Paper

The Myth of AI Introduction: Transformation from “AI Toolman” to “Knowledge Asset”

by | 12 month 25, 2024

Where do companies use AI?

In the era of digital transformation and rapid development of AI technology, many companies are thinking about how to effectively apply AI to daily business. However, a common misunderstanding is that as long as there are several "AI tool people" in the company (perhaps out of personal interest, enthusiasm, or company assignment) who are proficient in operating AI tools and have significantly improved work efficiency, it is considered that "We have successfully introduced AI."

In fact, this is just an "individual AI application" and not a true "enterprise-level AI introduction." On the surface, it seems to have “AI capabilities”, but it often hides the following three major problems:

  1. Dependent on individuals and unable to operate continuously
    If the "AI tool man" leaves his job, problems will arise in the entire output process that relies on him to use AI.
  2. Knowledge cannot be passed on effectively
    Other employees do not have the same skills or experience, making it difficult to expand the use of AI tools within the enterprise and spread them systematically.
  3. Lack of standardized operating procedures
    AI technology only exists in individual operations and has not been incorporated into the overall process and collaboration framework of the enterprise, making it difficult to maximize its benefits.

From "individual application" to "enterprise-level import"

People are assets, and knowledge is the long-term asset of an enterprise.

For an enterprise to truly complete the introduction of AI, the key lies in its ability to "organize" personal skills and experience. Rather than relying solely on one person's powerful AI technology, it is better to create an AI application ecosystem that can be widely copied, inherited, and upgraded.

1. Establish standardized AI application processes
  • SOP development
    Convert the use of AI tools into clear standard operating procedures (SOPs) so that all employees can follow the same steps.
  • Automated process
    Hand over highly repetitive or programmable tasks to AI to reduce labor costs and reduce human errors.
2. Knowledge management and sharing
  • Knowledge base construction
    Establish an internal knowledge sharing platform within the enterprise to transform individual AI usage experiences, examples and techniques into "organizational knowledge assets".
  • Improve the inheritance mechanism
    Allow employees to easily learn and copy previous experience and prevent key knowledge from being lost when individuals leave.
3. Continuous training and learning culture
  • Long-term training mechanism
    Regularly organize internal training or workshops to enable more employees to master AI technology and applied thinking.
  • Reduce dependence on a single individual
    When employees generally have basic AI capabilities, the company's dependence on the "AI technology" of one or a few people can be effectively reduced.
4. Incorporate AI into the core strategy of the organization
  • All-round integration
    AI should not be just a "personal skill", but should become a part of the organizational strategy and be integrated into the operational processes of each department.
  • Cross-department collaboration
    Let marketing, human resources, R&D, customer service and other departments be connected in series to share the efficiency improvements and value creation brought by AI.

Work log: Establishing a key bridge for knowledge inheritance

The work log is the core tool that connects personal experience with the corporate knowledge base. It can record problems, solutions and work progress in real time, becoming an important basis for enterprises to continuously optimize processes.

  1. Instant recording
    • Detailed records of daily work processes, problems and solutions can become the main source of content for the enterprise knowledge base.
  2. performance analysis
    • Management can track employee work progress through logs and provide timely support or guidance.
  3. Process optimization
    • Identify recurring issues or process bottlenecks from logs and incorporate best practices or corrections into company SOPs.
  4. Experience inheritance
    • Even if employees leave, the logs they leave behind can provide a learning basis for subsequent successors and avoid the loss of experience.

Log ↔ Knowledge Base ↔ AI Agent

  • Employees record work processes and solutions in logs.
  • Log content is extracted and incorporated into the enterprise knowledge base.
  • AI Agent learns through the knowledge base to provide more accurate task support; at the same time, it feeds the newly learned information back to the knowledge base.

The core of AI Agent: Knowledge is the real power

The power of AI does not come from "magic", but from the huge data and knowledge base. (The difference between archives, databases and knowledge bases)

  • Enterprise-specific knowledge
    Every company has unique processes, products, strategies and internal data. These are the "key materials" for AI to exert value in the enterprise.
  • Exclusive training
    Even powerful AI models (such as GPT and Claude) can only provide "general recommendations" without the company's internal expertise and cannot "tailor-make" solutions for the company.
Knowledge base is the "nutrition" of AI Agent
  1. Professional questions and answers
    • With a knowledge base, AI can provide solutions that meet the company's actual needs.
  2. Task execution accuracy
    • Only by understanding the SOP and business logic can AI perform various tasks correctly.
  3. Adapt quickly to changes
    • When corporate policies or processes change, AI can be "synchronously upgraded" as long as the knowledge base is updated.
  4. decision support
    • Combined with internal and external data, AI can provide management with more insightful decision-making recommendations.

Linkage between AI Agent and KPI, OKR, MBO

  1. with a clear purpose
    • The knowledge base allows the AI ​​Agent to clearly understand the task standards and measurement indicators to ensure that the goal is achieved.
  2. Performance tracking
    • AI Agent has data analysis capabilities, can provide accurate execution results reports, and assists in tracking KPIs.
  3. Problem diagnosis
    • If performance fails to meet standards or task deviation occurs, possible causes can be found through the knowledge base to assist managers in optimizing strategies.

Tasks, goals, performance → Knowledge base optimization → AI Agent capability improvement

  • If the KPI performs well, successful cases can be refined and incorporated into the knowledge base; if the KPI performs poorly, problems can be analyzed and improvements made.

The leap from "tool man" to "organizational wisdom"

The true introduction of AI is not simply to have an "AI tool man"; it is to create an "organized and systematic" AI application environment so that the technology no longer relies solely on individuals, but can be copied, inherited and used by everyone. Keep evolving.

  1. Individual abilities are limited, knowledge inheritance is unlimited
    • The departure of an individual should not be a risk for the business to lose its AI capabilities.
  2. AI automation and knowledge base are the capabilities of the entire enterprise
    • Through logging, knowledge base construction and SOP standardization, AI can truly be integrated into the organization.
  3. Sustainable AI automation transformation
    • Only by continuously updating and optimizing the knowledge base and cultivating employees' AI capabilities can enterprises remain invincible in the ever-changing market.

Only in this way can enterprises move forward steadily in the AI ​​era and truly completedigital transformationAndAll People Flood Risk Management.

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