Current discourse within the tech ecosystem assumes that Artificial Intelligence (AI) and Machine Learning (ML) are silver-bullet solutions to every problem involving data. However, voices across all industries are beginning to express their concerns that AI, under the influence of hostile actors, could threaten our own existence – from mass unemployment to nuclear war.
However, the reality of AI’s capabilities is far more nuanced than fabricated headlines allow room for. This lack of nuance is feeding into companies and boardrooms across all sectors – whether they use AI technology or not.
The issue for many companies looking to implement AI is not fully understanding the problem that they need to solve before they look to the technology to solve it. Not only does this lead to complications, but it also encourages businesses to deploy AI as a strategic goal, rather than as an application that will genuinely revolutionize their company. Avoiding the pitfalls of poorly articulated strategies for solving data problems is therefore a fundamental challenge.
This checklist is a starting point to help identify problem-shaping methods and ensure businesses prioritize organizational impact over solution technology.
Director of Government and Defense at Mind Foundry.
Prioritizing the human over the system
Optimizing with just the system in mind and solely prioritizing the technology rather than focusing on the effectiveness of the human is a common mistake. Conventional wisdom assumes that optimizing the system will speed up processing times and computation capabilities, or data architecture will be made neater. While this makes sense from a systems engineering perspective, if they aren’t usable or do not provide meaningful impact to the end user, the overarching system will not improve. Increasing efficiency in areas which are not a bottleneck is a misplaced effort, and improving system efficiency at the expense of usability can have a negative impact. Efforts should always focus on designing with the human in mind, rather than the system.
Don’t confine the solution within an existing system
Designing within the bounds of existing systems causes assumptions to be baked in and creates problems that artificially inflate complexity. Instead, stepping back outside of an existing system enables a curiosity-driven approach to discover how best to use the system and identify what the problem really is. A constrained starting point only leads to immediate inefficiencies as the system is optimized for some misplaced notion of the true goal.
A blank slate is not always the best
Balancing between what exists and what could be is a challenge. It is easy to talk about completely starting from scratch and implementing an AI-drive backbone, but the reality of implementation is more challenging. The ‘blank slate’ approach runs the risk of losing years of hard-won knowledge, understanding and best practices. Replacing this with an ill-designed and generic AI system only confuses existing users. Combining old systems with new improvements will make the most significant strides in productivity. However, this combination requires a strategic approach with collaboration between AI providers, system engineers and end users.
Automation is not the same as AI
The term “AI” can sometimes be misleading. The goal frequently becomes to deploy AI, which means sometimes we become distracted by the question of whether a technology is or is not “AI”. The priority, however, should be to find the best tool for the job, not the best job for a tool. The most technologically advanced solution may not actually be the optimal one. This is why it is of critical importance that there is an understanding of AI alongside a variety of other tools so that the best can be selected for any given scenario. The outcome of this should enable more simplified deployment, governance, and maintenance.
The data itself might be the most valuable
The assumption that all data is valuable leads to long journeys in search of this value, which ultimately wastes time. Instead, businesses should consider how extracting value from the data increases the likelihood of achieving a business or operational goal. We often collect data simply because we can or because it’s a by-product of ‘business as usual’. Many organizations waste time speculatively refining data in areas of the business that aren’t top of the list simply because they can. Collecting the right data to solve important problems should be the priority.
These recommendations provide a starting point for companies looking to deliver impact-focused, data-driven projects. Asking the right questions is a critical first step, and it must be followed with an approach that designs solutions with deployment in mind right from the outset.
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