Intro to governed AI Enablement: Everything you need to know

Imagine a company where decisions aren’t just data-informed, they’re driven by intelligent systems that anticipate change, optimize operations, and personalize every customer interaction in real time.

This is no longer a futuristic vision. It’s the reality made possible by AI enablement, and it’s redefining how modern businesses operate.

According to McKinsey, at least 78% of companies already use AI in at least one bus

Reference: McKinsey

In this article, we’ll break down what AI Enablement means, why it’s now a strategic priority for the C-suite, and how leaders can create the right foundation to scale artificial intelligence with confidence.

What Is AI Enablement?

AI enablement goes far beyond deploying a few machine learning models. It’s about building an ecosystem where AI can thrive by ensuring that your organization’s people, processes, and data infrastructure are aligned to support intelligent decision-making at scale.

While many companies treat AI as a tactical “add-on,” true AI-enabled organizations take a holistic approach. That includes:

• Defining business goals where AI can drive measurable value

• Ensuring that data is available, reliable, and usable across systems

• Aligning business, IT, and analytics teams around shared KPIs

• Embedding AI into operational workflows, not just dashboards

• Establishing a culture of iteration, governance, and continuous improvement

Why It Matters to the C-Suite

For executives, AI is no longer an optional technology project; it's a strategic enabler. When done right, AI drives agility, resilience, and growth in a constantly evolving market.

🔍 Strategic Insights: AI can identify trends, risks, and opportunities faster and more accurately than traditional analytics.

⚙️ Operational Efficiency: Automating repetitive tasks frees up teams to focus on higher-impact work and reduces error rates.

🛍️ Customer Experience: Hyper-personalized interactions powered by AI drive engagement, retention, and lifetime value.

🚀 Competitive Advantage: Companies with strong AI foundations respond to change faster, innovate earlier, and stay ahead.

A recent study from PwC, found that nearly 50% of CEOs see AI as a critical part of their strategy for the next 3 years.

Reference: PwC

From Vision to Execution: What You Need to Enable AI

1. Start with Your Data

AI systems are only as good as the data they consume. And today, most organizations struggle with fragmented, inconsistent, or unstructured data. Before AI, you need to fix your foundation.

That means centralizing, validating, and preparing your data from legacy systems, Excel files, APIs, and more to ensure it’s usable by AI models.

2. Integrate Systems and Maintain Context

Enterprise systems, such as ERP, CRM, HCM, and SCM, as well as platforms like Oracle Fusion Cloud, store rich business logic. But AI needs this data to be connected, contextualized, and machine-readable. It's not just about ingesting data, it's about preserving meaning and structure as you scale.

3. Build on a Scalable Platform

AI initiatives must move beyond pilot mode. You need a data platform that can:

• Automate ingestion, transformation, and validation

• Manage metadata, lineage, and versioning

• Govern access and ensure compliance

• Feed AI tools like Databricks, Azure, and Snowflake

• Scale from department-level use cases to enterprise-wide intelligence

Real-World Cases of AI Enablement

Use Case 1: Premier Unlocks Faster Healthcare Insights with AI/BI Genie

Premier serves two-thirds of U.S. healthcare providers and captures data from over 45% of hospital discharges, but fragmented systems and manual reporting were delaying critical insights.

With the Databricks Data Intelligence Platform and AI/BI Genie, Premier:

• Enabled natural language queries for instant insights

• Delivered 10x faster SQL generation

• Deployed Genie into production in just 3 days

• Empowered healthcare providers with self-service analytics

• Strengthened data security and governance with Unity Catalog

• Used Delta Lake and Delta Sharing to support real-world evidence for clinical trials

Premier’s data teams and healthcare partners now benchmark care, reduce readmissions, optimize treatment decisions, and improve outcomes, all with intuitive access to AI-powered insights at scale.

Source: Databricks

Use Case 2: SEGA Europe unlocks AI with data from more than 40 million players

SEGA Europe was processing over 50,000 events per second from more than 40 million players, but fragmented data and inconsistent quality kept their AI ambitions on hold. Insights were slow, trust in the data was low, and critical decisions lagged.

Their solution? A unified platform built on Databricks, combining Delta Lake, Unity Catalog, AutoML, and AI/BI Genie. With clean, governed, and centralized data, SEGA:

• Achieved 10x faster time-to-insight

• Enabled natural language queries for executives

• Ran real-time sentiment analysis and player retention models

• Boosted retention across multiple game titles

As SEGA’s Head of Data Services put it:

“We couldn’t capitalize on our data without a single source of truth.”

Source: Databricks

Use Case 3: Mondelēz Scales AI Across 160+ Countries

Mondelēz International, the $31B company behind Oreo, Ritz, and Toblerone, set out to become an AI-native organization by 2030. But siloed data, duplicated model development, and limited scalability were major blockers.

By adopting the Databricks Data Intelligence Platform, Mondelēz:

• Centralized global analytics across 160+ countries

• Enabled 20,000+ machine learning models, with ~3,000 in production

• Boosted store-level topline sales by 2–4% with SKU recommendations

• Reduced inventory waste by 2–3% and improved forecast accuracy by 3–5%

• Used LLMs and tools like SnackGPT to accelerate insights for brand and category teams

From sales execution and supply chain to revenue growth and GenAI assistants, Mondelēz now runs AI at a global scale with governance, speed, and measurable ROI.

Source: Databricks

Common Challenges (and How to Solve Them)

Challenge	Solution
Poor-quality or fragmented data	Implement centralized validation and governance
Internal resistance to change	Communicate business impact and deliver quick wins
Lack of AI-ready talent	Leverage low-code platforms and cross-functional teams
Lost business context in data pipelines	Preserve structure and logic during integration

The Future of AI Enablement

Emerging trends like explainable AI, real-time ML, AI ethics, and automated retraining will further accelerate the impact of AI in business. But none of this will work without the right foundation.

You can’t scale AI without data that’s structured, governed, and ready for machine learning.

That’s where the right platform makes all the difference.

Ready to Enable AI? Start With Your Data Foundation

Arkon Data Platform (ADP) helps organizations unlock the true value of artificial intelligence by fixing what comes before the models: the data.

• Ingest and centralize data from legacy systems, Oracle Fusion Cloud, CRM, POS systems, and more, while preserving structure and context.

• Validate and standardize your data at scale with automated rules, recipes, and pipelines.

• Load your data into modern environments, such as Databricks, Azure, Snowflake, and more. Ready for models or analytics without duplications or manual prep.

AI enablement begins with data enablement, and Arkon Data Platform makes it possible.

👉 Ready to move beyond experimentation and scale AI with confidence?

Arkon Data Platform

Explore Arkon Data Platform now

Frequently Asked Questions: AI Enablement

1. What’s the difference between AI enablement and AI implementation?

AI implementation refers to the deployment of specific AI tools or models. AI enablement is a broader, strategic process that prepares your data, infrastructure, governance, and teams to support scalable, trustworthy AI across the organization.

2. Can we use Excel-based data in an AI-ready platform?

Yes. Tools like Arkon Data Platform allow you to centralize and validate Excel data—along with structured and unstructured sources preserving meaning, traceability, and readiness for AI models in platforms like Databricks or Snowflake.

3. How does AI enablement impact existing business functions like supply chain or marketing?

With AI enablement, departments gain access to real-time, reliable insights, enabling use cases such as demand forecasting, route optimization, predictive maintenance, hyper-personalized marketing, and more. Case studies like Mondelēz and SEGA show measurable improvements in efficiency and outcomes.

4. What role does governance play in AI enablement?

Strong data governance ensures that the AI models are trained on accurate, secure, and compliant data. Features like access control, lineage, and validation (available in platforms like Unity Catalog and Arkon Data Platform) are critical for scaling AI responsibly.

5. How long does it take to become AI-enabled?

The timeline depends on your organization’s current data maturity. With the right data platform and strategic alignment, many companies (like Premier) begin unlocking AI-driven insights within weeks, starting with focused pilots before scaling enterprisewide.