Why social skills are essential for competitive intelligence professionals in the future

A few years ago, the Swedish Foundation for Strategic Research published an article named “Every other job is automated within 20 years – Challenges for Sweden” (translation). The article points out that it is very likely that 2.5 million jobs will disappear in Sweden in the next two decades.

What does this mean to competitive intelligence professionals? Let’s say like this, if you do a lot of your analysis in Excel, you need to read this article.

Automation, Big Data, Machine Learning and Artificial Intelligence

Automation, Big Data, Machine Learning (ML) and Artifiical Intelligence (AI) are concepts that are closely associated with each other.

• In short, automation is based on replacing the human factor with hardware and software. A fire alarm is an example of automation. Instead of a human being, it is the alarm that warns when the sensors sense heat or smoke.
• Big Data means that everything that can be quantified is quantified! According to IBM, about 90% of all data on the Internet has been produced over the past three years. Fantastic.
• These massive data sets are more or less structured and in order to get order in the chaos you use Machine Learning.
• And finally, AI, Artificial Intelligence, is the interface between man and machine. AI emulates human behavior.

It is thanks to these concepts that we will soon have self-driving cars on our streets. The concepts have been around for a long time and it is only recently that we have started to see concrete solutions.

Automated text analytics tools are becoming quite advanced

As an analyst, I think the concept of text mining and tools that do automated text analytics is really cool. I read a lot. Reading takes time. Tools that automate the text analytics part would free up my time. Today there are many different tools out there and they are getting really sharp. One feeds the tool with written text from searches on the Internet, social platforms, emails etc and then the tool summarizes the texts based on different parameters. The value to a competitive intelligence professional can be quite broad:

• Regular text summaries.
• Sentiment analysis, i.e. identifying the nature of commentary on an issue.
• Information extraction, i.e. extracting key entities.
• Investigation, i.e. discovery of the causes of a specific issue.
• Classification of the text’s subject or key content.

There are many different suppliers of text analysis tools. As a user you have to weigh the price against functionality when choosing your supplier and also see what other services are offered.

Smart machines don’t just analyze texts today, they are starting to write them as well. Actually, the research institute OpenAI recently announced its natural language generator, called GPT-2, might be too dangerous for the public. It easily mimics a writer’s style and generates convincing texts full of ‘fake news’.

The number of AI platform suppliers is increasing

There are many AI platforms out there; Meya, Premonition, MindMeld, Wit, Infosys NIA, Ayasdi, Baidu, Amazon, Microsoft Azure are just a few names…

• Rainbird offers real-time decision support based on Big Data.

• The IT player Wipro’s AI platform Holmes has been used internally for several years to streamline processes (and to reduce the number of employees by about 30%). Today it is used to improve delivery efficiency in customer projects. Customers also use it to save time in contract renewals.

• IBM’s famous platform Watson has many different functions. One, of importance to competitive intelligence professionals to know about, is the automated win/loss-analysis functionality.

The market for BI and analytics is growing too

The number of BI and smart analytics tools suppliers is increasing, and this means that the functionality is constantly broadened and improved. Research and advisory services company Forrester has stated that “by the end of 2019, all enterprise BI deployments will include Natural Language Generation” for example. And Gartner, another famous research and advisory company, predicts that “by 2020, natural language generation and artificial intelligence will be a standard feature of 90% of modern BI and analytics platforms.”

With self-service solutions for business intelligence, companies are starting to really address the challenge of workers having access to data for analysis anytime and anywhere. Companies tend to underestimate the need for training the users, but the fact is that with self-service BI you do not have to be a technology wiz to use it. It doesn’t matter if the data is on Hadoop, AWS or Excel. With some training a user easily get an answer to questions like “What market share do I have today?”.

Will competitive intelligence professionals be replaced by robots

Do the latest technology developments make me redundant? Will people ask an online tool, not me, about the market developments? Will I be replaced by a software robot?
My answer to that question is no. However, the market will change! And like other living creatures on this planet, the competitive intelligence professionals will need adapt to survive!

The brain has not developed at the same rate as technology. People are social beings. We like to discuss and argue. We like to laugh. We are arrogant and mean sometimes. Other times we are kind and full of empathy.

I think that is important to remember because competitive intelligence is very much about dialogue! Analytical skills are important for competitive intelligence professionals. And social competence is essential!

According to Gartner, 95% of all business managers 2020 will make decisions based on gut feeling, despite all available analysis and facts. The risks of making the wrong decision increase and that’s just how we work. Intuition is a factor we must be able to relate to as AI and smart machines move into the realm of decision making.

Benefits of the smart machines

An AI platform can get the answer to a specific question in a nanosecond. If, I say if, the question is “right”, i.e. if the algorithm is designed so that it can provide an answer. Questions that can be answered with analysis of existing, quantified data are perfect for smart machines to work with. They can combine and cross-reference huge data sets and present summaries and weighted results in a split second. A human requires more time to read through articles and fiddle with Excel sheets.

But what if the question is forward-looking? When it is not possible to extrapolate historical information, machines have difficulties in providing value to decision makers. It takes time and costs a lot of money to program an algorithm to come up with an answer that is useful. And if there is a new question, the algorithm has to be updated or completely redone.

When it comes to answering forward-looking questions, the competitive intelligence professional has an advantage because the human being man is creative and has imagination! Imagining answering a question about how a certain country’s political situation will develop. You need knowledge about the country, its people, the culture. How do the political leaders perceive their surroundings? And how do they interact with other important people?

As an analyst, I think it’s great to get help from a machine to answer questions like ‘what is my market share?’. That means I have more time to focus on finding answers to more important questions, like ‘How will my market share develop due to the digitalization of the industry?’. The answer to the second question matters much more.

The competence needs of the competitive intelligence professional

The competence needs of competitive intelligence professionals can be divided into two major areas, social and technical.
Today, when it comes to social skills, a competitive intelligence professional needs to be a good communicator. It is key to be able to interpret and clarify what is being said in interviews and meetings. It is also important to have the integrity and ability to argue for a perspective that might differ from that of the client.

As far as the technical competence needs go, I believe it is very much about hygiene factors still. The analyst must understand the industry, know what methodology to apply when and how to carry out the analysis. In many ways it is about understanding and interpreting data. Write and present reports.

-Malcolm X, human rights activist

In the future, however, the competitive intelligence professional must become more forward-looking. The future looks complex. ‘It is not what it used to be’, as someone said… Industries melt together. New competitors, customer needs and business models are emerging. A competitor is a customer is a partner.

I believe that intelligence professional must become selling generalists to stay relevant to decision makers in the future.

“Selling” because the competitive intelligence professional must become even better at communicating; more responsive, social, open and adaptable. AI and self-service solutions will produce a lot of ‘truths’ that need to be validated and questioned. Here the competitive intelligence professional plays an important role by making clients considering different perspectives before making a decision.

And “generalists” because in order to be able to provide different perspectives, one has to be able to relate to many more concepts and ideas than today. The generalist knows something about everything.

The telecom industry is merging with the IT industry. To provide better insights, IT analysts and telecom analysts discuss and compare notes. In the future the demarcation lines between many industries will become blurry. Hence, competitive intelligence professionals must feed the curiosity and become more knowledgeable about neighboring industries. The best way I think is through collaborating with peers.

Intelligence professionals must also collaborate more with data scientists in the future. For example, the analyst sets up a hypothesis about the market development and leverage on the skills of a data scientist to test it, using AI and large amounts of data. It is important to start learning how to use other tools than Excel. There are many visualization tools for example. One does not have to become an expert, but basic knowledge will help a lot in making the dialogue with a data scientist more efficient.

So. No, I’m not being replaced by a robot. Instead, I see a future where smart machines help me free up dig deeper into the questions that matters. I will collaborate more with people that have different skill sets. In the end that will lead to me generating better insights. Better insights and a closer dialogue with decision makers will lead to more informed decisions.

“The future depends on what we do in the present.”
-Mahatma Gandhi, Indian activist

What are your thoughts on how AI will impact the competitive intelligence professional’s job role? Let me know.

Saludos / Ingemar