Managing knowledge at scale is tough. Here's how AI can help and what to watch out for.
Balancing automation and human expertise in knowledge management
The challenge
Large organisations often face a flood of proposals and documents. In a recent client conversation, we heard that a knowledge team were tasked with ingesting each one of the hundreds of proposal and pitch documents created in a month. They were only able to get through 20-25% of this. The challenge lies in curating, maintaining, and governing this content so it remains accurate, accessible, and useful. Limited bandwidth and manual processes make it hard to keep up, leading to gaps and reactive updates rather than proactive knowledge sharing.
Overcoming the challenge
AI offers powerful tools for automating ingestion, deduplication, and quality checks, but it's not a silver bullet. Human oversight is still essential for governance and accuracy. Here are key considerations:
Bandwidth constraints:
Even with automation, teams of dedicated knowledge managers can struggle to process high volumes of content. Prioritisation and trend forecasting are critical.
Manual bottlenecks:
Tasks like sanitisation and resolving contradictory answers require human judgement. Automating these without oversight risks errors.
Content gaps:
AI can help identify trends, but proactive curation needs organisational sponsorship and feedback loops.
Governance and quality:
Automated scoring can support audits, but manual reviews remain vital for compliance and accuracy.
Dependencies:
Success depends on collaboration across teams, not just technology. Without input from marketing or innovation teams, knowledge bases risk becoming outdated.
The lesson? AI should augment - not replace - human expertise. Organisations that combine automation with strong governance and proactive planning will build knowledge systems that truly support proposal writing.
How we can help
At SP, we help clients design and implement knowledge management strategies that scale. Our services include:
Designing curated content libraries to be AI-enabled
Improving ingestion and governance processes
Performing independent audits on libraries for quality and accuracy
Integrating AI tools for efficiency without compromising quality
Training and coaching teams to balance automation with human oversight
