Top Genai Automations Every Business Needs

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Jun 27, 2024

3 minutes read

Top 10 generative AI automations every business needs

Unlock the transformative power of AI automation with large language models by identifying the right game-changing project for your business.

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_Updated: February 3, 2026_

The potential of GenAI automation with large language models (LLMs) is huge, but finding the right game-changing project for your business can be tough.

While projects should be bold and visionary, they also need to be achievable in practice. The good news is that cutting-edge LLMs — enhanced with features like retrieval-augmented generation ( RAG), multilingual support, and tool integration — offer countless new and exciting automation possibilities for businesses.

##### How to start integrating generative AI automations into your business

To kickstart your journey, we’re unveiling the top 10 LLM automations that leading companies have already deployed and the cookbooks you can use to start building them yourself.

##### Top 10 generative AI automations

1. Data analysis and reporting

LLMs can analyze large datasets, generate insights, and produce comprehensive reports, aiding decision-making processes within enterprises.

→ Get started: build a data analyst agent.

2. Advanced financial analysis

Enterprises can automate financial, operational, and tabular data analysis by leveraging LLMs to analyze various factors and generate reports. Equip your LLM with a Python console and start analyzing spreadsheets and financial data.

→ Get started: build a financial AI agent.

3. Automated document processing

LLMs can be used to automate the creation, review, and approval of documents, such as contracts, reports, and compliance paperwork. This reduces the manual effort and accelerates document workflows. LLMs can extract information from documents, review and generate reports, and ask questions with your documents.

→ Get started: use a multi-step PDF extractor.

4. Enhanced IT support

Integrating LLMs into customer support systems to handle complex queries, provide detailed responses, and escalate issues as needed significantly improves customer service efficiency and satisfaction. For example, check out Atomicwork’s recently launched Atom AI for IT support powered by Cohere models.

→ Get started: build a Q&A Bot from technical documentation.

5. Automated customer support

LLMs can facilitate seamless communication between APIs, enhancing customer support by integrating with existing CRM tools.For example, keep your CRM up-to-date by automatically analyzing sales call transcripts.

→ Get started: build robust API calls for enterprise workflows.

6. Automated meeting scheduling

LLMs can be used to coordinate meeting times, send invitations, and manage calendars, reducing the administrative burden on employees.

→ Get started: build a calendar agent.

7. Content creation and summarization

LLMs can generate summaries, marketing materials, internal communications, and social media content, ensuring consistency and saving time. Other use cases include summarizing customer support threads and reports from technicians.

→ Get started: build summarization automation.

8. Human resources automation

HR teams can streamline processes, such as recruitment, onboarding, and performance reviews, using LLMs to analyze resumes, generate evaluation reports, and provide feedback.

→ Get started: build an HR agent.

9. Legal and compliance automation

Legal teams can automate legal research, contract analysis, and compliance checks using LLMs to ensure adherence to regulations and reduce their workload. Cohere customer Borderless AI recently launched their legal agent, Alberni, to help with global compliance and employee onboarding.

→ Get started: build an agentic RAG pipeline for complex data.

10. Enhanced multilingual services

LLMs can be used to automate translation tasks to support multilingual communication within global enterprises.

→ Get started: build multilingual search and generation.

Experimenting with LLM automations, and collaborating with interdisciplinary, diverse, and cross-functional teams, can lead to groundbreaking enterprise innovation. You don’t need big R&D budgets to do it. The cross-pollination of ideas mixed with GenAI automation that can scale to support enterprise-grade workloads is at the heart of how we begin to reinvent work.

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