Artificial intelligence has officially arrived in the workplace — and its impact could rival the Industrial Revolution. According to McKinsey, AI has the potential to unlock up to $4.4 trillion in annual productivity gains for businesses worldwide. With large language models (LLMs) now available from leading developers like Anthropic, Cohere, Google, Meta, Mistral, and OpenAI, we’ve entered a new era of technology that’s reshaping how organizations operate.
But while the long-term promise of AI is enormous, the short-term reality is more complex. McKinsey reports that 92% of companies plan to increase their AI investments in the next three years — yet only 1% consider themselves truly mature, meaning AI is fully integrated into daily workflows and producing measurable results.
For small and midsize businesses (SMBs), this gap represents both an opportunity and a risk. The question isn’t whether to use AI — it’s how to adopt it securely, strategically, and responsibly to create real business value.
Public AI vs. LLM Platforms: Understanding the Difference
AI tools are everywhere today — powering automation, streamlining workflows, and transforming how teams operate. According to Stanford’s AI Index, about 78% of organizations reported using AI in 2024, up from 55% the year prior. But not all tools are created equal.
- Public AI tools (like widely available chatbots and productivity assistants) are accessible, user-friendly, and often low-cost or free. In simple terms, public AI tools are designed for everyone. They’re great for experimentation and quick tasks, but they also introduce risks:
- Data entered into public tools may be used to train external models.
- Businesses have little control over privacy, retention, or access.
- Compliance and governance are nearly impossible to enforce.
- Enterprise LLM platforms, on the other hand, are purpose-built for organizations. It allows businesses to safely integrate AI into daily operations. Rather than individual employees experimenting in silos, organizations gain a secured, centralized environment for AI adoption. They provide:
- Secure, private environments for company data.
- Centralized governance with logging, permissions, and audit trails.
- Integration with internal systems for seamless workflows.
- Scalable access for teams across the business.
In short, public tools prioritize accessibility — while LLM platforms prioritize security, compliance, and control.
Why Businesses Are Turning to AI — and Getting It Wrong
Companies are racing to adopt AI to improve efficiency, streamline decision-making, and drive innovation. But in the rush, many skip the strategy.
Common mistakes include:
- Deploying AI tools without a governance framework.
- Ignoring data quality, privacy, and compliance.
- Scaling too fast before defining measurable goals.
- Underestimating employee training needs.
As referenced in this previous blog post, a study found that while most organizations are investing in AI, only 23% of employees feel fully confident using it — leaving 77% uncertain about how to apply AI effectively in their day-to-day work. This disconnect between investment and execution is where many AI initiatives fail.
The Security and Compliance Risks of Public AI
Using public AI tools might seem harmless — until it’s not. When employees copy and paste company data into public systems, that information can end up outside the organization’s control.
A recent TELUS Digital Experience survey found that over half of employees using generative AI tools at work have shared sensitive company data through public AI platforms — often without realizing the risks.
The result? Hidden compliance violations, intellectual property exposure, and reputational risk.
A real-world example came in 2023, when Samsung engineers accidentally leaked confidential code through a public chatbot. It’s a cautionary tale for SMBs: without clear governance, even the best-intentioned teams can introduce serious vulnerabilities.
Barriers SMBs Face When Adopting AI Safely
Despite the benefits, small and midsize businesses face unique challenges when it comes to adopting AI safely, such as:
- Limited budgets for secure solutions.
- Lack of internal AI or data expertise.
- Weak IT infrastructure or governance frameworks.
- Overwhelmed from too many tool options.
- Unclear regulations or compliance standards.
Keep in mind, adopting AI doesn’t require building a data science department. The best AI tools for small businesses are those that simplify complexity — not add to it.
How to Evaluate the Top AI Tools for Business
When choosing the top AI tools for your business, start with strategy — not software.
Ask these key questions:
- Does this tool align with our core business goals?
- How does it handle sensitive or proprietary data?
- What governance and compliance safeguards are built in?
- Will this scale as our AI maturity grows?
Category | Example Tools | Use Case |
Productivity & Collaboration | Microsoft Copilot®, Google Duet AI | Automate repetitive office tasks and emails |
Content & Marketing | Jasper, Writesonic, ChatGPT™ | Create content, campaigns, and copy faster |
Data & Analytics | Power BI with Copilot, ChatSpot, IronAI™ | Turn raw data into insights and dashboards |
Workflow Automation | Notion AI, Airtable AI, Zapier AI | Streamline reporting, tracking, and workflows |
Managed AI Platforms | IronAI™, Anthropic Console | Secure, governed, multi-model AI access |
The best AI tools for business aren’t always the most popular ones — they’re the ones that integrate securely, deliver measurable value, and grow with your organization.
Public AI tools may seem cheaper upfront, but unmanaged use leads to mounting hidden costs:
- Time wasted troubleshooting and training.
- Compliance fines from accidental data exposure.
- Duplicated subscriptions across teams.
- Inconsistent results and rework.
- Security breaches or brand damage.
Why LLM Platforms Are the Best AI Tools for Business
For organizations looking to balance innovation and security, managed LLM platforms represent the next evolution of AI adoption.
Here’s why more SMBs are moving toward these enterprise-grade environments:
- Centralized Security and Governance
LLM platforms isolate company data, enforce permissions, and provide audit trails — ensuring AI use aligns with corporate policies and industry regulations (HIPAA, PCI, GDPR, etc.).
- Private Integration with Company Data
Instead of training public models, businesses can securely connect internal data sources to enhance insights while keeping proprietary information protected.
- Consistent Performance and Control
With managed platforms, organizations can fine-tune models, standardize prompts, and prevent disruptions from public model updates.
- Cost Efficiency Through Enterprise-Wide Access
Rather than paying for individual licenses across multiple tools, LLM platforms offer a consumption-based pricing model — giving every employee access to multiple AI models under one secure platform. This approach reduces software waste and simplifies management.
How MSPs Like IronEdge Are Leading the Way
As one of the first Managed Service Providers (MSPs) in the nation to offer a comprehensive ManagedAI™ Services, IronEdge Group is redefining what “AI for SMBs” looks like.
Our solution provides enterprise-level access to multiple large language models (LLMs) in a secure, governed environment — supported by training, governance advisory, and consulting services.
IronEdge’s ManagedAI™ Services gives SMBs predictable pricing, measurable ROI and the peace of mind needed to harness AI safely and strategically — without the complexity or risk of managing it all in-house.
By working with a partner like IronEdge Group, SMBs gain:
- A secure LLM platform with governance and data protection.
- Expert-led consulting to identify ROI-driven use cases.
- Employee training to promote safe, confident AI adoption.
- Ongoing strategy and optimization to scale efficiently.
Conclusion: Building the Future with Responsible AI
The race to adopt AI is on — but success won’t come from rushing in. It will come from choosing the right tools, partners, and governance framework to support sustainable growth.
By combining accessibility with security, and strategy with innovation, managed LLM platforms like IronEdge’s ManagedAI™ Solution help small and midsize businesses transform AI from an experiment into a competitive advantage.
A ManagedAI™ approach provides your business access to enterprise-grade technology, security, and expertise without the cost or risk of going it alone.
The best AI tools for business don’t just generate results — they build trust, efficiency, and long-term resilience.