[ad_1]
Artificial Intelligence has captured the public’s attention and is driving business transformation at an unprecedented rate. However, the excitement surrounding AI often blurs the line between proven capabilities and overhyped expectations. AI is frequently misunderstood and used as a blanket term for a range of technologies, from traditional machine learning to emerging tools such as generative and agentic AI. While traditional AI has delivered measurable value in areas such as supply chain planning and risk mitigation, newer forms, including generative AI and agentic AI, present both opportunities and risks. Yet despite the rapid adoption of these advanced tools, many companies still do not fully understand how they work. These tools can potentially produce inaccurate, biased or opaque results due to their reliance on probabilities and imperfect data.
To address these challenges, companies must adopt strong data governance, minimize algorithmic bias, and prioritize transparency to build trust in AI-driven decisions. Moreover, educating teams, initiating pilot projects and identifying new roles, responsibilities and governance are crucial steps in integrating AI.
As organizations transition from manual processes to intelligent systems, traditional AI provides a reliable foundation, particularly for those in the early stages of digital transformation or still using spreadsheets and legacy systems.
At the same time, generative and agentic AI represents the future — one that must be navigated with caution, accountability and ethical foresight.
As AI becomes more influential in decision-making, the need for transparency becomes great. Understanding how AI arrives at recommendations or conclusions — often referred to as solving the “black box” problem — is essential for building organizational trust.
Ultimately, companies that embrace AI must also embrace trust in the technology to scale successfully and avoid costly setbacks.
Read AI: Real or Hype? for more information.
[ad_2]
Source link
