The International Data Corporation (IDC) says the worldwide spending on artificial intelligence (AI) systems is estimated to reach USD 97.9 billion by the end of 2023.

Despite the global spending, many AI initiatives were a failure. Why? A 2019 MIT study stated nearly 40 percent of organizations that managed to make investments in AI barely report business gains, thus the downfall.

For a successful AI program, an approach called AI alignment needs to be strictly followed, according to a briefing by the MIT Center for Information Systems Research. Since 2019, the MIT research center (CISR) investigated around 52 AI solutions that were defined as applied analytics models having a certain level of autonomy. And amongst these 52 AI solutions, around 31 solutions were said to have been deployed at a larger scale.

Barbara Wixom, principal research scientist, CISR University of Queensland lecturer Ida Someh along with Robert Gregory, professor at the University of Virginia found that the most successful AI programs can be achieved via the three interdependent states of consistency. They are:

1. Scientific consistency
2. Application consistency
3. Stakeholder consistency

The three AI model revealed by Barbara and Robert helps achieve and maintain AI alignment between these three states. One of the reasons is because maintaining a balanced from the inside and outside requires adaptation within and across frequently, which is quite challenging.

For instance, if you make changes to the setting it may alter a change in an algorithm as well. Therefore, a change in current conditions like the pandemic could transform the conditions of the stakeholders. More so, since all three states require interdependency, altering within one state may also require adjustments from the other two states.

Nearly, 31 of the AI solutions studied were successfully deployed. As a result, an AI engineer has started learning the significance of AI alignment.

Here’s what all these three models actually mean. 

1. Scientific consistency:

A prerequisite is needed between what happens in reality and the AI model. Artificial intelligence programs need to undergo a preconditioned training, therefore, being likely to represent reality. Not to mention, every successful model needs to show accuracy. To achieve scientific consistency, comparing activities were used to make a comparison of the output of AI’s model with the empirical evidence. As a result, if any type of inconsistency is seen, the AI team corrects its course by adjusting the data, algorithm, features, and domain knowledge.

2. Application consistency:

Application consistency between the artificial model and solution. Besides being accurate, the AI model also needs to achieve the set goal to prevent inconsistencies. For this, teams need to use scoping activities. Such activities’ major focus relies on the consequences and impact while adjusting the model’s boundaries, automation, oversight (if needed), and restrictions.

A new AI program launched by the Australian Taxation Office encouraged taxpayers to file online making the review process simple. The program created was intended to boost noncompliance by taxpayers while also having the advantage to get through the scrutiny from the regulators.

3. Stakeholder consistency:

Maintaining the balance between the solution and the stakeholders. This program is intended to facilitate maximum benefits across the network of frontline workers, customers, citizens, regulators, and managers.

A stakeholder consistency takes place only when the program generates a value the stakeholder full understands, support, and gain benefit from. Companies need to stress in activities like value-creating to manage costs, handle the risks, and benefits or fix any problems.

However, it can get challenging in achieving AI alignment in all these three areas since factors can change anytime, says researchers. For instance, if changing the setting can alter the algorithm, then it is quite likely that the same setting can also alter the pandemic or any other crisis a stakeholder is looking to gain benefit from.

Leaders need to embrace dynamism and adopt new activities to have a sustainable AI solution.