Leveraging Generative AI for Competitive and Sales Intelligence

GenAI is beyond ChatGPT and the ecosystem is changing at a rapid speed

ChatGPT has somehow become synonymous with Generative AI. Just like Google, Uber or Photoshop has become the new verbs in our language. Generative AI is not new, it has evolved in a way it is being used and there’s more to it than just ChatGPT. It’s almost a misnomer now because of the word “generative” which comes from a branch of AI called natural language generation. However, the attention mechanism allows the algorithms to derive the exact meaning of a sentence, including semantics and context. This allows for application of this tech in a wide variety of use cases – like prediction, classification, summarization, and content interpretation (quantitative data, charts, and graphs etc.). Gen AI can help in three broad categories:

1. User productivity. This is where ChatGPT, Bard, Copilot, AutoGPT, Midjourney, OpenTable, Zillow search etc. falls into. These platforms make the lives of users easier and more productive. We will continue to see quite a few tools and plugins in this space to make our daily tasks simpler.
2. Enterprise Impact – This where a specific organization can benefit from the collective technologies. This typically needs to translate into P&L impact for both the clients and your own organization. There are many use cases here that lend well to this technology. Think of thematic and arena scanning, automatic creation of pitchbooks, rapid financial analysis, modernizing BI by deriving insights from charts and graphs.
3. Transformative change into existing industries and value chains – This may include process overhauls, new market adjacencies, new products, and solutions around AI. For example, at Evalueserve, we figured out a way to extract text from blurry and pixelated images by using language context. This is a game changer for the document processing industry.

We still have a lot of unknowns, but the future is exciting for the B2B and B2C industries overall.

Consulting, Advisory, Asset and Wealth Management and other Professional Services firms are proactively leveraging Generative AI

1. First and foremost, we are seeing several announcements in the news regarding consulting, advisory and other professional services firms launching new centers of excellence, or new capabilities around Generative AI that they can take to clients. These firms are also announcing partnerships with Open AI, Intel, and Microsoft to launch enterprise-grade Generative AI solutions. Firms are bringing in Gen AI into existing service areas like Tax or Legal.
2. Firms are very enthusiastic about GenAI, they are also a little nervous of how their employees would use it. Most firms are still in the stage of putting guidelines for employees regarding usage of GenAI, especially ChatGPT. It is a wait and watch approach that many firms are following, especially with so much being discussed from a policy perspective
3. Finally, some firms are implementing initiatives at a strategic and central level, these initiatives have not reached various teams across functions or regions. The bulk of the employees in most firms are still trying to figure out “What does GenAI mean to me” and “How does GenAI allow me to deliver insights at scale”.

Generative AI is impacting how firms approach their market and competitive intelligence programs

Below is how Competitive and Market Intelligence was done before GenAI

• PPTs, Excels, Newsletters
• Focus on tags, taxonomy, filters, across Publications, TLs, strategy movements, etc. 
• SaaS based platforms that make it easy to centralize, search and disseminate insights

While all these approaches are extremely beneficial and useful, what Gen AI allows you to do, is to enhance the user experience, and bring in more efficiencies to the insights program.

How can this be done?

Existing insights programs allow you to have a repository of rich data set that is sitting on existing systems in different formats. Now imagine if you are able to point GenAI to this massive data set and start querying it or asking it to do things – the output you get will be extremely robust.

The three key pillars for applying Generative AI

The first pillar is of course an LLM (a large language model) e.g., Open AI’s DaVinci, Google’s PALM 2 etc. These are massive models trained a huge universe of data to a point where the output may even pass a Turing test. So, picking the right LLMs is key to success.

• Now, Prompt Engineering is a new area where you must ask the right question to get the right answer. So, designing your prompts to get an optimal response is now becoming its own science
• Data corpus that you send along with the prompt to the LLM is another key pillar. It could be a single document such as a 10-K filing of a company, or it could be several hundred patents that you are trying to mine insights from
• To exemplify, we incorporated this abstraction into our architecture and recently released researchbot on insightsfirst. It’s a micro-front end that can plug into any of our existing products and can be configured to an LLM and can generate parallel prompts on a curated datasets to get a noise free output

Bringing in GenAI to support existing sales or opportunity intelligence program

1. Stage 1: Opportunity or Deal Identification: You can train the AI engine to look for opportunities aligned to your mandates – say growth or distressed or ESG and using GenAI you can do this at scale. Also, the ability to query this large data set and get interesting insights is substantial – for example, which companies in asset & wealth management have increased AUM by X%? or, which companies are hiring ESG specialists?
2. Stage 2: Opportunity Insights: By leveraging various GenAI based plugins or autonomous bots, one can leverage these APIs to create quick company profiles or executive profiles. Say you are going to meet a John Doe from Company X; you can ask the API to provide an overview of all the conferences this individual has attended
3. Stage 3: When it comes to deep dive insights, the human will need to take center stage, but Gen AI can operate like a co-pilot on various things to increase efficiencies. There are tests being undertaken by firms on the ability to help create pitch decks using GenAI. This is quite revolutionary in itself
4. Stage 4: Finally, in the end, what GenAI allows you to do is to get into your knowledge assets in a very structured and intuitive way to help you identify past proposals and approaches, and allow you to write specific sections of the proposal

Key considerations and challenges while working on Gen AI

Broadly speaking, Generative AI is here to stay. It is our collective job as industry experts to figure out how to use the technology safely and effectively. To get started on this journey, it is vital that there is a prioritization framework using factors like RoI impact, solution differentiation, effort required etc.