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SLA Webinar - How Technology is Changing the NexGen of Competitive Intelligence

Michael Irving


April 30, 2019

Last week Cipher CEO, Peter Grimm, spoke to members of the Special Libraries Association (SLA). If you missed the webinar, the full recording is below, along with the webinar slide deck and the Q&A session that followed. 

IN THE WEBINAR [0:51:00]: Peter discussed what traditional CI techniques are being adapted for a new era of CI and the technologies that are fueling both an industry transformation and wide scale industry disruption. 




Key Takeaways: 

  • How to adapt your traditional CI techniques to the modern era 
  • How to strategically use technology to improve CI efficiencies and generate stronger insight 
  • What you can do to integrate new technologies into you existing CI frameworks



We received several questions after the webinar ended and have posted all questions with Pete's answers below. 

Question: Hi Peter, Thanks for the great presentation. My question is: Do you think that niche research skills (looking up government documents, retrieving statistics, etc.) will still be relevant for CI professionals in the age of AI? 

Answer: The short answer, No. I don't think they're going to be relevant. It's just a matter of time. The AI being used is still very expensive.  

Think about how technology advances have evolved in cars. GPS technology was first available in luxury brands. Now it's kind of standard for everybody. The same thing will be true for AI, it is going to become commoditized over time. It's not anywhere close to that yet. It's still really expensive and very specialized.

But as AI becomes commoditized, more and more manual skills, even if they're specialized to some extent like, knowing how to look up government contracts, will be completed with AI. These are all things a computer can learn.

It's a question of when it becomes cost effective enough for those niche use cases to switch from paying a person to search, to a scenario where they are paying for a person to build a model that trains a machine to search. We’re not answering a technology question, but an economic question. I think the more specialized the skill, the longer until this will happen, but it's going to happen when it is economically beneficial.

Question: The EU recently came out with a set of guidelines on AI ethics, what are the speaker's thoughts on them?

Answer: This is a great question, I’m by no means an expert on the ethics of AI. Recently we had a conversation with an insurance client around the use of AI in the automotive industry and autonomous vehicles. Consider a scenario where a self-driving car gets into an accident causes damage to property, or harms a person, who is liable for that? Who should be required to have insurance? Of what if I’m a CI professional and I make a strategy recommendation based on insights gathered with AI and those insights are incorrect, who’s fault is that? Having these types of conversations and understanding who is responsible for what are key conversations that should be a part of your AI strategy.

Question: Any thoughts on new applications such as kompute.com & nichefire.com

Answer: No, I’ve never heard of either of those companies.

Question: Need to run. Have another appointment. Thanks for the very informative session and Peter is a fantastic speaker! Will the slides be available later?

Answer: Yes, visit this URL for the SLA Webinar Slide Deck

Question: Really enjoyed the presentation and think it's applicable in other areas. I work in legal and wonder if Peter has any thoughts in getting the message across that you must understand the tool in order to use it to make decisions. This question is based on the last 2 slides he shared.

Answer: I mean, I wish this question had come up during the webinar. It’s a great question. I think it would be easy to get to the message to resonate with lawyers. If you think about it, a lawyer is not going to trust a machine. They're going to be skeptical when they're presented with a box that tells them this is the legal strategy and here's all the relevant case law. If I were a lawyer, I'm going to be really skeptical. If you think about what lawyers do, at least in my experience not being one, they're always looking to understand how to construct an argument. What are the related arguments and different positions that can be taken? How do I support a position or counter it?

If I was an analyst who was worried about being replaced by some new AI tool capable of replacing my work with the same output and insights, I might turn that around to the lawyers and say, would you trust a machine to tell you what the relevant case law is and how you ought to litigate? Maybe they would in the future, but not yet.

I think for someone to trust the information they receive from AI, I would expect that person is going to want to understand the ins and outs of what the AI is basing the inferences and decisions on.

So, if a machine is going to tell me how to do that and I'm going to go stand up in court and say, this is what I think, and this is the position I'm taking… I'm betting that the attorney is going to want to know damn well how, how that conclusion was arrived at.

Question: In AI do we see organizations expanding the CI team or reducing. Taking from your note the AI will create more jobs than reduce

Answer: It's kind of a complicated answer. The shortest version is, it's going to expand, and it may not necessarily be inside of the CI team. I see the concept of a CI team is kind of a legacy concept. Right? There will continue to be a need for CI specialists in the future, but more and more this is a question of understanding data and not competitive intelligence.

If I'm a strategist advising the CEO or VP of strategy, someone who historically is or sits within the CI team, AI is going to significantly transform my role. The responsibilities of this person or team is going to transition from knowing everything about our markets, and our competitors and providing facts, to those planning our strategy to something new. These new responsibilities are more focused figuring how the analytics expert or a data scientist or whatever the proper term is, who's going to help me, apply tools to data to get the insights I need.

So, you can see then that person or people I need don’t necessarily have to be competitive intelligence experts. I’m going to need more and more people who understand the tools we use, who understand what they are, who understand the insights that the customer is looking for and know how to put all those pieces together.

One of the biggest challenges CI has had is how do you show ROI? Answering this gets a lot easier when you have AI. Now instead of spending all my time digging through all of this information, collecting and organizing it and spending very little of my time actually adding value to the business, AI allows me to spend much, much more time adding value to the business. And my ROI should be much more apparent.

Now I don't have to worry about reading the news every day and manually tagging it and maintaining the taxonomy and my ontologies and all this knowledge so that I know where things are. I don't have to do that anymore. In this new world. I can now focus on solving strategic problems that and value to the business. All that manual labor doesn't add any value to the business, the value of competitive intelligence doesn't appear until you get to the insights.

Question: Interesting take on how AI will disrupt the intel cycle. When you get to Outputs and say people will "interrogate [information] at their leisure," doesn't that open organizations up to wildly misinterpreted data and wayward decisions? That sounds like a whole set of corporate disasters just waiting to happen!

Answer: Thinking back to that slide, I think I will make a small but important edit. You make a good point, if you just open up the data warehouse to everybody to play with and they don't have any understanding of where the data came from and how credible the various pieces of it are, then yes, you could run into that kind of scenario. But when you have analysts, who do understand the voracity of all your various data and how much confidence to put in one source versus another, and then you apply technology or AI, you're able to automate the production of that first level of insights and make that available. That's where you get that kind of network effect, right?

So, interrogating the data at their leisure is probably a poor choice of words. What I’m really talking about is more interrogating the insights at their leisure.


There is little debate around how recent advances in technology, particularly AI and Big Data, have had a impact on how market researchers gather information and a broad impact on the competitive intelligence industry. These advances have expanded both the size and quantity of data competitive intelligence professionals have access to and at times, reduced the human capital need to perform research. 

To hear more on the disruption of the competitive intelligence landscape, watch Peter's previous SLA webinar, Trend Monitoring 101: Structuring Our Thinking. In this webinar [60 mins] originally presented to members of the Special Libraries Association, Peter Grimm defines market intelligence terms: Trends, Indicators and warnings, walks through each and discusses how to identify your market differentiation, define your risks and build a competitive market landscape.


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Michael Irving
Michael Irving

Michael Irving is an accomplished professional who has more than 20 years of experience building successful sales teams for growth-oriented businesses. Michael has held various leadership roles for both Fortune 500 and SaaS start-up companies leading all selling activities for multiple divisions.

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