Short answer — the “best” depends on the workflow you want to extend. Broadly speaking, pick from three classes that excel at integration:

• Native productivity-suite copilots (deep, no‑code embedding): Microsoft 365 Copilot (built into Word/Excel/Outlook and available via Copilot APIs) and Google’s Gemini (formerly Duet) for Workspace — they let you add AI directly inside the apps people already use. (learn.microsoft.com)

• Developer-first APIs & model hubs (flexible programmatic integration): OpenAI’s APIs (function‑calling / agents, SDKs) and multi‑cloud model platforms such as Amazon Bedrock give you programmatic control to wire LLMs into services, pipelines and custom apps. These are the best option if you need custom agents, RAG, or back‑end automation. (help.openai.com)

• Point solutions & connectors (fast wins, low code): Automation platforms (Zapier, Make, n8n, Slack Workflows / Slack GPT), CRM copilots (Salesforce Einstein GPT), and model marketplaces (Hugging Face, Anthropic Claude) provide prebuilt connectors or apps you can drop into an existing workflow without building everything from scratch. (zapier.com)

Recommended picks by typical use case

• Knowledge workers / Office productivity: Microsoft 365 Copilot (if you’re on M365) or Google Gemini for Workspace (if you’re on Workspace). They provide in‑app suggestions, templates and admin controls with minimal change to user habits. (learn.microsoft.com)

• Developer workflows / code authoring: GitHub Copilot (IDE plugins, workspace-aware completions) combined with an LLM API (OpenAI, Anthropic, Google) if you need server‑side automation or agents. GitHub Copilot also supports multi‑model options and agent-style features for repo tasks. (code.visualstudio.com)

• Customer support & CRM automation: Salesforce Einstein GPT / Einstein in Flow — integrates with CRM data, Flow automations, Slack and MuleSoft connectors to automate responses and generate personalized content. (salesforce.com)

• Enterprise-grade / regulated environments: AWS Bedrock, Azure OpenAI, Google Vertex AI and Anthropic’s enterprise offerings — these give model choice, private customization, governance, VPC/data‑protection patterns and MLOps tooling for production deployments. (aws.amazon.com)

• Quick automations & integrations: Zapier, Make, Slack Workflow Builder, and native Slack GPT let non‑developers set up triggers/actions and inject AI steps into business processes quickly. (zapier.com)

Integration patterns to consider

• Native plugin / in‑app copilot: fastest adoption, minimal UI change, admin controls required. (Good for knowledge workers.)
• API + SDK + function calling / agent: maximum flexibility for building custom logic, orchestration, and enterprise connectors. (Good for developers.)
• RAG / retrieval + vector DBs: essential if you need the model to work with internal documents or large context windows.
• Low‑code connectors & orchestration: use when you need many quick integrations across SaaS apps with minimal engineering.
• MLOps & governance: choose platforms that provide audit logs, model evaluation, fine‑tuning / private model hosting and data controls.

Practical selection checklist (quick)

• Primary place people work? (Inbox/Docs/Sheets → use Copilot/Gemini; Slack → Slack GPT)
• Need custom business logic / agents? (Use OpenAI / Bedrock / Anthropic + LangChain / agent SDKs)
• Data residency / compliance needs? (Prefer cloud vendor enterprise offerings with SOC/ISO/SSO and admin controls)
• Want fast ROI with low engineering? (Zapier / Make / Slack Workflows / Salesforce Flow)
• Long‑term MLOps & monitoring? (SageMaker / Vertex AI MLOps / Azure ML + CI/CD + model registry)

If you want, I can:

• recommend 2–3 concrete stacks for your team (tell me platform(s) you already use), or
• map a one‑week pilot plan (tools, minimal architecture, metrics to prove ROI).

Sources for the integration claims above:

• Microsoft Copilot APIs and Microsoft 365 Copilot integration docs. (learn.microsoft.com)
• Google Duet / Gemini integration into Google Workspace (Docs, Gmail, Slides). (workspace.google.com)
• OpenAI function calling / Agents platform documentation. (help.openai.com)
• Amazon Bedrock documentation describing a unified API to foundation models and AWS integrations. (aws.amazon.com)
• Zapier AI/AI‑ML connectors and Salesforce Einstein GPT integration details. (zapier.com)

Would you like tailored recommendations for a specific environment (Microsoft, Google, Slack, or a developer stack)? I can lay out a 1–2 page plan with connectors, sample architecture and security considerations.