10 ways to boost your Competitive Intelligence with Artificial Intelligence - Part I

10 ways to simplify, scale, or accelerate your competitive intelligence processes thanks to AI

I bet not a week goes by without an « AI » company telling you that they can revolutionize your daily CI routine.

So, let’s review 10 concrete ways to simplify, scale, or accelerate competitive intelligence processes with Artificial Intelligence, and name-drop AI tools to consider for each of them.

These 10 AI-powered CI processes can bolster your CI, but they are not miracle solutions that will work from day one or solve all problems. Moreover, always verify AI output, as AI tools are still prone to making mistakes. But we will discuss the risks of AI in another blog post.

Competitor news tracking

AI can minimize the time you spend each day tracking, analyzing, and summarizing competitors’ news, as well as preparing weekly competitive news debriefs for your Executives.

Indeed, AI-powered news aggregation tools can find and monitor considerably more sources than humans can (AI finds websites difficult to find via manual web browsing).

AI can also filter out the noise by identifying all the notifications related to the same news and notifying you only once. How often do you get a wall of notifications for the same news just because media outlets copy-paste the competitor’s announcement and publish it on their website?

Finally, AI can instantly draft a news summary that highlights the facts you care about, an impact statement, and calls to action. So you just have to fine-tune it before sharing the news.

AI search platforms, such as Perplexity Research and OpenAI ChatGPT Deep Research, offer a quick and cheap way to experiment with AI-powered news tracking. Create a contextualized prompt and schedule it to run at the desired frequency via the Tasks feature.

If you have about $20,000, you can get a 1-year subscription to thoroughly track about 5 competitors with pre-built CI platforms such as Klue or Crayon. They include AI-powered competitive news tracking capabilities that are easy to set up and customize.

Mass research

Generative AI can save you time when conducting market-wide research. AI can simultaneously find the same public information for numerous competitors, saving you from manually researching each one individually.

AI can help gauge the competitive landscape for a new market, pricing for a specific use case across competitors or competitors’ integration with specific technologies. It can also help identify competitors that you face in deals, according to the capabilities that customers mention.

For instance, you can prompt AI to:

• Document if each competitor listed in the prompt offers the [XYZ] functionality, and for the vendors that do, add a full description of how they deliver it.

• Document how your top 10 competitors, listed in the prompt, position themselves against your company in their marketing, covering the differentiators they claim and the objections they raise for you. You may find objections for which you should equip Sales with a rebuttal.

The simplest way to get started is to prompt AI search platforms with deep research options, such as Perplexity Research and OpenAI ChatGPT Deep Research.

Large-scale survey and interview campaigns

AI can gather, analyze, and summarize competitive insights from customers, partners, Sales representatives, industry experts, and more at scale, all while getting richer insight.

Let AI conduct information-gathering interviews on your behalf to gather insights from more people. Some people like AI-powered interviews for their convenience, as they can do them at any time, and may feel more comfortable speaking their mind freely, since they do not face a human. For instance, it is a time-saving way to interview your own sales force. However, I do not recommend it for strategic accounts or high-ranking decision-makers.

AI can gather deeper insight in surveys and interviews than automated systems with pre-set questionnaires or interviewers without industry expertise, thanks to dynamic questions. You first train AI on your industry, company, and competitors, then instruct it on the information you want, and provide it with a set of core questions. Then, in surveys or interviews, the AI adapts and adds questions based on the interviewees' responses.

Finally, AI can identify patterns and summarize the results for large pools of surveys and interviews way faster than humans can. Favor pre-built AI analytics or AI analytics engineered by your Data Science team over prompting generative AI tools via their frontend console, since it is a multi-step, sophisticated task for a large amount of data.

I prefer pre-built AI-powered survey and interview platforms, such as Klue and Clozd, for win-loss surveys and interviews, as they reduce implementation time and complexity, while ensuring higher-quality output.

Some platforms may even offer to find interviewees who fit your target persona outside of your own pool of interviewees or to analyze large pools of surveys and interviews you conducted yourself, by uploading your own transcripts to their platform.

Also, look for AI-powered platforms that can analyze data from user review websites, such as TrustRadius and G2.

CRM data analysis

CRM systems, such as Salesforce, contain valuable competitive information. Yet, many of the gold nuggets are buried in typically poorly documented text fields that require hours or days of manual review.

That is one more challenge that AI-powered analytics can solve by identifying and summarizing patterns across your CRM opportunities at scale. You can even configure them to slice the data per segment, region, use case, vertical, competitor, or more.

Once these AI analytics have ingested all your CRM textual data, you can use an AI chatbot to get answers to specific questions about your competition, making it easy to mine your CRM data in preparation for strategic meetings or for answering questions on the spot during these meetings.

I do not recommend prompting generative AI tools for this use case, since you will have too much data for them to process, and that sophisticated task requires too many steps and double checks for AI not to hallucinate with a single prompt. Identifying patterns, quantifying them, ranking them, describing them, generating quotes, and more is too sophisticated for that approach.

Instead, have your Data Science team build a more robust analytics platform or invest in prebuilt AI-powered CRM analytics. I do not have specific platforms to recommend, but a web search for "CRM AI win loss analytics" lists several vendors worth trying.

Resource creation

Creating CI for different stakeholders, like Sales, Technical Sales, and Marketing, can be challenging, as they care about different aspects of the competition and want different levels of detail.

You can now thoroughly document your competitive insights into a knowledge base or wiki once, and then use AI to generate different resources from it for your various stakeholders.

For instance, you can create a 10-page deep dive on a competitor, covering your differentiators, their objections and your rebuttals, their differentiators and your deflections, as well as a technical comparison. Then you can have AI use that information to create a 101 battlecard for Sales, a platform comparison for Technical Sales, and a positioning brief for Marketing. It will save you time as you just have to proofread and fine-tune these deliverables, and maintain only the source document afterward, while AI updates the related resources at your desired frequency later.

Start by experimenting with Copilot. With well-contextualized prompts, it can create simple 1-pagers or short competitive presentations that Sales can use with customers. You can find numerous AI document generators on the Internet.

Calls to action

These were the first five ways to power up your CI with AI. We will cover the next five AI use cases, which focus on CI creation and Sales support, in a future blog post.

Get started by reading the Competitive Intelligence Alliance AI in CI Playbook and the Gartner Market Guide for Win/Loss Analysis Solutions (ID G00808237).

Then, obtain demos from Klue, Clozd, and Crayon, and get a deep research AI subscription to experiment with these AI use cases.

Consult with your AI and legal councils to ensure you use AI in accordance with your company's policy before implementing AI in any of your CI processes, as CI teams handle sensitive data.