The Intelligent Organization – Implementing AI to maximize efficiencies
- AI, when implemented properly, has shown significant returns and improved competitive edge.
- Properly implementing AI requires careful evaluation and planning.
- Organizations should be aware that there is no one magic algorithm that can solve all problems.
As artificial intelligence (AI) continues to grow and permeate seemingly every aspect of business, it’s important to cut through the noise and focus on where AI fits in within the organization, along with how to best implement it. The technology is anything but a ‘plug and play’ kind that will deliver immediate returns to organizations.
For starters, to implement AI within an organization successfully, one would need to understand what an AI is, where the market currently stands, what value the technology can provide to businesses, and perhaps most importantly, how it can be successfully adopted. It is a known fact that AI provides enormous value in a variety of industries. Because of its high-value potential, many companies have been scrambling to implement AI within their organizations.
And the projects, when implemented properly, have shown significant returns and improved the competitive edge for many companies. Experts reckon that firms that have yet to start implementing AI, may lag behind their competitors. But it is important to note that implementing AI should be a carefully thought-out process, in order to avoid turning into a costly failure.
So at TechHQ, here is a simple framework to help kickstart the process of turning the company into an AI-driven ‘intelligent organization’.
The right AI strategy for the organization
First, it’s important to understand roles AI can play, and to research what it can and can’t do for the organization. Ideally, CIOs and CTOs can get more familiar with AI by collaborating with a data scientist because it’s important that the C-level has a good understanding of AI and its implementation and cost hurdles, before they define where in the company, and to what degree to implement it.
What often happens is that AI is not holistically understood, and the overall project misses out on the desired value. Once the role AI is expected to play within the organizational structure is better understood, the next step is to get a grasp on the problem the organization intends to solve, or the opportunity costs that are presented as a result.
Feeding AI with the right data
Any application of AI and machine learning (ML) will only be as good as the quality of data that is gathered and analyzed. Monica Rogati’s Data Science Hierarchy of Needs shows a pyramid of what’s necessary to add intelligence to the production system. At the bottom is the need to gather the right data, in the right formats and systems, and in the right quantities.
For many companies, it’s a task in and of itself to keep track of the types of data, let alone where it’s stored and in what way(s). After understanding the data types and how AI might best help implement efficiencies, then AI algorithms can be brought in to connect the dots between these datasets and draw meaningful connections for analyses. This enables organizations to identify key insights, solve business problems and, in some cases, unlock vast troves of useful data insights.
Start small and scale quickly
According to a white paper by IBM, it is best to start actual AI implementation with ‘minimal valuable products’ (MVPs). In this phase, bring in experts to help quickly develop solutions to business problems. This can only be done once the above-mentioned steps are completed and the business is equipped organizationally and technologically. This also means that the experts you bring in should be both business- and technologically-savvy.
The white paper also recommends setting comprehensible key performance indicators, or KPIs. “To make sure that a project will succeed, you need to define KPIs that are understandable for your business— including employees and other stakeholders. These KPIs will help you evaluate whether a project is successful.”
In general, IBM suggests taking a second look at these KPIs after an appropriate duration to decide whether the project is successful or if you should discontinue it. “If your business can’t pinpoint the right KPIs to measure success, the project is too complex,” the paper reads. The last step would be rolling out AI-powered solutions across the company, wherever applicable and adequate feasibility studies have been done to ensure AI can resolve the inefficiency in that aspect of the organization.