Artificial intelligence is perceived by many people as a tool to automatically make companies smarter. But the reality is different: AI is an accelerator. It helps to process information, automate workflows, and make decisions much quicker than any person. The only condition for making high-quality decisions is high-quality information, process, and knowledge.
This point is becoming more crucial for many organizations that decide to adopt such technologies as LLMs, AI agents, and workflow automation.
Speed Is Not Intelligence
The most popular misconception about the implementation of artificial intelligence in a company is that this step will automatically improve its business decisions. AI will definitely help to work more efficiently, but not necessarily better in terms of judgment.
If the company has such factors as:
- Bad documentation
- Inconsistent business processes
- Outdated knowledge
- Poor data
The results from AI implementation will be the same problems but on a higher speed level.
Those companies that have good governance, reliable knowledge source, and proper workflows are likely to gain a lot from AI.
The Secret Cost of Loss of Institutional Knowledge
Many businesses automate routine tasks. And while it does enhance the efficiency of work, it carries one danger that is seldom mentioned - the loss of institutional knowledge.
The institutional knowledge is possessed by employees who work in a company for years. They know what are the exceptions, previous decisions, customer needs, and informal process flows that cannot be derived by AI alone.
If not saved, the institutional knowledge will get lost during the automated workflow creation process.
Why Information Retrieval is Important
And this is why enterprise AI is different from general purpose chatbots.
While the latter rely only on pre-trained knowledge, the enterprise AI is able to search through documents, policies, contracts, technical manuals, and knowledge bases of the organization to find the required information.
Technologies such as:
- Semantic search
- Vector databases
- Retrieval-Augmented Generation (RAG)
- Knowledge graphs
allow AI systems to use current knowledge of the organization instead of using only the knowledge stored in the AI system itself.
AI Should Complement Human Expertise
The best AI systems usually do not replace human decision-making.
Rather, they aid humans in:
- Faster search for information
- Lessening repetitive tasks
- Summarization of difficult papers
- Providing necessary context
- Enhancing decision-making
Humans continue to interpret results, deal with edge-cases and make strategic decisions.
Governance Is Key
With increased AI adoption, governance has become just as important as performance.
One might have to ask questions such as:
- Can AI provide a source for its answer?
- Is the information fresh?
- Are outputs verifiable?
- How is confidential information protected?
- What happens when there is uncertainty in AI?
These will ensure development of trustworthy AI systems as well.
Responsible Scaling of AI
Organizations that get the most value out of AI aren’t the ones using the biggest models. They’re the ones that incorporate their AI into well-architected workflows with good quality data and knowledge management.
As a developer, architect, or engineer, this means designing for retrieval, transparency, and governance in addition to what your model can do.
For more information on approaches to semantic search, explainable AI, RAG, and knowledge management at the enterprise level, PowderForge AI provides valuable insights into these fields.
In Conclusion
AI is a force multiplier like no other, but it won’t solve your problems if you don’t have well-designed systems and expertise within your organization.
A well-designed organization will become more efficient and agile because of AI. A poorly designed one may become faster at repeating its errors.
Enterprise-level AI of the future won’t be limited to building intelligent models. It’s going to be about building intelligent systems.
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