Implementing artificial intelligence into an existing business is about more than algorithms. In fact, many AI researchers believe that algorithms are the easiest part of an artificial intelligence implementation. Algorithms need data, and for a business to assess, organize, clean, and use it's data requires ways of thinking that are entirely foreign to most existing enterprises. Partnering with Corinium Global Intelligence, we asked six experienced AI and analytics professionals (all speakers at Corinium's Chief Analytics Officer Spring event in on May 14th-16th in San Francisco) the following three important questions: In the sub-sections of the article that follows, we'll explore each of these questions in depth, highlighting the best insights from the professionals we corresponded with. IT procurement, software development, and software aren't new concepts to many experienced executives.
Artificial intelligence is unlike traditional software in one very important aspect: It has to learn how to do its job. This provides a key benefit for product life cycles in that instead of having to wait for coding wizards to manually upgrade their creations once per year (or even less frequently), the system itself can add new tools, create new features and otherwise alter itself to better satisfy user requirements. The downside, of course, is that few AI programs will provide top-flight performance right out of the box; only through continuous use will they come to understand what is expected of them and how best to achieve their objectives. A key factor in this evolution is the data that AI-driven systems are exposed to. Good data, properly conditioned and placed in the right context, will allow services to make informed decisions and take appropriate actions, while bad data will lead to poor results and steadily diminishing performance.
It has become abundantly clear by now. Successful business adoption of Big Data and analytics initiatives is largely a function of overcoming cultural impediments. These "cultural" factors have been the principal barrier to deriving value from data and analytics investments for most large firms. Among the principal impediments these firms have faced are challenges of organizational alignment, communication between business and technical constituencies, transformation of key business processes, governance and leadership of data initiatives, business sponsorship and assurance that technical investments can be linked to measureable business outcomes. For the most part, the barriers to success for Big Data initiatives are not about technology – they are due to business issues.
IDC estimates that all 40 percent of all technology spending will go toward digital transformations, with enterprises spending in excess of $2 trillion in 2019. "IT leaders who have not fundamentally changed their organizations to focus on digital will find that their business colleagues will turn to outsourcing to handle development needs," says Joseph Pucciarelli, an IT executive adviser at IDC. Going digital on such a broad scale requires CIOs to tackle change management and other challenges. Committing to digital often requires CIOs to partner with business peers more closely to achieve desired business outcomes -- a striking change in its own right. Indeed, a Gartner survey shows that 95 percent of 3,160 CIOs expect their jobs to change or be remixed due to digitalizationDigitalization describes the process of digitalizing everything which can be digitalized. This process is found in workplaces but also in every day home life.
As organizations attempt to create new digital services that drive additional revenues, many of them are realizing existing approaches to data management no longer meet their requirements. SAP today launched a series of initiatives intended to make it simpler for organizations to employ a more holistic approach to managing data as a true business asset. Announced simultaneously at the SAP TechEd event and Strata Data Conference, the latest SAP offerings span everything from the general availability of SAP Data Intelligence Service on the SAP Cloud that provides tools for managing data and machine learning algorithms used to create artificial intelligence (AI) models to a conversational user interface that has been added to the SAP Analytics service. At the same time, SAP also announced it is extending an existing alliance with Microsoft to ensure interoperability between their respective blockchain platforms. As organizations attempt to monetize data to create new digital services that drive additional revenues, many of them are starting to realize existing approaches to data management no longer meet their requirements, says Juergen Mueller, CTO and executive board member at SAP SE.