AI for SMBs: Maximizing Value

In today’s rapidly evolving business landscape, 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.

At AIriam, we’re actively helping our clients through this transition strategically to maximize value and drive key business outcomes.

Based on our experience, this article provides 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.

The Business Case 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[^3]. 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

Integration & Ease of Use

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

Data Scope & Knowledge

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

Security & Compliance

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

Customization & Flexibility

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

Deployment & Maintenance

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

Cost Considerations

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:

1. Divide by Task Type

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.

2. Data Sensitivity and Compliance Split

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.

3. Training and Augmentation

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.

4. Staged Implementation

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.

Technical Implementation Considerations

Microsoft Copilot Readiness

To successfully implement Microsoft Copilot, organizations should prepare their Microsoft 365 environment:

1. Licensing and Prerequisites: Ensure appropriate Microsoft 365 licensing (Business Standard/Premium or E3/E5) plus Copilot licenses. Microsoft has removed the previous 300-seat minimum requirement, making Copilot more accessible to SMBs.
2. Semantic Index Setup: Configure the Semantic Index for Copilot, which maps your organizational data for AI retrieval. This index respects existing permissions and security boundaries.
3. Data Governance: Review and organize your Microsoft 365 content. Clean up outdated information, apply appropriate sensitivity labels, and ensure permissions are correctly configured.
4. Access Controls: Verify that your permission structure accurately reflects who should access what information, as Copilot will respect these settings when retrieving data.

Private LLM Architecture and Deployment

A typical private LLM implementation relies on Retrieval Augmented Generation (RAG) architecture:

1. Select Model Approach: Choose between hosting an open-source model like Falcon or Meta’s Llama 3, which offer affordable, customizable AI that SMBs can deploy locally or on cloud platforms, or using managed services like Azure OpenAI or AWS Bedrock.
2. Data Preparation: Prepare and index your business data in a vector database, which allows efficient semantic search and retrieval.
3. Orchestration Layer: Implement the logic that takes user queries, retrieves relevant information from your data sources, and constructs prompts for the LLM.
4. User Interface: Create appropriate interfaces (web applications, chatbots, API endpoints) for users to interact with the LLM.
5. Monitoring and Feedback: Implement logging, performance monitoring, and feedback mechanisms to continuously improve results.

Security and Compliance Considerations

For both solutions, prioritize:

1. Access Control: Implement strong authentication and authorization to ensure users only access appropriate information.
2. Data Protection: Use encryption for data at rest and in transit, and implement appropriate retention policies.
3. Audit Logging: Maintain comprehensive logs of AI system usage for compliance and troubleshooting.
4. Content Filtering: Implement guardrails to prevent inappropriate outputs or data leakage.
5. Governance Policies: Develop clear guidelines for AI usage, including review processes for AI-generated content.

Strategic Assessment Framework for SMB Decision-Making

Step 1: Needs Assessment

Evaluate your specific business challenges and opportunities:

• What are your primary pain points that AI could address?
• Which processes consume the most time but deliver relatively low value?
• What specialized knowledge or data could be leveraged with AI?
• Do you have industry-specific requirements that need customized solutions?

Step 2: Resource Evaluation

Assess your available resources:

• What is your budget for AI implementation and ongoing costs?
• What technical expertise exists within your organization?
• Are you already invested in the Microsoft ecosystem?
• What timeline do you have for implementation and seeing results?

Step 3: Risk Assessment

Consider potential risks and constraints:

• What data privacy and security requirements apply to your industry?
• What would be the impact of AI errors or limitations in your use cases?
• Are there regulatory considerations that might impact AI implementation?
• How sensitive is the data you plan to process with AI?

Step 4: Implementation Pathway Selection

Based on your assessment, choose the most appropriate implementation pathway:

Copilot-First Approach

• Best for: SMBs with limited technical resources, need for quick implementation, and use cases centered around Microsoft 365 applications
• Starting point: Implement Microsoft Copilot for core productivity tasks
• Future expansion: Evaluate private LLM needs as specific use cases emerge

Private LLM-First Approach

• Best for: SMBs with high data sensitivity, specialized industry needs, or strategic AI differentiation goals
• Starting point: Implement targeted private LLM solutions for highest-value use cases
• Future expansion: Add Microsoft Copilot for broader productivity enhancement

Hybrid Implementation

• Best for: SMBs with diverse needs and moderate technical capabilities
• Starting point: Implement Microsoft Copilot for general productivity and a private LLM for one specialized use case
• Future expansion: Gradually expand both solutions based on demonstrated ROI

Real-World Implementation Examples

Example 1: Manufacturing SMB

A 200-employee manufacturing company implemented a hybrid AI approach:

• Microsoft Copilot: Deployed to engineering and management teams for document creation, email management, and meeting summaries. This immediately reduced documentation time by 35%.
• Private LLM: Developed a custom LLM solution that integrates with their equipment management system. The AI analyzes maintenance data, predicts potential failures, and suggests preventive measures. This reduced unplanned downtime by 22% in the first quarter.
• Integration Point: Created a Power Automate flow that takes Copilot-generated maintenance reports and feeds relevant data to the private LLM for deeper analysis, creating a seamless workflow between systems.

Example 2: Professional Services Firm

A legal services SMB with 75 employees implemented:

• Microsoft Copilot: Used by all staff for drafting correspondence, summarizing case documents, and preparing meeting notes. Associates reported saving 7-10 hours weekly on routine writing tasks.
• Private LLM: Built a secure, specialized LLM trained on their case history and legal precedents. This system powers an internal research tool that helps attorneys quickly find relevant case law and precedents, while maintaining strict confidentiality of client information.
• Complementary Workflow: Attorneys use the private LLM for specialized legal research, then use Copilot to incorporate those findings into client-facing documents, accelerating their overall workflow.

Example 3: E-Commerce Retailer

A growing online retailer with 120 employees implemented:

• Microsoft Copilot: Utilized across marketing and operations teams to draft product descriptions, analyze sales data in Excel, and manage internal communications. The marketing team reduced content creation time by 40%.
• Private LLM: Deployed a customer-facing chatbot on their website, trained on their product catalog and support documentation. This AI assistant handles 65% of customer inquiries without human intervention and has improved customer satisfaction scores by 18%.
• Data Flow: Customer service representatives use insights from the private LLM’s customer interactions to identify common issues, then use Copilot to draft improved product descriptions and FAQ content to proactively address these concerns.

Example 4: Financial Services Company

A 50-person financial advisory firm implemented:

• Microsoft Copilot: Used by advisors to summarize client meetings, draft communications, and prepare initial financial reports based on internal templates and data.
• Private LLM: Developed a secure, compliant LLM that analyzes client portfolios against market data and company research. This system generates personalized investment recommendations while maintaining strict data security and regulatory compliance.
• Security Boundaries: Established clear policies where sensitive client financial data is only processed by the private LLM with appropriate safeguards, while general market analysis and communication happens via Copilot.

Getting Started: Step-by-Step Adoption Guide

1. Start with Your Use Cases

Identify 3-5 concrete business challenges that AI might help solve. Focus on specific pain points and opportunities rather than adopting AI for its own sake.

2. Assess Your Microsoft 365 Readiness

If you’re already using Microsoft 365, evaluate your readiness for Copilot:

• Review your licensing and eligibility
• Organize your SharePoint and OneDrive content
• Verify your permission structure reflects actual access needs
• Consider a pilot deployment with a subset of users

3. Identify Complementary Private LLM Opportunities

Look for areas where a custom AI solution would add unique value:

• Customer-facing interactions requiring brand consistency
• Domain-specific knowledge applications
• Scenarios requiring integration with non-Microsoft systems
• Cases where data sensitivity requires maximum control

4. Plan a Phased Implementation

Start small and demonstrate value before expanding:

• Select a pilot group of tech-savvy, enthusiastic users
• Choose one high-impact use case for initial deployment
• Establish clear success metrics to evaluate results
• Gather feedback and make adjustments before scaling

5. Develop Governance and Training

Prepare your organization for effective AI usage:

• Create a clear AI usage policy with guidelines for appropriate use
• Provide hands-on training for users on effective AI prompting
• Establish a process for reviewing and improving AI outputs
• Designate AI champions who can help others learn best practices

6. Measure, Impact, and Iterate

Continuously evaluate and improve your AI implementation:

• Track quantitative metrics like time saved or customer satisfaction
• Collect qualitative feedback from users and stakeholders
• Identify areas where the AI is underperforming and make improvements
• Test new use cases and expand successful implementations

Conclusion: Building an AI-Powered SMB

The AI landscape presents unprecedented opportunities for SMBs to enhance productivity, deliver superior customer experiences, and compete more effectively with larger enterprises. By strategically implementing Microsoft Copilot and private LLMs in complementary ways, SMBs can create an AI ecosystem that addresses their unique needs while managing costs and technical complexity.

The key to success lies in thoughtfully assessing your business needs and deploying AI tools that align with your specific challenges, resources, and strategic goals. Whether you begin with Microsoft Copilot’s accessibility or private LLMs’ customization, the important step is to begin your AI journey with clear objectives and a commitment to measuring outcomes.

As AI technology continues to evolve at a rapid pace, the businesses that gain experience now will be best positioned to leverage future advancements and maintain competitive advantage in an increasingly AI-driven business landscape. Starting your AI journey today will pay dividends in driving efficiency, innovation, and growth for your business.

Reach out to AIriam today to learn how AI can help drive the business outcomes you need!