Gen AI Trends and Scaling Strategies for 2025

Generative AI isn’t just moving fast—it’s on turbo mode. Gartner confirms it in their popular Hype Cycle, compared to other evaluated technologies: gen AI tech is rocketing through the stages faster than anything else. In under three years, it’s already crashing into the trough of disillusionment, while prompt engineering shot to peak hype almost the second it emerged.

In this blog post, we bring insights for AI leaders Svetlana Sicular, Research VP, AI Strategy, Gartner and  Yaron Haviv, co-founder and CTO, Iguazio (acquired by McKinsey). To see the complete conversation and dive into their insights, watch the webinar here.

What Gen AI Trends is Gartner Seeing?

Some of the significant fast-paced trends Gartner is seeing include:

• AI-ready data - Large players are announcing partnerships and acquisitions of data-related companies, for purposes ranging from access to data for AI purposes to explainability to synthetic data.
• AI governance - Merely two and a half years after gen AI entered the ecosystem, the market is comfortable with generative AI. Now, it's looking for ways to make it safe. This includes responsible AI, Gartner’s concept of AI TRiSM (Trust, Risk and Security in AI Models) and Sovereign AI.
• AI engineering - AI is being democratized for developers and engineers, expanding beyond the limited pool of data scientists. Companies are building AI tools and frameworks that empower engineers to integrate AI into applications without needing deep expertise in ML.
• AI Agents and multi-agent systems.
• And more. See the webinar for more Gartner trends.

How should organizations respond to these changes? Gartner sees this as a matter of appetite and strategy.

• Enterprises seeking immediate productivity should look at implementing mature technologies on the right-hand side of the hype cycle.
• Enterprises seeking competitive differentiation should focus on emerging technologies on the left-hand side of the hype cycle. However, they should be mindful of skill availability. Established technologies have more implementers, whereas cutting-edge innovations require experts and ongoing skill development. At the early trigger stage of the hype cycle, enterprises should expect to invest in growing expertise rather than relying on readily available talent.

What Does it Take to Productize Gen AI Applications?

The technologies evaluated in Gartner’s hype cycle are used to productize gen AI applications. Doing so requires a series of steps, going beyond development.

Development includes the important following steps:

• Exploring relevant data assets
• Building data ingestion and transformation pipelines 
• Developing data enrichment and RAG logic
• Developing the gen AI application and agent or multi-agent workflow
• Developing the front-end application
• Developing fine-tuning and RLHF workflow (optional)

Then, organizations should follow with:

1. Data Management, Security, and Governance

• Automating, scaling, versioning and productizing data pipelines
• Ensuring data security, lineage and risk controls
• Adding application security
• Adding real-time guardrails and hallucination protection

2. Quality, Scalability and Continuous Delivery

• Implementing modularity with LLM, data, and API abstractions to ensure flexibility
• Implementing tests for models, prompts, application logic, etc.
• Optimizing performance, costs and supporting workload elasticity
• Add observability, and experiment tracking
• Building containers, microservices and cloud resource integrations
• Developing automated CI/CD pipelines (for data, models, and apps)

3. Live Operations

• Automating deployment processes and rollbacks
• Supporting health checks, recoverability and disaster recovery
• Managing resources, implementing FinOps and chargebacks
• Monitoring application performance, accuracy, drift, risk, etc.
• Monitoring business KPIs
• Creating custom dashboards

Altogether, these requirements take months and a large team of engineers.

What are the Key Guidelines for Productizing Gen AI Applications?

To accelerate and simplify productization, follow these best practices: 

1. Consolidate the technology stack for data engineers, data scientist and ML engineers to allow collaboration, resharing and reusing.
2. Automate productization, like auto-gen batching, real-time data pipelines, automated model training and CI/CD.
3. Get end-to-end observability by auto-tracking data, lineage, experiments and models and real-time monitoring.
4. Build for scale and elasticity, with distributed data processing, model training and serving, serverless architectures and on-demand container and VM allocation.
5. Use open-source and extensible architecture that support cloud and on-prem, and all frameworks and LLMs.

The expected impact:

• Enhanced efficiency with 90% reduction in manual tasks
• 12X faster time to production
• Responsible Al for high quality, governance, reproducibility, etc.
• 6x reduction in computation costs
• Risk reduction and future-proof architecture

This is achievable with an enterprise AI factory, which allows for continuous delivery, automatic deployment and monitoring of AI apps.

The Enterprise AI Factory

The enterprise AI factory includes:

• Data management - Curating data, preparing it, ingestion, etc.
• Development - Building pipelines and creating, training, testing and fine-tuning models
• Deployment - CI/CD and rapid deployment of scalable real-time serving and application pipelines that use LLMs and the required data integration and business logic
• LiveOps - Monitoring the assets to ensure they meet expectations

Based on work with global enterprise clients, we’ve curated a list of the features each pipeline includes. Watch the webinar to see.

To enhance development, government and conformance with organization methodology, the enterprise AI factory includes reusable assets. Some of the reusable assets in the platform:

• Data sources connectors
• Data Loss Prevention (DLP)
• Model serving functions/ servers
• CI/CD
• Integrations
• Structured/ unstructured data processing
• Model training and tuning functions
• Prompts and agents/tools library
• Data exploration and analysis functions
• Model evaluation and test functions
• Risk control components (real time guardrails)
• Monitoring functions and dashboards
• Application templates/ blueprints

See what an example project looks like in the webinar.

What’s Next in 2025?

Generative AI is evolving at an unprecedented pace, and organizations that want to stay ahead must take a strategic approach to adoption and scaling. Whether leveraging mature AI technologies for immediate gains or investing in emerging innovations for long-term differentiation, success hinges on robust data pipelines, governance, security, and automation. Taking an Enterprise AI Factory approach allows companies to accelerate AI productization while minimizing risk and costs. 

The future of Gen AI belongs to those who build with foresight. Watch the webinar to see how leading enterprises are navigating this transformation.