Transform Your Business with AI: Maximizing Value
Artificial intelligence has transitioned from a future technology to a present-day competitive necessity for small and medium-sized businesses (SMBs). Recent surveys indicate that 42% of SMBs are already using AI, with over half seeing financial savings as a result.
By 2025, nearly one-third of small businesses plan to prioritize AI investments, underscoring an urgent need for a thoughtful implementation strategy.
We're AI-riam
At AIriam, we’re actively helping our clients through this transition strategically to maximize value and drive key business outcomes.
Based on our experience, we provide an overview of two powerful approaches that SMBs should consider in starting and continuing their AI journey – Microsoft 365 Copilot and private Large Language Models (LLMs). We also provide a high-level framework for SMBs to determine the optimal AI implementation approach.
Business Cases for AI in SMBs
The adoption of AI technologies offers SMBs significant advantages in today’s competitive marketplace:
Operational Efficiency
AI automates routine tasks, reducing labor costs and human error while allowing staff to focus on higher-value activities. Nearly 75% of employees report increased productivity in companies that have deployed AI effectively.
Enhanced Decision Making
AI-powered analytics provide deeper insights from business data, enabling more informed strategic choices. This is particularly valuable for SMBs that may lack dedicated data analysis teams.
Competitive Differentiation
AI capabilities can create unique customer experiences and service offerings that distinguish SMBs from competitors, helping smaller businesses compete with larger enterprises.
Scalability
AI systems can handle growing workloads without linear increases in overhead, making them ideal for growing businesses with limited resources.
AI Implementation Options: Microsoft Copilot and Private LLMs
Microsoft 365 Copilot
Microsoft 365 Copilot is an AI assistant embedded within the Microsoft 365 ecosystem. It appears seamlessly in familiar applications, combining advanced language models with your organizational. Microsoft-based data—including documents, presentations, emails, files, meetings, and chats. Acting as an AI colleague in your workflow, Copilot helps draft emails, summarize meetings, analyze data, create presentations, and more—all within the tools your team already uses daily.
Private Large Language Models (LLMs)
Private LLMs are custom AI models deployed in your own environment or cloud. Unlike public AI tools, private LLMs can be trained or configured on your specific business data—whether that’s product information, internal knowledge bases, customer records, or industry research. They offer maximum flexibility, allowing you to deploy AI capabilities wherever needed, not just within Microsoft applications.
Both Microsoft Copilot and private LLMs are complementary technologies that solve different problems in different ways.
Microsoft Copilot excels at general productivity enhancement within the Microsoft 365 world, while private LLMs can be targeted to specific needs, especially for custom applications or with unique datasets.
Comparison: Microsoft Copilot vs Private LLMs
Microsoft Copilot:
- Seamlessly integrated within Microsoft 365 applications (Word, Excel, PowerPoint, Outlook, Teams)
- Minimal learning curve as it appears in familiar interfaces
- “In-the-flow” productivity with no new applications to learn
- Quick deployment for users already on Microsoft 365
Private LLMs:
- Requires custom integration but can be embedded anywhere—websites, custom apps, existing business systems
- More development work needed, but offers freedom to implement AI beyond the Microsoft ecosystem
- Can be designed for specific workflows and user experiences
- Potential to create streamlined, purpose-built AI tools
Microsoft Copilot:
- Leverages content in your Microsoft 365 tenant (SharePoint, OneDrive, Teams chats, Exchange emails)
- Uses a Semantic Index to map your internal data for context-rich responses
- Knowledge is bounded by what’s in Microsoft Graph—data in external systems must be imported to be accessible
- Benefits from regular updates to Microsoft’s underlying models (currently GPT-4)
Private LLMs:
- Can be trained or configured on any data you choose—internal wikis, product databases, proprietary research
- Capable of incorporating industry-specific knowledge, jargon, and data
- Can be connected to live data sources (using Retrieval Augmented Generation) for up-to-date information
- Customizable for specific domain knowledge and continuously adaptable as you feed new data
Microsoft Copilot:
- Operates within your existing Microsoft 365 security and compliance framework
- Inherits all your existing security, privacy, identity, and compliance settings, ensuring people only see what they have permission to access
- Does not use your prompts or content to train public models
- Data stays within Microsoft’s cloud boundary for your tenant
- Effectiveness depends on proper setup of permissions and data governance
Private LLMs:
- Keeps data in-house, reducing risks associated with data breaches and ensuring compliance with data protection regulations
- Provides maximal data control with security by isolation—sensitive data never leaves your infrastructure
- Can be deployed in your controlled cloud or on-premises environment
- Requires you to actively maintain security and compliance measures
- Can be configured to meet specific regulatory requirements like HIPAA or GDPR
Microsoft Copilot:
- Limited to Microsoft’s provided functionality and settings
- Designed to generalize across many business domains
- Great for common tasks but less adaptable for highly specialized functions
- Updates and improvements are controlled by Microsoft
Private LLMs:
- Can be customized and tailored specifically to your business needs, providing better accuracy and relevance for specific use cases
- Can be fine-tuned on your proprietary data to understand your specific jargon, processes, and domain
- Allows implementation of Retrieval Augmented Generation (RAG) to connect to live data sources
- Provides long-term adaptability as your business and data evolve
Microsoft Copilot:
- Simple enablement once prerequisites are met (proper licensing, tenant setup)
- Microsoft handles the AI service management behind the scenes
- Requires proper setup of semantic indexing, permissions, and data governance
- Regular updates and improvements handled by Microsoft
Private LLMs:
- More involved technical project requiring infrastructure (cloud or on-prem)
- Requires ML engineering expertise for setup and integration
- Ongoing maintenance needed (model updates, performance monitoring, security patches)
- Higher initial complexity but potentially greater long-term control
Microsoft Copilot:
- $30 per user per month if billed annually ($31.50 if billed monthly), with a 12-month minimum commitment
- Requires qualifying Microsoft 365 license for each user
- Predictable subscription model that scales linearly with user count
- Microsoft has introduced a new Copilot Chat plan with pay-as-you-go pricing, providing a more flexible option for smaller businesses
Private LLMs:
- Variable costs depending on deployment approach (infrastructure, cloud usage, ML operations)
- Can scale based on usage rather than per user
- Potentially lower cost per query at scale
- Higher upfront investment but may be more cost-efficient for specific high-volume scenarios
Making Them Work Together: Hybrid AI Implementation Strategies
Rather than choosing one solution over the other, many SMBs will find value in implementing both Microsoft Copilot and private LLMs in complementary ways:
Use Copilot for general office productivity tasks (drafting emails, summarizing meetings, creating presentations) and deploy private LLMs for specialized tasks like customer-facing applications or domain-specific queries.
For example, a manufacturing company might use Copilot for internal document creation and communication while implementing a private LLM to analyze equipment sensor data and predict maintenance issues.
Use Copilot for less sensitive contexts and private LLMs for highly sensitive data processing. For instance, a professional services firm might use Copilot for marketing content generation while relying on a private LLM to handle confidential client information in a secure environment.
Use insights gained from one system to improve the other. For example, a sales team might use Copilot to draft initial client proposals, then feed that content into a private LLM trained on successful past deals to refine the messaging and pricing strategy.
Start with one solution to build AI competency and culture, then expand to the other. Many SMBs begin with Copilot due to its ease of deployment, then add private LLM capabilities as they identify specific use cases that require more customization or integration.
