Explore the 16 key strategies that enterprise CIOs are adopting to budget, acquire, and deploy Gen AI effectively in 2025 and beyond.
Introduction
As enterprises navigate the rapidly evolving landscape of artificial intelligence (AI), Chief Information Officers (CIOs) are at the forefront of integrating Generative AI (Gen AI) into their organizations. In 2025, the strategies for building and buying Gen AI have become more sophisticated, reflecting the maturity of AI technologies and their critical role in business operations. This post delves into the top 16 strategies that enterprise CIOs are leveraging to successfully deploy Gen AI, ensuring their organizations remain competitive and innovative.
Budgeting: Scaling AI Investments
1. Expanding AI Budgets Beyond Expectations
Enterprise AI budgets have surged, outpacing initial forecasts. CIOs anticipate an average growth of approximately 75% over the next year, driven by the discovery of new internal use cases and increased employee adoption.
2. Transitioning AI Spend to Core Budgets
Gen AI expenditures are moving from innovation funds to permanent line items in centralized IT and business unit budgets. This shift underscores the recognition of Gen AI as essential to business operations rather than experimental.
Model Selection: Embracing a Multi-Model Approach
3. Adopting a Multi-Model Strategy
CIOs are deploying multiple AI models to optimize performance and cost. This approach helps avoid vendor lock-in and allows for the selection of models best suited for specific use cases.
4. Navigating a Crowded Model Landscape
With OpenAI, Google, and Anthropic leading the market, enterprises are leveraging the strengths of each provider. For instance, Anthropic excels in coding tasks, while OpenAI’s models are preferred for complex question-answering.
5. Evaluating Cost-Effective Closed Source Models
Closed source models like Google’s Gemini 2.5 Flash offer compelling price-to-performance ratios, making them attractive for enterprises seeking cost-effective solutions without compromising quality.
6. Shifting Focus from Fine-Tuning to Prompt Engineering
Improved model capabilities have reduced the necessity for fine-tuning. Instead, enterprises are utilizing prompt engineering to achieve desired outcomes, enhancing flexibility and reducing costs.
7. Optimism Around Reasoning Models
CIOs are optimistic about the potential of reasoning models to handle more complex tasks. Although adoption in production is still early, the anticipated scalability promises to unlock new use cases.
Procurement: Adopting Rigorous Evaluation Processes
8. Implementing Traditional Software Procurement Practices
The buying process for AI models now mirrors traditional software procurement, incorporating rigorous evaluations, security assessments, and cost considerations.
9. Diverse Hosting Preferences
Enterprises are increasingly hosting models directly with providers or through platforms like Databricks, favoring direct access to the latest models and performance enhancements.
10. Managing Rising Switching Costs
As AI workflows become more intricate, switching models incurs higher costs. CIOs are cautious about making changes that could disrupt established workflows and require significant engineering efforts.
11. Leveraging External Benchmarks for Model Selection
External benchmarks, akin to Gartner’s Magic Quadrants, are becoming essential in filtering and selecting appropriate AI models, providing a standardized assessment framework.
Application Usage: Transitioning from Build to Buy
12. Shifting from Building to Buying AI Applications
There’s a notable trend towards purchasing third-party AI applications rather than developing in-house solutions. This shift is driven by the maturity of the AI ecosystem and the desire for incremental ROI gains.
13. Addressing Challenges with Outcome-Based Pricing
While outcome-based pricing is gaining traction, CIOs express concerns about setting clear metrics and managing unpredictable costs. Consequently, usage-based pricing remains the preferred model.
14. Prioritizing Software Development as a Key Use Case
AI-driven software development has seen exponential adoption. Tools like Cursor and Claude Code are being utilized extensively, transforming traditional development processes and enhancing productivity.
15. Harnessing Prosumer Market Influence
Early growth in enterprise AI applications is heavily influenced by the prosumer market. Consumer familiarity with tools like ChatGPT drives enterprise demand, accelerating the adoption of next-generation AI solutions.
16. Favoring AI-Native Vendors Over Incumbents
AI-native companies are outpacing traditional vendors in product quality and innovation speed. Enterprises recognize that AI-native solutions offer superior outcomes, particularly in dynamic and specialized use cases.
Integrating Genspark into Your AI Strategy
Amidst these strategies, platforms like Genspark are revolutionizing information engagement through their AI-powered agentic engine. Genspark’s unique multi-agent framework, Sparkpage technology, and robust data protection measures align perfectly with the strategies outlined above, providing enterprises with the tools needed to navigate the complex AI landscape effectively.
Conclusion
Enterprise CIOs are adopting a comprehensive and strategic approach to building and buying Gen AI, driven by expanding budgets, sophisticated model selection, rigorous procurement processes, and a shift towards buying over building applications. By embracing these 16 strategies, organizations can effectively deploy Gen AI, driving innovation and maintaining a competitive edge in 2025 and beyond.
Ready to transform your AI strategy? Discover how Genspark can empower your enterprise today!