It’s hard to go anywhere nowadays without coming across the topic of artificial intelligence (AI).

The subject is all over the media, while it is hard to find a presentation deck (on any subject) that does not mention it.

However, an important question is: when do I need artificial intelligence, and when do I just need to use real intelligence (RI). Do we need always AI or can we improve the service and fix our pain points with RI?

It’s worth trying real intelligence before thinking about artificial intelligence.

From a practitioner mindset, I have been through different experiences where companies and organizations are falling behind by implementing AI projects, while their business is in need of simpler solutions that require real intelligence.

Here are the questions you should be asking before moving forward with AI ideas and solutions…

1.Where are you with omnichannel Customer Experience?

Many organizations are scattered through facing customers in different channels without a unified system. This keeps the customer in the confusion zone.

We can see it when the customer is sending mail to inquire or complain about the service, with no action or response from the organization. Often the customer is calling the call center asking for a resolution and realizing that the contact center is not aware of the mail he or she sent earlier.

When a company has different channels, and different team managing each channel, while the teams are not talking to each other and no system is connecting the back-end to update different teams – well, such a case requires RI before AI.

First, strengthen your back-end through unifying the system and the database behind it.

2. Are you running your Customer Experience with solid standard operation procedure (SOP) and clear performance indicators?

Running your CX with solid SOP that outlines how things get done, who does what, and how results are measured, is giving customers a seamless experience on different channels.

Nowadays, some organizations which are still struggling with answering customers on-time or abiding to a clear service level agreement, are discussing AI-related projects to develop chatbots or virtual assistance.

However, it’s vital to stop the bleeding first to sustain the business. Substantial change is needed in such a company’s operation before thinking of advanced technology that might double the pain.

3. Are you equipped with the right capabilities to run AI projects?

Employees who are trained in the new technology offered are key for its successful implementation. It’s similar to building an aircraft without having a pilot.

It is always recommended to run education programs in parallel to digital transformation programs so that customers can find the right guidance in case of facing difficulties.

4. What is your final goal with AI projects?

Customer centricity and operational efficiency should be the objective behind AI projects.

Resource utilization, being closer to customers, exceeding customer expectation, cost efficiency, and optimizing the operation – these should your KPI’s to measure the impact of AI projects.

Companies who are willing to implement AI projects with no clear objective or clear KPI’s to measure, before and after the implementation, should to rethink their approach and apply RI rather than AI.

5. Do you have data? Do you own data?

As we learn in computer science, ‘Garbage In, Garbage Out’ – GIGO.

GIGO, from an AI perspective, says that going into AI projects with no data, or with unclean data, will lead to failure. Going into AI implementation without owning the data leads to the same results. My advice is to ask the below questions on data first before thinking of AI projects:

What data I have?

Is it clean data?

Is it structured data?

Do I own the data?

Is the data dynamic or static?

Can we add more data, from other entities, to provide a full story representing the customer journey?

AI research uses tools and insights from many fields, including computer science, psychology, philosophy, neuroscience, and cognitive science.

AI research also overlaps with tasks such as robotics, data mining, speech recognition, facial recognition, and many others.

However, it is essential to implement AI projects with the sufficient human intelligence to utilize the technology and improve business and work efficiently.

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