Triangulate Knowledge’s founder and CEO, Charlie Ross pioneered the expert network industry in Australia. He went on to established Australia’s first home-grown and most experienced local expert network firm in 2020.
Before joining the industry, Ross was accustomed to being limited to data-dependent decisions. His own experience had reinforced the value in bringing humans in to interpret, and put data-led insights, into context. He thrived on the intersection of data and lived experience.
When Data Alone Isn’t Enough: The Role of Human Context
Artificial intelligence has turned the process of analysing data, into rapidly processing large datasets and detecting patterns that may be evasive to humans. While AI may miss the reasons behind the patterns, the “why” is what is critically important where informed decisions need to be made.
For example, a model may recognise a 30% decrease in user interaction following a product update and suggest rolling back the update. While the response is data driven, it lacks context. A Subject Matter Expert may suggest that the decrease is the result of negative feedback from a specific cohort of users and has additional context to the issue, that can then be resolved.
At Triangulate Knowledge, Ross gets excited by human-AI synergy. By linking organisations and Subject Matter Experts together, his company bridges the gap between raw data and context that lead to actionable insights. Along the way, it ensures that decisions are not just data-driven but contextually relevant. An example is the case of a major investment firm in Australia seeking to learn more about the allied health industry. Despite having large-scale quantitative data at hand, the firm was still facing information gaps relative to other more data-intensive areas of General Practice and Pharmaceuticals. Triangulate Knowledge facilitated expert interviews and B2B surveys that gave the firm a balanced perspective of market forces, entry risk and competitive landscape that was data backed. This marrying of qualitative and quantitative data enabled the firm to base their strategic acquisition decisions on real knowledge.
While there is ongoing improvement with AI, it has inherent limitations
1. Historical bias: Historical data that the model is trained on may contain inherent biases
2. Inadequate ethical consideration: AI is coded to optimise efficiency, not ethical implications or reputational damage
3. Sensitivity to change: Market conditions changing rapidly and changing regulations can render AI models outdated.
Subject-matter experts give context, ethical judgment, and adaptability to resolve these problems effectively.
Integrating AI with human expertise involves:
1. Employing machine learning in data processing and recognising patterns
2. Engaging SMEs in interpretation, contextualisation, and strategic insight
3. Developing feedback loops, in which specialists curate AI inputs and models, to be tailored based on emerging qualitative conclusions
Overall, the optimal decision-making process combines the processing power of the computer and the subtle judgment of professional humans. This combined approach ensures that organisations can resolve complex problems with speed and depth and create more concrete and informed outcomes. What happens next? We will see, but most would agree, it’s almost certainly a new frontier.