This article is a compilation of pieces I’ve developed over the last couple of months to assist marketing and sales departments in their efforts to communicate value of the proposed solutions to the potential or existing clients.
Today AI implementation is a necessity to maintain a competitive advantage in the market regardless of industry. It just makes things easier.
However, AI transforming business ain’t happens out of thin air. The business needs to adapt and build itself up towards implementing machine learning applications and making the most out of them.
AI strategy 101 - Understanding that AI / ML is a tool and not a magic wand
It is as simple as that. The first thing the company should understand about AI implementation in the business process is that it is not a magic wand by any means. It is a solution to particular part of the workflow. That is one of the first steps to implement AI into the business workflow.
Artificial intelligence and machine learning are capable of outstanding results if the rest of the enterprise infrastructure is there to enable it. It cannot pull rabbits out of a hat. As Thomas Davenport put it, “AI will improve products and processes and make decisions better informed.”
The reality is that it is a tool that can help with specific aspects of the business operations - by providing appropriate scope and gaining valuable insights by mining existing data assets and gathering new ones.
As such, the plan for implementing AI in business is shaped by its role in the workflow. No less, no more.
Here are the key AI adoption challenges you to need to address:
The most common challenge comes from the underestimation of what AI is capable of doing and how the organization can leverage on it. AI can't point out what decision to make. It can only make the patterns in data more apparent for that.
On the other hand, there is an underestimation of the company’s capabilities in implementing the required solutions and maintaining its infrastructure.
AI implementation strategy starts as a long and winding process of experimentation and learning. It takes time (sometimes a lot of time) for AI/ML solution to start generating value within business workflow.
Due to a variety of options on the market - there is a need to navigate in available AI/ML solutions and understand what fits your needs, whether it is compatible with the rest of the technologies and to what extent you need to adapt the system to implement a particular solution. Because of the limited understanding of impact - it is hard to estimate all pros and cons correctly.
Traditional performance indicators for AI solutions are Quality of results and Return on investment estimation. The latter is out of the picture until the solution has proven its effectiveness in solving the business need.
There are numerous regulations regarding data collection, data processing, and data privacy. European Union got GDPR, USA got HIPAA, and local privacy acts, Canada got PIPEDA. The solution needs to stay within the boundaries of relevant regulations.
Understanding the Technology behind AI transformation
The next step in the AI transformation playbook is exploring viable technologies. The adoption of machine learning artificial intelligence solutions in the business operation relies on the three pillars:
Data to process and gain valuable insights;
The infrastructure that processes data and gets valuable insights;
Human Resource or Talent who processes data via infrastructure and generates value;
The three pillars mentioned above correspond with three main requirements for AI business transformation. As Andrew Ng defines it:
Understanding of the organization’s AI/ML use cases - what AI can do for the company and how it will affect the business?
Resources to do AI operations systematically. This aspect includes both technologies to enable AI operation and talent to handle it.
Strategic direction - the understanding of why the company is doing what it is doing with AI projects. For example, the company is making data analytics solutions, and its goal is to expand the availability of tools and increase the quality of performance.
According to O’Reilly Media survey, one of the reasons why the AI implementation strategy fails is because of:
Lack of understanding of the business need;
Failure to implement appropriate technological solutions for business needs;
Lack of knowledge of what kind of data the organization needs and where to find it. Later on, the issue also includes poor quality of data;
Shortage of talent and failure to attract one;
Now let’s get back to three pillars of AI transformation.
Infrastructure is the easiest to explain out of all three. In a nutshell, it is a technical solution for a particular task - a combination of hardware and software that makes the whole thing ticking. For example: