You need your intelligence to be accurate, relevant, and timely. But you also want it to be as efficient and cost-effective as possible. Automation and artificial intelligence (AI) can improve your competitive intelligence (CI) efficiency in some areas, though it may frustrate and hinder it in others. When it comes to best practices in CI, some tasks are best automated while others are better performed by human analysts. We examine the differences between the two resources below.
Human intelligence and AI at a glance
CI tasks: Who does it better?
We’ve discussed the benefits and limitations of AI for specific purposes in previous posts, but now we’re going to look across a wider spectrum of CI functions and showcase which are best handled by people, and where AI could give you the best results.
Here is how the two approaches stack up in some common CI tasks:
Customized context and insights
The nod here goes to a dedicated intelligence analyst that understands your business, your competitive landscape and the broader market. Analysts can utilize this knowledge and experience to deliver the most impactful insights. Automated solutions tend to deliver a more generic report.
Bulk scanning and filtering for known issues
As long as you are looking for the same set of “signals” consistently, an automated solution is the best for this task. A trained AI can make quick work of large datasets and give you a filtered shortlist of relevant information.
Identification of emerging issues, opportunities and threats
Whenever you are faced with an unknown, uncertain or evolving scenario, a human analyst will deliver the best results. While AI can be good for bulk scanning, they are less capable of spotting new issues or making connections across different areas. AI needs to be trained to identify specific issues, and this training takes time and resources.
Early warning for known risks and opportunities
If you know exactly what you are searching for, it might be worth automating your early warning function, for the same reasons noted for bulk scanning above.
Innovation and disruption analysis
Because innovation and disruption involve new developments and rapidly evolving issues by definition, we find that a human analyst is most effective at these tasks. AI would be far less effective and more costly, for similar reasons as discussed in the identification of emerging issues section.
Broad industry-wide indicators and dashboards
If you are seeking industry-wide data and basic visualizations of that information, an automated system may be the preferred option. We find these can provide a good resource for a dedicated analyst, who can combine such information with additional intelligence “dots” to deliver a more relevant and contextualized view of your competitive landscape.
Long-range and strategic analysis
Looking ahead, incorporating scenarios with multiple variables, and answering strategic questions all require the ability to make connections that may be new or novel. For these reasons, a human analyst would supply the best results here.
Speed is not a task, per se, but speedy delivery of your CI reporting is important. As noted above, who is faster depends on the task. If you have thousands of documents or sources to search (and if the AI has been trained for the task), an automated tool is best. But if you are searching for new threats, opportunities, disruptions or innovation, a human analyst will generally deliver the results faster.
Human intelligence vs AI: Which one is better?
Ultimately, you want a combination of both. The fact is, our team of intelligence analysts and researchers use a range of powerful automation tools on a daily basis to deliver insights to our clients. We understand where AI can add value, and where you need a dedicated analyst to create the greatest return on investment.