AI was expected to eclipse BI, but the two are developing a powerful synergy. BI lays the data groundwork AI needs, and AI feeds insights back to improve BI. That cycle breaks down data silos and moves organizations toward data mastery.
In 2025 AI moved from buzzword to a pillar of enterprise strategy, and midsize companies are adopting it at a fast pace. Adoption is maturing but uneven. CEOs who can separate AI fact from fiction and deploy it strategically will be best placed.
Mid-market financial services firms see AI reshaping fraud prevention, underwriting and personalization, yet many are unsure how to begin. The survey points to better treatment of data and equal attention to BI. Outside guidance can speed the move from stabilizing to optimizing to monetizing.
Commercial real estate runs on data such as leases, valuations and occupancy, yet AI adoption remains modest. Our survey points to better data treatment, advanced BI as a gateway to AI and impactful use cases. Leadership support and external expertise make the difference.
Midsize companies can use AI to drive efficiencies, improve customer experiences and open new revenue, but success takes more than buying tools. It requires a strategy tailored to goals, resources and challenges, with leadership behind it. A knowledgeable guide helps build the roadmap.
Data strategy and AI strategy are often treated as separate efforts, which leads to missed opportunities and misaligned goals. They are complementary and interwoven, whichever one you start with. Developing them together charts a clearer path to innovation.
Founder Jeff Roberts presents Innovation Vista's iterative, ROI-driven approach to AI strategy in this webinar. It covers an example workshop agenda, a framework for use-case ideation and strategic planning, and guidelines for making brainstorming sessions more effective.
Companies face three AI options: build custom systems, buy existing solutions or wait for AI to arrive in current products. Each path has unique advantages. Expert advice helps align the choice with the company's overall objectives.
Excitement over AI often runs ahead of identifying the needs and opportunities where it could be used. A practical approach first categorizes high-volume tasks by AI feasibility, then analyzes the economic return. Targeting areas where AI complements or supersedes human effort is where the efficiencies appear.
ChatGPT reached 100 million users in 60 days, and AI has inverted the traditional IT and business challenge. Now coming up with the right problems is the hard part. Finding them is a collaborative exercise pairing your knowledge of strategy and process with an expert's knowledge of the art of the possible.
AI is seldom as simple as business leaders want or some AI service companies claim. Without significant preparation, there are substantial risks of false positives and false negatives from AI models. A set of readiness questions helps you judge whether your organization is ready for AI.
Founder Jeff Roberts was interviewed by Corey Prator, founder of Consortium Partners, about Innovation Vista's purpose and approach. The conversation explores how the firm helps leaders broaden their minds on the business potential of IT.
