Telecom & Technology Consulting
Artificial Intelligence (AI) has spawned an new era of innovation. The technology has reformed entire industries, and in the process delivered heightened levels of automation, productivity, and growth.
But AI adoption can’t be undertaken without a clear understanding of its potential ramifications. Here are some issues to consider as you plan your implementation:
Focus on your objectives: As with any emerging technology, leaders must peer through the hype and ask: Do we truly require AI to achieve our goals, or are we simply being swept up in the hoopla? Sit down with key stakeholders and assess the organization’s strategic objectives, and pinpoint how – or even if – AI can foster those goals. Don’t force it; instead, be sure that AI is the right fit.
Determine how you will measure AI value: While AI can certainly deliver results, you have to understand exactly how you would assess its actual value. Also, examine if your AI strategy will align with desired outcomes. Without that congruency, you may end up completing projects that appear “successful”, but which don’t create true value for the organization.
Value, of course, has much to do with return-on-investment. To this end, don’t be vague about anticipated ROI. Certain AI deployments will provide decent ROI, while others can create remarkable returns. Have something specific in mind when it comes to efficiency, productivity, profits, and cost savings.
Don’t go halfway: Whatever your AI strategy, be sure that you implement it across the company rather than only within a few departments. A partial deployment can create an imbalance of employee capabilities and, ultimately, inconsistency in performance and results.
Instead, create an enterprise-wide AI implementation plan. No organization can do it all at the same time, but spreading the technology to every facet of the company will provide maximum value.
Gather the right tools, people, and resources for real-world success: Seasoned tech leaders know that a positive use case test doesn’t always translate into good results after implementation. That’s why it’s critical to have the data, talent, and complementary resources to successfully move forward after testing.
If you’re not prepared, hold off on the implementation until you are; it’s worth the wait.
Be secure with your data: Organizations must be extremely familiar with the quality of their data; they must also truly understand their data ecosystem. This is of supreme importance, as you have to know if the data is strong enough for the particular AI initiatives you have in mind.
Prepare for potential audits: It’s impossible for data to be completely compliant with regulatory requirements. However, you can take steps to ensure that the data is properly secured, and that you perform best practices around security and privacy.
In addition, leaders have to be sure that algorithms are created with ethical guidelines in case they must be defended during an audit or deposition.
Indeed, it’s paramount to be prepared for the potential ethical, legal, and compliance implications of AI deployment. This is an issue that’s gaining more of the spotlight, with governments now considering the implementation of more AI regulations. Courts are increasing handling AI-related cases, which is the result of society (both in business and in public) trying to get a grip on the sometimes unintended consequences of AI.
To be sure, organizations must recognize the ethical, legal, and compliance ramifications from their AI strategies. When prepared, they can anticipate issues and prevent – or drastically minimize – the legal and financial damage to the brand. In practice, this involves developing policies and guidelines, establishing oversight committees, and gaining advice from legal experts on how to best meet regulations.
Understand your risk tolerance: Tech leaders, in collaboration with others in the C-Suite, have to implement AI safeguards for a variety of factors. These include safety, security, and ethical matters. As such, the company can feel confidant that their AI programs are responsible, and that they minimize risks.
To do so, many enterprises are forming AI governance frameworks. As a result, internal teams are free to innovate safely and efficiently. Even more, they’re ensured to construct more rigorous safeguards, security measures, and bias mitigation techniques for use cases involving sensitive data.
AI is certainly not a flash-in-the-pan technology. In the short time of its widespread use, we’ve seen measurable benefits across not just multiple industries, but also for society as a whole. AI viability is no longer the question; the question now is, is it viable for your organization at this time? Plan accordingly.
