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Integrating AI into your Competitive Intelligence Program for a Sustainable Advantage
Blog Feature
Erin Pearson

By: Erin Pearson

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November 9th, 2021

Integrating AI into your Competitive Intelligence Program for a Sustainable Advantage

Competitive intelligence (CI) is vital in any hotly contested marketplace. With increasing competition and ever-changing industries, getting a handle on what the market is doing and how your competitors are addressing current trends, is essential.

As a recent article in Forbes put it “the key is to identify challenges, advantages and white spaces to build a strategy that creates competitive differentiation.” In this way, you’re complementing, rather than directly competing with your rivals. You use this information to develop a different delivery model, new products, or appeal to a different market sector. 

As with any data-driven information source, CI relies upon systems able to sift through mountains of data and make sense of it. This has to happen efficiently and with a minimum of error. This is where AI comes in. The speed and efficiency with which AI can deliver insight make this a tool well worth considering. 

Pros of using AI for CI

  • Speed and Productivity — AI works infinitely faster than a team of human researchers. It can run all night and over weekends if you need it to. At the end of its deliberations, you’ll have all the data you need.
  • Reach and Scope — While human workers might select a shortlist of sources to focus upon, your AI can be trained on a much larger dataset.
  • Reduction of Repetition — While your colleagues dislike mindlessly repetitive tasks, your AI loves them. Machine learning works by drawing inferences from repeated patterns of activity and becoming more efficient. While your AI is beavering away, your human employees can get on with more interesting tasks.
  • Customizability — A good AI can be trained to look for a whole range of signifiers in the data, meaning you can fine-tune it for whatever purpose you choose.
  • Interactivity and Engagement — The best such tools can be tweaked and altered as you go if they are not delivering. Change the parameters or increase the breadth of the data source; the AI will not complain.
  • Improved Distribution and Impact — Data-scraping crawlers can roam far and wide throughout the net and need not be limited to geographic areas or native language. Knowing that you haven’t missed anything makes it easier to make a convincing business case.
  • Detailed Data Breakdowns — The best AI for CI systems will allow you to get very granular with the results they unearth. Gaps should be easy to identify.

Cons of using AI for CI

  • False Positives — Since with machine learning you won’t necessarily know exactly HOW your AI is identifying suitable data points, it is possible for a programming oversight to lead to falsely positive results. You will want to run random spot checks on the data to ensure the AI is identifying what you need it to.
  • Lack of Context — There’s a certain “black box” quality about AI-driven insights, i.e., you feed data into one end, and answers emerge from the other, but the process in between is opaque. Your AI might identify an unexpected trend, but it won’t be able to tell you why it’s happening. Those investigations will remain your job.
  • Obsolescence — There is currently so much innovation going on in AI that there’s a danger of discovering your bold new investment has been superseded by something even more powerful. Keeping an eye on what’s current can ward off this danger.
  • Overreliance — Sometimes something as fashionable as AI can blind us to the other tools we have at our disposal, both manual and machine-led.

Common use cases

So which industries and sectors are using AI for competitive intelligence, and how? Here are just a few of the popular uses of this technology:

  • News Tracking — Identifying and collating mentions of your brand or your competitors’ brands in written and audio-visual media content.
  • Social Media Monitoring — Similar to the above but focused on social media content. This necessitates the ability to identify logos onscreen and contextualize casual conversational content.
  • Auto-Tagging — Applying hashtags to content across a range of media to maximize its reach and shareability.
  • AI-Enabled Search and Content Recommendation — Comprehending a user’s speech, or written questions to accurately deliver what they want.
  • Opportunity Tracking — Looking for user searches or requests for a particular product or service, going beyond brand mentions.

Conclusion

AI is more than “just another tool." Although its use can be misunderstood or misinterpreted, in terms of reach, efficiency, and neutrality little beats this method for gathering competitive intelligence at scale to develop a sustainable competitive advantage.

Want to start using AI to source market-leading competitive intelligence? Evalueserve can help. Click below to learn how.

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About Erin Pearson

Erin Pearson is Director, GTM at Evalueserve.

Disclosure: Marketing AI Institute writes about and recommends AI-powered marketing and sales technology. In all cases, content and recommendations are independent and objective. In some cases, Marketing AI Institute may have business relationships with companies mentioned, which may include financial compensation, affiliate compensation, or payment in kind for products or services. View a list of Institute partners here and MAICON sponsors here.