How to Ensure AI Delivers the Results You Want by Setting Clear Goals and Clean Data

Noam Cohen, founder of CritiqeIL, highlights that the main obstacle to extracting value from artificial intelligence (AI) is the input of disorganized data and vague objectives. AI functions as an amplifier of human input, meaning incomplete or unclear data leads to generic and disappointing outcomes. Cohen explains that many organizations blame AI tools when results fall short, but the real issue lies in what is fed into the system.

Typically, organizations provide outdated, scattered data with ambiguous requests like "organize this," which results in AI producing polished but confused outputs. AI does not read minds or clean data automatically; it magnifies whatever it receives. Therefore, achieving optimal results requires clearly defined goals, cleaned and comprehensive data sets, and precise business context to enable AI to act as a strategic partner.

Cohen recommends five critical questions before using AI: Have you defined what a good result looks like? Is the data clean and complete? Have you provided organizational context or just a general query? Do you have a plan, or are you expecting AI to decide for you? Would a new employee manage with the same input? If the answer is no to these, AI will likely fail to deliver meaningful results.

He stresses that the less glamorous but essential work is organizing data and sharpening objectives before engaging AI tools. This preparation distinguishes organizations that gain real value from AI from those that become frustrated and give up. Since everyone has access to similar AI models, success depends on the groundwork of cleaning data and clarifying goals. AI does not fix disorder; it amplifies what already exists. Provide order, and AI will return strength.

Noam Cohen is an AI consulting and implementation expert working with Israeli organizations to improve their AI outcomes through better data management and goal setting.