Each era of rapid technological advancement has been shaped by distinct mindsets. “Unless you are breaking stuff,” one well-known tech leader declared in a 2009 interview, “you are not moving fast enough.”
Latter decades have largely shifted toward a paradigm of stakeholder accountability. But today, the era of artificial intelligence (AI) – especially Generative AI (Gen AI) – looks set to have an impact that will reverberate for generations to come.
It can feel like there is a race to incorporate AI into everything. The question is why and at what cost?
Balancing speed with stakeholder accountability
Gen AI is transforming industries, offering unprecedented opportunities to enhance decision-making, improve efficiencies, and drive innovation.
Rule-makers and rule-takers have historically been slow to adopt the latest innovations. This is not necessarily a bad thing and indeed, this isn’t the time to act recklessly. As an interviewee in our Legislative Q&A series put it, sometimes efficiency is not what the legislature is about. It is a deliberative process and as a result, requires deliberate innovation. However, there are risks associated with falling too far behind.
1. Changing user expectations: an iPhone moment
The arrival of the iPhone is often cited as a technological watershed moment. Not simply a technology suddenly shooting to instant mainstream popularity, the iPhone set new standards for user experience and functionality and ushered in the app economy.
AI is not without its challenges. These challenges are magnified in the context of rule-making and rule-taking as the stakes are far higher. If bias, inaccuracies, or plain errors creep into such processes, the ramifications for society at large are serious.
2. Missed opportunities
Technological solutions to managing AI-related challenges are emerging. Knowledge graphs, used in conjunction with a technique called Retrieval Augmented Generation (RAG) improve the accuracy of AI outputs and allow organizations to manage content more effectively.
There are opportunities for Gen AI to support rule-makers and rule-takers in achieving their jobs to be done. Gen AI tools are adept at tasks like summarization, simplification, identifying material changes, and the impact of changes.
Summarization
Simplified language
Another area in which AI could introduce efficiencies is in the generation of Simplified Technical English (STE) and other controlled languages. Combining language summarization with translation can transform long technical documents into concise summaries in multiple languages. An example use case is creating an English summary of a long Japanese patent document.
Managing regulatory change
One of the biggest challenges for knowledge management professionals is identifying meaningful changes within evolving legislation, standards, and guidance. SMEs may already have the capability to view change timelines. Adding AI can automate the detection of ‘material’ changes by comparing versions of text and highlighting the updates that necessitate action such as training revisions or compliance updates.
AI-enabling your published content
Knowledge graphs and RAG are not only effective methods of mitigating AI-related risks, providing LLMs with greater context without sharing data; there are other benefits.
- Large LLMs take months to train. RAG allows organizations to bring in, for example, what the standards say today without needing to rerun training.
- RAG and knowledge graphs make LLM outputs far more powerful for the user who can essentially ‘converse’ with authoritative content.
This opens multiple avenues for value creation and progress on both sides of the rule-maker/rule-taker coin while reducing hallucinations and inaccuracies. If you are a standards developer, for example, your AI agent can connect to your customer’s AI agent, facilitating the connection between knowledge graphs and delivering invaluable insights.
There are opportunities for Gen AI to support rule-makers and rule-takers in achieving their jobs to be done.
Protecting intellectual property (IP) in the AI era
Rule-makers and rule-takers are all stewards of critical IP, including:
- Laws and legislation
- Standards and regulation
- Internal policies and procedures
This content is crucial, underpinning business operations, governance, and compliance. This content ensures the fair, safe, and progressive running of everyday life. However, as AI tools become more ubiquitous, they’re also being misused by bad actors.
Organizations must address existing vulnerabilities while becoming AI-ready. Key actions include:
- Making content more readily consumable by machines
- Ensuring the ability to establish the provenance of digital documentation
- Ability to demonstrate to end users the authenticity of digital documents
- Provide internal teams with high-quality, easily navigable information systems so they won’t be tempted to use unverified sources.
Deliberate innovation for deliberative processes
Rule-makers and rule-takers need to get ready for an AI-driven future. However, this is not a call to move fast and break things; this is a call for deliberate innovation for deliberative processes.
Standards, regulations, and guidance are developed with high levels of necessary rigor. In these processes, the audit trail is fundamental. In lawmaking, the audit trail is what ensures transparent and democratic processes. In audit and accounting, the audit trail is what ensures accountability to regulators and other stakeholders. These records demonstrate the validity of processes.
As AI continues to advance at a rapid rate, there is a more pressing need to act – to move fast. But the technology must be applied in the right way. We cannot risk breaking things.
