Making Enterprise AI an Organizational Asset

Enterprise AI is no longer about deploying one-off AI projects — it is about integrating AI, GenAI, and analytics automation into core business operations to create lasting business value. Organizations that succeed in AI do not treat it as a standalone initiative. Instead, they embed AI into workflows, decision-making, and strategic execution to drive efficiency, innovation, and competitive advantage.

The shift from one-off AI projects and proofs of concept (POCs) to enterprise-wide AI orchestration is accelerating. Generative AI (GenAI) and AI agents are transforming how businesses automate complex workflows, augment decision-making, and optimize productivity. But GenAI is not a replacement for more traditional methods — it extends the capabilities of machine learning (ML), predictive analytics, and AI-driven automation to create a continuously improving AI ecosystem.

Companies leading in Enterprise AI go beyond adoption. They build a scalable AI foundation that ensures AI delivers compounding business value through governance, collaboration, and adaptability. The organizations that get this right drive operational efficiencies, accelerate innovation, and unlock new revenue opportunities.

At Dataiku — The Universal AI Platform™ — we have seen organizations take vastly different approaches to Enterprise AI. Some start with promising pilots but struggle to move beyond isolated projects. Others integrate AI into daily operations, making it a critical driver of business strategy. The difference between these groups is not technology but strategy. Enterprise AI must be structured for scale from the start, ensuring that AI becomes an enterprise-wide capability rather than a series of disconnected efforts.

To build a scalable AI strategy, organizations need:

• Cross-functional collaboration: Success with Enterprise AI requires company-wide alignment. It takes a village to build enterprise-grade AI, with business leaders, IT, analysts, AI engineers, and data scientists all playing a critical role in making AI work at scale.
• Scalable governance: Enterprise AI systems should be transparent, adaptable, and compliant as they evolve.
• AI as a business asset: AI should move beyond analytics and be deeply embedded into real-time operations, automation, and revenue-generating processes.

Organizations that adopt this structured approach transition from isolated AI projects to enterprise-wide AI ecosystems that deliver sustainable, compounding value.

Scaling AI requires the right operating model — a structured framework for how AI is managed, developed, and deployed across an enterprise. Organizations typically evolve through these five models as they mature in AI adoption.

1. Decentralized / Siloed
Teams experiment independently, using different tools and methodologies with little collaboration across the business. While this phase is often short-lived, it serves to determine AI’s value before investing further.

However, as teams begin generating results, the need for shared infrastructure and specialization becomes evident. This realization often drives organizations to transition to a more centralized model, reducing costs and improving efficiency.

2. Centralized Center of Excellence (CoE)
A centralized team develops and maintains AI products for multiple business units, driving strategic alignment and accelerating AI adoption. Success depends on collaboration between technical experts and business teams to create a unified strategy.

Key tasks include prioritizing AI projects with measurable return on investment (ROI), building scalable data infrastructure, and promoting success stories to drive engagement. While this model jumpstarts adoption, the CoE typically evolves as organizations scale their AI efforts further.

3. Hub and Spoke
In the Hub and Spoke model, AI experts are in a central hub. Business units or functions take more control of AI product development. The hub focuses on infrastructure, governance, and innovation tracking, while the spokes prioritize AI use cases and drive adoption.

This structure helps data teams work better with business units. It makes sure that AI projects, including cutting-edge initiatives like GenAI deployments, align with business goals. Companies that successfully use AI are more likely to adopt this model. It balances central control with local execution.

4. Center for Acceleration
As organizations mature, they often transition to a Center for Acceleration, which promotes widespread AI adoption among domain experts. This model gives business units the responsibility for developing AI products. It still keeps centralized guidance on governance and infrastructure.

The result is increased innovation and agility, as domain experts bring their deep knowledge to the development process. This structure enables organizations to scale AI across multiple functions while driving measurable results.

5. Embedded
The Embedded model represents the most decentralized and innovative approach to AI. Here, business units fully integrate AI capabilities with minimal central oversight. Shared resources, such as responsible AI guidelines and curated datasets, provide consistency, but business functions operate largely independently.

This model works best for mature organizations that have a strong data culture. It helps them innovate quickly while staying true to their core principles.

The right model depends on organizational maturity and AI adoption goals. As organizations move through these AI operating models, the challenge shifts from structuring AI teams to ensuring AI delivers sustainable impact. This is where AI scaling strategies come into play.

Hub-and-Spoke Organization Provides the Control Needed to Create AI at Scale

Most companies already have the skills needed to succeed with Enterprise AI — they’re just fragmented across teams, tools, and functions. Data and IT teams bring expertise in scaling infrastructure, ensuring security, and monitoring model performance. Business teams offer deep process knowledge, real-world use case insight, and day-to-day operational context. But without alignment between them, even strong capabilities fail to translate into enterprise-wide AI impact.

The hub-and-spoke model solves this.

• The hub provides centralized governance, infrastructure, and AI expertise — especially critical in regulated environments.
• The spokes enable business teams to move fast, building AI solutions that are relevant, cost-effective, and aligned to real-world needs.

This structure enables a massive multiplication of AI creators across the organization — while maintaining the control and oversight needed to scale AI responsibly. And it’s already delivering impact across industries. Among Fortune 500 life sciences companies using this model:

• 85% reduction in time-to-market for AI use cases.
• $200M+ in net new trade sales in North America.
• 150+ AI products deployed in production.
• 750+ AI creators collaborating across the business.

Dataiku makes this orchestration possible. As The Universal AI Platform™, it connects hubs and spokes through shared infrastructure, governed workflows, and accessible tools — turning distributed talent into scalable business impact.

Scaling Enterprise AI is not about simply expanding adoption — it is about ensuring AI compounds in value over time. Leading organizations do not just launch exponentially more use cases; they orchestrate, optimize, and refine AI continuously.

Common AI scaling pitfalls include:

• Expanding AI without efficiency: Without governance, AI complexity grows faster than its impact.
• Struggling to drive sustained value: Initial AI projects may succeed, but later ones stagnate without a clear scaling strategy.
• Operating AI in silos: If AI remains fragmented across teams, it risks becoming a collection of disconnected tools rather than a strategic differentiator.

To scale successfully, Enterprise AI must be structured as a continuously evolving system. The most effective organizations prioritize AI reuse and iteration, ensuring that each new initiative builds upon existing analytics, model, and agent capabilities.

Scaling AI Through Reuse and Optimization

AI is most effective when organizations build on existing analytics, machine learning models, or agent successes rather than launching new use cases in isolation. A scalable AI strategy focuses on:

• Reusing AI assets across teams: Instead of reinventing AI solutions, organizations should standardize models, automation frameworks, data sources, data pipelines, and more. Having a standard repository for these types of assets becomes even more useful when building AI agents, as they can be leveraged as tools in agentic systems.
• Expanding use cases strategically: AI development isn’t always linear — some teams may start with GenAI, others with analytics or models. What matters is building on what exists, evolving use cases rather than reinventing them every time.
• Orchestrating AI across the enterprise: AI must be integrated into business processes, creating a connected, self-reinforcing ecosystem.

Without a structured Enterprise AI scaling strategy, AI remains fragmented, duplicative, and inefficient. Organizations that embed AI into business processes as a continuously evolving intelligence system sustain long-term impact and differentiation.

Leveraging AI to Drive Efficiency and Maximize Value

For years, AI was framed as a cost-cutting tool — automating repetitive tasks, streamlining operations, and reducing inefficiencies. While these benefits remain valuable, today’s enterprise AI leaders recognize that efficiency alone isn’t the goal. The real opportunity lies in scalability, differentiation, and business transformation.

GenAI and AI agents have fundamentally changed how organizations approach AI investments. Rather than simply offsetting costs, leading companies now use AI to optimize enterprise workflows, enhance productivity, and free up talent for more strategic work. This shift transforms AI from a cost-reduction tool to a proactive driver of enterprise-wide transformation.

Scaling AI Without Wasted Effort

Scaling AI is no longer just about pushing models to production faster — it’s about ensuring AI, including GenAI and AI agents, continuously improves, remains trustworthy, and drives meaningful business impact. Organizations must focus on four critical areas:

1. AI as a Continuously Learning and Adaptive System

Unlike traditional machine learning models that follow a fixed train-deploy-optimize cycle, modern AI must evolve dynamically to remain effective. To scale AI effectively:

• AI must learn from real-time interaction: AI agents and GenAI models should continuously adjust based on live data, evolving business needs, and direct user feedback.
• Automation should enhance, not replace, human expertise: AI should amplify human expertise by providing intelligent recommendations, not replace business-critical judgment.
• AI-generated outputs require continuous oversight: GenAI systems and AI agents must be monitored for accuracy, bias, and reliability, ensuring they align with business objectives and compliance standards.

2. Unified AI Governance Across Models, AI Agents, and Automation

As AI adoption expands, organizations face a major risk: governance fragmentation. Without a structured AI governance approach, AI agents, automation, and models operate in silos, leading to inconsistent decision-making, regulatory gaps, and business misalignment.

To establish enterprise-wide AI governance:

• Governance frameworks must unify models, AI agents, and automation workflows: AI oversight should not be split into disconnected structures. A single governance model ensures AI applications remain accountable, auditable, and aligned with business priorities.
• Standardizing AI performance metrics are critical: AI success isn’t just about technical accuracy. AI effectiveness must be measured on its business relevance, security, explainability, and long-term impact.
• Risk management must evolve alongside AI complexity: Continuous monitoring should detect model drift, unreliable AI agent responses, and compliance risks before they escalate.

3. Differentiate With AI, Don’t Just Optimize for Cost

Ask a room of executives who’s using AI — and every hand goes up. Ask how many are doing something with it their competitors can’t — and the room gets quiet. Differentiation doesn’t come from having AI. It comes from how you scale it, embed it, and evolve it into something uniquely yours.

The real risk is AI commoditization — organizations that adopt GenAI and automation without a strategy for competitive advantage may end up using the same tools in the same ways as everyone else. That’s not transformation — that’s table stakes.

Leading organizations go beyond automation and cost savings. They treat AI as a strategic capability: deeply embedded, constantly improving, and difficult to replicate at scale. That’s what sets them apart — and keeps them ahead.

4. Modernize AI Infrastructure to Support GenAI and Automation at Scale

Enterprise AI can’t scale on outdated infrastructure. GenAI, AI agents, and automation require an AI foundation built for real-time execution, seamless data access, and enterprise-wide interoperability. A fragmented AI stack slows innovation and creates governance risks, making modernization essential.

To build an enterprise AI foundation that supports GenAI and automation at scale:

• Unify AI, GenAI, and automation under one system: AI must operate as a connected ecosystem, not isolated tools. A centralized infrastructure ensures AI agents, models, and workflows integrate seamlessly.
• Eliminate bottlenecks in AI-driven decision-making: AI systems need fast, reliable data pipelines to power real-time automation and adaptive learning.
• Ensure flexibility for enterprise growth: AI should function across cloud, on-prem, and hybrid environments without reconfiguration barriers.

Companies that modernize AI infrastructure remove roadblocks to AI execution, enabling faster innovation, real-time automation, and enterprise-wide intelligence.

Scaling Enterprise AI requires a dual approach — balancing immediate, high-impact wins with long-term transformation. Many organizations struggle by focusing too heavily on either short-term AI automation or moonshot innovation, when true enterprise value comes from doing both in parallel.

Successful AI adoption involves:

• Mundane AI use cases that augment countless day-to-day business processes, enhancing efficiency, accuracy, and decision-making.
• Moonshot AI initiatives that redefine business models and create entirely new capabilities, driving significant competitive differentiation.

Organizations that focus only on automation risk stagnation, while those that over-invest in moonshots may face uncertain ROI. The key is orchestrating AI across both fronts — embedding AI in everyday workflows while enabling transformative advancements.

How to Deliver on Quick, High-Impact AI Wins

1. Define an Initial Set of Use Cases

Start with use cases that strike a balance between impact and feasibility. These projects should answer key questions:

• Who benefits from the project?
• How will it improve outcomes, and how can improvements be measured?
• Why is AI better than existing processes for this task?
• What are the potential risks and rewards?
• Where will the data come from, and does it already exist?
• When will prototypes and final solutions be delivered?

2. Accelerate Use Cases With Dataiku

Dataiku simplifies AI adoption through pre-packaged solutions designed to fast-track deployment. These include:

• Customizable dashboards tailored to specific business needs.
• Comprehensive training materials to onboard teams effectively.
• Tools for rapid prototyping and operationalization.

This approach enables organizations to achieve real-world results faster while ensuring flexibility for future use cases.

3. Engage Ambassadors and Early Adopters

Scaling AI requires champions to drive adoption, share successes, and inspire cultural change. Key ambassadors include:

• Business teams: Communicate AI’s value in relatable terms.
• Power users: Evangelize AI’s benefits and recruit colleagues.
• Team leads: Promote upskilling efforts and collaboration.
• IT managers: Ensure smooth rollouts while balancing governance and data access.

These champions are instrumental in transitioning from isolated wins to widespread AI adoption.

How to Achieve Long-Term AI Transformation

1. Redefine AI Governance for the GenAI Era

Enterprise AI governance must evolve as GenAI and AI agents become more embedded in real-time decision-making. Traditional MLOps frameworks are no longer sufficient — organizations must implement dynamic oversight that continuously adapts to AI’s evolving role in business operations.

Leading organizations are strengthening governance by:

• Expanding risk management to account for AI-generated outputs, including biases, compliance gaps, and security vulnerabilities.
• Automating AI guardrails to proactively detect and mitigate risks in real time, ensuring AI-driven decisions remain aligned with business objectives.
• Unifying AI oversight across models, automation, and AI agents to maintain consistency, accountability, and transparency at scale.

2. Move Beyond MLOps to AI Lifecycle Orchestration

Managing AI as isolated models is no longer sustainable. The shift toward AI lifecycle orchestration ensures that AI — from predictive analytics to GenAI — operates as an interconnected system that learns, adapts, and improves continuously.

Key advancements in AI lifecycle orchestration include:

• Real-time model optimization: AI must adjust dynamically based on shifting business conditions, not just periodic retraining cycles.
• Integrated AI observability: Governance should extend beyond model drift to encompass data pipelines, automation workflows, and AI-generated content.
• Continuous iteration: AI agents and models should be refined in an ongoing loop, optimizing outputs based on user interactions and performance benchmarks.

By embedding governance and lifecycle orchestration into the AI strategy, organizations create a foundation for scalable, resilient, and trustworthy AI operations — bridging the gap between innovation and long-term value.

3. Upskill Teams to Scale Enterprise AI

Scaling AI isn’t just a technology challenge, it’s about empowering people to use AI effectively. The companies that get AI right don’t just train a few experts; they make AI part of how everyone works.

• AI literacy needs to go beyond technical teams. AI can’t remain in the hands of specialists. Decision-makers, analysts, and frontline employees all need to understand how to use AI tools and insights in their daily work.
• AI is only valuable if people know how to apply it. Simply having AI isn’t enough — teams need to be able to refine, validate, and act on AI-generated insights to drive real business impact.
• The right tools make AI accessible. Enterprise AI platforms should eliminate technical barriers, making it easy for non-technical users to integrate AI into their workflows without relying on specialists.

AI should not be a black box controlled by specialized teams. The companies that scale AI successfully don’t just train a few AI experts — they create a workforce that thinks, works, and operates with AI as second nature.

Successfully scaling enterprise AI requires balancing quick wins with long-term transformation. Organizations can achieve this by:

• Orchestrating AI initiatives that build on past successes, ensuring AI efforts compound in value rather than remain isolated experiments.
• Implementing governance and AI engineering operations practices to maintain compliance, mitigate risks, and drive continuous improvement across all AI applications, from traditional ML to GenAI.
• Upskilling teams to ensure AI is not just a technical capability but a fundamental part of decision-making and innovation at every level.

As The Universal AI Platform™, Dataiku empowers organizations to bridge the gap between AI adoption and AI scale. Whether you’re deploying GenAI, AI-driven automation, or predictive analytics, Dataiku provides the foundation for AI that continuously learns, scales, and delivers measurable value.

• Accelerated Time to Impact: Deliver value faster with pre-built tools, automated workflows, and intuitive interfaces that simplify even the most complex AI projects.
• End-to-End Governance: Built-in safeguards, including Dataiku Govern and LLM Guard Services, ensure compliance, mitigate risks, and enable sustainable scaling.
• AI Literacy and Democratization: With both low-code and full-code capabilities, Dataiku makes AI accessible to diverse teams, fostering seamless collaboration between business users, data scientists, and IT.

Dataiku doesn’t just simplify AI — it transforms it into a competitive advantage. By embedding AI into everyday operations, organizations can scale AI efficiently, maximize ROI, and future-proof their business.

Dataiku provides the best-in-class data analytics and AI platform. The platform was able to natively integrate with enterprise systems and meet all security and compliance requirements. We have been able to successfully scale to multiple business units across designers and explorers. Also, there is flexibility in customizing and building custom components that can be integrated with the platform. Most of all, it provides the ability for teams to collaborate across all projects.

— Senior Director of IT in the Banking Industry (Source: Gartner Peer Insights)

Working with Dataiku empowers us to imagine the full potential of AI and GenAI, allowing us to embed it into Doosan’s business strategies and operations. It’s a crucial initial step in allowing us to imagine the concept of “AI Everywhere” and bring it to life, driving agile innovation that’s practical and breakthrough.

— Robert Oh, EVP, Head of Corporate Digital, Doosan Corporation

Gartner Peer Insights are trademarks of Gartner, Inc. and/or its affiliates. All rights reserved. Gartner Peer Insights content consists of the opinions of individual end users based on their own experiences, and should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product, or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose.