It’s that time of year when the gods come closer to the earth to reveal the mysteries of the time-enshrouded future. Several of our trusted consultants have had clandestine rendezvous with the gods of yore and have lived to tell what they learned. They have graciously agreed to share their insights with the Datalere audience.

AI Will Drive the Convergence of Data and Software Development. 

Elliott Cordo, founder of DataFutures

For decades, the data engineering profession lagged software engineering in maturity and tooling. But that gap is closing fast. Today, data professionals are building AI products more like applications and less like pipelines. Also, as data workloads become more software-like and business-critical, data developers are adopting more software engineering practices. And AI is accelerating this shift — coding assistants and intelligent copilots are closing knowledge gaps and empowering data teams to build faster and more confidently. I believe that 2026 will be the year the line between data and software engineering nearly disappears.

Chatbots Replace BI Dashboards

Mike Spiessbach, Senior Consultant, Data Architecture & Engineering, Datalere

In 2026, dashboards will start to become relics of the past. Why would data analysts or developers spend weeks designing static charts when generative AI can answer any question about a company’s data in real time? Why would business users navigate filters and drill-downs when they can simply ask an AI tool, “What were last quarter’s margins by region?” and get instant, contextual insights? This shift isn’t just about convenience—it’s about agility. Gen AI chatbots will democratize analytics, eliminate dashboard fatigue, and empower decision-makers with conversational access to data. The future of BI isn’t a dashboard—it’s a dialogue.

AI Giants Will Focus More on Enterprise Needs

Kevin Petrie, VP of Research and Head of Data Management Practice, BARC

Under pressure from Wall Street, tech gorillas such as Microsoft will scale back their capital expenditures on GenAI model training and instead invest more in model inference, application development, governance and cost controls. By pivoting in this fashion, the gorillas will better address their customers' priorities. They will support growing consumer and corporate demand for cloud-hosted GenAI platforms. They will give organizations new applications that integrate AI into their business workflows to boost efficiency. They also will focus more on helping organizations reduce governance risks such as data quality, hallucinations, or rogue model behavior. And finally, they will provide new capabilities to predict, measure, and optimize AI-related costs such as tokens and compute. In short, the gorillas will invest more in profitable endeavors and less in building super-intelligent models.

Companies Get Serious About AI Governance 

Sean Hewitt, Operating Partner, Succeed Data Governance Services

Organizations that are serious about AI will make significant investments in AI governance capabilities to reduce the number of failed AI projects. The majority of organizations are struggling to produce results with their AI programs. This fact is highlighted in a 2025 MIT study that estimates "95% of AI project fail to deliver desired results". Failures are often due to data and organizational issues that can be addressed through data governance rigor. Organization that are committed to AI need to address their data governance gaps before they can achieve successful results with AI.

Data Interoperability Becomes a Primary Focus of Data Management

Dave Wells, Data Management Architect and Author

After decades of relying on copy-based integration—extracting, transforming, and replicating data across countless systems—the limits of this approach are clear. Each new copy adds cost, delay, and fragility, creating technical debt that slows change and amplifies risk. The future lies in data interoperability: enabling systems to communicate meaningfully without constant duplication. Instead of ever-expanding pipelines and transformations, interoperability builds on shared semantics and standard data contracts that allow systems to interpret data where it resides. In 2026, data management priorities shift decisively toward interoperability as a key organizing principle for scalable, adaptive, and sustainable architectures.

Universal Semantic Layers Start to Take Root

Wayne Eckerson, Strategic Consultant, Datalere

In the world of data & analytics, semantic layers have always been critical components of our reports, analyses, and predictive models. In the world of GenAI, semantic layers are even more important. Without them, large language models are apt to hallucinate and provide wrong answers or recommendations. Data developers are now building universal semantic layers that works with all kinds of applications, from BI and transformation tools to machine learning and LLMs. That's because developers don't want to build separate semantic layers which are difficult to integrate and synchronize. The new semantic models leverage design elements from graph technology to connect concepts and relationships across structured and unstructured data sets and across multiple toolsets.

AI Changes Data Engineering Development While Data Platforms Consolidate

Carlos Bossy, CEO, Datalere

I see big changes afoot for how data engineers work and the platforms they use. In terms of development, data engineering is evolving rapidly due to AI integration. Data engineers will become more adept at using AI to assist in developing data integration workflows rather than coding data pipelines. At the same time, data management tools are consolidating onto all-in-one platforms, such as those from Snowflake and Databricks. Those platforms increasingly support the end-to-end application and data development process while embedding AI support to speed data development tasks.