Half of the executives from large companies cite data challenges—whether privacy and security, or usability—and lack of talent as the biggest obstacles to the implementation of Generative AI. The good news is that the introduction of this technology is receiving support from senior management, overcoming the common barrier of resistance to change at higher levels.

Problems with Privacy and Data Quality

Concerns about privacy and data quality are leading many companies to develop experimental projects that struggle to move to the large-scale adoption phase.

“Hallucinations” and intellectual property (IP) infringement are significant risks inherent in the use of Generative AI that relies on public data. For this reason, many companies are looking to build organization-specific tools trained with corporate data instead of public data.

However, corporate data is often incomplete, not integrated, and not formatted for effective use. In addition, data quality is a particularly difficult problem to solve. One option is to use synthetic data, a process that uses statistical algorithms to fill gaps in data sets.

This approach comes with several concerns, including the cost of building and maintaining consistency with the original data in addition to the tendency to imitate and replicate the inherent biases in the original data.

Companies considering synthetic data to build their own Generative AI tools must work closely with data and ethics professionals to manage these risks.

The Race for AI Talent

As for talent, demand is changing rapidly. Generative AI requires specialized skills. Although many companies are looking to improve and retrain their current employees, the search for new skills outside the company remains a key strategy.

Issues of Bias and Malicious Use

Ethics in AI is a concern that only 14% of respondents identified as a main one. However, it is an issue that cannot be ignored.

Consumers and shareholders are becoming increasingly aware of issues of bias and transparency in AI, and companies must be proactive in building ethical governance and oversight of their Generative AI tools.

Other business challenges for companies using Generative AI include the malicious use of AI-generated malware and disinformation, as well as copyright issues.

The Path to Responsible AI

Generative AI is redefining what it means to be competitive in the 21st century.

Business leaders are taking note of the ethical and data challenges that this technology presents. The key to success is not only to adopt Generative AI but to do so in a way that is ethical, transparent, and responsible.

With executive oversight and careful planning, companies can overcome obstacles and harness the transformative power of Generative AI.

The future belongs to those who not only innovate but also respect data and human dignity at every step of the way.