Collaboration is Crucial for AI Success
A recent study highlighted that the global Artificial Intelligence market is expected to reach about $300 billion dollars by 2026. While this is an astounding number and growth in a short period of time, this doesn’t create a sense of urgency for companies to adopt AI, let alone make it a necessity.
So, why the disconnect?
The fierce competition in multiple industries spurs a sense of urgency for many organizations, but the buy-in for AI is still a struggle for many people within those organizations. The struggle is exacerbated by the lack of harmonious collaboration and communication across the finance, operations, technology, and line of business leaders. While each area is critical for the business, each area also has different objectives in how the overall company goals are reached. This creates friction, and this friction leads to stagnation.
What Makes AI a Necessity?
Many times, artificial intelligence efforts are seen as an “IT thing” or “tech only initiative” without realizing the impact that AI could have across the organization. This lack of understanding leads to a breakdown in collaboration.
Imagine a CIO or IT Manager that understands how AI could deliver long-term, beneficial outcomes for the company. They create a small proof of concept to measure the outcomes and the impact it will have across finance, operations, and other lines of business. The lessons learned from the proof of concept provide a way for the CIO to have effective dialogue with colleagues. This dialogue creates higher levels of interest and a high level of adoption across the organization.
A great example of a dialogue topic is the concerns of AI adoption. The CIO equipped with the details to address these concerns can have a collaborative conversation, for example, with finance. Finance and Tech are now working together to solve their specific issues. This harmonious framework helps the organization reap the benefits which make AI a necessity to stay competitive.
Securing the Operational Process
As the artificial intelligence and machine learning framework are being created, security must be woven into everything. Since AI/ML relies on data, the culture of data must be understood.
Security typically falls into the realm of the tech area of the business, but how the company functions across the board fall under operations. This, again, makes working in harmony an imperative. Tech and Operations now need to build upon their collaboration to define the processes.
The need for AI and ML to work together securely to deliver a strong, secure DevOps process surfaces some obstacles. To this end, DataRobot recently announced its acquisition of Algorithmia. DataRobot enables organizations to leverage AI at scale through their Augmented Intelligence platform. Algorithmia’s MLOps Enterprise platform delivers machine learning models securely across the organization.
Now, the opportunity exists for enterprise-level processes to be created for AI and ML using secure, scalable solutions for organizations.
Closing Thoughts
According to DataRobot, there are three critical areas in the concept of trust in AI. Trust in the…
- Performance of your AI/machine learning model.
- Operations of your AI system.
- Ethics of your workflow,
The only way to trust AI is to have a collaborative foundation, and in order to stay competitive, AI can’t be treated as optional.
Make a plan for AI and execute the plan. Put governance in place, but be nimble and flexible.
Remember this: Your adoption and use of AI will determine if you can compete fast or concede faster.
