Artificial intelligence has become one of the most talked-about technologies in business communications, but for small and midsize businesses (SMBs), success will not be determined by how much AI they adopt. It will be determined by how effectively they apply AI to real business challenges.
As AI moves from experimentation to deployment, a new theme is emerging across the SMB market: practical AI. Rather than pursuing fully autonomous operations or replacing employees, SMBs are prioritizing AI tools that help people work more efficiently, improve customer interactions, reduce complexity, and generate measurable business outcomes.
Frost & Sullivan shows that small businesses with 10 to 99 employees primarily expect AI to:
- Improve quality of work — 58% of surveyed organizations
- Reduce human error — 49%
- Improve customer experience — 49%
- Increase productivity — 43%
At the same time, channel partners are playing an increasingly important role in helping SMBs navigate AI adoption. Many customers understand the potential of AI but need guidance on where to start, how to measure success, and how to avoid costly mistakes.
AI Adoption Is Accelerating, But Most Organizations Are Still Learning
Despite the hype, AI adoption remains a work in progress. According to Frost & Sullivan’s 2026 UCC buyer research:
- 16% of small businesses have deployed agentic AI across multiple use cases
- 20% have deployed it in at least one use case
- 39% are in the proof-of-concept stage
- 24% are still watching and learning
However, adoption is expected to accelerate rapidly — 25% of small businesses are projected to have deployed agentic AI across multiple use cases by 2029.
The research also shows that AI is becoming a strategic priority across communications environments. Communications infrastructure and operations analytics, along with customer experience and conversational intelligence, rank as the highest-priority AI capabilities, with 18% and 17% of small businesses respectively rating them as “crucial.”
81% of small businesses prefer a single integrated platform that delivers common AI capabilities across calling, meetings, messaging, and contact center functions — reflecting a growing recognition that AI delivers the greatest value when embedded within existing workflows rather than introduced as another standalone application.
What Works: Practical AI Use Cases That Deliver Results
The most successful SMB deployments focus on augmenting people rather than replacing them. A survey of Intermedia customer organizations found that 80% are more interested in AI that enhances employee productivity than AI that replaces employees.
AI Agents for Front-Line Customer Engagement
AI agents such as Intermedia’s AI Receptionist are gaining traction as a practical first deployment. By answering calls, addressing common questions, and directing callers to the right person or resource, these tools help businesses deliver responsive, consistent service — even after hours. The result: fewer missed calls, employees freed for higher-value interactions, and more inquiries handled without adding staff.
Intermedia refers to these technologies as “digital teammates” — distinguishing between AI agents that automate specific tasks and workflows, and AI assistants that support people.
AI Assistants for Real-Time Employee Support
AI assistants that help employees during customer interactions are also proving valuable. Intermedia’s AI Agent Assist capability provides real-time answers, sentiment analysis, prior interaction summaries, and knowledge-base guidance.
According to data drawn from actual customer call records, agents using the “Ask AI” capability were four times less likely to place callers on hold. When holds did occur, they averaged only 3 seconds — compared with 81 seconds for agents not using the feature.
Meeting and Communications Automation
AI is also simplifying everyday communications by automating note-taking, transcription, summarization, and follow-up activities. Because these tools fit naturally into existing workflows, adoption tends to be straightforward and ROI becomes visible quickly.
What Doesn’t Work
While AI adoption is accelerating, several common pitfalls continue to undermine outcomes.
Leading with technology instead of business outcomes
Organizations often begin by purchasing AI capabilities before identifying the business problems they want to solve. A better approach is to start with measurable objectives — reducing customer hold times, improving first-contact resolution, increasing productivity, reducing administrative work, improving customer satisfaction. The technology should support the objective, not define it.
Adding more complexity
Many SMBs already struggle with communications fragmentation. Frost & Sullivan research found that small-business employees cite too many communications applications (30%) and too many communications devices (31%) among their biggest workplace challenges. Adding disconnected AI tools often increases complexity rather than reducing it — which is why integrated platforms are becoming increasingly attractive.
Underestimating change management
Technology alone does not create business value. Even effective AI solutions can underperform if organizations fail to train employees, establish governance policies, define success metrics, adjust workflows, and continuously optimize usage.
Why Channel Partners Matter More Than Ever
Many SMBs lack the internal expertise needed to evaluate, deploy, manage, and optimize AI-powered communications solutions. Frost & Sullivan research identifies AI and communications skills shortages as a major challenge, cited by 41% of small businesses globally.
This creates a significant opportunity for channel partners. The most successful partners are moving beyond product sales and acting as strategic advisors by:
- Assessing customer readiness
- Identifying high-value AI use cases
- Defining measurable success metrics
- Managing deployment and integration
- Providing user training
- Delivering ongoing optimization services
Partners are uniquely positioned to translate AI capabilities into business outcomes.
Spotlight: Intermedia’s Practical AI and Partner-First Approach
Intermedia provides an example of how vendors can align AI innovation with SMB realities and channel requirements. The company serves more than 145,000 businesses globally through over 7,500 channel partners and generates approximately 90% of its revenue through partners.
Its AI strategy focuses on practical, integrated use cases rather than standalone point solutions — AI receptionists, conversation intelligence, workflow automation, sentiment analysis, real-time assistance, and call and meeting summaries, all delivered through a unified communications and contact center environment.
A key differentiator is Intermedia’s emphasis on an integrated orchestration layer and shared communications data foundation. Rather than requiring SMBs to stitch together separate tools, the company embeds AI across communications workflows while maintaining a unified administrative experience.
Equally important, Intermedia continues to invest heavily in partner enablement — including AI-powered marketing tools, automated data-cleaning capabilities, competitive intelligence resources, and sales support platforms designed to help partners improve go-to-market execution and customer outcomes.
The Bottom Line
The future of AI-powered communications in the SMB market will not be defined by the most sophisticated algorithms. It will be defined by the most practical applications.
- SMBs should focus on AI that improves productivity, customer experience, and operational efficiency.
- Channel partners should focus on guiding customers toward measurable outcomes rather than feature-driven deployments.
- Vendors must continue to reduce complexity by embedding AI into the communications tools employees already use.
The organizations that win will not necessarily be those with the most AI. They will be the ones that apply AI most effectively to real business challenges.
