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As a Chief Information Officer (CIO) or Information Technology (IT) leader, building trust in your knowledge management AI is crucial if you want to drive adoption and engagement across the enterprise, and see an ROI on your AI system. Your stakeholders are more likely to use AI for market insights if they trust the underlying system. If they doubt its accuracy, fairness, or security, they may bypass it, leading to poor adoption..
Ultimately, the return on investment (ROI) of your AI system depends on its adoption and consistent use. By fostering trust in its accuracy, security, and fairness, you can drive engagement and ensure that AI becomes an integral part of decision-making—delivering real value to the enterprise.
Trust encourages participation — leading to better insights, stronger decision-making, and greater business impact. Building trust in AI-powered knowledge management enterprise systems requires a multi-faceted approach that includes strong AI governance, transparency, explainability, security, and user participation.
This blog gives CIO, IT professionals, and Transformation Leaders strategies for fostering confidence in your knowledge management AI solution across the organization.
The rise of AI in knowledge management — and why trust is a major challenge
Generative (gen) AI is transforming knowledge management (KM) by enabling enterprises to process, organize, and extract market and customer insights from vast amounts of structured and unstructured data in a way not previously possible.
Today, 83% of CIOs globally believe AI will deliver the most value in productivity gains, whether it’s from streamlining previously labor-intensive tasks, enhancing employee experience, or better information management. Furthermore, 77% of IT leaders expect AI to deliver competitive value in some form over the next two years, whether it’s from improved customer experience, new product lines, more effective marketing, or enhancing product/service lines.
While AI offers significant advantages in knowledge management, your organization may be hesitant to fully embrace it. Here’s why:
- Data accuracy and hallucinations: Gen AI models sometimes produce incorrect, biased, or misleading information (hallucinations). If an AI-generated market or customer insight is inaccurate, not only can it erode trust in your AI for insights, it can lead to poor business decisions that could result in losses.
- Lack of explainability: Many AI models provide insights without explaining why they reached a certain conclusion. Business leaders need transparency in decision-making, especially when justifying insights to stakeholders.
- Data quality and source reliability: AI models depend on the quality of input data. If the underlying data is incomplete, outdated, or biased, the AI can produce unreliable insights. Your stakeholders may worry about how their knowledge management AI determines which data sources to trust.
- Security and confidentiality concerns: Knowledge management involves proprietary data, competitive intelligence, and sensitive customer insights. AI systems that ingest and analyze this data must be secure to prevent leaks, compliance violations, or unauthorized access.
- AI vs. human expertise: Employees may resist AI-driven knowledge management if they feel it undermines their expertise. Trust issues arise when AI contradicts your stakeholders’ established industry knowledge.
Building organizational trust in AI-driven knowledge management systems
Establishing trust in AI-powered enterprise knowledge management systems involves a comprehensive strategy that encompasses robust AI governance, transparency, explainability, security, and active user involvement.
By implementing AI guardrails, continuously refining governance frameworks, and ensuring best-in-class security measures, organizations can drive adoption and maximize the value of their AI-powered knowledge management systems.
Top four pillars of building trust in your knowledge management AI system
1. Credible and reliable AI outputs
The first thing your stakeholders will likely test when they start using your AI-powered knowledge management system is whether it produces precise and reliable answers. They need to know that you as an IT leader have done your due diligence to ensure your knowledge management AI output is relevant and factual — no one wants to make important business decisions based on incorrect or fabricated content (hallucinations).
A generic Retrieval-Augmented Generation (RAG) AI tool can fail to answer business questions with truly relevant content because relevant information has been drowned out by the sheer volume of tangentially related information. When answering market research questions, these
tools need to be fine-tuned and calibrated to deal with the nuances of exploring market research, competitive intelligence, and customer feedback so they can provide contextually accurate and relevant answers. If not, your stakeholders will quickly (and justifiably) lose trust in the practicality of the platform.
Rather than stretching the limits of what the AI can do, a good rule of thumb for your knowledge management AI is accuracy before breadth. It’s more beneficial to have a focused, reliable AI for market insights than one with numerous but inconsistent functions.
You can ensure outputs are accurate and dependable by using a proprietary RAG tool that’s purpose-built for market and consumer insights — an AI solution with built-in safeguards that ensure reliability and prevent unwanted responses.
In the back end, this may look like constraining the model’s temperature to zero to limit hallucinations and only include responses based on your organization’s knowledge assets. For example, instead of attempting open-ended conversations, DeepSightsTM — a proprietary RAG AI, purposely-built for market insights — focuses the interaction on a question-answer process. Its models are trained to judge if a question can be answered based on a given piece of evidence.
In the research and insights sector, getting the same result from the same data is crucial both for trust in the system and for factual accuracy. DeepSights sets itself apart from generic AI tools by prioritizing precision and reliability and maximizing consistency to reduce variability in outcomes.
2. Explainability, interpretability, and bias mitigation
It’s important your stakeholders understand how your knowledge management AI processes information, so they can make insights-driven decisions confidently. Your AI system should provide justifications for its outputs so users can verify them.
Employees should be trained to understand your knowledge management AI’s strengths and limitations, helping to foster trust in AI-assisted workflows. Be clear about your AI’s algorithmic and data processing assumptions and review them regularly for any underlying biases.
DeepSights is a great example of an AI for market insights that provides explainability and data source controls. Every piece of information generated by DeepSights can be traced back to a known and vetted source, so your stakeholders can easily verify original data if they need to. They can also narrow or expand source inputs to tailor the AI’s answers to your top resources on a particular topic or project, further reinforcing confidence in the AI’s output.
Adopt a purpose-built AI for market insights that has data limitation alerts, so your stakeholders can avoid misinterpretations and mistakes. For instance, DeepSights has a feature called AI Watchouts, which automatically pinpoints and alerts you to potential knowledge concerns, such as conflicting data, reliability issues, and outdated sources. You instantly know when and where to delve deeper and how to communicate data caveats to decision-makers.
3. Best-in-class data security and privacy
Without robust security measures, your stakeholders may be reluctant to share or rely on AI-generated insights. Security is the most important component of any AI — especially AI that works with your organization’s proprietary knowledge assets.
Your customer and market insights are not only crucial to your competitive advantage but they also need to comply with the highest standards of your industry and regional regulations for customer data. Adopt a knowledge management AI with best-in-class data security and privacy foundations.
Adherence to data protection standards
Make it clear to your stakeholders that your AI-powered knowledge management system was developed to comply with the EU’s General Data Protection Regulation (GDPR), which is of the best personal data protection standards in the world. Your AI knowledge management application provider operations should be aligned with ISO 27001 best practices and undergo regular penetration tests by a third party to verify the robustness of their application.
LLM Security
Market Logic’s DeepSights addresses the top ten most critical vulnerabilities for LLM applications identified by the Open Worldwide Application Security Project (OWASP), from malicious prompt injection and training data poisoning to sensitive information disclosure and insecure plugin design.
DeepSights’ LLMs are trained using clean, verified datasets, and feedback loops and continuous monitoring to protect against training data poisoning, malicious inputs, and misleading or harmful outputs. All actions taken DeepSights’ LLMs are logged and monitored to ensure compliance with security policies and to prevent unauthorized activities. Any user-provided data undergoes thorough checks to ensure it’s free of harmful content.
Use of customer data
Whichever AI-powered knowledge management system you adopt, in order to enable the highest security, you’ll want to make sure your knowledge assets and customer data are:
- NOT stored at third-party AI model providers (like OpenAI)
- NOT available or connected in any way with ChatGPT
- NOT used to train or improve Open AI or Microsoft AI models.
- NOT used by your AI Saas vendor to train the AI to answer questions based on learned facts from your customer data.
- ONLY seen by the AI initially to pre-process new documents of finding extraction and on-demand to answer questions, at which time only fragments immediately relevant to the question are used.
DeepSights uses Microsoft Azure OpenAI’s GPT-series of LLMs, but the GPT models employed by DeepSights are not trained on customer data or content. In other words, DeepSights AI models are not trained to learn facts from customers’ data and generate answers from this training. Rather, the evidence from which the AI generates answers is always presented on-demand for a specific question. Read more about how DeepSights LLMs work here.
4. Governance frameworks and quality assurance processes
To maintain trust, organizations must continuously monitor AI applications and refine their governance structures. Best practices include:
- Regular bias assessments: AI models should be reviewed frequently to identify and mitigate potential biases.
- User feedback loops: Allow users to correct errors and improve AI outputs to refine AI responses over time.
- Specialized risk management processes: AI model operations should integrate risk management throughout development, testing, deployment, and ongoing operations.
For example, DeepSights aggregates user feedback to retrain classifier models, improving accuracy without exposing proprietary knowledge. Feedback channels allow users to provide input on AI outputs, and this feedback loop helps in refining and further improving accuracy over time, ensuring continuous refinement and increased trust in the system.
Conclusion: Trust Is the foundation of AI-driven knowledge management success
Building trust in AI-powered knowledge management systems is not just about technology — it’s about creating a culture of confidence, transparency, and security. By ensuring accurate and reliable AI outputs, promoting explainability and bias mitigation, implementing best-in-class data security, and establishing strong governance frameworks, organizations can drive AI adoption and maximize its value. When AI is purpose-built, properly governed, and aligned with enterprise needs, it becomes a powerful tool for unlocking insights and driving competitive advantage.
Solution: DeepSights empowers IT leaders with secure knowledge management AI they can trust
As an IT leader, you understand that achieving business success with AI requires seamlessly integrating the right, trusted AI solution for critical use cases. DeepSights enables you to incorporate market and consumer data intelligence into your business workflows and AI ecosystem effortlessly and securely.
Powered by our cutting-edge, purpose-built RAG model, using an award-winning AI insights solution like DeepSights helps to deliver accurate, actionable insights — designed to elevate strategic decision-making and strengthen your competitive edge.
Curious to see DeepSights in action? Request a free trial and explore how gen AI can revolutionize your organization’s knowledge management today.
Want to know more? Check out our guide How to choose an AI-powered insights & knowledge management platform. Stay tuned for more content that supports IT professionals and CIO experts in the coming months.
