Insights

Ⅰ. Data quality & Tech
 

Successful adoption of AI models requires quality data, yet most tech leaders recognize creating and managing the data sets as their biggest pitfall. While the open-source datasets might suffice when conducting trials, the fully-featured solutions require clean and organized data to help achieve strategic goals more effectively. Any AI application is only as smart and as efficient as the information it has been fed with. One example of data quality influencing the quality of the overall AI system was observable in 2018 at Amazon. The system was trained on resumes submitted to the company over the previous decade, which were mostly from men. As a result, the algorithm downgraded resumes that contained certain words and phrases that were associated with women, such as “women” or “female”. 

When dealing with irrelevant or inaccurately labeled datasets, the application’s abilities become limited. Another reason behind a lack of quality data is linked to organizations’ tendency to collect excessive amounts of data. In this case, data decay is a commonly observed outcome which results from accumulated inconsistencies and redundancies. A potential solution to help organizations access high-quality data suggests practices such as manual tagging, labeling, cleansing and randomization. To improve the quality of data, it is recommended to streamline the collection process, as well as standardize the structure of all the data management processes.  

 
Ⅱ. Governance

• • • Data governance

Both, quality and maintainability of data are crucial aspects of data governance. With growing concerns around transparency and privacy raised by data collection, more factors come into play. To address these concerns, organizations have to ensure that they can monitor and restrict their AI algorithms, as those need to be properly managed at all stages including data extraction, processing, usage, and storage. 

• • •  Cybersecurity 

In the face of rising cybercrime, it is more important than ever to address and prevent the risks associated with AI implementation. Data collection as part of the AI initiatives is highly vulnerable to breaches. In an attempt to mitigate the impact of data breaches and preserve the safety of user information, network segmentation is the key. Network segmentation is the technique that divides a network into smaller, distinct subnetworks, each with a designated, unique set of security controls and services.

• • •  Regulatory compliance 

As more and more people are becoming concerned with how companies access and use their confidential information, transparent data collection policies can also help alleviate the suspicion related to this process. Legal regulations have been gaining substantial prominence when addressing the risks associated with AI and other data-centric operations. As this subject becomes increasingly concerning for the users, organizations that adopt AI technologies have to evaluate their approach towards compliance policies and other regulations. Legal compliance to these restrictions is especially applicable for those organizations that operate in highly regulated industries such as finance and healthcare. As the field of AI-related regulations becomes trickier to navigate, using the help of third-party auditors appears as the most sensible option. 

In late 2022, the EU ministers presented their position on the AI Act, the planned EU law to regulate artificial intelligence. The EU’s approach to artificial intelligence focuses on excellence and trust to strengthen research and industrial capacities and ensure fundamental rights. However, the proposal is already debated critically inside the AI ecosystem.

 
Ⅲ. Workforce readiness & Skills gap
 

The right skill set is essential to make AI achieve its full level of effectiveness. Yet, according to Juniper’s survey, 73% of organizations reveal that they struggle with preparing and expanding their workforce for a smooth AI integration. A key obstacle at this stage of the digital transformation process is an AI skills gap. In the early phases of AI adoption, many organizations appear hesitant to attract experienced AI professionals, such as machine learning modelers and data scientists, since it is a challenging and expensive task. However, scaling a data team is a crucial step in building the AI model and requires the technical expertise that might be lacking internally. Even with a certain degree of in-house expertise, a lack of proper experience in AI-based technology is a potential hazard that could obstruct the realization of the full-value AI models can offer to organizations. We can see the strong economic potential for the AI companies themselves. For example, the developing company of ChatGPT, OpenAI, are striving for a $30 billion evaluation at the time of us writing the article! To make the transition towards AI-optimized workflow as seamless as possible, the best solution would be to first focus on isolating the skills required for each unique strategic goal. To further drive the digital upskilling experience, finding an expert partner that could provide a team of specialists could be a costly yet extremely valuable investment into the organization’s transformation journey. The right specialists would both help with the training of the current teams to make up for the skills’ shortage, and address the technical pitfalls of AI implementation, such as inefficient processes and integration issues.