Sharing is caring!

The role of chief information officer is changing, as organizations shift their digital transformation strategies from a focus on data to a focus on machine learning and artificial intelligence.

No longer can CIOs decide *if* they should use AI, but instead where and how to apply it. As a result, CIOs are facing increased pressure to take on a more advisory role, helping the entire enterprise, rather than a single department, improve their KPIs by embracing AI-driven technology.

The application for AI-driven decisioning spans across departments, including marketing, finance and human resources. The CIO should be guiding adoption and execution throughout the organization in myriad use cases to drive better outcomes across the business landscape. It’s not enough to just collect information, then analyze noise in addition to valuable data, and make rear-view mirror predictions about what to do next. The CIO should be helping the organization to make better use of their data and continually improve decisions based on AI learning from past outcomes.

Here’s how to ensure AI adoption is successful across the business:

#1: Get AI into the Hands of Business Stakeholders

CIOs should ensure that the powerful tools being used are accessible by more than just a few analytic gurus in the organization. If that is the case, it’s more than likely that the usage will be confined to a few initiatives and won’t scale to the meet the needs of the business across departments. Perhaps more importantly, such a process will leave out the knowledge of domain experts. This knowledge is critical to the success of any initiative and should not be overlooked. In fact, the programs with the greatest success put the AI tools directly into the hands of the business stakeholders.

#2: Ruthless Prioritization: Program Management not Science Experiments 

If an organization is trying to incorporate AI as part of the digital transformation journey, the primary goal is to find a faster, smarter way to get from data to action. This isn’t going to happen overnight and will require prioritization and focus to be successful. The CIO can bring a wealth of experience with iterative processes – the foundation of agile development methodology – and can help the organization prioritize AI implementation, focus on generating success in those areas, and bring consistency across departments.

#3: Understand the Why: Make AI Explainable

The days of “black box” AI are numbered. As AI systems are given more responsibilities and more complex tasks, business leaders must understand the basis of the decisions so that they can be trusted.  If a business is making important bets on a machine’s decision, it’s critical to understand why the machine is making a certain decision and on what basis the decision is being made. Increasingly, the CIO will need to work with business leaders to describe the decision-making process, understand the actions taken as a result, then measure the effectiveness of those decisions and actions.

Explainable AI should produce more transparent models and maintain a high level of predictive accuracy while enabling users to understand, trust and manage the system. This is particularly important when AI-derived decisions impact consumers.

Nearly every business is figuring out how to use AI and machine learning to better compete. This means CIOs are now responsible for leading the organization through the journey and shepherding the employees successfully through the process. Starting with a clear grasp of where the business stands now – employees, customers, assets and infrastructure, CIOs should understand the vision of where the business wants to be and create the roadmap to get there. While the journey will have small steps and huge leaps, dotted with surprising discoveries and a few failures, the CIO can lead the organization with the right blend of people, data, analytics and machines to a future that is uniquely it’s own.


Sharing is caring!

Leave a Reply

Your email address will not be published. Required fields are marked *