We have recently witnessed how digital technology can be used to overcome obstacles in the real world. For instance, several businesses moved outside their comfort zones to adapt with new virtual experiences, services, and conveniences when customer ties were threatened by pandemic limitations in order to maintain or even increase those crucial customer relationships. Could it have been quicker to respond? The response is “yes” for a lot of organizations. Data and analytics experts have discovered that our data and AI foundations weren’t as prepared as they could have been for significant changes. Today, the topic of almost every client interaction I have is the implementation of new operating model plans or the acceleration of investment and modernization.

Additionally, we’re getting an unusually high volume of client inquiries about integrating data and AI into real-time and event-driven capabilities. Personalization, intelligent automation, and on-the-fly adaption based on the customer’s location are becoming more and more important for connecting with customers at scale. The awareness that a cloud data and data science platform is simply one piece of the solution has been reached by many businesses as a result. The applications, mobile devices, and machines where customers engage and interact with the business need to be at the cutting edge of data and AI.

Businesses that embrace linked intelligence employ AI to cross functional boundaries and create holistic user experiences that gather, exchange, and synthesize intelligence across all touchpoints and channels. The best way to proceed is to have a defined framework to align the data and AI operational model with technology.

In light of all of this, the following is what analytical, business, and technological executives might anticipate as they move forward with connected intelligence strategies:

Ecosystems develop from partners. The number of subject matter experts (SMEs) needed to design, develop, and deploy data and AI for linked intelligence is increasing in sectors like insurance, energy, and pharmaceuticals. In addition to building the models, these SMEs are enlisting internal partners from other areas, such as legal and risk, to define the overall AI capabilities in order to improve the customer experience and commercial outcomes. External business partners are joining the fray to offer extra knowledge and enhance omnichannel experiences with AI as organizations continue to change and broaden their ecosystems.

Practices adopt a highly collaborative style. How they collaborate becomes increasingly crucial as there are several partner SMEs at the connected intelligence table. Operating model design involves concentrating on how many roles interact, collaborate, and produce data and AI rather than just supporting certain tasks and organizational structures. One major energy producer has integrated journey mapping into its practice area for data and AI. By better matching roles and responsibilities to objectives and imagining new procedures and methods to maximize collaboration, coordination, and support, this dictates how roles and teams interact. Furthermore, we observe how service providers facilitate distributed training of models across clients in order to validate and improve these models prior to their introduction into production.

Platforms are designed for a competitive edge. Without the appropriate platforms to supply linked intelligence, enable best practices, and reinforce those practices, the new partner and practice models could not possibly be successful. Investments in the cloud, edge computing, blockchain, and 5G are enabling connected intelligence by strengthening the network and backbone for AI. But there are also new platforms and collaborative tools developing to build secure settings where data, models, training, and insights may be leveraged to produce AI. Exchanges of data and AI are emerging both within and across sectors. Platforms for AI collaboration offer trusted model co-development amongst outside parties in a trusted network. To strengthen the link between data, AI, and real-time edge applications, integration platform as a service is starting the shift from data engineering to application development.