It’s the time of year when we think about trends that will affect data & AI for the next 12 months. In truth, the market is moving so quickly you could (and should) revisit trends every 3 months! However, it’s useful to pause and think about the trends that will affect your business, and how you can capitalize on them.
These top 5 trends are based on our work with Chief Data Officers, Chief Analytics Officers, and the emerging role of the Chef AI Officer, along with their data, analytics and AI leadership teams.
(1) Domains become the Primary Focal Point – Customer DNA (Data, aNalytics, & AI) Emerges as a Practice
Much of the efforts around data engineering, lakehouses analytics and AI have been enterprise-wide – a place to put all data, analyse it and use it. In 2025, the next step will be to organize it to make sense to the organization and make it easier to find and use for analytics and AI. The first domain to become a practice will be the most important domain (and concept) for an organization – Customer.
There are multiple drivers for this already in place in 2024. Within CDO organizations, more organizations have appointed a VP leader to be in charge of ‘customer data’. Similarly, customer analytics leaders and specialist teams work across business functions. Many data and IT teams are busy building data mesh architecture, which is domain-centric. Most have started with the customer domain, building customer-centric data sets and collections in their lakehouses, and customer-centric business services to find and consume customer data. Those services will be vital to powering customer-centric AI agents that emerge in 2025.
Organizations should take a holistic approach to the customer domain, and be sure it expands beyond just data to include AI and analytics. That organization structure will create the foundation for organizations to deliver transformative customer-centric AI agents.
(2) AI Agents will Quickly Evolve from Single-agent functions to Multi-Agent Processes
Customer-centric AI will evolve rapidly in the year ahead. A collection of single-agent functions are already available from applications – CRM, ERP, eCommerce, and the like. Those AI agents will help automate and improve individual functions, and therefore individual tasks that impact the customer.
However, we all know that the customer experience is much broader than single-agent functions. Sure, it’s great that a chat-bot can tell me my order status, but if it can’t answer a billing question, then my overall experience remains frustratingly average. Companies that are serious about customer-centricity will race ahead to create multi-agent customer processes that measurably improve customer experience.
Here’s a tip – you can’t do multi-agent customer-centric AI with data patched together from applications or even with ‘core’ data from master data management. In fact, those AI projects will fail without a multi-modal data foundation. Data modernization is required in order to provide multi-agent AI with the data it requires, and so …
(3) Data Modernization Ramps Up to Support AI Initiatives
Many organizations increased data modernization spending in 2024. However, that investment went to moving legacy apps to more modern cloud versions of the same software. The SAME capabilities. That’s the problem. Functionally, data management has been standing still (or falling behind) for the past 2-3 years.
Yet some organizations are moving in the right direction and adding new capabilities. The most common one? Customer domain capabilities. Data mesh business services. Customer-centric connected data sets. Deployment of entity resolution inside data lakehouses to make customer data sets customer-centric.
In 2025, organizations must keep up the investment in data modernization to keep pace with AI investments. Data is essential – it is what will make AI either succeed or fail. The true leaders will focus their efforts on adding new data capabilities and concentrating on their most important domains, such as customer, to support their AI strategy.
(4) Too Many Chiefs? Convergence of CDO, CAO, CAIO to a … CDNAO?
During 2024, there was noticeable consolidation of Chief Data Officers (CDO) and Chief Analytic Officers (CAO) into the role of Chief Data & Analytics Officer (CDAO). And just as that was happening, a new role, the Chief AI Officer, started popping up. AI is important, so why not appoint a new chief to lead it? Different leaders equal different agendas. Data management doesn’t keep pace with AI’s needs, and AI doesn’t take advantage of modern data initiatives.
If you believe data, analytics and AI should be well aligned to ensure all three are successful, then your organization structure should reflect that. CDO, CAO, CAIO roles should be combined in a single leader for data, analytics and AI. A Chief Data aNalytics and AI Officer – a CDNAO.
(5) Customer 360 View Evolves into the Customer 3D View
Customer 360 is an old term. It’s always meant to encapsulate “everything about the customer”. However, it has always been a data-centric term used by data management solutions. Master Data Management (MDM) vendors use it. Customer Data Platforms (CDP) use it. CRM claims they have a customer 360. Data lakehouses talk of customer 360s. What’s the deal?
Well, many of those systems need a customer 360, or specifically, they need ‘their version of a 360’ – the data in context for their processes (analytics, customer service, sales, marketing, etc.). And that’s the problem. A 360 of all data doesn’t provide just the data needed for any one function.
In 2025, customer-centric orgs will revisit their customer 360 strategy and realize that their current customer 360 is actually one-dimensional. At best, it contains current and past data about the customer. And let’s be clear, that is at best. Many orgs are still struggling to keep up with matching and linking customer data across various silos to achieve that. But a 3D Customer View has two other ‘dimensions’ that make your customer understanding much more realistic. The second dimension is “future data”. Predictions of future actions. Lifetime value. Predicted next actions. Much of the ‘future data’ needs to be predicted or extrapolated. And that’s where customer analytics comes in. Customer analytics already exist, but their output (insights and data analytics) often exist in reports and dashboards. But not in the customer domain. And that’s a huge problem for AI. All of that insight should be connected and accessible with the customer domain. The third dimension is the most important – context. There is an overwhelming amount of customer data. Too much data. The quality and value of that data varies depending on how it will be used. That is what contextual data is all about. It puts the data in context for various use cases. Context means presenting not only relevant data, but also data of the required quality and value. Creating a customer 3D view that puts data in context will allow you to build better AI agents and operate them without hallucinations and errors.
Start with your existing customer 360 strategy and the various technologies it encompasses. Gather your customer-centric AI requirements and then look for capabilities that will help evolve a flat customer 360 into a realistic Customer 3D View.
2025 Will Be A Pivotal Year for AI and Data
Organization structures, data technologies and AI capabilities will dramatically evolve in the coming year. It can be overwhelming. Dealing with years of legacy data management and analytics investments is a challenge. Selectively leveraging those past investments while building new capabilities to support AI is a must.
What’s our advice? Focus. No company can modernize all data systems, analytic investments, and build new multi-agent AI across the board. Focus on your most important domain. For customer-centric organizations, the choice is obvious – the customer domain. Combine your customer DNA (Data, aNalytics, and AI) practices together. Prioritize customer-centric AI projects and then evaluate your current customer data management and analytics capabilities. Select the technologies that can be leveraged and determine which ones need to be modernized. Most important, plant a marker for this new shift – evangelize your Customer DNA strategy and organization to business leaders and get them on board. Let’s make 2025 the year of Customer DNA – Data, aNalytics and AI in order to truly transform the customer experience!
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