On January 20th 2017, independent consultant Andrea Sutter lost yet another project to a larger consulting firm. She was baffled. Her skills and experience perfectly suited the client’s requirements. Her pitch was flawless. She was sure that her proposal was much more cost-effective than that of her larger competitor. Still, after the effort, all that she got was a polite rejection note and maybe some goodwill for a future attempt. The reason for the loss: a “lack of analytical breadth”.
A lack of confidence in one-person operations
The project was a market entry study focused on China’s FMCG industry – her specialty. However, it required a large-scale analysis of competition, pricing and product offerings in seven Chinese cities. While she had already delivered over a dozen studies on the topic for other corporations, the client’s executives challenged her capacity as a one-woman show to research a landscape of around 3,500 products with enough detail. They were probably right.
Independent consultants often face this challenge when developing business or executing projects. They can have the right practical training, often honed during years at top-tier management consulting firms. They can present an outstanding value-for-money proposition. However, when they are independent, they are also alone. They miss the wealth of information available to peers at bigger firms and the access to junior analysts to support research and content development work.
Enhancing the independent consultant’s capabilities
That is precisely where artificial intelligence (AI) and automation technologies can help. Independent consultants can benefit from them to gain productivity and analytical muscle, compensating the small scale of their operations. What is important is for them to correctly frame AI use cases within their business. For example, after the setback outlined above, Andrea recognized three ways in which she could leverage these emerging technologies in similar projects.
First, she realized that she could have applied an AI-based data mining system to automate the collection and classification of online information on FMCG products. Prices, user reviews, product characteristics, technical specifications and images of thousands of products can be automatically saved to an Excel spreadsheet for further analysis. This saves the independent consultant from having to pay for access to specialized databases or hire additional team members to support data collection. Andrea calculated that, for a portfolio of close to 3,500 products, the total time required to integrate the information would be reduced by at least a factor of 10. Furthermore, once the system was set up, human intervention in the data collection process would be minimal.
Second, beyond standard spreadsheet analysis, Andrea understood she could have used machine learning to analyze pricing ranges, consumer behavior and competitor clusters in the dataset. For example, classification and sentiment analysis algorithms could have been used to link product characteristics to positive customer sentiment, spot unmet needs and sources of negative sentiment, and establish sensitivity to pricing based on consumer experience. All variables in the product portfolio could have been classified according to their influence on each other, allowing Andrea to deliver hard evidence on key purchase factors and propose new product profiles with high market potential.
Third, Andrea could have deployed an automated PPT slide design system to generate both detailed profiles for each of the products reviewed and overview analyses for each of the clusters recognized. The system would have allowed fast development of customized reports for different product management and marketing teams within the client organization. With minimal manual work, she would have been able to facilitate market entry planning at both the strategic and the tactical level.
Building your robo-intern battalion
Given Andrea’s example, how can independent consultants brainstorm AI use cases to increase their productivity? A good starting point is to identify what data collection, data analysis and data reporting tasks might normally be assigned to an intern. That is, what work requires human level cognitive skills but might be lengthy or repetitive? Then consider AI as a way to build a battalion of robo-interns to tackle the challenge. Thinking this way is the first step for independent consultants to greatly expand their project delivery capacities and gain new business. Hopefully it will also make the tasks assigned to talented interns more interesting.
