Uncertainty is all you need // to understand
McKinsey, BCG, and Deloitte are facing unexpected operational challenges despite their expertise in strategic forecasting and advising
The best way to predict the future is to make it. New ideas and new technologies always win.
The major consulting firms McKinsey, BCG, and Deloitte are facing unexpected operational challenges despite their expertise in strategic forecasting and advising large corporations. This ironic situation highlights the difficulty of accurately predicting future trends and adapting strategies, even for firms renowned for their predictive capabilities.
The root cause seems to be a failure by these consultancies to anticipate disruptive shifts within their own industry, such as the impact of new technologies and changing client expectations. Their traditional forecasting models based on historical data and expert analysis proved inadequate for foreseeing the rapid pace of market evolution.
As a result, these firms made strategic missteps by over-investing in proprietary methodologies while nimbler competitors leveraged innovative technologies like Enterprise Knowledge Graphs to offer more scalable and efficient planning solutions. This exposed a critical gap in the established consultancies’ approach — their lack of adaptability and technological integration.
To address these challenges, McKinsey, BCG, and Deloitte must undergo significant transformation by:
1) Embracing innovation and integrating advanced data analytics and technologies into their forecasting models and service offerings.
2) Increasing organizational agility to quickly adapt strategies as market conditions change.
3) Rethinking their over-reliance on past frameworks and expert opinion in favor of more dynamic, data-driven approaches.
The difficulties faced by these consulting giants serve as a pivotal moment for the entire industry. To remain relevant, strategic consulting must evolve toward more technology-driven, responsive methods that can better anticipate future disruption across sectors.
The unforeseen challenges confronting McKinsey, BCG, and Deloitte despite their predictive prowess demonstrate that even the most prestigious firms cannot rest on traditional models alone. Continuous innovation, digital transformation, and adaptability are crucial for accurate foresight and thriving amid rapidly changing business landscapes.
Knowledge graphs
Large Language Models (LLMs) have significantly advanced healthcare innovation on generation capabilities. However, their application in real clinical settings is challenging due to potential deviations from medical facts and inherent biases. In this work, we develop an augmented LLM framework, KG-Rank, which leverages a medical knowledge graph (KG) with ranking and re-ranking techniques, aiming to improve free-text question-answering (QA) in the medical domain. Specifically, upon receiving a question, we initially retrieve triplets from a medical KG to gather factual information. Subsequently, we innovatively apply ranking methods to refine the ordering of these triplets, aiming to yield more precise answers. To the best of our knowledge, KG-Rank is the first application of ranking models combined with KG in medical QA specifically for generating long answers. Evaluation of four selected medical QA datasets shows that KGRank achieves an improvement of over 18% in the ROUGE-L score. Moreover, we extend KG-Rank to open domains, where it realizes a 14% improvement in ROUGE-L, showing the effectiveness and potential of KG-Rank.
In this work, we proposed KG-Rank, an enhanced LLM framework combining medical knowledge graphs with ranking techniques to boost free-text medical QA. KG-Rank, as far as we know, is the inaugural integration of ranking models with KG for long-answer medical QA. It demonstrates over 18% improvement in ROUGE-L across four medical QA datasets. Its application to open domains yields a 14% ROUGE-L score enhancement, underscoring KG-Rank’s effectiveness and versatility.
In this research, we propose an LLM framework augmented by UMLS to improve the quality of the content generated. However, there are some limitations, which we will address in the next phase. Firstly, we plan to incorporate physician evaluations to validate the factual accuracy of KGRank’s answers. Secondly, we aim to assess the performance of more medical-specific base models on medical QA tasks. Lastly, while the ranking method may increase computational time, we recognize the need to optimize its efficiency.
