AI tools for SMEs in 2026: a practical comparison

Why "which tool" is the wrong question

Knowledge-intensive organisations often ask which AI tool to choose as if it were a single-tool decision on a par with "which accounting software should we buy". That is the wrong frame.

The AI tools in 2026 have different strengths, different weaknesses and different economic models. They are not competing to be the best at everything. They are competing to be the best at specific task types. A lawyer writing a complex agreement needs Claude. The same lawyer searching case law needs Perplexity. The same lawyer summarising a meeting needs Microsoft Copilot or Gemini depending on which Office stack sits underneath.

That is the frame this pillar uses. Not "which is best" but "which portfolio fits". We go through the five major tools the way they should actually be evaluated: per task type, per industry, and per data sensitivity.

The five major tools in 2026

Claude (Anthropic)

Claude Opus 4.7 and Claude Sonnet 4.6 are Anthropic's flagship models in 2026. The strongest of the five on long documents (200k token context), complex logical reasoning and quality in long written deliverables. Detailed justification for its output, which makes it especially valuable for law, audit and advisory where you need to be able to stand behind the conclusion.

Strong areas: contract review, legal drafts, audit notes, summarisation of long research documents, strategic deliberation, code with high quality requirements.

Weak areas: real-time data (no built-in web search on Pro tier), image generation, plugin ecosystem smaller than ChatGPT.

Price (2026): Pro DKK 215/mo, Team DKK 250/mo/user, Enterprise individual pricing with DPA and EU residency via Anthropic Enterprise or AWS Bedrock in Frankfurt.

ChatGPT (OpenAI)

GPT-5 and o1-pro are OpenAI's flagship models in 2026. The broadest ecosystem of the five: custom GPTs, plugins, code interpreter, built-in image and video generation, agents functionality in Pro tier. Strong on real-time data via built-in search, and the most widely used tool among end users.

Strong areas: broad tasks, multimodal output, code, rapid prototyping, third-party integration via plugins, demo-friendly.

Weak areas: consistent quality on very long documents is uneven, EU residency only on Team and Enterprise tier, more "personality" and less consistency in tone than Claude.

Price (2026): Plus DKK 145/mo, Pro DKK 1,450/mo, Team DKK 200/mo/user, Enterprise individual pricing with DPA and EU residency.

Gemini (Google)

Gemini 2.0 Pro and Ultra are Google's models in 2026. Deepest integration with Google Workspace (Gmail, Docs, Sheets, Drive, Calendar, Meet). A long context window (1-2 million tokens on Ultra) makes it particularly suited to summarising entire document folders at once.

Strong areas: Workspace-heavy work, meeting transcripts directly from Meet, automatic document summarisation, integration with YouTube and Maps for research, image analysis on large volumes.

Weak areas: quality of reasoning is uneven compared to Claude and GPT-5, plugin ecosystem smaller than ChatGPT, less verbose in explaining its output.

Price (2026): Gemini Advanced DKK 145/mo, Gemini Workspace Business DKK 175/mo/user included in Workspace Business Plus, Enterprise individual pricing.

Microsoft Copilot for M365

Microsoft Copilot for Microsoft 365 in 2026 runs on a blend of OpenAI models (GPT-4, GPT-5) and Microsoft's own models. Deepest integration with Outlook, Teams, Word, Excel, PowerPoint, SharePoint and OneDrive. For organisations already running M365 Enterprise, it is easiest to roll out.

Strong areas: mail summarisation, meeting transcripts from Teams, Excel formulas and pivots, PowerPoint drafts, integration with existing SharePoint data, DPA built in via M365 Enterprise.

Weak areas: less flexible than Claude or ChatGPT on ad-hoc tasks, deeper reasoning typically weaker than the specialised models, per-user price higher when you count the M365 licence as well.

Price (2026): DKK 220/mo/user on top of the M365 Business Standard or Premium licence.

Perplexity

Perplexity is the tool that differs most from the other four. Optimised for research with source citations: every claim in the output is linked to the web page or article it comes from. For research-heavy industries (law, audit, financial advisory) it adds value in a way the other four do not match.

Strong areas: source-based research, quick overview of a new topic, comparison across sources, Pro Search for deeper analysis, academic and industry-specific filters.

Weak areas: not a general-purpose assistant (weaker on long documents or complex reasoning), no full EU residency yet, less capable for written deliverables than Claude or ChatGPT.

Price (2026): Pro DKK 145/mo, Enterprise individual pricing with DPA. Self-serve free tier covers many research tasks.

Industry-specific recommendations

Law firms

Primary portfolio: Claude Enterprise with EU residency for contract review and legal drafts (sensitivity demands it). Perplexity Pro for case law and legislative research. Microsoft Copilot for M365 for mail handling and office tasks.

Avoid: consumer-tier ChatGPT or Gemini for client agreements. For highly sensitive matters: self-hosted Llama 3.3 via OpenWebUI on the firm's own infrastructure.

Accounting practices

Primary portfolio: Claude Team for audit notes and management-letter drafts. Gemini Enterprise for Workspace-heavy work with clients (sharing, meeting transcripts). Microsoft Copilot for M365 for Excel-heavy tasks and report drafts in Word.

Special note: AML screening and risk assessment that can lead to client rejection are typically EU AI Act high-risk. Use self-hosted models or Anthropic Enterprise with full audit trail.

Financial advisers

Primary portfolio: Perplexity Pro for market research and macro overview. Claude Enterprise for client reports and strategic deliberation. Microsoft Copilot or Gemini depending on the Office stack for ongoing communication.

Special note: AI-based credit scoring and investment advice to retail clients are high-risk under the EU AI Act. Audit trail and human oversight are not optional. This likely requires either ISO 42001 readiness or self-hosted infrastructure.

IT services firms

Primary portfolio: ChatGPT Pro or Claude Pro for general productivity and code assistance. GitHub Copilot or Cursor for code writing. Perplexity for research. Microsoft Copilot for M365 if you deliver M365 services to customers.

Special note: if you build AI solutions for customers, document the AI stack choice in your delivery process and audit each deliverable with audit trail. Show the customer which models were used and which data was sent where.

Comparison on five core dimensions

The table is deliberately simplified. A real evaluation requires testing on your concrete tasks, because models improve frequently and nuances change month by month. Use the table as a hypothesis, not a verdict.

Open source alternatives

For client-confidential data or use cases where data must not leave your infrastructure, self-hosted open source models are a real alternative in 2026. Three candidates stand out.

Llama 3.3 70B (Meta)

The strongest of the open source models in 2026 for English reasoning. Typically hosted via Ollama or vLLM on Proxmox or an Azure tenant. Resource requirements: 2x H100 GPUs or 4x A100 for production. Total cost of ownership DKK 4,000-12,000 per month depending on scale. The licence permits commercial use up to 700 million monthly users.

Mistral Large 2 (Mistral AI)

Competitive with Llama 3.3 and particularly strong on French and other European languages. The licence is slightly more restrictive than Llama (commercial use above certain thresholds requires a Mistral agreement), but technical performance is comparable. Mistral AI is French and offers an explicit EU residency narrative.

Qwen 2.5 (Alibaba)

An open source alternative with strong performance on Danish and other European languages. More controversial for some organisations due to its Chinese origin, and typically ruled out by Danish public institutions on political grounds. For private organisations where licence restrictions are not problematic, a usable choice.

A self-hosted setup typically requires infrastructure work (a Proxmox host, GPU passthrough, OpenWebUI or LibreChat as the frontend, n8n for orchestration). EnterpriseIQ runs Llama 3.3 and Mistral via OpenWebUI on Proxmox for client-confidential use cases, documented openly on our ai-stack page.

The real gain comes from orchestration

A typical knowledge-intensive organisation using a single AI tool sees 10-15 percent productivity gain on tasks that tool is suited to. One using multiple tools manually sees 15-25 percent gain but also more context switching. The real significant gain (30-50 percent on documentation-heavy tasks) typically arrives when multiple tools are orchestrated via agent platforms.

A typical law firm flow looks like this:

• Perplexity finds relevant case law and cites sources
• Claude takes the sources plus client background and proposes argument structure
• Microsoft Copilot integrates the draft into a Word template with correct formatting
• n8n orchestrates the flow and logs audit trail in Archer or a Google Sheet

Each model is used where it is strongest. That is the hybrid architecture EnterpriseIQ implements as part of pilot projects, not as a single-tool delivery. Read more on our Pilot Project service page.

Three steps you can take this week

Step 1: Assess your current portfolio

• List which AI tools are actually in use (including shadow IT)
• Note the account tier (consumer, Pro, Team, Enterprise) per tool
• Identify which tools are missing from the portfolio based on the industry recommendations above

Step 2: Run GDPR plus EU AI Act screening

• Check which tools have a DPA signed for your tier
• Verify EU residency where data sensitivity demands it
• Identify gaps where you use consumer tier for tasks that should be on Team or Enterprise
• Add missing DPAs to a quarterly review cycle

Step 3: Define a model selection policy

• 1-2 pages specifying which model may be used for which type of task
• Clearly delineated for each data sensitivity (public, internal, confidential, secret)
• Share with staff at a team meeting and in the AI policy
• Set a quarterly review date because models and pricing change quickly

These three steps give a deliberate portfolio choice. They replace the situation where every employee uses the tool they are most familiar with, with no regard for data sensitivity or business criticality.

FAQ

Next step

Three paths depending on where you stand: