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The Rise of Small Language Models (SLMs)

The Rise of Small Language Models (SLMs)

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Over the past year, large language models (LLMs) have dominated the AI conversation, but a quiet revolution is happening behind the scenes. Enterprises are shifting their focus from experimentation to implementation, and in that transition, Small Language Models (SLMs) are emerging as the practical choice!

From AI Hype to Real-World Adoption

Between 2023 and 2024, many enterprises experimented with large, general-purpose AI services from major LLM providers. While these tools proved powerful, organizations quickly realized that relying on third party platforms introduced challenges around data privacy, regulatory compliance, and cost control. By 2025, the narrative has shifted from experimentation to practical integration. Businesses are now seeking AI solutions that are tailored to their needs, systems that fit neatly within existing infrastructure, align with compliance requirements, and deliver measurable ROI. 

Smaller language models (SLMs) have become the preferred choice for companies looking to embed AI directly into their operations. Unlike large, externally hosted LLMs, SLMs can often be deployed and managed within a company’s own environment. They offer greater control over data privacy, regulatory compliance, and ongoing operational costs. For example, a regional bank adopting a 1B-parameter SLM to handle customer query automation internally, reducing data transfer to third-party clouds, cutting operational expenses, and maintaining full control over customer data privacy and compliance.

What’s Driving the Shift Toward SLMs

SLMs are smaller and more efficient, allowing organizations to run them within their corporate infrastructure budget without the need for massive, AI focused data centers. This approach delivers faster performance, reduced dependency on external providers, and stronger control over sensitive data. For instance, a manufacturing company recently implemented an SLM to automatically summarize maintenance tickets directly on its local servers, improving response times by 40% and reducing downtime across production lines.

Security and compliance have also become decisive factors. Many industries, especially healthcare, finance, and government, need data to stay inside their walls. With SLMs, they can keep sensitive information within their own networks. A healthcare provider, for example, can fine-tune a local SLM on anonymized patient data to securely generate visit summaries while maintaining full compliance with HIPAA standards.

Finally, cost control is a major motivator. The usage-based pricing of commercial LLM APIs can quickly escalate, especially for enterprises running thousands of queries daily. SLMs provide a more affordable alternative, offering comparable intelligence for many operational tasks at a fraction of the cost. One logistics company saw its monthly AI expenses drop by 60% after moving from a commercial API to a lightweight, open-source SLM hosted privately, demonstrating that efficiency and economy can go hand in hand.

Where SLMs Are Making the Biggest Impact

SLMs are proving their value across several key business functions. At customer experience, they power real-time chatbots and knowledge assistants trained on company data, providing faster and more relevant responses than generic models. In operations, SLMs automate document processing, report generation, and task triage, all while running efficiently on internal infrastructure. This helps teams streamline workflows without relying on external systems. For analytics and insights, SLMs are being embedded into BI dashboards to enable contextual Q&A and analysis without exposing proprietary data, giving decision-makers secure, on-demand access to insights. And in IT and security, SLMs act as internal AI copilots that assist staff with coding, anomaly detection, and compliance documentation, all within tightly controlled, secure environments.

How CCG Accelerates Enterprise Adoption

Many organizations understand why they want to adopt SLMs, but struggle with how to deploy them effectively. CCG helps identify and validate specific, ROI-driven use cases for AI and automation. Our team develops and aligns the optimal technical approach, sourcing the right technology partners as needed to ensure seamless execution and measurable outcomes..

To maximize performance, we also oversee implementation and help ensure the application is properly managed, maintained, and optimized for long-term success. Adoption is accelerated this way by ensuring that there is good data on which to base the SLM! Finally, we assist in building governance frameworks that promote AI compliance and risk management.

In one example, CCG supported a mid-size financial institution in deploying an on-prem SLM to automate Know Your Customer (KYC) document reviews. The result was a 70% reduction in turnaround time while maintaining complete data control and regulatory compliance!

Ready to make AI work for your business? Connect with us to learn how purpose-built SLMs can help you move from experimentation to real-world impact!

Boost Agent Performance and CX with Five9

Boost Agent Performance and CX with Five9

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Today’s customers demand more than just quick answers; they seek fast, personalized, and consistent support across all channels. The customer experience (CX) has emerged as a crucial differentiator for businesses across every industry, whether it’s through phone, chat, SMS, or social media.

Unfortunately, many organizations are still relying on legacy contact center systems that weren’t built to meet these demands, especially at scale. That’s why businesses are moving to cloud-based solutions that offer flexibility, AI-driven intelligence, and omnichannel support.

CCG partners with Five9, a leading provider of cloud contact center solutions, to help clients modernize and scale their CX operations. Together, we empower organizations to not only improve customer satisfaction but also boost agent performance and operational efficiency.

Five9’s Core Capabilities

Five9 delivers a comprehensive, cloud-native platform designed to meet the needs of modern businesses and their customers.

CCaaS Platform: Omnichannel by Design

With Five9, customer engagement happens on a single, unified platform that supports voice, chat, email, social, SMS, and video. Built for scale, Five9 offers 99.999% uptime and flexibility that’s ideal for hybrid and distributed teams.

Seamless CRM and UC Integrations

Contact center success often depends on how well it connects to existing systems. Five9 integrates seamlessly with Salesforce, ServiceNow, Microsoft Teams, Zoom, and many other platforms. CCG ensures these integrations are customized for each client’s tech stack, creating workflows that save time and reduce friction.

AI and Automation

Five9 puts artificial intelligence to work where it matters most, empowering both customers and agents. Features like intelligent virtual agents, predictive analytics, real-time transcription, and automated workflows help speed up resolutions and improve accuracy. With Five9 Genius and GenAI Studio, businesses can design smarter interactions and provide real-time guidance that boosts both CX and agent confidence.

Workforce Engagement Management (WEM) Tools

Agents are the heart of any contact center. Five9’s WEM suite provides tools for forecasting, scheduling, performance tracking, and quality management. Add in gamification and coaching features, and organizations have everything they need to engage, retain, and empower their teams.

Professional Services and Ongoing Support

Beyond the platform, Five9 offers training, consulting, and optimization services. Partnering with CCG amplifies this value; we bring proven deployment expertise, industry insights, and continuous support that ensures clients get the most from their investment.

How CCG Enhances Five9’s Value for Clients

While Five9 provides the technology foundation, CCG ensures that it’s implemented and optimized to fit each organization’s unique needs.

Smooth Cloud Migrations & Implementation

CCG helps businesses move from legacy or on-prem systems to Five9’s cloud platform with minimal disruption. Our proven processes reduce risk and accelerate time-to-value.

Tailored Integrations and Workflows

From Salesforce to Microsoft Teams, CCG ensures every integration is aligned with business needs. We also design AI-powered routing logic, automation flows, and custom workflows that streamline operations and drive measurable improvements.

Industry-Specific Expertise

Different industries face different CX challenges. With experience in financial services, healthcare, retail, insurance, and more, CCG delivers tailored solutions that account for compliance requirements, customer expectations, and scalability needs.

Workforce Optimization

Technology alone won’t improve performance, strategy matters. CCG helps configure Five9’s WEM tools, building custom dashboards, gamification strategies, and coaching frameworks that make agents more effective and engaged.

Our support doesn’t stop after deployment. CCG provides post-launch services including platform updates, AI enhancements, and ongoing optimization plans. Clients gain a partner who evolves with them as their needs change.

Real-World Value for Clients

When businesses choose Five9 with Cloud Communications Group, they gain more than just a technology upgrade where they unlock a more scalable, efficient, and customer-centric way of working. With the flexibility of the cloud, organizations can deploy faster while reducing IT burden, all while leveraging AI-driven automation to streamline service and personalize every interaction. Agents benefit from better engagement tools, performance tracking, and coaching resources that make their jobs easier and more rewarding. Seamless integration with CRM, UC, and other business systems ensures workflows stay connected and efficient, while CCG’s tailored approach keeps every solution aligned with specific KPIs and industry requirements. The result is improved operational efficiency, happier agents, and a customer experience that drives loyalty and long-term growth. Contact us today to elevate your CX!

How Businesses Can Safely Harness the Power of Artificial Intelligence

How Businesses Can Safely Harness the Power of Artificial Intelligence

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Table of Contents

AI is transforming the way we work, but it’s also creating confusion. From ChatGPT to “agents,” the AI landscape can seem like an alphabet soup of technologies that promise everything from instant insights to fully automated workflows. 

In a recent conversation between CCG’s Mike Stute, resident AI and machine learning expert, and Tyler Dool, an AI power user and client partner, the two explored what’s really behind terms like Chat AI and Agentic AI, how they differ, and how organizations can use them safely and effectively.

Chat AI: Your Digital Research Assistant

When most people think of AI today, they’re thinking of Chat AI, tools like ChatGPT, Claude, or Gemini. These are built on large language models (LLMs) that have been pre-trained on massive amounts of text data to predict and generate human-like language.

But Chat AI can do more than just “chat.” When properly used, it can:

  • Summarize lengthy documents or research papers
  • Draft content and client communications
    Analyze meeting transcripts or contracts
  • Generate quick insights from unstructured data

As Mike explained, Chat AI becomes truly powerful when combined with three key elements:

  1. Data: What information the model has access to.
  2. Model Selection: Choosing the right LLM for the job.
  3. Prompting: The art of asking the right question for the right output.

Tyler and our CCG team use Chat AI models privately, within secure environments, to create dedicated “workspaces” for clients. Each workspace acts as a knowledge hub where project data, notes, and transcripts are stored safely and can be queried on demand. The result: faster analysis, better memory, and more accurate client deliverables without exposing sensitive information.

Agentic AI: Your Workflow Automator

If Chat AI is like a digital analyst, Agentic AI is more like a digital employee.

Agentic AI systems go beyond answering questions, they perform multi-step workflows. These agents can make decisions, interact with software, and execute tasks based on changing conditions.

Mike described it this way: Chat AI helps understand and summarize data, while Agentic AI helps act on it.

Examples include:

  • Automating HR onboarding and offboarding processes
  • Managing logistics, like assigning truck routes based on cargo type and conditions
  • Processing customer requests through AI-driven ticketing or service systems

Unlike traditional automation (which depends on rigid “if/then” rules), agentic systems use reasoning and natural language understanding to adapt. For instance, an AI can interpret a driver saying, “I can’t see because of snow,” the same as “Visibility is bad due to a blizzard,” and act accordingly.

Public vs. Private AI

With all this potential comes risk. Many users don’t realize that when they upload data into public LLMs (like free ChatGPT or Gemini accounts), that information could be used to further train those models.

That’s why we developed a private AI environment, an internal AI server running smaller, specialized models. This setup gives their team:

  • Full data privacy (no information sent to public servers)
  • Control over model behavior and updates
  • Flexibility to fine-tune models for specific clients or workflows

Mike also outlined the three tiers of AI deployment:

  1. Public models: Inexpensive but risky; limited control and potential data exposure.
  2. Intermediate models: Managed by third parties that act as a compliance filter between you and the LLM.
  3. Private models: Fully self-hosted, secure, and customizable. The best option for organizations prioritizing data security and consistency.

Tyler emphasized the real-world importance: IT leaders don’t want to block innovation, but they also can’t risk sensitive data leaking. Segmenting access by role or data sensitivity lets teams safely use both public and private models under one roof.

The Future of AI in Business

When asked which industries can benefit from AI, Tyler’s answer was simple: all of them.

From marketing and sales to construction and logistics, AI is already finding its way into every corner of business. One example he shared involved a construction firm whose field staff use AI-powered mobile tools to record spoken site updates. The AI automatically organizes their comments, photos, and videos into clean reports, saving hours of manual note-taking and reducing errors.

As younger, tech-native generations enter the workforce, the expectation to use AI tools will only increase. Businesses that ignore AI risk falling behind, not because the tech replaces humans, but because it amplifies what humans can do.

Safe Adoption Starts with Strategy

Mike and Tyler agreed on a few best practices:

  • Start with defined use cases: Don’t deploy AI for its own sake, use it where it saves time or improves accuracy.
  • Build a governance model: Determine which data can go to public tools and which must stay private.
  • Invest in education: Train employees not just to use AI, but to understand its limitations and risks.
  • Fine-tune for your business: Smaller, customized models can outperform generic ones when focused on your organization’s data.

Learn more about how CCG can help your business harness AI responsibly and be sure to watch the full video here!

Building Custom Automations: Crawl → Walk → Run Framework

Building Custom Automations: Crawl → Walk → Run Framework

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Why Crawl, Walk and Run?

Businesses today are under increasing pressure to adopt AI and automation. Leaders see the potential for faster operations, smarter workflows, and better customer experiences. However, many initiatives fail because they are rushed, unstructured, or lack a solid foundation.

Employees often experiment with “shadow AI” tools that introduce compliance and security risks. IT teams struggle to keep up with the demand for AI while ensuring governance and protection of sensitive data. Leadership, eager for results, may invest in large-scale automation before the groundwork is in place, which leads to low adoption and wasted resources.

The “Crawl → Walk → Run” framework provides a phased roadmap for adopting automation responsibly. Each stage builds on the last: Crawl focuses on laying the foundation, Walk on streamlining processes, and Run on scaling with custom builds.

Crawl: Establishing the Foundation

The objective is to build trust, introduce AI safely, and eliminate shadow AI within the organization. By introducing a company AI assistant, employees gain access to a sanctioned, user-friendly tool that supports everyday tasks such as summarizing documents, drafting responses, and answering company FAQs. This approach delivers immediate value without requiring deep integrations or extensive technical effort. Compliance and visibility are built in, every interaction is logged to create a clear audit trail, and all sensitive information remains within approved environments to ensure data protection!

In addressing the risk of shadow AI, employees no longer need to rely on external tools like ChatGPT or other consumer-grade assistants. Centralizing AI access mitigates data leakage risks and ensures consistent guardrails. Beyond security, the initiative fosters cultural benefits: employees develop confidence and AI literacy in a safe setting, while leaders gain visibility into AI usage patterns and emerging needs. Ultimately, this establishes a secure, trusted foundation for AI adoption, achieving early wins, reducing risk, and gathering valuable insights for future opportunities.

Walk: Automating Processes

The purpose of this stage is to expand beyond individual productivity and begin streamlining team and organizational workflows. This marks a shift in focus, from supporting specific employees to removing bottlenecks that slow down cross-departmental work. Here, automation evolves from being a convenience to becoming an operational necessity! 

Practical use cases span functions. Within HR, it can automate onboarding, document collection, and answering employee questions. In the Finance sector, it can help handle invoice approvals, expense processing, and regulatory reporting. In Operations, streamlining ticket routing, inventory tracking, and scheduling; and at Customer Support, managing first-line inquiries, ticket triage, and maintaining the knowledge base. The benefits are clear that repetitive, rules-based tasks are consistently managed at scale, freeing employees to focus on strategic work. 

As a result, workflows become faster, more predictable, and less prone to error. The business impact is tangible, productivity gains are measurable, cost savings are realized, and both customers and employees experience faster, more reliable service. Ultimately, AI becomes embedded into daily business operations, proving its ROI and building momentum for more advanced initiatives.

Run: Scaling with Custom Builds

The goal at this third stage is to move beyond operational efficiency and begin creating true business differentiation through AI. While off-the-shelf automation tools can deliver immediate value, they rarely provide a lasting competitive edge. Real advantage comes from custom AI solutions, built around an organization’s unique systems, data, and goals, that unlock innovation competitors cannot easily replicate. 

In this “Run” phase, companies develop bespoke AI workflows, models, and applications that integrate deeply into enterprise systems such as CRMs, ERPs, and data warehouses. AI is customized to meet specific compliance, regulatory, and customer requirements, and specialized models are trained using proprietary company data. Examples of such custom builds include a bank deploying real-time fraud detection powered by AI-driven signals, a retailer offering personalized promotions based on individual customer histories, or a logistics company predicting supply chain disruptions and automatically rerouting shipments. 

To scale effectively, organizations must establish strong governance and monitoring frameworks to maintain compliance, while continuously refining models based on real-world performance. Training and change management are equally vital to ensure adoption across the enterprise. The outcome is a transformation of automation from a support function into a strategic differentiator, one that fuels growth, strengthens resilience, and drives long-term innovation.

The Roadmap to Responsible Automation with CCG by Your Side

Moving too fast invites risk and moving too slowly risks being left behind! A phased, structured approach unlocks compounding value at every stage while keeping governance, security, and adoption in check.

CCG is the right partner to guide organizations through every stage of their AI and automation journey. With deep strategic and technical solutions, we provide the strategic guidance and technology advisory needed to navigate the complexities of infrastructure, integrations, and vendor selection, ensuring every automation initiative is both technically feasible and aligned with business goals. 

First, assess where your organization currently resides in the “Crawl → Walk → Run” continuum. Then, engage with CCG to map out the right next steps, whether that’s selecting your first AI assistant, designing process automations, or building bespoke models. Together, we can begin or accelerate the journey toward structured, scalable automation. Contact us today to unlock value at each phase while maintaining control, compliance, and alignment to business needs!

What Are Small Language Models (SLMs) and Why Enterprises Need Them

What Are Small Language Models (SLMs) and Why Enterprises Need Them

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Enterprises are quickly realizing that large language models (LLMs) aren’t always the best fit for every task. While powerful, they’re often expensive, resource-intensive, and difficult to control.

This is where small language models (SLMs) come in. These models are designed to be lighter, faster, and easier to deploy, making them highly practical for enterprise use cases. As AI adoption matures, SLMs are becoming a strategic tool for companies that want both efficiency and performance without the high costs of LLMs!

What Are SLMs?

SLMs are compact AI models designed to handle language-based tasks with fewer parameters than LLMs. Instead of aiming for broad, general intelligence, they focus on doing specific jobs well. They require less computing power, deliver faster responses, and can often be run on standard enterprise infrastructure. This makes them more accessible for businesses that don’t want to rely solely on heavy cloud setups.

How SLMs Differ From LLMs

LLMs often need clusters of GPUs and massive cloud spend, while SLMs can run efficiently on local servers or private cloud environments. Additionally, LLMs shine in open-ended, creative scenarios, but SLMs excel at speed, focus, and precision: qualities that matter most in enterprise operations. Models like Microsoft’s Phi, Mistral, and Meta’s LLaMA variants showcase how the AI landscape is shifting toward smaller, more specialized options.

SLMs vs. LLMs: Choosing the Right Model

LLMs should be used in broad, creative, and open-ended tasks where general knowledge is needed. SLMs should be used in predictable, structured, or compliance-heavy workflows where speed, security, and cost matter most. Many enterprises find value in combining the two by using LLMs for exploration and SLMs for execution.

Why Enterprises Need SLMs

SLMs offer clear advantages when it comes to cost efficiency and scalability. Their smaller size significantly reduces infrastructure and cloud costs, making it possible to scale AI across multiple teams or locations without ballooning budgets. They also deliver greater speed and agility. Faster response times are crucial for real-time applications like customer support, and because SLMs are easier to fine-tune, they can be quickly adapted for niche use cases within an industry.

Data security and privacy are another strength. SLMs can be deployed on-premises or in private clouds, giving enterprises full control over sensitive data. This approach also reduces dependence on third-party APIs that may expose information to external providers. When it comes to high performance on specific tasks, SLMs often outperform LLMs in narrow, domain-focused work. They excel in areas such as compliance reviews, automated internal support, or industry-specific workflows where accuracy matters more than creativity.

Finally, SLMs are far more energy efficient and sustainable. With smaller compute needs, they consume much less energy than their larger counterparts. This not only lowers costs but also helps companies meet sustainability goals and ESG commitments.

Enterprise Use Cases for SLMs

SLMs have a wide range of enterprise use cases. They can automate customer service chatbots with faster, more reliable responses, ensuring smoother interactions and are valuable for reviewing compliance and risk-related documents at scale, helping organizations handle large volumes of critical information more efficiently.

Another application is in building knowledge management and search tools that improve employee productivity by making information easier to access and use. SLMs can also power edge AI in IoT devices, retail kiosks, or field operations where resources are limited, making them practical in environments that demand lightweight solutions. They support everyday tasks like summarization, drafting, and workflow automation, all of which contribute to greater efficiency across teams.

How CCG Can Help Enterprises Leverage SLMs

We specialize in building and optimizing cloud, hybrid, and on-premises environments which is exactly the kind of setups where SLMs thrive. They help enterprises right-size infrastructure to balance performance and cost. With partnerships across major providers, we can help organizations navigate the crowded AI ecosystem to select the right mix of SLMs, LLMs, and supporting platforms. Our deep focus on security ensures that enterprises deploying SLMs in regulated industries (finance, healthcare, government) can maintain compliance while keeping sensitive data protected. Beyond deployment, we can guide enterprises to build governance frameworks to ensure AI is accurate, ethical, and aligned with business goals

From initial proof-of-concept projects to full enterprise-wide adoption, CCG guides companies through every stage of integrating SLMs into business operations. AI isn’t set-and-forget! Contact us today for ongoing support, monitoring, and optimization to ensure enterprises like yours continue to see ROI as AI models evolve!

Accelerating Business Transformation with Dialpad on the Google Cloud Marketplace

Accelerating Business Transformation with Dialpad on the Google Cloud Marketplace

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For today’s business leaders, digital transformation isn’t optional, it’s essential. The challenge? Driving efficiency, reducing costs, and scaling collaboration while staying agile. That’s why Cloud Communications Group (CCG) partners with Dialpad on the Google Cloud Marketplace: to help executives modernize communications faster, smarter, and more cost-effectively.

CCG is a leading IT advisory and expense management firm, guiding enterprises of all sizes in modernizing technology strategies without overspending. Our vendor-agnostic approach ensures we recommend solutions based solely on what delivers measurable impact for your business. One of our most powerful partnerships is with Dialpad, whose AI-powered communications platform transforms how teams connect, collaborate, and serve customers.

Why the Google Cloud Marketplace is a Game-Changer

Purchasing Dialpad through the Google Cloud Marketplace isn’t just about convenience, it unlocks strategic advantages that traditional procurement processes can’t match.

Commit Drawdown Advantage

If your organization already has a committed Google Cloud spend, CCG ensures your Dialpad purchase counts toward that commitment. This means you can modernize communications while meeting contractual spend obligations.

Flexible Procurement

Every organization’s procurement process is different. CCG enables clients to purchase through the Marketplace or our trusted partner network, managing coordination between procurement, finance, and IT. This reduces administrative overhead and keeps your focus on business outcomes.

Trusted & Recognized

Dialpad is a proven leader in cloud communications, validated by top Google certifications:

  • Google Cloud Tech ISV Partner of the Year – Productivity & Collaboration (2023)
  • Chrome Enterprise Recommended
  • Google Workspace Recommended

These designations confirm Dialpad’s performance, reliability, and seamless integration with Google Cloud solutions.

Business Benefits of Dialpad on Google Cloud Marketplace

Faster Procurement

Organizations can speed up acquisition by as much as 45% through the Marketplace. With CCG managing the process, approvals move quickly and bottlenecks are minimized, ensuring teams begin using Dialpad sooner.

Faster Deployment

Deployment cycles are up to 50% faster because Dialpad integrates seamlessly with Google Workspace. CCG handles setup, migration, and user training, ensuring teams adopt the platform smoothly and efficiently.

Lower Software Costs

Dialpad consolidates multiple communication tools into a single platform, reducing third-party software costs by up to 21%. CCG amplifies these savings by identifying and eliminating redundant technology, maximizing your technology investment.

Increased Productivity for Finance Teams

Finance teams experience a 25% productivity boost thanks to simplified billing and reduced technical disruptions. Consolidated invoices and CCG’s guidance in tracking ROI free up time for higher-value financial tasks.

Maximizing Dialpad’s Value

Purchasing Dialpad is only the first step. True value comes from how it’s implemented, optimized, and supported. CCG evaluates whether Dialpad is the right fit for your communications strategy, considering your existing tech stack, business goals, and budget. Our end-to-end project management ensures a seamless experience from Marketplace purchase to full user onboarding, while cost optimization strategies align your Dialpad investment with Google Cloud commit drawdowns for maximum savings.

We also provide strategic technology roadmapping to ensure Dialpad supports your long-term vision for unified communications, AI integration, and scalable growth. For example, one of our clients onboarded 7,500 users in under 60 days, simultaneously reducing overlapping software spend by 18%—a clear demonstration of how CCG turns software purchases into enterprise-wide transformation wins.

Let’s Transform Your Communications

If you’re ready to accelerate collaboration, cut costs, and modernize your communication tools, Dialpad on the Google Cloud Marketplace is a smart, future-ready choice. Schedule a strategic conversation with CCG today to explore how we can help you purchase, deploy, and optimize Dialpad for maximum business impact.

How CCG and Five9 Are Powering the Next Era of Agentic CX with Intelligent AI Agents

How CCG and Five9 Are Powering the Next Era of Agentic CX with Intelligent AI Agents

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Customer expectations are evolving fast. People want intelligent, frictionless service that’s both immediate and deeply personalized. Traditional automation and scripted chatbots can’t keep up. That’s why Five9 recently unveiled a major leap forward in customer experience (CX): the launch of AI Agents and a robust AI Trust & Governance framework, designed to enable what they call Agentic CX.

As a strategic Five9 partner, CCG is helping organizations harness these innovations with speed, strategy, and full control – turning advanced AI into real, measurable outcomes.

What Is Agentic CX and Why It Matters

Agentic CX is the next step in AI-powered service. Instead of bots that follow fixed rules or static workflows, Agentic CX uses AI that can reason, decide, and act on its own. These AI Agents understand intent, use contextual knowledge, and adapt to different scenarios in real time. It’s not just automation, it’s intelligent orchestration of customer journeys.

At the core of this new approach is the Five9 Agentic Experience Engine, which takes customer inputs and turns them into intelligent actions. Whether that means answering a billing question, booking a service, or routing to the right live agent, the system makes real-time decisions while preserving a natural, human-like flow.

Smarter Self-Service with AI Agents

Five9’s new AI Agents are designed to deliver natural, hyper-personalized self-service at scale. They combine generative AI, intent detection, and enterprise knowledge integration to provide real-time answers, execute tasks, and escalate seamlessly when human support is needed.

Conversational and Context-Aware AI supports voice and digital channels, including multilingual text-to-speech and voice biometrics and handles both simple questions and complex workflows using natural language. Built in the low-code/no-code AI Agent Studio, they offer fast deployment with industry-specific templates that reduce development time and cost.

Advanced capabilities include intent detection and entity extraction using LLMs, the Knowledge Node for real-time contextual answers, the AI Summary Node for automatic interaction summaries, and Code Crafter for rapid JavaScript generation. Their omnichannel design ensures consistent experiences across preferred customer channels while enabling smooth, context-rich handoffs to live agents without losing history. With these features, businesses gain greater automation coverage, faster time-to-value, and a better overall customer experience.

Built-In AI Governance: Trust, Control, and Safety

Powerful AI needs powerful guardrails. Five9’s AI Trust & Governance layer ensures that businesses can scale AI securely, with full transparency and control.

Why does governance matter? Generative AI introduces risk: erratic outputs, compliance violations, data exposure. Five9’s AI Trust & Governance toolkit embeds safety and oversight across every AI layer.

Key Governance features include Granular Guardrails, which define autonomy, tone, and permissions by task, team, or channel. 

  • Prompt Monitoring tracks and optimizes prompt performance and completeness. 
  • Hallucination Detection flags and corrects false or off-brand outputs in real time. 
  • Threat Detection identifies risks such as prompt injection or data misuse before they escalate. 

FlexLM for Long-Term Flexibility lets teams choose or change LLM providers per use case without added security audits and future-proofs AI investments while supporting evolving enterprise needs. Together, these features give your team real-time observability, safe scalability, and confidence in every AI interaction.

How Companies Are Succeeding with Five9 AI

The impact of Five9’s AI Agents and governance tools isn’t theoretical, it’s happening now.

At Exact Sciences, AI Agents delivered a 45% self-service containment rate for carrier pickup calls and reduced handling time by 60%, boosting both agent productivity and customer satisfaction. Teladoc Health used AI Agent Assist to enable HIPAA-compliant, real-time call guidance, achieving 97% customer satisfaction and 99% uptime while empowering agents to respond faster, stay compliant, and provide higher-quality care. 

These real-world examples prove that when implemented thoughtfully, AI-powered CX can drive both operational efficiency and exceptional customer experiences!




CCG Presents at CTO Roundtable Unlocking Business Value with AI & Data in Montana

CCG Presents at CTO Roundtable Unlocking Business Value with AI & Data in Montana

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On August 20th, Cloud Communications Group (CCG) hosted a roundtable in Montana, bringing business and technology leaders together to explore how AI is transforming organizations today.

The session, led by CCG’s Keith Hatley, Chris Moffett, and Mike Stute, traced the rapid evolution of AI and its move from experimentation to real business impact.

From Potential to Practical

The conversation began with the history of AI’s exponential growth and how its adoption has accelerated as tools become embedded in the platforms companies already use. While early exploration often focused on “what’s possible,” today’s leaders are asking: How do we drive measurable results?

Real-World Use Cases

To answer that, the roundtable highlighted tangible examples of AI in action:

  • Logistics & Dispatching: Optimizing Routes with AI

In the roundtable, the team shared how AI is transforming logistics by helping drivers quickly match loads to their truck capacity, location, and destination. Instead of filling out complex forms or waiting on slow systems, a driver can simply ask, “I’m in Missoula heading to Nebraska, what loads are available?” The AI then pulls real-time data from GPS, load boards, and company databases to recommend the best options and most efficient route. This shift reduces wasted time, improves resource allocation, and allows drivers to focus on the road rather than navigating cumbersome processes.

  • Financial Reconciliation & Compliance: Automating Accuracy

During the roundtable, the team explained how AI can tackle one of the toughest back-office challenges: reconciling financial records across messy, inconsistent data. For example, “ABC Company” might appear in systems as ABC, A.B.C., or ABC Inc., a nightmare for traditional reconciliation software. AI quickly recognizes these as the same entity, cleans the data, and then matches incoming receivables with payments across divisions, sales teams, or regions. The result is faster, more accurate reconciliations, reduced manual effort, and stronger compliance oversight. By automating these repetitive reviews, organizations save significant time while improving confidence in their financial reporting.

These examples underscored how AI isn’t just a theoretical tool but a driver of efficiency and better decision-making in data-heavy industries.

What Executives Need to Know

Another recurring theme was the responsibility of leadership teams. Executives must understand both the opportunities and risks of AI in order to guide their organizations forward, balancing innovation with governance, compliance, and trust.

A Space for Connection

Beyond strategy, the event offered a chance for leaders to step away from daily demands, connect with peers, and share candid perspectives. The setting fostered collaboration, helping participants not only exchange insights but also build relationships that extend beyond the session itself.

Continuing the Conversation

CCG is proud to facilitate these discussions and remains committed to helping organizations embrace AI with confidence.

Watch the full roundtable discussion here: Unlocking Business Value with AI & Data

From Fiction to Fact: A Night with CCG on the Future of AI

From Fiction to Fact: A Night with CCG on the Future of AI

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Last week, Cloud Communications Group hosted an exclusive dinner event designed to spark relationships and ideas. Held in an intimate setting, the evening brought together a curated group of executives and IT leaders for good food, great conversation, and an eye-opening presentation on the rapid rise of artificial intelligence.

Accelerating into the Future

Keith Hatley and Mike Stute from CCG led a 25-minute session titled From Fiction to Fact, diving into AI’s historical roots and its exponential rise in the modern business landscape. Their message was clear: the pace of change is no longer linear, it’s exponential. Businesses that don’t plan for that acceleration are already behind.

They shared jaw-dropping projections, including AI’s expected $15.7 trillion impact on global GDP within the next five years. With examples of billion-dollar companies built on exponential tech models, it became clear that artificial intelligence isn’t just a trend, it’s a paradigm shift.

A Journey Through AI’s Origins

Mike took attendees on a fascinating journey through the history of artificial intelligence. From the chess-playing automaton of the 1700s to HAL 9000, Eliza, and Deep Blue. The presentation made one thing clear: while AI may seem like science fiction, it has long been part of humanity’s technological aspirations. Now, that fiction is fast becoming fact.

Looking Ahead

As the evening wrapped, the conversation continued around the table with ideas of future webinars, potential AI roundtables, and even a whiskey-and-cigars night with a recording session already in the works! For CCG, this event was about more than thought leadership, it was about creating lasting partnerships with forward-thinking leaders ready to embrace tomorrow. If you missed this dinner but want to be part of the next conversation, reach out! AI is moving fast and so are we.

How Are Large Language Models (LLMs) Trained? A Deep Dive into AI Learning

How Are Large Language Models (LLMs) Trained? A Deep Dive into AI Learning

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Even if you haven’t heard of the term “Large Language Model,” you’ve probably heard of some LLM products; ChatGPT and DeepSeek are some of the most popular LLMs available on the market today. Large Language Models (LLMs) are a type of AI that can generate text and even emulate human language based on context from written or even spoken prompts. They have several uses that make them invaluable to organizations all over the world, such as data summarization, report generation, idea creation, and much more. In order to achieve this level of capability, LLMs need to be trained to generate the results that you want, and the process for doing so requires large amounts of GPUs and TPUs to accomplish. However, it’s still important for IT leaders to understand how this process works on a high level. Let’s review the steps needed to effectively train a Large Language Model and why each step is important to creating a useful AI application.

Collect Data and Create Datasets

LLMs require vast amounts of data to perform a desired task, so the data collection step is crucial to the success of the LLM. This data needs to be converted into usable datasets for the LLM to reference, and the text must have corresponding sources to ensure accuracy. Generally speaking, the more data that is fed to an LLM, the more accurate its answers and responses will be. 

Set Up The Transformer Neural Network

The transformer neural network is the architecture that defines how an LLM processes data; it’s like a massive network of synthetic neurons that determines a LLM’s operational capabilities. An important part of this process is tokenization, which involves turning large datasets into smaller, encoded units that are easier for the LLM to understand. These “tokens” are essential to forming the foundation for LLMs, as the transformer neural network can more easily handle these pieces of information to accomplish NLP tasks.

Train the LLM

Now that the LLM has its datasets and parameters in place, it’s finally time to begin training the system. As mentioned previously, training an LLM requires vast amounts of processing power to complete; not only do they have billions of parameters that need to be processed, but they also need to be fine-tuned, which requires even more processing power. However, there are some training methods that require human involvement. One effective training method is creating a system where the LLM adjusts its responses to queries based on human feedback. Doing this allows for more accurate responses and eliminates the possibility of grammatical, factual, and other types of errors.

Fine-Tune the Model and Make Adjustments

The process for configuring a LLM does not end with the training process; it’s crucial to conduct a series of tests on the model to fine-tune its capabilities. Creating a reward model–a bit of code that can label data as ideal or not ideal–can further fine-tune the LLM at a faster pace than human testers. The fine-tuning process is also an ideal time to input highly-specific information that the LLM could use to create more realistic results. For example, if an LLM is being fine-tuned to instantly solve mathematical problems, it can be fine-tuned with knowledge of specialized equations to help it solve a wider variety of problems. 

Explore the Possibilities of LLMs with CCG!

LLMs have tremendous potential in the world of business, but there are a lot of options available for IT leaders to choose from. At CCG, our team can work with you to evaluate your business’s short and long-term needs and find an LLM application that addresses your goals. We’re partners with several of the industry’s top IT providers, and we’re committed to finding solutions that can create an immediate impact on your operations. Ready to join the AI revolutions? Start a conversation with our team today to get started!

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