Frequently Asked Questions
This FAQ is focused on practical deployment of Large Language Models (LLMs) and AI-powered tools within corporate environments. It helps to address key concerns shared by business users to date.
ChatGPT-like tools are universally relevant for many industries and domains:
- Process and analyze vast amounts of business documents in minutes instead of hours, extracting key insights and answering specific questions
- Automate routine tasks like report generation, email drafting, and document summarization, freeing up employees for strategic work
- Enhance knowledge sharing by making corporate expertise easily accessible through intelligent search and Q&A across internal documentation
- Begin with well-defined, low-risk use cases that can demonstrate quick wins and ROI
- Implement on-premise LLM solutions to maintain data security while leveraging AI capabilities
- Develop internal AI expertise through pilot projects to make informed decisions about larger implementations
- Start with basic automation tasks, then gradually expand to more complex applications as your team gains experience
- Create a feedback loop with users to identify pain points and opportunities for AI implementation
- Deploy on-premise LLMs (on your servers) that can safely process business data while maintaining data security and control
- Integrate AI tools with existing data systems to enhance analytics capabilities without disrupting workflows
- Enable natural language queries across corporate databases for faster insights
- On-premise LLM offers cost-effective implementation with minimal hardware requirements (single GPU server) and no expensive model training needed (use pre-trained models)
- Avoid recurring subscription costs per user
- Scale gradually based on actual usage and benefits
- On-premise deployment ensures complete data control within your security perimeter. No data sharing with external providers.
- Role-based access controls protect sensitive information. Ability to restrict specific data sources or topics.
- Compliance with industry regulations and internal security policies.
- Audit trails for AI interactions.
- Pilot is a low risk and modest effort way to build basic expertise in business and IT community
- Practical implementation reveals which features and capabilities matter most
- Build evaluation criteria based on real use cases rather than theoretical benefits
- Gain insights into integration requirements and potential challenges before major investment
In the end, a later but well-informed decision is likely to serve your company better than a premature one.
- Start with high-quality, structured data sources to demonstrate quick wins. Incrementally expand to more complex data systems as capabilities grow
- Data validation and cleaning tools ensure quality input
- Modular approach allows selective integration based on data readiness
- If you start with pilot it enables low-risk experimentation without vendor lock-in
- Developing in-house expertise helps to maintain strategic control over AI initiatives
- Understanding implementation costs and maintenance needs helps deciding in build-vs-buy space
- Create balanced approach between in-house capabilities and vendor solutions
Ah, that's a million bitcoins dollar question.
Plan reasonable onboarding, provide targeted training sessions for key roles (e.g., IT, operations, finance) to increase AI literacy. Encourage a culture of collaboration between domain experts and data/AI teams. Establish a change management plan to handle process adjustments and employee concerns.
Dont hesitate to ask this question to your favourite LLM and use this as thinking basis.
Plan reasonable onboarding, provide targeted training sessions for key roles (e.g., IT, operations, finance) to increase AI literacy. Encourage a culture of collaboration between domain experts and data/AI teams. Establish a change management plan to handle process adjustments and employee concerns.
Dont hesitate to ask this question to your favourite LLM and use this as thinking basis.
- Define measurable performance indicators before project kickoff. When possible agree on measurable metrics separately for every AI-tool (use case) developed. For example:
- % of errors/non-conformance reduction
- review/approvals cycle time acceleration
- direct/indirect cost savings
- business users structured feedback
- Conduct phased rollouts to track incremental value and identify quick wins.
- Capture ad-hoc benefits reported by end users and try to replicate this.
- Regularly evaluate AI-tools performance against benchmarks to justify further investment or pivot as needed.
Modern models become significantly more reliable and accurate.
Your staff will be trained how to ask effective questions and minimise risk of misleading or incorrect answers. We can also embed validation loops in solutions if your corporate data contains sufficient reference data.
Your staff will be trained how to ask effective questions and minimise risk of misleading or incorrect answers. We can also embed validation loops in solutions if your corporate data contains sufficient reference data.
Humans make mistakes. AI-tools are not much different.
We will ensure pilot team is very conscious of this and relevant review steps are included in decision making process.
AI tools are not designed to replace your experts, but to speed up manual data handling and support engineers in their decision making.
Whenever staked are high, company's experts should aways validate AI-generated recommendations.
We will ensure pilot team is very conscious of this and relevant review steps are included in decision making process.
AI tools are not designed to replace your experts, but to speed up manual data handling and support engineers in their decision making.
Whenever staked are high, company's experts should aways validate AI-generated recommendations.
Why integrating LLM with corporate data?
- Basic use of LLM relies only on training dataset.
- Connecting LLM to corporate documents unlocks many more options and brings significantly more value.