The past year has shown the power of digitization to master challenges in the physical world. When customer relationships were threatened by pandemic restrictions, many companies left their comfort zone in order to respond with new virtual experiences, services and amenities, in order to maintain them or even to maintain them grow these important customer relationships.
Could this answer have been faster? For many organizations the answer is yes. If data and analytics professionals had to learn one important fact over the past year, it is that our databases and artificial intelligence weren’t as well prepared for this challenge as they could have been. Investments and modernization or the introduction of new operating model plans.
We also see an unprecedented number of questions about integrating data and artificial intelligence into real-time and event-driven functions. Growing connections with customers is increasingly about personalization, intelligent automation, and instant adaptation to the customer’s location. For many companies, this has led to the realization that having a cloud data and data science platform for building models is only part of the puzzle of engaging with business. This is the new world of connected intelligence, and it’s the required status for every modern enterprise, not just for large tech companies.
It’s time to think of AI as more than just a demolition model, chatbot, and speech processor. Companies advocating connected intelligence are using AI to bridge business silos and generate holistic experiences where intelligence is captured, shared, and combined from all touchpoints and channels. Aligning AI data and operating models with technology is the way to go.
With all of this in mind, here is what business, technology, and analytic leaders can expect as they push ahead with connected intelligence strategies:
- Partners evolve into ecosystems We’re seeing industries like insurance, energy, and pharmaceuticals expand the number of subject matter experts (SMEs) deployed to design, develop, and implement data and artificial intelligence for connected intelligence not just to build the models, but to design the entire AI capability to improve customer experience and business outcomes. As businesses further shift and expand their ecosystems, external business partners are also coming into the fold to provide additional expertise and to support omnichannel experiences with AI.
- Practices become hyper-collaborative As multiple SMBs are linked at the Connected Intelligence table, their collaboration becomes more and more important. The design of operating models goes beyond supporting specific tasks and organizational structures and focuses on how different roles interact, coordinate and provide data and AI. The energy producer has applied trip mapping techniques to its data and artificial intelligence practice area. This determines how roles and teams work together by better aligning skills and responsibilities with goals and developing new processes and ways to optimize collaboration, coordination and support. Optimize these models before going into production.
- Platforms are shaped for the business edge Without the right platforms to provide networked intelligence and enable and enforce best practices, new partner models and practices could in no way be successful. But new collaborative skills and platforms are also emerging to create trustworthy environments in which data, models, training and insights can be used to generate artificial intelligence. Data exchange and artificial intelligence appear across industries and industries; AI collaboration platforms enable collaborative model development between external parties in a trusted network, and the integration platform as a service begins the transition to include data engineering and application development to strengthen the real-time connection between data, AI and edge applications.
