It’s not only about the technology…
In the last few posts I posited that we are at an inflection point in the capabilities of AI and its potential to deliver major benefits IF used correctly and for the right use cases. The last post teased that we need different forms of AI to achieve deterministic outcomes - where we can rely on the results without the delay and cost of human oversight.
LLM based systems rely on prediction based upon statistical correlations from training data. This is very powerful when dealing with language but less so when we need precision. LLMs struggle with logic, maths and problems requiring a chain of thought. All kinds of workarounds have been developed to break down problems, overcome memory limitations, provide richer context, use specialised systems for parts of tasks etc. These all improve performance markedly, but they do not guarantee reliable results.
There are different AI models that rely less on syntactics (language) and more on knowledge (ontology) and logic (axioms, rules expressed formally). These systems are not new, but are more difficult to design and take more effort to configure - for example, we need to reach consensus on agreed and capable ontologies in a given domain (e.g. healthcare, manufacturing). Fortunately, these efforts are now starting to pay off, leading to AI systems that are reliable and orders of magnitude more efficient. They can be incorporated into production systems with real time response and little to no human supervision.
Benefits are not achieved by technology alone. We have to employ it properly. This involves selecting the right technology for particular use cases, selecting applications that are within the capabilities available, considering the full architecture of a solution, including:
human concerns (roles, safety, desirability, ethics, experience)
organisational (stakeholders, goals, customers, offerings, process, resources)
systems (functionality, workflow, interfaces, integration, security, efficiency)
data (scope, definition, design, sourcing, management, security, privacy)
technology (types, integration, standards, infrastructure, performance, scale, security)
governance (principles, policy, projects and change, quality assurance, risk management)
So we should remember the adage:
“There is almost always a management solution to a technology problem.
There is almost never a technical solution to a management problem”
AI can deliver enormous benefits, if we use it well. That is the bigger challenge.
