Is AI-Driven Productivity & Insight an Illusion? [Enterprise Edition]

Big data turned organizations into the world’s primary data stations. Yet, many companies struggle to extract actionable insights from ML projects — reducing AI to just an overrated technology with insignificant operational/monetary value. Meanwhile, productivity is hit as a combined consequence.

But what if we tell you that by ensuring accessible and reliable data and IT talent, companies can easily excavate great business value out of AI investments? Let us explore how, and all the particulars of it.

Order of Prioritization with Data Infrastructure & Talent

Rather than starting with outright investments in emerging AI capabilities, the focus should be on making data accessible across systems and ready for analysis – followed by welcoming it with a scalable data science team. This enables businesses to quickly compare and draw insights, enhancing the effectiveness of both ML projects and productivity endeavors. Setting priorities in this order saves valuable researcher time from being wasted on data categorization, validation, and preparation.

Challenges with Data & Talent Management?

Encountering plenty of difficulties when embarking on data science journeys is fine as long as they are not typical and repeated. One of the most infamously common pitfalls is attempting to achieve returns without establishing a foundation of reliable data. Without this foundation, data engineers spend a significant portion of their time maintaining data pipelines due to changes in APIs or data structures. Companies looking to maximize AI/ML investments must not afford repeating such mistakes. A quick fix – hiring the right experts and letting them establish automated processes for data integration can enable the existing teams to reclaim their time while ensuring access to accurate data, ultimately reducing costs, and in-housing a more-than-ever robust IT workforce.

The Impact of Narrow Data on Insights

Machine learning relies on comprehensive and properly formatted data for optimal performance. Gaps or formatting issues in the data can lead to ML failures or, even worse, inaccurate results. To address uncertainties regarding data, many organizations reroute their prized possession of a workforce to manually label datasets for supervised machine learning. However, it turns out to be not only an unnecessary jeopardy to workforce productivity, but also introduces additional risks to the project. Moreover, trimming the training examples too much due to data issues limits the scope of ML models, resulting in repetitive and unenlightening insights.

Comprehensive Data and Shared Understanding Across IT Workforce

The key to overcoming data and AI-related challenges lies in creating a comprehensive and central data repository from diverse sources. This approach ensures a shared understanding of the data and enables data scientists to maximize the return on investment from ML models. A data science program can only evolve if it’s based on reliable and consistent data, accompanied by a clear understanding of the confidence level associated with the results.

Balancing Volume and Value of Data

An essential aspect of a successful AI investment is striking a balance between the volume and value of data when making predictions. For instance, a social media company analyzing billions of interactions daily can leverage large volumes of relatively low-value actions to generate reliable forecasts. However, organizations aiming to predict contract renewals, where datasets are smaller but the consequences are significant, face limitations. Overcoming these limitations requires breaking down data silos and combining various data sources with the help of competent IT teams constantly looking into them. This includes zero-party information, first-party website data, and customer interactions. This comprehensive approach provides the necessary depth and breadth to make confident predictions.

Steps to Maximize ROI, Productivity, & Competitive Edge from AI/ML

To avoid the pitfalls that lead to diminishing confidence, productivity, and returns from AI/ML programs, companies can follow these steps:

Conclusion

Securing budgets for flashy data science programs may seem tempting — it’s still the “less thrilling” infrastructure and talent tasks that create the foundation for success. Re-prioritizing them can pave the way for significant ROI while putting valuable insights, productivity, faster time-to-market, competitive edge, and cost-savings back on track. And us being the go-to IT talent providers for both the Fortune and SMB companies globally, we have been at the center of it all – supporting countless AI projects. For assistance or additional information, write to us: [email protected]