How To Join the Connected World With Data and AI

Image credit: iStockphoto/Thomas-Soellner

The past year demonstrated the power of digital to overcome the challenges seen in the physical world. When customer relationships were threatened by pandemic restrictions, many firms stepped outside their comfort zones to respond with new virtual experiences, services, and conveniences to maintain or grow those vital customer relationships.

Could that response have been faster? For many organizations, the answer is “yes.” If there was one key learning for data and analytic professionals in the past year, it’s that our data and AI foundations weren’t as ready for this challenge as they could have been. Nearly every client conversation I have today focuses on the acceleration of investment and modernization or the deployment of new operating model plans.

We’re also seeing an unprecedented number of client questions about embedding data and AI into event-driven and real-time capabilities. Connecting with customers at scale is increasingly about personalization, intelligent automation, and in-moment adaptation based on where the customer is. For many organizations, that’s led to the realization that a cloud data and data science platform to build models is only one part of the puzzle. Data and AI need to be at the edge of business in the applications, mobile devices, and machines where customers engage and interact with the business. This is the new world of connected intelligence, and it’s not just for the big tech companies — it’s the required state for any modern enterprise.

It’s time to envision AI as more than a churn model, chatbot, and language processor. Organizations embracing connected intelligence use AI to bridge business silos and generate holistic experiences where all touchpoints and channels capture, share and combine intelligence. Having a clear framework to orient the data and AI operating model with technology is the right way to move forward.

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 see industries such as insurance, energy, and pharmaceutical expanding the number of subject matter experts (SMEs) used to design, develop, and deploy data and AI for connected intelligence. These SMEs are tapping internal partners from diverse teams, including legal and risk, to build the models and shape the entire AI capability to enhance the 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 support omnichannel experiences with AI.
  • Practices become hyper-collaborative. With multiple partners, SMEs at the connected intelligence table, how they work together is increasingly important. Operating model design is expanding beyond supporting specific tasks and organizational structures and focusing on how different roles engage, coordinate, and deliver data and AI. For example, one global energy producer has applied journey mapping techniques to its data and AI practice area. This determines how roles and teams work together by better aligning skills and responsibilities to goals and envisioning new processes and ways to optimize collaboration, coordination, and support. Additionally, we also see how service providers are brokering distributed training of models between clients to validate and optimize these models before launching to production.
  • Platforms are shaped for the business edge. In no way could the new partner and practice models be successful without the right platforms to deliver connected intelligence and enable and reinforce best practices. Investments in the cloud, edge computing, blockchain, and 5G are providing a stronger backbone and network for AI to enable connected intelligence. But new collaboration capabilities and platforms are also emerging to create trusted environments where data, models, training, and insights can be used for generating AI. Data and AI exchanges are popping up within industries and cross-industries. AI collaboration platforms are allowing trusted codevelopment of models between external parties in a trusted network. And integration platform as a service is beginning the transition to span between data engineering and application development to tighten the connection between data, AI, and real-time edge applications.

The original article by Michele Goetz, vice president and principal analyst at Forrester, is here.

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/Thomas-Soellner