While business leaders are paying attention to change brought about by AI technology, many make costly mistakes or fail to move forward due to factors that include bad advice and myths of AI. The table below dispels common myths about AI.
| Myth | Reality | |
| 1 | Technology companies will be the primary beneficiaries of AI | Many non-tech companies have created or expect to generate value from AI |
| 2 | The availability of sophisticated AI tools will level the playing field | The gap between leaders and laggards is widening, with the leaders doubling down on AI investment |
| 3 | The primary benefit of AI is cost savings from automation | Most companies use AI to increase revenues and improve quality in addition to reducing costs |
| 4 | Senior leaders expect AI to reduce the size of their workforce | The more the senior the executive, the less likely they are to believe that AI will lead to overall job loses |
| 5 | Organizations can scale AI successfully by increasing the number of pilot projects | Scattered pilot projects do not fully unlock the value hidden in data and organizational knowledge; they add risk to the organization |
| 6 | AI is available as software packages and plug n’ play solutions | An AI solution is built for a specific problem, using specific data and specific domain knowledge; no two AI solutions are identical |
| 7 | Organizations need PhDs, data scientists, coders, machine learning experts, and huge budgets to use AI successfully | There are several enterprise-grade AI software tools that are accessible to the common user and requiring modest budgets and their number is increasing |
| 8 | Giant internet companies have a monopoly over data that can be used to make AI applications | Data is verticalized; companies can have more data in their domain than the giant internet companies, enabling them to create something unique |
