Discover how to harness AI and machine learning to transform your data strategies, navigate risks, and drive enterprise success.
As labor shortages, growing data volumes, and evolving consumer needs pressure companies to innovate, forward-thinking IT leaders are doubling down on their artificial intelligence (AI), machine learning (ML), and data analytics investments to stay competitive. And it’s paying off.
According to a recent MIT Sloan Management Review article, approximately 92% and 98% of organizations demonstrated tangible value—higher efficiency, better decision-making, and improved customer experiences—from their investments in 2022 and 2023, respectively. This is good news, and we expect the trend to continue in the foreseeable future.
But there’s one problem. As companies invest heavily in AI, ML, and data analytics, many forget that culture, people, and processes are key to becoming truly data-driven. Without tackling digital transformations from both a technology and human perspective, desired returns will remain elusive. Luckily, that doesn’t have to be the case.
This article explores leveraging AI and ML for more intelligent analytics while empowering technology end users to maximize returns. Keep reading.
AI, ML, and Data Analytics in The Modern World
AI can solve some of the biggest data challenges businesses face today.
Take healthcare, for instance. A field like genomics has massive data volumes that require complex analysis beyond what standard statistical procedures can handle. By leveraging AI and ML, tasks like genome annotation, phenotype-to-genotype correspondence, and variant calling and classification can be handled faster and more accurately, allowing for more effective treatments.
Similarly, medical image analysis can be automated for faster and more accurate diagnosis. This, too, can have far-reaching consequences. For example, with algorithms scanning magnetic resonance imaging (MRI) films for abnormalities, radiologists can use the time saved by the offloaded task on higher-value areas where their expertise is truly needed.
Beyond healthcare, AI-powered analytics can help predict when manufacturing equipment will break down for preventative maintenance, deeply understand customer preferences for more targeted marketing, and automate various security and compliance aspects.
AI is an excellent analytics tool because it is extremely good at applying the rules provided during training to similar data and can independently learn to handle tasks beyond what it has been instructed on. So now, you have an analytics tool that’s fast, accurate, and smart to augment your human resources.
Avoiding the Shiny Object Syndrome
While AI and ML are powerful, they are not always the best solutions to every pain point. Organizations must avoid falling victim to the “shiny object syndrome,” which in this context means rushing to adopt AI and throwing it at every problem because it’s the latest and greatest technology. Instead, IT leaders must be thoughtful in their strategies.
AI adoption must be intentional and aligned with business objectives. You need to consider why you want to solve the problem at hand with AI, what the impact will be, how AI fits into your technology ecosystem, and whether better solutions are available in your existing toolbox. Carefully considering these questions leads to more informed decisions, ensuring investments drive measurable impact and minimize waste.
Navigating AI Risks
While AI offers immense potential, it comes with many risks. As Joseph Kulnis, Principal Consultant at Core BTS, put it in a recent webinar titled “Navigating the Risks of AI,” “There are hundreds, if not thousands, of (AI) risks that we need to consider.”
Addressing these challenges can quickly get overwhelming, so it’s essential to break them down into two categories:
1. Governance and Ethical Risks
AI can inherit biases from skewed training data, leading to unfair conclusions based on age, race, religion, or other factors. Using appropriate training data is paramount if you want reliable analytics results.
Another challenge is ensuring privacy. Joseph adds, “We have to think about what laws and regulations are in place (e.g., GDPR, CCPA, and HIPAA) that are going to govern how we treat the data we’re collecting, the data in our test environment, the data that the AI is gonna output.” Proper controls should be enforced to ensure protected data isn’t exposed.
2. Technical Risks
Organizations need to protect their AI the same way we secure other assets. They also need to avoid undertraining and overtraining their AI. Undertraining could lead to biased output, while introducing too much information can confuse AI models and create difficulty generating legitimate solutions.
While navigating these risks, don’t forget the human factor.
Building a Data Culture
To become truly data-driven, technology alone isn’t enough. Organizations must foster a culture where data literacy thrives and employees are encouraged to explore AI tools. This can be achieved by providing in-depth, interactive training programs that get the message across while democratizing data usage and access to AI tools across the organization.
Remember to get buy-in from all stakeholders, from top executives to the front-line staff. When everyone in the organization trusts and speaks the language of data, your AI and ML initiatives will have a far more significant impact.
Leverage AI-Powered Analytics for Business Advantage
Appropriately implemented and with the right partner, AI and ML can unlock unprecedented opportunities. They can drive more intelligent decisions, streamline operations, help meet ever-evolving market demands, and boost customer satisfaction. Download our e-book Leveraging AI for Business Advantage to dive deeper into actionable data analytics strategies.
Core BTS meets the most impactful companies where they are in their AI journeys and helps them chart the fastest path to desired destinations. Whether you need help discovering the best areas to implement AI or want an expert team to tackle the complexities of AI implementation, we’ve got you covered.
Contact Core BTS to level up your AI, ML, and data analytics strategy.
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