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The 2026 Top 100 CLG Awards honored the leaders rewriting the post-sale playbook around AI. In the Utilizing AI in Customer Programs category, four winners stood out for the discipline of their work. Christopher Irwin-Dudek, Vice President of Corporate Communications at NiCE, rebuilt customer marketing as a GPT-trained intelligence engine. Sil Cleary, Senior Program Manager of Community Experts at Adobe, consolidated Adobe's advocacy programs across four organizations and seven teams into one AI-enabled community. Sneha Iyer, Lead Business Value Consultant at Observe.AI, proved advocacy is downstream of value architecture by building the infrastructure that produces $20M+ in influenced revenue. And Pankaj Bhardwaj, Senior Vice President of Global Customer Support at Saviynt, replaced brute-force support scaling with a multi-agent AI ecosystem that added $7.7M in incremental revenue. Four functions. Four companies. One shared move: AI stopped being a pilot and became the operating layer of post-sale.
The customer engagement function is being reshaped twice over.
The first shift is in how customers buy. Half the market now distrusts AI-generated brand content. Buyers ask ChatGPT to compare vendors before they ever visit a homepage. The vendor shortlist gets stitched together from G2 reviews, podcast clips, LinkedIn posts written by real customers, and analyst notes. Marketing's homepage is no longer the first impression. The first impression is whatever an AI agent assembles from public signals about your customers' experience with you.
The second shift is in how customers use what they bought. Renewal windows are tighter. Boards want NRR above 110% on the same budget that delivered 95% last year. Customer Marketing, Customer Success, customer support, and community all touch the same post-sale journey, and they all run on different tools that do not talk to each other. The CSM platform shows "healthy" because the customer logged in. The lifecycle automation platform sends a generic email anyway. The advocacy program is a spreadsheet maintained by one person. The CFO asks why NRR is not moving and the answer is fragmentation.
Both shifts pressure the same place: the operating layer between the customer and every team responsible for their outcome. That layer is what 2026's top CLG leaders are rebuilding. They are not running AI pilots. They are designing AI as the substrate that connects customer programs, surfaces the right signal at the right moment, and produces outcomes the board can measure on one dashboard.
Base AI is the AI engagement OS for customer-led growth. The bet underneath the platform is that customer marketing, customer success, advocacy, lifecycle, references, community, and support do not need another point solution. They need one operating layer that orchestrates every customer program in one place, fed by the signals that already exist across product, CRM, and support data, and instrumented to produce the metrics every CFO already trusts: NRR, expansion pipeline, retention, influenced revenue, advocacy participation.
The four winners below are doing pieces of this thesis at their own companies. Christopher built the customer-truth layer that makes every other program more credible. Sneha built the value architecture that makes advocacy possible downstream. Sil consolidated four advocacy organizations into one AI-enabled community. Pankaj rebuilt support as a multi-agent operating model that turned a cost center into a growth engine. Each of them rewired one part of the post-sale function around AI. The pattern under their work is the pattern Base AI is built for.
If you lead Customer Marketing or Scaled Customer Success in 2026, your board has stopped asking whether you are using AI. They are asking what it is producing. The honest answer for most teams is: a few pilots, a copilot license per CSM, and a backlog of ideas waiting on RevOps or IT.
The structural problem is not access to AI. It is that the post-sale function was designed before AI existed. Customer stories live in PDFs. Reference quotes are buried in slide decks. Health scores are reactive. ROI cases get rebuilt from scratch on every renewal. Adoption programs are one-off campaigns instead of governed lifecycle systems. Support tickets and CSM notes never make it back into the marketing layer. The infrastructure is fragmented, the signal is trapped, and the assets a CSM or customer marketer needs in the moment (vertical-specific proof, value modeling, behavioral nudges, intelligent triage) are exactly the assets AI is built to surface.
That is the gap the 2026 Top 100 winners in the Utilizing AI in Customer Programs category are closing. None of them ran a "Year of AI" initiative. Each rewired one part of the post-sale lifecycle around AI as the substrate. Each can point to revenue, retention, or efficiency outcomes that show up on the board deck. Here is what they actually built.
Most enterprise Customer Marketing functions sit on the same problem: customer truth is everywhere and usable nowhere. Case studies live in PDFs. Reference quotes are buried in slide decks. Sellers hunt for proof manually. Voice of Customer signal never reaches the people who should act on it.
Christopher rebuilt NiCE's entire function around the inverse premise. In his words, "our success as a Customer Marketing team comes from reimagining our function as the architect of an AI-powered growth engine. We moved from a content publishing model to a dynamic intelligence model, where customer stories are continuously enriched, analyzed, and activated across marketing, sales, customer success, and leadership."
His team redesigned the lifecycle of customer stories end-to-end. Every case study, executive interview, Customer Advisory Board session, and NPS comment is now captured in a structured format and automatically tagged by industry, persona, use case, and solution footprint. The corpus feeds a custom GPT-based system trained on NiCE's full customer content library. A seller preparing for a healthcare CIO conversation surfaces vertical-specific proof, executive quotes, and outcome metrics in seconds. A CS leader pulls expansion proof tied to a specific product module. Leadership queries themes across hundreds of customer conversations on demand.
The discipline that makes the system stick is that AI adoption is not optional at NiCE. Every team member owns at least one AI-driven KPI. Within the first year: sales content utilization +45%, time-to-surface relevant proof down 60%, win rate +12% on AI-recommended content deals, sales cycle compressed 8%, advocacy participation +25%, and manual tagging and research time cut 30%. The function shifted from content publisher to a measurable revenue lever, and customer truth, in Christopher's framing, became the most activated growth asset in the business.
Sil's career spans nearly a decade of community, program, and advocacy management across King (Activision Blizzard), YouTube, Microsoft, LinkedIn, and now Adobe. The throughline is the same in every chapter: building programs from scratch in newly created departments, then engineering them to scale.
At Adobe, the work that earned her Top 100 recognition is the full revamp of the Community Experts Program, which had run with minimal changes since 2006. "I made our Influitive Hub," Sil says, "and now we consider all the contributions of our Community Experts to the Adobe Creative Ecosystem." The new Hub has the highest retention and engagement of any Hub at Adobe and surpasses industry standards.
The deeper move is structural. Sil is consolidating all of Adobe's Customer Advocacy Programs across the Digital Media market into one Adobe Community Program umbrella spanning four organizations and seven teams. One experience serves creative professionals, students, educators, and the wider Adobe customer base. The CMS award category for her work is Utilizing AI in Customer Programs, and the reason is the operational layer underneath the consolidation: AI is what makes one umbrella across four organizations possible without losing the personalization Adobe customers expect.
The measurable outcomes reflect the system. Membership grew from 245 to over 600 worldwide. NPS climbed from 44 to 83. Member retention rose from 75% to 98%. Gender diversity moved from 89% male to 73% male, with 7× more young creatives (18–30) in the program. Sil's philosophy ("it is all about the people") is the framing every Customer Marketing leader should internalize. The technology is the leverage. The human discipline is the substrate.
Most Customer Marketing teams measure advocacy by case studies produced. Sneha rejected that framing entirely. "I do not run customer marketing," she says. "I build the engine that makes it possible." Advocacy, she argues, is downstream. The real question is whether the customer can prove value to their own CFO, and the answer depends on infrastructure built long before a case study is ever requested.
She built two engines at Observe.AI. The first, First Mile Intelligence, converts 300+ past deployments into reusable context, KPI logic, and data-readiness briefs that prepare account teams before major customer engagements. "I built it," Sneha explains, "because customer outcomes should not depend on tribal knowledge or on who happens to be assigned to the account." Discovery stops restarting from zero on every account. Institutional knowledge becomes operational instead of tribal.
The second engine, the Value Modeling Engine, is a financial proof system. It is not a generic ROI calculator. It maps every type of customer objection, the realistic improvement rate at 60 versus 180 days, the variance between strong and weak execution, and produces projections a CFO will defend in a budget meeting. She also leads Impact Series, a monthly thought-leadership forum, and CxI, a customer-experience index that scores 100% of interactions.
The outcomes are the kind that get board attention: 40% improvement in time-to-value, 50% faster CS ramp, ROI models produced in 20 minutes instead of 6 to 8 analyst hours, $20M+ in influenced revenue, a 73% upsell win rate, 80%+ renewal rates on deals supported by Value-Modeling business cases, and 35%+ of the customer base participating in advocacy programs. Sneha's framing is the durable lesson here. Advocacy is the visible part. Value architecture is the load-bearing part. AI lets you build the load-bearing part at scale.
Pankaj inherited the version of post-sale that most fast-growing companies recognize. Hyper-growth had outpaced support. The backlog was bloated with cases aged beyond 90 days. CSAT was stagnating. The instinct in most companies is to hire faster. "Our success," Pankaj explains, "is defined by rejecting the industry's brute-force scaling model and embracing a Global Unified Model powered by a Multi-Agent AI Ecosystem. We instilled the Japanese philosophy of Omotenashi (anticipatory hospitality) into our culture, moving beyond just ticket-handling to actively predicting customer needs."
Predictive Triage eliminated the 4-day back-and-forth that had become the industry standard. An AI Co-Pilot for engineers lifted daily case closures 15.9%, freeing the team for high-value problem-solving. AI-powered chat now autonomously resolves 41% of incoming interactions. A Knowledge Agent drove a 24.9% case deflection rate, a 267% increase. Average Resolution Time fell 40% and 50.8% of 90+ day aged cases were liquidated. The team did not scale by adding headcount. It scaled by adding judgment.
The commercial outcomes are the part that matters for any executive defending the AI investment. CSAT vaulted from 4.12 to 4.71 (above the TSIA industry median of 4.6). Customer retention reached 98%. Premium "Diamond Success" package adoption surged 169%. The entire transformation added $7.7M in incremental revenue, converting support from cost center to growth engine. Employee morale tracked with the customer story, landing at eNPS 89+. Pankaj's nomination reframes the AI conversation for any VP Scaled CS. AI's job is not to replace the team. It is to remove the brute-force scaling tax that was burning the team out.
Four different functions (customer marketing, advocacy and community, business value, customer support) and the same architectural move underneath each story.
The reason these four stories sit in the same category is that each leader built one piece of the same operating layer.
Base AI is that layer, productized. Customer marketing, customer success, advocacy, references, community, lifecycle, and support programs run on one platform, fed by the same signals (product usage, support data, NPS, CRM context, advocacy intent), governed by the same rules, and measured on one dashboard. AI agents inside the platform orchestrate the routing. The right intervention reaches the right customer at the right moment without a CSM in the loop. The right reference quote surfaces in the right deal without a customer marketer hunting for it. The right adoption nudge fires when behavioral signal warrants it without a lifecycle marketer building a campaign from scratch.
That is the operating model the 2026 Top 100 winners are demonstrating. The next five blogs in this series unpack what it looks like across the rest of the post-sale function: scaling customer success without adding headcount, scaling customer marketing into a revenue engine, unifying post-sale to drive expansion, turning advocacy and community into a measurable growth function, and compressing time-to-value with AI-driven onboarding and adoption.
Next in the series: How to Scale Customer Success with AI in 2026 (Without Hiring More CSMs) →
→ See how Base AI operationalizes this pattern: Introducing the AI Engagement OS (webinar) · Base AI Platform · The CLG Playbook
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