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Turning Disruptions into Opportunities - AI Series #2

In the second article of our artificial intelligence series, Cocoa Gao, CPA, CA examines different AI solutions and puts a case forward for how to turn disruptions into opportunities.

29 Oct 2020 | 5 min read

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The traditional consulting business model, which is primarily human-driven, is at risk. In the second article of our artificial intelligence series, Cocoa Gao, CPA, CA examines different AI solutions and puts a case forward for how to turn disruptions into opportunities.

Disruptive Innovation

‘Disruptive innovation’ was first coined by Clayton Christensen in the 1990s. Since then, many fields have experienced all sorts of disruptions. An industry that is most vulnerable to disruption usually displays the following characteristics: a few major players dominate the market, business practices are relatively outdated, and technology adoption is very slow. The consulting industry displays all of these characteristics but surprisingly, not a lot of people mention it when discussing the industries that are vulnerable to disruption.

Around 70% of companies will adopt at least one AI technology by 2030. Management consulting firms are involved in many digital transformation projects, helping their clients to adapt to the disruption smoothly. However, consulting firms need to realize that AI will not only impact their clients, it will also impact highly skilled knowledge-based industries, such as the consulting industry itself. The traditional consulting business model, which is primarily human-driven, is at risk.

In the traditional consulting business model, consultants add value by collecting and interpreting data, and providing expertise. Insights are accumulated through experiences. However, many clients now understand the value of data and have started to invest in technologies, such as inventory management systems, point-of-sales systems, and manufacturing systems, to track and store data that are generated. When it comes to analyzing the collected data, tools like Tableau and Power BI enable these companies to interpret data more easily. In addition, consultants’ value-add in the past was driven by information asymmetry. In today’s digital age, this advantage is slowly disappearing as market research has become more accessible than ever before.

AI Solutions

AI solutions are customizable, scalable, and adaptable. Consulting firms can utilize AI solutions in their operations to turn disruptions into opportunities:

1. Using AI solutions to log and share data

Consulting is a data-driven business which means that consulting firms can use proprietary AI solutions to capture and quantify all the insights and knowledge they have gained through their experiences. Every consultant acquires rich industry knowledge and insights through different projects.

However, this knowledge is often informally maintained. If a centralized AI solution can be implemented to log, organize, and share this data, the overall value of the data will be significantly larger.

2. Employing expert assist solutions to analyze client data

Even though many companies already generate a significant amount of data from their business operations and have invested in various analytics tools to analyze the data collected, the process is far from streamlined. The analytics tools available today are useful for questions that are generally smaller in scope. This means that even with quality data, Power BI won’t be helpful for analyzing the business at a deeper level because it doesn’t know where to start. This is where consulting firms will continue to add value (albeit with support from AI technologies).

An expert assist solution is an AI-based system that enables users to retrieve and produce information in a highly efficient manner. Consulting firms can invest in an expert assist solution to help analyze a client’s data more efficiently and effectively. It usually takes a long time for users to search for information in database systems powered by keyword search technology; in many cases, the results are not ideal in terms of content. Expert assist can reduce search time by 50% or more using automatic clustering, ontologies, and visual recognition technologies to identify the correct information and content. The paragraph below explains how each AI technology works.

Automatic clustering algorithms perform clustering on large-scale data sets without prior knowledge or additional guidance from human data (known as unsupervised learning technique in AI) and determine the optimal number of clusters even when noise and outliers are being presented. Ontologies function like a brain; they process information in ways that are similar to how humans perceive interlinked concepts. Ontologies improve data quality by allowing users to make better sense of their data. Visual recognition uses deep learning algorithms to analyze images and give the user insights into the visual content. For example, this can help to organize image libraries and recognize and detect specific objects from an image.

3. Utilizing AI solutions for content discovery

Even though the internet enables market research to be more readily available, many key data points are still hard to uncover. Consulting firms can use content discovery AI-solutions to analyze unstructured big data. These AI-solution algorithms can read, analyze, and interpret big data from the web in an efficient manner.

These are some examples of how AI can be applied in the management consulting industry to help consulting firms better advise their clients.

What’s Next?

In the next articles, we will look at AI application in the back office and the distinguishing characteristics of the new operating model, and then complete the series with a discussion regarding the new competitive landscape that is emerging.

Further Readings:

Beck M., and Liber., B. “Management Consulting’s AI-powered Existential Crisis.” MIT Sloan Management Review, September 14, 2018. [https://sloanreview.mit.edu/article/management-consultings-ai-powered-existential-crisis/].

Bughin, J., Seong J., Manyika J., Chui M., and Joshi R. “Notes from the AI frontier: Modeling the impact of AI on the world economy.” McKinseyGlobal Institute (2018). [https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-modeling-the-impact-of-ai-on-the-world-economy].

CB Insights, “Killing Strategy: The Disruption of Management Consulting”, September 24, 2019. [https://www.cbinsights.com/research/disrupting-management-consulting/].

Gesing, B., Peterson, S.J., and Mchelsen, D. “Artificial Intelligence in Logistics.” DHL Customer Solutions &Innovation (2018). [http://www.globalhha.com/doclib/data/upload/doc_con/5e50c53c5bf67.pdf].

IBM, “Waston Visual Recognition: What it can do for your business”. [https://www.ibm.com/ca-en/marketplace/visual-recognition].

Odebode, Afees Adegoke. “Fast Clustering with Noise Removal for Large datasets.” Dalhousie University, August 2017. [https://dalspace.library.dal.ca/bitstream/handle/10222/73283/Odebode-Afees.MCS-CSCI-August-2017.pdf?sequence=1&isAllowed=y].

Ontotext, “What are Ontologies?” [https://www.ontotext.com/knowledgehub/fundamentals/what-are-ontologies/].

Wheal, Alex. “How Artificial Intelligence might affect management consultants.” Hiscox, June 12, 2018. [https://www.hiscox.co.uk/business-blog/artificial-intelligence-might-affect-management-consultants].


Cocoa Gao