Challenges for onboarding AI fuelled CRM
Getting onto the AI isn’t just a choice anymore for enterprises, it is a necessity to stay in the foray and to give customers the level of services that they expect. Having said that, it is important for companies to tread their steps into AI fuelled CRM carefully. Too much haste or a wrong direction can wreak havoc!
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Of course, one of the biggest challenges is to get the business leaders and creators of the enterprise to fully understand the impact of the change. Change is sometimes difficult to digest. Even though people may agree to the needs in theory, but in practice, they may be averse to change and thus oppose or look for ways to derail the AI adoption plans. Therefore, aligning AI plans to the overall business roadmap and targeting it to key business objectives is crucial for a systematic top-down approach to AI.
It’s a mindset change really!
In terms of AI fuelled CRM and customer-centric processes of a business, a mindset that focuses on empathy and fitting into the shoes of the customers are key factors to deliver what they want. For instance, many players are going gung-ho about the capabilities of new chat bots, based on NLP and machine learning, yet many of the bots fail to impress or find a connect with the customers for wanting the true replication of ‘human-like’ interaction. Time and energy gone waste is the typical scenario in this type of AI.
It’s a mesh out there!
What gets more complex in a technical scenario is the widespread spectrum of platforms and channels over which companies interact with their customers. With the cloud-based apps on the rise, it is quite possible that one function of a large organization is unaware of how customer engagement is happening in another department or geography. Any AI tool that needs to go across the board needs IT intelligence and synergy between all the parties involved. This can become a collaboration or coordination issue especially with different timelines or different priorities between departments. The internal workings are encapsulated from the customer and two disjointed sets of customer engagements can thus cause more harm than good.
How good is your AI system, for example, a bot? Is it expecting customers to feed it technically-correct queries or can it cater to natural and lesser descriptive questions, say from a customer who is not that technically savvy? Context building and leading from one intent to another is very important for a customer facing digital assistant to continue a conversation. The algorithms deployed behind may need a combination of deep learning, and self-learning techniques to dole out a wonderful service experience.
Is it secure? Million-dollar question!
Then, there is the never-ending debate about privacy and security. AI platforms rely heavily on the cloud today, so there is every possible chance that sensitive customer information may actually be travelling across unreliable channels that are prone to attack and misuse. This becomes grave in cases such as financial asset or identity management services. Companies rely on external vendors and experts to make AI-based solutions and integrate them with their own workflows. This poses a bigger threat to security as well.
Impartial and omni-serving?
The program is as good as the programmer who wrote it. This is true for AI-based algorithms as well. Many times, unintended bias creeps into the logic and the software starts to behave in a certain way, leaning towards a certain set of customers. This is dangerous as it may lead to total rejection or dissatisfaction of customers who do not fall in this ‘assumed’ category.
It can get out of your hands!
At the same time, too much intelligence can be a problem. As was the case with the Facebook bot which started to develop its own language, turning out ‘smarter’ than the creators. This can create havoc as AI machines start to outdo their own masters. Companies need to monitor and measure the responses with human intervention to ensure that their solutions stay within the ‘lines of control’. This can be a major challenge with limited understanding of the core AI fuelled CRM related tech skills, especially when an external consultant has been used to incorporate AI .
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Hence, it is imperative that business leaders weigh the pros and cons of choosing the correct use cases where AI fuelled CRM can bring in good ROI’s and also assess the risks carefully. A step-phase approach to AI adoptions will ensure that it can empower the customer relationship management to stay ahead of the explosive curve of customer expectations.