A successful AI transformation involves the execution of several implementation phases such as a proof of concept, pilot program and ad-hoc tweaks in between each phase to make sure the AI solution caters to the exact business needs of a client. From a technical standpoint, it’s an undertaking that can turn challenging for companies, especially for those at an enterprise level, because business environments are much larger and more importantly, early-adopters of new, cutting edge technology like AI are entering uncharted waters. Undoubtedly, the popular sentiment amongst C-suite executives is that AI can offer huge returns on their investment, but are unclear as to what process to follow implementation-wise and beyond to secure those desired results.

It’s also important to note that we’re talking about an AI transformation here. There is no end-date or culminating phase for this sort of project; it is a continual process where much has yet to occur past the initial installation phase. For example, gaining the trust and support of employees and customers around artificial intelligence is a hurdle that needs to be surpassed before a cognitive technology can truly begin to flourish and bring about its tangible benefits.

Below are a series of steps early-adopters have followed to ensure company dollars are invested smartly and a soon-to-be implemented AI product is properly set up for success.

  1. Identifying a Compelling Business Case

It’s paramount to never implement artificial intelligence without first pinpointing the area(s) where AI is needed. This is a matter of delivering results in the present versus theorizing the potential of AI. Of course, cognitive technology such as Amelia can be deployed in a wide array of use cases, but only after an initial business challenge has been identified and resolved through AI. By learning from this initial deployment, AI’s role can be further expanded to other segments of a business operation in a more effective manner without encountering as many installation problems.

The scope of the use case should be large enough that C-suite executives will turn their heads at the news of its success, yet small enough that if the AI deployment encounters difficulties, it won’t severely affect the overall flow of business. It’s a delicate balance without a doubt, but these are the types of use cases that foster enterprise-wide support behind an AI offering. At the same time, the AI transformation team itself should only include the necessary individuals to initiate the project and curate the testing parameters throughout the program. With a smaller, agile team, shifting gears during a testing phase is much easier and could mean the difference between a successful or unsuccessful project outcome.

  1. Initiating a Digital Renovation

Without a solid foundation, a new home won’t stand the test of time and neither will your AI deployment. Placing artificial intelligence on top of a stitched together business process could potentially derail an organization’s AI project and result in a multitude of unforeseen problems that could prove costly to fix.

At our Digital Workforce Summit, Tom De Carlo, Managing Director, Head of Client Services at UBS, stated, “Once you have well-trained people and clean procedures and policies, then you can layer technology on top of that and show a good investment in ROI.” In essence, auditing and optimizing existing procedures and policies before an AI product is implemented goes a long way in terms of accelerating returns on investment and amplifying an AI offering’s business impact. Furthermore, the idea of optimizing existing procedures is also meant to increase the technological sophistication of key parts within a business system so that an AI technology can be seamlessly deployed and perform as it should going forward.

Aside from strengthening the foundation of a business operation, examining and improving upon current processes serves an introspective purpose, allowing AI adopters to better understand where the points of AI integration exist within their business. On the other hand, this inward examination of existing business processes may reveal that perhaps a complete redesign of such procedures and IT infrastructure might be necessary in order to accommodate the presence of an AI-driven solution.

  1. Breeding a Digital Culture

Once you’ve selected the right business case, made the requisite digital renovations to your business infrastructure, and succeeded in driving the first business case to a positive outcome, it’s time to build upon that success and create company-wide momentum by surveying more use cases and executing them effectively. Stringing these “quick wins” together in consecutive fashion builds substantial trust amongst C-level executives and initiates the breeding of a digital culture.

In an effort to perpetuate this digital culture throughout an organization, a champion needs to be chosen who will help drive the assimilation and acceptance of AI within the business, or in other words, keep the momentum going. Sustaining this type of momentum means winning the hearts and minds of both your employees and customers and as such, employing different messaging to each group about how the presence of AI can positively impact their day-to-day work or customer service experience.

First off, company employees need to know artificial intelligence is going to complement a company’s business efforts by working side by side with them instead of replacing them entirely. In terms of a company’s customer base, AI should be introduced in a manner where they can experience firsthand the positive difference in the quality of service they are receiving as well as be given a choice to engage with an AI-driven agent.

Last but not least, establishing a Center of Excellence (COE) within an organization should be a primary objective for AI adopters who are engaged in multiple deployments and require a central control point from which to monitor the progression of those projects. In addition, a COE serves as a beacon of AI knowledge and skills that can be leveraged to teach staff about integrating AI into various types of business systems; ensuring that a sufficient amount of engineers and solution architects are always available to push forward AI projects.

 

Conclusively, cognitive technologies like Amelia can become an unprecedented force of transformation and bring about greater levels of workplace productivity to an organization, but in order to attain such benefits from artificial intelligence, it has to be supported by a thorough, well thought-out transformation plan. This strategy involves first solidifying the foundational processes within a business operation before layering AI on top of such processes, defining the key business areas in need of AI, implementing artificial intelligence strategically and creating a digital-first environment supportive of the implemented AI.

Ultimately, making the decision to become an AI business is a bold move, but it is the direction multiple industries are headed. Given the early results of AI pioneers, the benefits of artificial intelligence are real and carry the potential for more ROI growth in the long-term. The sooner businesses can start to analyze the areas where AI can enhance their business operations, the better positioned they’ll be to remain competitive in an increasingly digital market.