More and more enterprises plan to adopt Artificial Intelligence, realizing that it is the ultimate source of business value. Meanwhile, CXOs of many organizations debate whether to implement AI or not. And it stands to reason that AI deployment comes with several challenges.
Digital transformation through AI has its formula of success: right People + cutting-edge Technology + smooth Processes = Business Value. This way, all AI adoption roadblocks lay in terms of people, technology, and processes. As Artificial Intelligence becomes more and more pervasive and, most likely, will become the standard, it is vital to understand the bedrock hurdles and put a strategy for successful dealing with them.
Before diving into AI challenges, here is a list of the minimum an enterprise requires for AI implementation:
- A lot of data.
- A database to store these data.
- Algorithms to organize the data.
- A clear idea of the issue AI will solve.
- ML/DL algorithm to solve this issue trained on a large data set.
- A strategy of reaching end-users and receiving feedback from them.
It is a lot for such an entity like an enterprise with its established procedures, processes, and workflows. That is why experts advise starting small, covering, for instance, a commonsensical part of some system and creating a standalone team for solving the clearly stated business need. This team creates an MVP (Minimum Viable Product) and receives feedback from its first users. In case of success, it is time to develop a fully-featured product and then scale.
Let’s get back to the challenges of adopting and implementing AI.
Data quality and volume are the most critical issues not only for enterprises but also for other organizations. There is always a lack of relevant data which do not infringe on the users’ privacy. Moreover, even if you have enough volume of data, it doesn’t come with a value.
Thus, even if an organization collects a lot of data, it doesn’t mean there is always a possibility to derive any valuable insights. And these data won’t be useful when you feed them to your AI algorithm.
The only solution here is starting not with collecting a lot of data but gathering data from a segment able to provide the most valuable insights accompanying your AI algorithm.
Cost of development
The common roadblock for the massive implementation of AI is the cost of development and necessary tools. When creating a strategy for adopting AI, many organizations might tend to build everything from scratch in-house, which can be costly. AI experts, technologies to use – all these require a lot of effort and budget.
Deloitte enterprise research states that about 50% of AI adopters buy their capabilities rather than build in-house:
That is the solution: by leveraging the existing readily available capabilities, an enterprise may save a lot. That is so-called smart consuming. Such an approach helps to increase competitive advantage:
Too many stakeholders
When starting an AI project within an enterprise, a lot of stakeholders are involved. Often they are all C-levels and have varied responsibilities, which implicates no clear leadership and no single product owner, accompanied by too many conflicting opinions.
Sharing the same vision is a must when starting an emerging technology initiative. It is even viable in order not to fail the entire project. Coordinating teams, aligning with strategies and plans, reducing costs, and delivering a project within a planned time are the main tasks for a product owner. And the only reasonable solution in this situation is taking ownership of AI systems and projects across the enterprise by someone from C-suite.
Imagine a large enterprise machine with its long-held processes, general lack of AI-related education, deeply-rooted workflows (some of them might be legacy). And here comes the disruptive AI project! There seems it can ruin the existing cohesive structure of the entire business, which may stop staff from trusting in such a project and putting any decent effort into this.
Moreover, such a project comes with some strategic, operational, and ethical risks to tackle:
The reasonable solution for integrating AI in the existing business processes without user rejection is implementing AI in some part of the already used systems, like CRM or ERP.
- AI implementation challenges can regard People, Technologies, and Processes, as these are the main components of the enterprise digital transformation success formula.
- Technology challenges mostly touch on a lack of data quality and volume. Also, the cost of tools and development may be high.
- People’s challenges focus on a lack of leadership and skilled experts.
- Process challenges mostly come from deep-seated workflows in the enterprise.
- All AI adoption issues have reasonable solutions helping to implement AI systems and derive the benefits.
AI adoption is inevitable despite all roadblocks. And if a business wants to stay on top, it needs to put a lot of effort into leveraging AI. Let’s team up to make the most out of AI for your business.
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