Our societies and economies are in transition to a future shaped by artificial intelligence (AI). To thrive in this upcoming era, companies are transforming themselves by using machine learning, a type of AI that that allows software applications to make accurate predictions and recommend actions without being explicitly programmed.
There are three ways that companies successfully transform themselves into AI-driven enterprises, differentiating them from the companies that mismanage their use of AI:
Commercial payback from AI comes when companies deploy highly-accurate machine learning models that operate robustly within the systems that support business operations.
While hard data is scarce, anecdotal evidence suggests that it is not uncommon for companies to train more machine learning models than they actually put into production. Challenges to organisation and technology are in play here, and success requires that both are addressed. From an organisational perspective, many companies see AI enablement as a technical speciality. This is a mistake.
AI is a business initiative. Becoming AI-driven requires that the people currently successful in operating and understanding the business can also create tomorrow’s revenue and be responsible for both building and maintaining the machine learning models that grow revenues. To succeed, these business drivers will need collaboration and support from specialists, including data scientists and the IT team.
Machine learning models must be trained on historic data, which demands the creation of a prediction data pipeline. This is an activity that requires multiple tasks including data processing, feature engineering, and tuning. Each task, down to versions of libraries and handling missing values, must be exactly duplicated from the development to the production environments, a task with which the IT team is intimately familiar.
The organising principle in the AI-driven enterprise is that machine learning is a business-led activity, which succeeds due to the collaboration and support from IT members and data scientists. Early attempts at enterprise-scale machine learning attempted to find data scientists with deep domain knowledge of a company’s business – professionals so rare as to earn them the moniker of “unicorns”.
The proven approach is to turn this thinking on its head: Teach business people how to identify business problems that can be solved with machine learning, and give them the support they need to create their own models. Because business people already own their business processes, they understand what drives specific responses from prospects, customers, and partners. This knowledge is critical for transforming business through AI.
It becomes the CIO’s responsibility to ensure that representatives from IT are on the team from the start of each project. By working with the business, IT representatives ensure that the context for each machine learning model is analysed and understood, while also planning for deployment within the information systems that currently support the operations.
Engaging early and regularly in machine learning projects allows IT representatives to develop sufficient knowledge of the business context for each model so that plans can be made for successful deployment into production, as part of an end-to-end process.
Business managers surveyed by Gartner identified time-to-value as one of their biggest challenges, reporting that it takes an average of 52 business days for their team to build a predictive model and even longer to deploy it into production. An automated machine learning platform must be capable of radically transforming the economics of AI, such that models can be produced in minutes or hours, not weeks or months.
The approach of data scientists being responsible for building machine learning models isolates business professionals from the process of business optimisation. An automated machine learning platform should allow business professionals to build the models they need to transform operations and collaborate with data scientists and IT representatives when needed. As many as 90% of all enterprise machine learning models can be developed automatically. A company’s data scientists or specialist consultants can be engaged to work with business people to develop the small percentage of models that are currently beyond the reach of automation.
Handover processes, from the team responsible for model deployment to the IT team responsible for putting the model into production, should also be simplified and automated. This is achieved when both teams collaborate on the same automated machine learning platform, offering different deployment options that support the needs of the business as identified by the IT team.
When the business context demands robust, real-time predictions along with model management features, such as interpretation, monitoring and error handling, the platform should offer APIs that let the platform serve as a dedicated prediction server. A broad range of deployment options supports the IT team in ensuring the run-time model fulfils the requirements of its business context.
AI and machine learning offer companies an opportunity to transform their operations. IT professionals play a critical role in ensuring that the models developed by their business peers and data scientists are suitably deployed to succeed in serving predictions that optimise business processes. Automated machine learning platforms allow business people to develop the models they need to transform operations while collaborating with specialists, including data scientists and IT professionals.
Choosing an enterprise-grade automated machine learning platform will certainly make IT’s life easier. By providing guidance on organising for successful model deployment and the choice of appropriate technology, IT executives ensure their teams are recognised for their effective contribution to the company’s success as it transforms into an AI-driven enterprise.