The fastest path to AI value may start before anything gets built. Organizations rarely struggle with a lack of AI ideas. The harder part is knowing which ideas are worth pursuing, where AI actually adds value, and how to turn promising concepts into capabilities people will use. Our experience across more than 50 AI use
People First, Then AI Read More »
AI ideas are not in short supply. The harder part is deciding which ones deserve investment. Requests may come from business teams, technology leaders, executives, and vendors. But moving too quickly from an interesting concept to a proposed build can lead to unnecessary work, unclear outcomes, and investments that are difficult to evaluate. Before funding
Two Questions to Ask Before Funding an AI Project Read More »
For decades, the front door of a community bank was easy to recognize. It was the branch on Main Street. The lender a local business owner already knew. The referral from a neighbor. The familiar name on a youth sports sponsorship, nonprofit event, or local chamber program. Those front doors still matter. But they are
The New Front Door to Community Banking Read More »
Community banks have spent decades building something many institutions would love to have: trust. They sponsor local events. They help first-time homebuyers navigate one of the largest purchases of their lives. They support small businesses through periods of growth, uncertainty, and transition. Their lenders, branch managers, and relationship teams often live in the same communities
Being Known Is Not the Same as Being Found Read More »
The Data and AI talent market is shifting quickly. In the early wave of generative AI, many organizations focused on prompt engineering, experimentation, and lightweight productivity gains. In 2026, the conversation is becoming more mature. Organizations still want AI skills, but they increasingly need the broader technical and delivery capabilities required to move AI from
The Most In-Demand Data & AI Skills in 2026 Read More »
The next stage of enterprise AI will not be won by isolated experiments. The next stage of enterprise AI will be won by cross-functional product teams that can turn ideas into governed, measurable, adopted solutions. For many organizations, early AI activity has been fragmented. A business team experiments with a tool. A data science team
The Rise of the AI Product Team Read More »
AI gets the attention. Data engineers make AI possible. As organizations rush to adopt generative AI, agentic AI, and advanced analytics, one reality is becoming harder to ignore: AI systems are only as useful as the data they can access, understand, and trust. Models can summarize, recommend, classify, and generate, but models depend on the
Why Data Engineers Are Becoming the Most Important Role in AI Read More »
The question every remote pipeline must answer Is this the candidate’s own work? In remote and hybrid hiring, that question is not theoretical. HR and industry reporting document impersonation/proxy interviews, deepfake‑assisted calls, and AI‑generated responses—all of which can pass an initial screen and drain panel capacity if not addressed up front. Curate leverages AI‑powered technology
Don’t guess, verify: ensuring human‑authored work in remote hiring Read More »
Start interviews on solid ground Generic coding tests alone don’t address today’s hiring risks. In remote and hybrid pipelines, misrepresentation has become more sophisticated—ranging from impersonation/proxy interviews to deepfake‑assisted calls and AI‑generated answers—which undermines trust and stretches interview loops. Curate leverages AI‑powered technology to test with human oversight to verify who you’re speaking with and
From puzzles to proof: Real‑work screens with verified integrity Read More »
Databricks has cemented its place as a leading platform for data engineering, analytics, and AI. As organizations increasingly rely on it to build scalable and reliable data pipelines, the demand for skilled Data Engineers who can harness its power effectively has surged. While many skills contribute to a great Data Engineer, a specific trio consistently
