In the race to harness AI’s transformative power, chief executive officers face a critical challenge: how to sprint ahead of competitors while avoiding risks that could derail their entire AI strategy. In fact, a KPMG survey this year found that 80% of leaders across the U.S. recognize generative AI (GenAI) as important to gaining competitive advantage and market share.
However, by establishing guardrails upfront, such as implementing a trusted GenAI framework that allows for agile development and deployment, while ensuring human oversight remains central to the decision-making process, companies can reap the benefits of GenAI without compromising on ethics or quality.
Here are three key areas chief executive officers should focus on to achieve this delicate equilibrium.
Governance
At the core of any organization is trust, and the foundation of a successful GenAI implementation lies in robust governance to ensure that trust. To do so, companies must develop an operating model and a governance structure to establish accountability and ensure transparency and fairness for strategic decisions.
By leveraging a clear roadmap with ethical, safe, and responsible decision-making anchored throughout the GenAI lifecycle, businesses can achieve the right level of governance and scale. It’s important to note that GenAI implementation is not a one-and-done affair. As the use of GenAI scales, so must an organization’s governance playbook. It should be humancentric, repeatable, scalable, and agile –ensuring that core principles and values are applied consistently.
Consider that the KPMG survey found that 70% of leaders in Q2 2024 now consider establishing ethical frameworks for GenAI as the most effective practice to ensure ethical use, up from 44% the previous quarter.
Data Quality
Data quality is the lifeblood of any effective GenAI platform because it ensures quality output. AI models are like sponges – they soak up everything they are trained on. So, it is critical for businesses to keep their data up to date, clean, validated, and enriched to make sure it is accurate and mirrors real-life situations.
Data quality remains a concern because poor data quality can lead to flawed insights and decisions. However, by maintaining robust data governance frameworks, this risk can largely be mitigated. Regularly assess data sets used for GenAI training, and ensure data is complete, accurate, and relevant to the intended business purpose.
Workforce Adoption
The success of any GenAI implementation depends heavily on workforce adoption. The KPMG survey found that 93% of leaders reported their participation in mandatory GenAI training, a spike from 19% earlier this year.