Engaging with AI: Six tips for CIOs
Artificial intelligence (AI) is at the top of the list of game changing technologies for chief information officers (CIOs). AI may also be the key to unlocking humanity’s problem-solving capabilities but engaging with AI is not necessarily intuitive. Business leaders may find it difficult to understand how specific AI advances can be applied to their organisations and how to begin integrating AI technology at scale.
Using AI may not always be the right -- or even necessary -- approach. Before considering the integration of AI into their IT organization, CIOs and business leaders should map out the most pressing problems, prioritise them, and then determine which technology would best solve those challenges. Don’t overlook simple solutions or force the use of AI within your organisation.
Once the problem is identified and it is determined that AI is indeed the right solution, start work in the most technologically advanced areas of your business because AI models need a rich data history (and ongoing data collection) to make beneficial recommendations. It can be helpful to prioritise the functions, verticals, assembly lines and assets within that problem domain that are further along in data readiness. As an added benefit, these areas are often the most critical to a business.
Starting to work with AI is no different than with any other technology: Understand the problem you are trying to solve, understand the capabilities of the technology, and reconcile the two.
Distilled in this article are some of the steps from our application of AI across a variety of real-world projects.
The six steps
Step 1: Identify the goal. Before you begin, define what you want to achieve and in which part of your organisation. In AI, we call this the objective function. Keep in mind, there may be multiple goals that you need to balance.
Prioritising here is also key. Set your most important goal as the objective function, and then ensure the model also takes your secondary goals into account when it makes decisions.
Step 2: Define the set of possible decisions. Once you have determined your objective, outline what levers you have at your disposal -- which parts of a system you can (and want) to improve using AI. This is the action space.
Take advice from domain experts in your organisation. These kinds of deep partnerships are vital for the successful application of AI and allow domain experts to use AI as a tool to enhance their impact.
Step 3: Keep the system safe. A critical step of any AI system setup is to understand the operating boundaries necessary to ensure the safety of the system. You should define these constraints at both the individual component and the overall system level.
An advantage of AI is that it can explore options within the boundaries you place on the system, but the stricter the guardrails, the less it can explore. Balance, without missing core constraints, is key.
Step 4: Audit the data. AI depends on data to make decisions, so you will need the data necessary to measure the actions and objectives you have chosen. At this stage, you can also address ongoing data questions, such as the cadence at which you need to capture system-level data, the latency of the data, maintenance and change logs, and so on.
Step 5: Clean the data. AI may depend on data, but success depends on good data. Identify and fix bad data. Merge data sources. Ensure data is representative of the problem you are trying to solve and that there is a diverse set of actions represented in the data history. A rich history and data variance are important for AI to make optimal decisions.
Step 6: Perform ongoing data maintenance. As the success of your AI project depends on good data, it is important to set-up routine, periodic checks to ensure ongoing data cleanliness.
These last three steps can be grouped into “data quality assurance.” They can be the most time-consuming part of the process and where most organisations will need to focus before embarking on an AI project.
Choosing the right optimisation metrics for your objective is also very important. Ensure that your AI system gets feedback on the decisions it makes so it can learn and improve over time.
The truth is there is no silver bullet
No single AI system will solve all your objectives. Most engagements require customisation, so your journey will likely require different AI systems for different applications. The best way to find what works for your use case is to start building models, iterate, and expand as you progress.
AI is not always the right answer, but it can be a powerful tool for improving current systems, building new processes, and solving complex problems. Whether your organisation is just starting its journey or has some experience applying AI, our aim is for these six steps to help simplify the process and kickstart the discussion.