How AI Can Help Identify Opportunities Before They Expire

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Read more about author Rigvinath Chevala.

Opportunity knocks but once. It’s true – great opportunities must be seized when they come. But it’s also important for organizations to watch for signals and identify opportunities to stay ahead of the curve. Trend identification can span across a broad range of industries and functions such as financial institutions, competitive intelligence, strategic advisory, innovation scouting, and business development.

For example, a product’s potential can be recognized and acquired by an entity before it blooms. Or a brand-new idea is patented and could rapidly increase your organization’s topline. Maybe a competitor starts hiring specific talent in a specific region, inferring possible intents. 

In a way, it’s like playing “Sherlock Holmes” at an organizational level, except it’s hard to find enough Sherlocks to help all businesses. That’s where domain-specific AI becomes an excellent tool to help uncover hidden opportunities buried in mountains of disparate data sources. The question is how? Fortunately, you don’t need to go far to find the clues.

Framing the Process

Fortunately, there’s a method to this madness. Here’s a four-step framework that should help you get started in your data exploration:

Step 1: Define Your Mind Map

Most mid-sized to large firms have a very complex structure and different focus areas. Each subgroup has different definitions of what an opportunity means.

A mind map illustrates the multi-level triggers and sub-triggers that define an opportunity. For example, if you want to acquire distressed organizations, your triggers would include “bankruptcy,” “covenant problems,” “cutbacks,” etc. Each trigger could have multiple levels of sub-triggers to focus on specific criteria.

Step 2: Pilot 

Assuming you spent quality time defining your mind map, this step allows you to test the concept in the field. In this context, a pilot requires you to actually use AI + human intelligence in live scenarios (and not just a simulation exercise). 

For the AI portion, it’s important to consider the following requirements:

  1. Data sources – Variety and quality. Think government websites, scientific journals, patent offices, job sites, obscure news channels, etc. 
  2. Data science – A skillset that’s available and dedicated, either internal or vendor supplied. This cannot be a side of the desk task.
  3. Reusability – Productize algorithms so they are configurable for mind maps from other sub-groups but remain domain-sensitive for the entire organization.

Extending from the example above, a simple keyword approach will easily confuse “distressed furniture company” vs. “furniture with distressed leather.” You need sophisticated purpose-built AI algorithms that not only understand the context but also reduce the noise to a manageable level where “human in the loop” will work more effectively.

Step 3: Steady State

Focus on your team’s culture and well-internalized data “sleuthing.” Providing alerts through technology and domain expertise is one important step, but this is just the beginning. Have a well-defined cradle-grave process for opportunities. For example, if an analyst announced a new assessment report with the best and emerging players, what does your team do? How do you ensure that an opportunity is seized before your competition? 

More importantly, as with any supervised learning algorithms, these actions or decisions must be fed back into the engine for the machine to become smarter and more relevant over time.

Step 4: Scale Up

As the name suggests, once you have a sub-segment or a use case figured out, repeat this process for other use cases within your organization. If you are a P&L leader of one division, it’s in your best interest to make a larger effort across the organization to achieve economies of scale. If you are part of a centralized function, this gives you a long-term assurance of providing stakeholder value. This is why step three of the prototype phase is important. It lets you reuse technology across other areas.

Key takeaways:

  • Invest time in defining your mind map. It may seem trivial, but it will affect your desired outcomes in a dramatic way.
  • Find the right resources (internal or external) to create the optimal mix of technology and domain expertise.
  • Don’t stop and revel at solving just one use case. Think of a roadmap ahead of time to leverage your resources in the most efficient manner. 
  • It is not an expensive endeavor. Capitalizing on just one opportunity pays for itself. Imagine the opportunities when you achieve scale! 

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