Why AI Use Cases Fail–and What to Do About It

Many businesses have learned the hard way that not every AI project leads to glory and success. In fact, a 2023 CIO.com survey found that more than half of AI projects fail to produce actionable results at all. There are many reasons for this, but one of the biggest causes we frequently see is a disconnect between the data scientists who are actually building the models and the end users who would consume or use the models.

 Most data scientists would agree that deep data exploration of all the relevant data is crucial to any analytics project. Unfortunately, these same data scientists are regularly faced with tight deadlines and often have no clear way to quantify the ROI for data exploration. As a result, data scientists frequently do not spend as much time as they would like when framing and scoping new projects and exploring the corresponding data. Additionally, the onus of data exploration typically falls to the data scientist who may be fairly removed from the end users within the organization. This means that when data exploration happens, it happens apart from the business analysts closest to decision-making. As a result, organizations miss out on domain expertise that could guide bigger data-based projects such as AI.

The New AI: Analyst-powered Intelligence

There’s an enormous opportunity for companies to upskill their analysts. With AI-powered analytics, they can accomplish data exploration without getting blocked by too much data, too many correlations between the attributes, or an inability to find signal in a dataset.

Say a financial services company wants to boost its business in lines of credit for SMBs. Maximizing this opportunity requires the company to understand who their ideal customer is and how best to reach them. Using AI-powered analysis, the analyst can find groups of businesses that would be strong candidates for credit extensions and understand why they were recommended.

Armed with this insight, the analyst then collaborates with the marketing and environmental-social-governance (ESG) teams to identify the ideal customer persona to target, then prioritize the appropriate business development projects, such as chatbots that can alert the sales team when these customers interact with the website.

From start to finish, the analyst partners with their business team to get the best results out of the right AI projects. Moreover, the same AI-driven analytics platform can be used by the data science team to solve more complex problems that an analyst may not have the specific skillset for yet. It’s a win all around for the organization.

Surface Hidden Opportunities

When analysts have the power of advanced analytics in their hands they can discover business advantages buried within mountains of data. Decision-makers can have confidence that any AI project proposal that emerges as a result of deep analyses has emerged organically from data and was put together in full collaboration with those on the business side—ensuring there’s value in pursuing it.

To Know More, Read Full Article @ https://ai-techpark.com/why-ai-use-cases-fail-and-what-to-do-about-it/

Read Related Articles:

Improve Clinical Efficiency with AI

Blockchain, AI, and Quantum Computing

Article source: https://article-realm.com/article/Business/56721-Why-AI-Use-Cases-Fail-and-What-to-Do-About-It.html