We must board the AI train to truly coexist with new technologies
In 2023, Generative AI (GenAI) became “ready for use,” enabling faster and more effective options to create human-readable content.
Morgan Stanley concluded that Generative AI might affect more than 40% of the workforce in the next three years.
Let’s align the definitions to avoid confusing AI with GenAI.
AI – The science of making machines that can think like humans.
GenAI – describing any artificial intelligence (AI) that can produce new text, images, video, or audio clips. This type of AI learns patterns from training data and generates new and unique outputs.
With this trend “crowned” as one of the most disruptive technologies since the smartphone, I am most intrigued by its impact on our mindset and the future job market.
Will the machine take over our jobs? A few of them will probably be made redundant, while new opportunities will be revealed for us. Let’s adopt a positive mindset and learn to adapt, just as we embraced the internet and smartphones.
The same approach applies to our future as we learn to “coexist” with AI and unlock new benefits. With that in mind, there are a few principles to follow:
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Allow ourselves to be curious and refrain from categorizing AI as “too complicated” or “not mature to make an impact.”
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Being judgmental. We can trust technology and, at the same time, be mindful of the potential downsides and pitfalls. For example, can we trust the data processed by AI to be accurate or biased?
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Move away from the comfort zone. Self-learning capabilities, trial-and-error, and other experiments will propel the effective use of new technology. Standing still now means we will lack the future skills to keep up with the evolving job market.
There is a “promise” that we will make faster and more informed decisions as AI performs advanced data analysis, crunches the numbers, and provides accurate forecasts. Professionals will enjoy having most of the tedious work performed for them. At the same time, they can focus on sharpening their decision-making process and potentially increase their impact on organizations, customers, and their ecosystem.
This improvement will save time, which we can leverage to develop additional professional and personal (human) skills. These skills should be our “competitive edge” amid the growing analytical capabilities of machines.
AI and GenAI will offer better outcomes in certain domains currently performed by humans.
The question is, which skillset should we prioritize and improve today?
Mainly the ones that machines will not easily automate and perform accurately.
Which skillsets will prevail?
According to Morgan Stanley, the following skills will be effectively performed by machines:
Does it mean we will not have authors, data analysts, and project managers in a few years? I highly doubt it.
I published two 100% “human-made” books and didn’t see myself fully “subcontracting” them to a computer algorithm. However, having a virtual “assistant” to refine my thoughts, language, and writing tone will be helpful.
The opposite aspect is the top skills that will be difficult to automate (according to Morgan Stanley):
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Management of humans
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Negotiation
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Assisting and caring for others (Medical staff, therapists, social workers, psychologists, etc.)
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Technology design
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Active listening
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Service orientation.
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Persuasion
The next step is to plan our professional path, whether acquiring new skills or honing on existing capabilities that we should strengthen.
Then, we must adjust our way of working (our processes). This will be a mutual change driven by organizations and individuals.
Let’s look at two of my favorite examples:
Trainers can assume GenAI will create a substantial part of the content. It will integrate product knowledge, support ticket information, and other sources, such as community and customer feedback, into structured training material. Then, GenAI tools can create AI-based clips and training courses. I can see an emerging new training and community specialist/designer role who will supervise the process, configure the tools, and verify the output.
Customer Success Manager (CSM)
CSMs will have to get used to working side-by-side with AI and GenAI, as existing playbooks will be partially or fully automated and executed by machines.
Examples of these playbooks:
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Onboarding new technology by a customer
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Driving product adoption and best practices
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Business reviews for long-tail customers
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Reporting and analytics
Given the prediction above, CSMs can evolve into “Success Architects” or “Customer Success Consultants” who will harness AI’s benefits.
We can consider a few use cases:
AI will analyze user interactions, feedback, and historical data to develop advanced customer and user profiles. These profiles will empower Customer Success to measure progress and outcomes and intervene when needed. For example, Success Consultants will offer personalized and tailor-made recommendations and solutions based on insights delivered by the machine. Moreover, success consultants will encourage customers to interact with the intelligent recommendation module provided by their product.
One of the rising trends in customer success is employing Digital CS to enable scalable, innovative, and targeted customer communication, which is effective and cost-saving. Not surprisingly, we now deal with a new type of customer service agent. These automated chatbots and virtual assistants are powered by natural language processing to provide instant and personalized support.
But there is more to come.
I expect AI to detect sentiment and tone, which should flag alerts and trigger a human response. This is where the CS consultant will be influential in resolving challenges and issues the machine might not be able to handle. Moreover, AI algorithms will have sufficient knowledge to advise optimal times for reaching customers and ensuring timely updates.
For example, AI will let the CS consultants know whether the customer has been informed about an important product or feature announcement. This will trigger an automated notification to the customer, or the CS consultant may engage the customer directly with a tailored message based on the recommendation provided by the machine.
Health score and churn prediction
AI will be instrumental in improving customer health scoring models, considering factors such as usage patterns, feedback, and support interactions.
Moreover, I trust AI to learn and better understand the customer’s journey and business outcomes to provide more accurate and real-time health assessment. With this level of insights and domain expertise, which I expect CS consultants to have, they will timely engage customers with a high probability of churn. Consequently, they will provide professional guidance, regain customer confidence, and lead them to achieve their objectives.
Change is coming, and we need to embrace it.
Technology changes have improved our lives. We are faster and more efficient today than the previous generation. We can access data from internet-enabled devices, learn independently, and enhance our knowledge without limitations. The next evolution of our professional lives introduces a better factual decision-making process supported by AI and more options to create content supported by GenAI.
This new superfast train is about to stop at “our” station, and we must board it.
It is an opportunity, not a threat.
The writer is Guy Galon, VP Customer Success at Hysolate
