CRN Best Of The Channel Awards 2026: How AI Is Transforming The Way Solution Providers Do Business

From streamlining operations to reshaping customer solutions, AI is changing the channel business. CRN’s Best Of The Channel Awards finalists for Best AI Solution Provider reveal where it’s having the biggest impact.

As the 2026 CRN Best of the Channel Gala in October approaches, we will be spotlighting some of the awards finalists over the next several weeks, taking a look at their achievements, what inspires them and what advice they have to share with others in the channel.

The gala will honor the winners of the second-annual CRN Best of the Channel Awards—spanning nine company categories and 6 individual awards—aiming to spotlight the executives, teams and companies who have demonstrated excellence, innovation, and leadership throughout the channel ecosystem in 2026.

The finalists were revealed on July 24, and the winners will be disclosed live at the gala on Oct. 13 in Atlanta (tickets are available here).

Here, we look at the finalists for the Best AI Solution Provider of the year category, which recognizes MSPs, VARs, systems integrators, consultants or other solution provider businesses for overall excellence and technical acumen in delivering AI-focused solutions to end customers over the last 12 months.

The finalists shared in their awards applications the impact AI had on their companies over the last 12 months regarding the way they run their business and the solutions they offer.

AHEAD

Over the last 12 months, AI has changed both how AHEAD runs its business and what the company delivers to clients. Internally, AHEAD has moved from ad-hoc experimentation to governed, company-wide adoption. The company established a formal Artificial Intelligence Standard aligned to NIST and global regulations that defines how AI is designed, developed, deployed and monitored across AHEAD, with clear requirements for privacy, transparency, accountability and bias mitigation. AHEAD has also embedded AI into its own operations using a structured, five-stage AI implementation methodology within AHEAD Managed Services first, helping improve incident resolution, automate repetitive tasks and increase platform ROI for both AHEAD’s teams and its clients.

Bespin Global US

Over the last 12 months, AI has transformed Bespin from a cloud manager into an AI orchestrator.

How It Runs Its Business

- Operational Autonomy: Moving from manual reporting to using Agentic AI for predictive marketing and real-time decision-making, cutting operational timelines by nearly 50%.
- Global Brain Trust: Unifying 1,300+ experts across 10 countries using AI-driven collaboration tools to align North American strategy with global R&D.

The Solutions It Offers

- Agentic AI (HelpNow): Bespin doesn’t just build chatbots; it deploys autonomous multi-agent systems that handle end-to-end enterprise workflows.
- AI-Driven Security (SecureAid): Bespin has shifted its MSSP offering to machine-speed defense, reducing response times for clients like Caesars to under 15 minutes.
- AI-Optimized Cloud: Integrating AI into FinOps tools to create "self-healing" infrastructure that automatically eliminates cloud waste.

Caylent

AI has fundamentally transformed both how Caylent delivers services and the nature of the solutions it offers customers. Rather than treating AI as an add-on, Caylent has adopted an AI-native delivery model that embeds agentic automation directly into its engineering workflows, cloud operations, and customer engagement frameworks.
Internally, Caylent leverages AI across the software development lifecycle, from automated code generation and testing to AI-driven project management and delivery orchestration. This has enabled the company to increase throughput, reduce delivery risk, and compress time-to-value for customers without proportional increases in headcount.
From a solutions standpoint, the launch of Caylent Accelerate™ in 2025 represented the most significant evolution in how Caylent delivers modernization services. The portfolio's agentic execution layer automates assessment, planning, code conversion, validation, and deployment, activities that previously required large teams of engineers executing sequential manual processes. The result has been dramatic improvements in speed and efficiency: up to 75% time savings for application modernization, up to 60% for database modernization, and up to 50% for cloud migration.
In 2026, Caylent is extending that same agentic pattern into managed services with the launch of Caylent Accelerate™ for Cloud Evolution, its Agentic Cloud Operations capability. This shifts managed services from reactive ticket-based support to a model where AI agents continuously anticipate issues, surface cost optimization opportunities, and accelerate remediation inside expert-defined guardrails, while Caylent architects retain accountability for outcomes.

CBTS

CBTS has been leveraging AI for its own operations for nearly two years. When the company counsels clients on adoption, it draws from its own experience.

While many competitors’ conversations center on a product, CBTS’ approach centers on partnership. The company has deployed AI software internally, resulting in a 40% increase in productivity. This includes reducing CBTS’ internal SOW cycle from five days to one.

During the readiness assessment, CBTS builds a comparable model for each client’s unique environment, tailored to its headcount, workflows and cost structure.

That internal depth powers Forge AI, CBTS’ external AI services portfolio launched this year. When helping clients deploy AI, CBTS draws on challenges it has already addressed internally, including governance, data readiness, adoption and security architecture, rather than solving those issues at the client’s expense.

Forge AI unites AI advisory, data readiness and engineering into a single end-to-end framework. It addresses what CBTS identifies as the biggest reason AI initiatives fail: companies lack the data foundation to support them. Forrester puts the scale of the problem at more than 90% of organizations having made limited progress in their data journey. Forge AI is designed to address that gap before a model is ever touched.

Chetu, Inc.

The company has integrated AI into software development processes, project management, business intelligence, customer support and sales enablement, driving measurable improvements in operational efficiency, decision-making speed and service delivery across the organization. This shift has evolved Chetu’s operating model into an AI-augmented delivery organization.

In the past year, one of the company’s major internal initiatives, Seamless AI Launch (Sai)l, demonstrates how Chetu uses AI tools internally to enhance execution and accelerate client delivery outcomes.

Chetu’s development teams now use AI to optimize and review code quality in line with modern enterprise engineering practices adopted across leading technology organizations. The time saved allows developers to focus on more complex issues for clients.

AI augments project development; it does not replace developers.

AI is used to improve code review efficiency, reduce redundancy, enhance performance and accelerate release cycles across development teams.

Chetu’s team also uses AI to generate documentation, optimize design artifacts, summarize client meetings and extract sentiment insights and key concerns. This gives developers more time to collaborate closely with clients, enabling more tailored software solutions.

Compucom

Compucom’s AI-Augmented Collaboration & Experience Services seamlessly integrate AI digital assistants, experience analytics, and automation within employees’ daily collaboration tools like Microsoft Teams, driving both adoption and measurable experience improvements. These services embed support and knowledge directly into workflow, reducing friction and enhancing productivity.

Key components include:

* AI Digital Assistants: Generative AI-powered assistants deliver self-help and self-service within MS Teams, AI Chat, AI Voice, and AI Mobile ap, providing natural language Q&A, ticket management, and insights by integrating with enterprise systems such as ServiceNow, Genesys, SysTrack, and knowledge repositories.
* Personalized, branded experience: Supports self-help, ticketing, password resets, endpoint automation, proactive updates, and SharePoint access, tailored to employee personas and journeys.
* AI-enhanced contact centre: Integrates Genesys-based quality, workforce, and performance management with sentiment and empathy analytics, improving agent coaching and operational insights.
* Experience Management: Ongoing analytics governance delivers insights and fosters outcome-based service improvement, enabling XLA (experience level agreement) readiness.
* Automation Platform: An agentic AI and automation roadmap underpins internal operations and customer self-help, driving continuous evolution in search, dashboards, and assistive capabilities.

Connection

AI has significantly reshaped how Connection operates and delivers value to customers over the past 12 months.

Internally, Connection has implemented targeted AI use cases to improve efficiency, streamline workflows, and enhance decision-making. These efforts focus on practical applications that deliver measurable value rather than broad experimentation.

Externally, the expansion of CNXN Helix has transformed how AI is brought to market. CNXN Helix introduces a consultative approach that helps customers navigate the complexities of AI adoption, from stakeholder alignment to identifying high-impact use cases to deployment.

This is particularly important as many organizations are still early in their AI journey and often lack a clear starting point. Connection addresses this challenge by combining advisory services, validated solutions such as AI-in-a-Box, and full-scale implementation support.

AI has also reshaped how Connection goes to market. As customer conversations have shifted from products to outcomes, Connection has evolved its marketing approach to support a more consultative sales motion. This includes developing vertical-specific campaigns and integrated content strategies focused on real-world AI use cases rather than broad technology messaging.

ePlus Technology

ePlus has built a differentiated, end-to-end AI practice that helps customers move from ideation to production with reduced risk and faster time-to-value. Over the past 12 months, the company has expanded its AI Ignite portfolio to support organizations at every stage of their AI journey, from AI Curious to AI Mature, through envisioning workshops, readiness and data strategy assessments, accelerated proofs of concept, AI infrastructure design and deployment, managed services and ongoing optimization. Its advisory, implementation and lifecycle services help organizations overcome the high failure rate of AI implementations by positioning them for success. The advance work of identifying infrastructure, data, security and other requirements, as well as helping customers select practical and impactful use cases to test, has positioned ePlus as a strategic AI transformation partner invested in customer success.

In addition, ePlus strengthened its AI credibility through advanced infrastructure capabilities, including achieving NVIDIA DGX SuperPOD Specialization Partner status, which validates its ability to design, deploy and optimize enterprise-grade AI environments for training, inference and research workloads.

In partnership with Digital Realty, ePlus’ AI Experience Center offers an accessible and convenient way for customer organizations to better understand and visualize what they need to be successful. At the center, ePlus hosts executive briefings that demonstrate automated AI infrastructure deployment, efficient management and advanced proactive monitoring, showcasing practical AI solutions and helping accelerate adoption.

ePlus also mandated AI training for its entire sales team, strengthening its ability to understand and pitch AI solutions. The training has enhanced the team’s ability to explain AI initiatives and requirements to customers and help organizations understand and navigate key considerations.

Internally, ePlus uses its own solutions and services to identify areas where it can enhance the customer experience for organizations that rely on the company.

Insight

Over the last 12 months, AI has fundamentally transformed how Insight runs its internal business by embedding intelligent workflows across its global operations. Today, 90% of the company's 10,000+ workforce utilizes AI daily through Horizon, a secure platform granting access to multiple AI models, specialized agents, and tools. This widespread adoption is supported by the Flight Academy, a structured and gamified training program boasting over 10,000 enrolled teammates. In its first year alone, the company's AI Center of Excellence (CoE) deployed three key use cases that generated $1M in verified impact. However, the transformation extends beyond top-down mandates; a culture of grassroots innovation has emerged, with teammates building their own custom AI tools to automate immediate challenges and unlock time for higher-value work. Currently, the organization is focused on "industrializing context"—transforming implicit institutional knowledge into explicit, scalable, and persistent digital workflows.
Simultaneously, these internal advancements have radically evolved the solutions Insight offers to its clients, shifting the business from a traditional technology vendor to an outcome-driven AI partner. This capability was dramatically expanded in Q4 2025 through the acquisition of Inspire11, which integrated approximately 400 professionals and 30+ accelerators into client-facing operations. Insight now delivers its services as a complete, interconnected system rather than a product catalog. This includes top-down Business Outcome Solutions powered by their proprietary Prism methodology, which utilizes a RADIUS discovery-to-execution pipeline to condense traditional four-week consulting timelines into mere days. Furthermore, successful internal tools like the Horizon platform and Flight Academy are now commercialized as "AI Enablers" for clients. This is bolstered by custom data foundations, strict enterprise guardrails within an "Operating Envelope," and a specialized "Context Layer" managed by the CoE to help clients operationalize their own organizational knowledge. Remaining vendor-neutral and hyperscaler-agnostic, Insight is so confident in this operationalized approach that it now offers risk-share pricing models, putting its own fees at risk to align directly with client success.

Mission Cloud, a CDW Company

AI has changed how Mission delivers services, how internal teams are structured, and what the product roadmap looks like going into the second half of 2026.

Traditionally, an AWS MAP migration assessment is a valuable but bounded deliverable: a directional document that gives customers enough to make a go/no-go decision, not a production-ready migration blueprint. Mission applied agentic AI to that process and the output is categorically different.
Where a standard assessment produces a high-level recommendation, Mission's AI-powered approach produces implementation-ready architecture: fully specified security controls mapped at the organizational unit level, complete network design with routing and segmentation, and an operations model detailed enough to hand directly to an engineering team. The depth increase across every dimension of the deliverable is transformational.

The result is an assessment so thorough and actionable that customers can move from Phase 1 directly into landing zone and pilot without the rework cycles that typically follow a lighter engagement. AWS has recognized this: Mission is ranked #1 in the Migration Acceleration Program for getting workloads launched on time and on budget. That ranking reflects what happens downstream when the assessment sets the right foundation.

Mission established a dedicated AI Solutions and Services team, purpose-built to move faster than the traditional consulting model allows. This team's mandate is rapid innovation: identifying emerging AI capabilities, prototyping solutions against real customer problems, and getting those solutions into production on an accelerated timeline. Mission Cloud customers now have a team whose full attention is on what AI can do for them next quarter, not just what it can do today.

Mission is bringing a suite of AI services to market with launches scheduled before the end of Q3 2026. The roadmap is built on patterns extracted from 400+ delivered AI projects: the use cases with the highest ROI, the implementation gaps that slow customers down most often, and the governance capabilities that separate AI pilots from systems an enterprise can actually bet its operations on.

Quantiphi

Over the past 12 months, Quantiphi transformed from an “AI-First” firm into an “AI-Native Digital Experience Powerhouse,” deliberately decoupling revenue growth from headcount and pivoting to value-driven, outcome-based monetization.

1. Impact on How Quantiphi Runs Its Business (Operating Model)
Quantiphi leveraged its proprietary Technology Services-as-a-Software (TSaaS) framework to drive non-linear internal growth, projecting a 700-basis-point EBITDA margin expansion by 2028.

2. Impact on the Solutions Quantiphi Offers (External Portfolio and Commercials)
Quantiphi’s external offerings shifted from basic experimentation to autonomous, enterprise-grade systems, supported by the acquisition of Candyspace to combine advanced AI with human-centered design.

rSTAR Technologies, LLC

Twelve months ago, AI was a growing practice area at rSTAR Technologies. Today it is the strategic center of gravity around which the entire business is organized.

On the solutions side, the shift has been from advisory and implementation into full-scale enterprise AI deployment. rSTAR Technologies moved from helping clients understand what AI could do to building and operating production-ready AI systems that run inside one of the largest utility enterprises in the United States. The engagement with our client (Fortune 200 Utility) alone delivered an AI Knowledge Base, AI Email Assist, AI Call Assist, and purpose-built Agentic AI agents for utility-specific workflows, all live, all measured, all generating verifiable outcomes at contact center scale. These are not pilots. They are operational systems that their customer service representatives use every day.

This shift required rSTAR Technologies to build a genuinely different kind of delivery capability. The company invested heavily in enterprise-grade AI architecture on Microsoft Azure, building with Azure OpenAI Services, Azure AI Foundry, Azure Data Factory, and Vector Databases. It built reusable AI accelerators and utility-specific AI agents designed to extend beyond customer service into grid operations, workforce transformation, and enterprise automation. Its AI solutions are now available on the Microsoft Azure Marketplace, making them accessible to utility and manufacturing enterprises across North America.

Internally, AI changed how rSTAR Technologies operates as well. AI is now embedded in software development workflows, proposal generation, solution design, sales enablement, and delivery methodology. Development cycles are faster. Proposal quality is higher. Delivery is more consistent. The firm moves at a faster speed than it did 12 months ago.

The commercial impact followed the capability investment. AI became the primary driver of new client engagements and strategic consulting opportunities. The Microsoft Solutions Partner Designations for Data & AI, Digital & App Innovation, and Infrastructure, all achieved this year, validated the transformation externally.

What changed most fundamentally is how rSTAR Technologies is perceived in the market. The company has moved from being known as a reliable systems integrator to being sought out as a strategic AI transformation partner for regulated, asset-intensive industries. That repositioning is backed by production deployments, measurable outcomes, and a growing roster of enterprise clients who trust rSTAR to build AI that operates at scale where reliability is not optional.

SHI International Corp

SHI has applied the same AI Factory capabilities internally, using its own organization as a proving ground for enterprise AI adoption. One of its initial internal development efforts was Project Mindspark, which began as an LLM chatbot initiative designed to improve workforce productivity. It has since evolved into SHI’s proprietary generative AI platform, supporting more than 300 active use cases and enabling the company to scale AI across core business operations and real-time employee workflows covering 75% of the company. SHI has since streamlined and packaged the approach as a model for customers’ own use cases.

SHI also implemented Microsoft Copilot to support workforce productivity, completing a secure implementation in six months. Nearly 12 months after implementation, 80% of the organization is using the tool, with SHI reporting nearly $2.5 million in internal cost savings.

Additionally, SHI invested in robotic process automation to automate manual workflows, including renewal notifications, processes and overdue renewals for more than 200,000 customer renewals. These efforts have streamlined internal operations while improving efficiency for customers.

Other key internal AI implementations include AI-enabled analytics and decision platforms such as Palantir, which support complex data integration, operational insights and strategic decision-making across large and diverse data sets. SHI has also embedded AI directly into ServiceNow workflows to automate intake, routing, issue resolution and operational reporting across IT and business operations. These implementations extend beyond chatbot use cases into process automation and operational intelligence, reducing manual effort and improving service consistency.

SHI also uses AI internally for forecasting, vendor and partner analysis, capacity planning and executive reporting, reinforcing the company’s focus on AI as an operational accelerator. These internal deployments give SHI experience in governance, change management, adoption and trust, which the company identifies as common points of failure in enterprise AI initiatives.

By running AI at scale internally across customer support, operations, IT service management, analytics and decision support, SHI has built institutional knowledge around model risk, data quality, explainability, user adoption and auditability. This internal AI maturity directly informs how SHI designs, delivers and governs AI solutions for customers through its AI Factory.

TOGA Technology

TOGA adopted a “practice what it preaches” approach by deploying three internal AI solutions before taking them to market:

This internal-first adoption validated reliability, demonstrated ROI and helped de-risk customer implementations. It also positioned TOGA to show clients that the company does not simply advise on AI but uses the technology to run its own business.

Market Impact: Solutions TOGA Offers
AI became the foundation of TOGA’s service transformation strategy. The company built a Ph.D.-led AI Innovation team with MIT-certified presales consultants and established a comprehensive AI practice spanning six functional areas: training, consulting, development, solution delivery, managed services and presales.

TOGA launched AI QuickStart services across five critical areas: AI Training & Enablement, AI Strategy & Consulting, Responsible AI, Data Engineering & Modernization, and AI Solution Development & Managed Services. These services enable the company to take clients from strategy through production deployment with measurable outcomes.

AI is no longer a standalone offering at TOGA. It is embedded across the company’s service lines, including Advisory + Modernization for AI strategy and consulting, Managed Services for AI-powered contact center and help desk automation, True Solutions for custom AI and machine learning development, and Lifecycle Services for AI-enhanced asset management and support operations.

This integrated approach is designed to prevent clients from getting stuck at the pilot stage by providing end-to-end transformation from strategy through production.

WBM Technologies

Over the past 12 months, WBM has moved from delivering AI projects to operating an AI adoption service.

Internally, AI is now embedded within the Enterprise Service Desk. It supports daily operations by improving consistency, making knowledge more accessible, and increasing efficiency across support interactions. This work earned the 2026 HDI Best Use of AI award and provided a working model for how AI can be applied responsibly within a live service environment.

Externally, that same model has been extended to clients. What began as a Modern Work practice has evolved into a structured approach for AI adoption. Organizations apply AI within real workflows, supported by ongoing training, curated content, and in-the-flow guidance through PowerBar.

The impact is measurable. Across the WBM customer community, more than one million hours of productivity have been captured through user interaction, training programs, and support engagement.

The shift to a service model has also changed the business. AI adoption is no longer delivered as a one-time engagement. It is delivered as a recurring capability that continues to improve over time. This has created sustained value for clients and contributed to rapid growth within the AI practice.

WBM is now operating an AI adoption system that runs across its organization and its customer base. The focus is no longer on introducing new tools. It is on helping people use those tools effectively and continuously improving how work gets done.