenge.  

Rather than manually reviewing maintenance records and reliability reports, an operator can use the knowledge graph’s natural language interface and ask it to perform a bad actor analysis across a pump circuit. 

The analysis surfaces Pump 301 as the issue and highlights the components most closely associated with the problem, making it the logical starting point for further investigation.

A Maintenance Knowledge Graph Shows Relationships 

Once Pump 301 has been identified, the next step is understanding how the issue connects to the broader maintenance environment. 

The graph is, effectively, an asset map, and it shows relationships. Looking at an individual asset, users can see the asset broken down into components and assemblies. Those assemblies are then broken out into condition information and connected to related work orders, permits, and other maintenance information, like replacement parts.  

In this example, one of Pump 301’s main bearings is going out. Based on vibration, temperature, and other available telemetry, the graph is making its best guess at the condition causing the failure. In this case, spalling.

You can see which zone the pump is located in, along with the associated work order. That work order is tied to a replacement part. The system also shows the confined space permit required to perform the work.

That is what a graph can do for you.

Once You Have the Graph and the Telemetry, What Can You Do?

The rest of the demo shows the power of what becomes possible once you have both the graph and the telemetry.

If we look at the telemetry for Pump 301, you can see the vibration spikes it’s suffering. An operator can use this information to see what’s going on.

There is also a time-travel capability, which is critically important for operators. When a bearing starts to fail, one of the first questions is: When did it start going?

To answer this question, operators can drag a slider back to previous points in time to examine vibration trends and spikes at that moment.

You can do this across all of the telemetry associated with the asset and its components, making it easier to see where trends developed and where spikes began to appear.

Drafting the Work Order

Diagnosing equipment problems is only part of the process. After the operator has looked at the asset and telemetry and determined there is an issue, the next step is initiating maintenance work.

In the natural language interface, the operator asks the system to create and submit a work order to address the issue affecting Pump 301. The system will then generate a work order that can be handed off to planning. The draft includes the information an operator would normally provide and identifies items such as:

• Required permits
• Required replacement parts
• Relevant equipment conditions

Planning can then review inventory, confirm resources, and move the work into scheduling.  

The result is a faster transition from diagnosis to action. 

Bringing It All Together 

Throughout this example, the maintenance knowledge graph connected information that would traditionally be spread across multiple systems. 

Operators could understand relationships between assets and components, identify recurring issues, review telemetry, examine how conditions developed over time, and generate a work order from a single environment.  

This is what it means to radically condense the amount of time required to diagnose a failure. 

By bringing together the graph, telemetry, and natural language interaction, operators can spend less time gathering information and more time taking action. Tasks such as preparing a work order, which normally takes 15 to 20 minutes, can be completed in seconds. 

See What Else a Knowledge Graph Can Do 

The maintenance scenario in this article is just one example of what’s possible when operational data, maintenance history, and asset relationships are connected. 

Explore how knowledge graphs help organizations preserve expertise, improve decision-making, and make critical information easier to access in our related article, “Retirements Are Inevitable. Knowledge Loss Doesn’t Have to Be.“ 

And if you’d like to learn more about the maintenance knowledge graph demo featured here, connect with Perficient’s Energy & Utilities experts.

The post How a Maintenance Knowledge Graph Accelerates Asset Failure Diagnosis appeared first on Perficient Blogs.

Every engineering team eventually hits the same wall. The system is growing. More services. More traffic. And all those services are talking directly to each other — synchronous HTTP calls, request-response, point-to-point. It works fine at small scale. Then it doesn’t.

Apache Kafka® was built for exactly this moment. In this first post of our three-part series, we’ll cover why traditional architectures break, what Kafka fundamentally is, and the core building blocks you need to understand before you use it in production.

Series Overview
Part 1 (this post) covers the problem space and Kafka’s core concepts. Part 2 walks through real-world production architectures. Part 3 is a deep-dive on testing Kafka systems end-to-end.

The post Apache Kafka® Explained: Why Every High-Scale System Needs an Event Streaming Platform appeared first on Perficient Blogs.

Who gets a voice in shaping what’s next? 

I’m thrilled to be moderating the third annual Women in Digital Breakfast at Sitecore Symposium. This annual event is a time to step off the conference floor, connect with peers over a beautiful breakfast and really good coffee, and engage in big conversations that just don’t happen anywhere else. This year we’re talking about the crucial role women in our industry play in defining a future with AI.

This year’s panel brings together an incredible group of leaders from the Sitecore Community with very different professional and personal backgrounds: 

• Jessica Chirambaguwa, Lead Product Manager, Global Digital Product at Colliers 

• Agata Nowakowska, President, Americas at Sitecore 

• Jennifer Smith, Senior Director of Digital Marketing & Ecommerce at Cracker Barrel 

• Stefanie Wittner, Chief People Officer at Sitecore 

Together, we’ll explore questions that many of us are asking: 

• Why does diversity of perspective matter when building and governing AI? 

• How can women move from being observers of AI innovation to leaders influencing its direction? 

• What practical steps can professionals take to strengthen their careers and opportunities in an AI-driven future? 

• And what gives us reason to feel optimistic about what’s ahead? 

Most importantly, this session is about empowerment. 

We want women to leave feeling energized, confident, and excited about the role they can play in shaping the future of technology. AI isn’t something happening to us. It’s something we all have the opportunity to influence. 

And for the many men who join us each year as allies: this conversation is for you, too. Understanding different perspectives, advocating for diverse voices, and helping create opportunities for others are essential leadership skills in today’s workplace. 

Whether you’re a marketer, technologist, strategist, leader, or simply curious about what’s next, we invite you to pull up a chair, join the conversation, and help shape a future that benefits everyone. 

We hope to see you there. 

The post 2026 Women in Digital Breakfast at Sitecore Symposium appeared first on Perficient Blogs.

The Key for Success

To use Personalization for Sales and Service Clouds, you’ll need to have:

1. User identity systems that are fully configured and working properly. The user needs to exist in the CRM, and you will need to match them using the same attributes you use in Personalization. For instance, if you’re with matching email addresses in Personalization, ensure that your Contact or Lead has the corresponding email.
2. The Personalization dataset needs to be fully configured with catalog items or promotions. In addition, you’ll be sending Einstein Recipes, so it’s crucial to ensure this functionality as well.

Ensure both elements are functioning seamlessly for immediate results.

Connect Both Tools

With Personalization now operational, you can now integrate with Sales Cloud. You need to install the “Interaction Studio Connector for Sales and Service Cloud” from AppExchange in Sales Cloud. This managed package includes Apex classes and four key Lightning components used to display Personalization data. To make it work, you’ll need to:

1. Create an API token in Personalization to send events and access the API. This token exposes all datasets in Personalization and their information.

2. Create a Named Credential in CRM using the API token from Personalization.

Access the Data

After you’ve set up the connection, it’s time to add the components to the Contact or Lead page layout. For these three components, I’ll show how they interact with your data.

1. Event Stream
2. Affinity Graph
3. Next Best Recommendation

Each component must align a CRM attribute with a Personalization Identity Type, and it’s crucial to specify the dataset, ensuring that known users in CRM (contacts or leads) are associated with the chosen dataset.Event Stream Component

This component gives you information about the interactions your contact or lead is having with the site. You’ll be able to know what the contact or lead is doing on the site, where they are going, and the date and time they did a specific thing. Take into account that it won’t refresh every 15 seconds like it does in Personalization, and it won’t give all the specific details you can see inside Personalization.

Affinity Graph Component

The affinity component gives you a better perspective on how your contact or lead interacts with a client’s product. This component works with the collected information about the different catalog objects you have set in Personalization.

You can filter by view time, purchases, views, and revenue, and you can add one for each catalog object you have.

Next Best Recommendation Component

This component requires additional refinement as it necessitates exposing a Server-Side campaign in Personalization to send the outcomes of a chosen Einstein Recipe.

This campaign will return recommendations based on how the recipe is set, from trending products to co-browsed items or any eligible item. Each time the contact record is open, you can get the triggered interaction for the campaign in the Event Stream of Personalization and in CRM.

The post How to Use the Personalization Connector for Salesforce Sales and Service Clouds appeared first on Perficient Blogs.

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.