Build the right data platform for AI-led transformation in 2025
Artificial Intelligence (AI) is reshaping every industry, every workflow, and every decision. From personalized customer experiences to predictive supply chains, AI’s reach is expanding, disrupting every process we have been accustomed to. 92% of companies are planning to ramp up their AI investments over the next three years. Yet, despite this momentum, many organizations are still struggling to understand how to extract lasting value from AI.
Why? Because most are moving in a reactive way, chasing tools instead of building the right platform.
In 2025, building the right AI platform isn’t only about assembling the latest technologies. One has to align strategy, technology, behavior, and governance in a structured way. It’s also about understanding and leveraging the rise of agentic AI, autonomous AI agents that can plan, act, and learn on behalf of users. These systems require new thinking across data, ethics, and control.
This blog discusses how you can build for AI in a structured manner and learn how to lay a strong foundation that scales with your business needs.
Three key dimensions to consider
1. Business outcomes: What are we trying to achieve?
Before diving into tools or models, define what success looks like for you and your business. Is it better forecasting? Lower operational costs? Personalized customer experiences? The goal is to prioritize based on impact, not to prioritize what is popular. Each business depending on the scale and trade may have various business outcomes, but understanding what it is you are trying to achieve is the first and most important step in building the right AI platform.
2. Technology outcomes: Is our infrastructure AI-ready?
As promising as AI may sound, it runs on a steady infrastructure. If your data is messy, siloed, or outdated, your AI will be too.
This is where data governance and architectural alignment come into play. Key technology outcomes should focus on:
• Ensuring real-time, high-quality data availability.
• Scalable and secure data platforms.
• Alignment of AI tools with long-term business architecture.
It is important to invest in the right stack, as it will help you future-proof AI capabilities and ensure they reach their full potential.
3. Behavioral outcomes: Will people actually use AI?
AI’s value isn’t in the algorithm, it’s in adoption. You need to drive a cultural shift where teams trust and embrace AI tools in daily decisions.
This requires change management, transparency, and training. Encourage experimentation while enforcing ethical use guidelines and empower users with self-service capabilities.
Step-by-step: Building your AI platform
Once you understand your desired outcomes, it’s time to build. Here’s a structured, scalable approach:
Step 1: Map and score use cases
Start by identifying high-impact use cases tailored to your industry. For instance, a retail chain might look at demand forecasting, dynamic pricing, and store optimization. A manufacturer might focus on predictive maintenance, quality control, and production planning.
Next, identify and score use cases. Fortude recommends using frameworks like the Gartner AI Opportunity Radar, which evaluates potential initiatives based on feasibility, value, and readiness. It helps clients make informed, ROI-driven choices. Drawing such a map and scoring use cases helps identify which approach best suits your business.
Step 2: Design the AI stack
Think of your AI platform as a layered architecture:
- Data platform (Foundation)
Includes data lakes, warehouses, real-time ingestion engines, and metadata management. This is where structured, unstructured, and streaming data converge. - Analytics layer
Traditional BI, reporting, and dashboarding tools live here. They offer descriptive insights based on historical data. - AI layer
Machine learning models, NLP engines, and generative AI tools. This layer drives predictions, recommendations, and autonomous decision-making. - Governance layer
Enforces policy, ethics, fairness, and compliance. Includes bias monitoring, explainability tools, and model auditability. - User empowerment layer
Self-service AI tools, embedded assistants, and agentic AI platforms that allow business users to interact naturally with AI and make autonomous decisions.
Your AI stack can be split into:
- Internal AI: Operational optimization, automation, and employee productivity.
- External AI: Customer-facing experiences, personalization, and digital interfaces.
A mature platform manages both effectively.
Why AI efforts fail: The data factor
Most AI-focused projects fail not at the model level, but much earlier and the main reason is either bad data or no data. According to a 2023 McKinsey Report, 70% of AI projects fail to meet their goals due to issues with data quality and integration.
Studies also show that data teams spend most of their time preparing data for AI, ensuring models can learn from it efficiently. What’s worse, many businesses operate in silos where crucial data is inaccessible, outdated, or inconsistent. Without a robust data platform, even the best AI strategy is bound to collapse.
According to Rand, more than 80% of AI projects fail and one of the root causes is because the organization lacks the necessary data to adequately train an effective AI model.
So how do you get the right data?
To succeed, you need a unified, scalable, and real-time data platform that breaks silos and supports advanced analytics. Here’s what it should include:
- Data storage: Centralized but flexible (data lakes, warehouses, or lake houses).
- Data accessibility: Real-time APIs, data virtualization, or streaming platforms.
- Metadata & lineage: Know where your data comes from and how it’s transformed.
- Data quality & monitoring: Detect anomalies, missing data, and data drift automatically.
- Security compliance & data governance: Role-based access, data masking, and audit trails.
Fortude’s approach
At Fortude, we always start AI platform initiatives with a data assessment. If the foundation isn’t strong, AI outcomes won’t scale. Our approach includes deep dives into your data landscape to identify gaps and recommend platform changes before any model is built. We’ve helped leading organizations, such as a leading Australian furniture retailer, successfully realign their data for AI-enablement.
If foundational issues are found, such as siloed data or governance gaps, we work with your teams to redesign the platform before any model hits production. Our structured, outcome-driven approach ensures AI success is sustainable, scalable, and secure.
We ensure your AI roadmap aligns with your Long-Range Plans (LRPs) and strategic goals. We also help you answer the following questions which will help build the right AI platform:
- Are your AI initiatives tied to measurable business KPIs?
- Are your primary data sources and systems integrated?
- Are your tech and talent strategies aligned to future demands?
We help enterprises build not just any AI platform but the right AI platform for their business. We begin with a comprehensive assessment that covers:
- Business priorities
- Data readiness
- Governance frameworks
- Tech stack alignment
- User enablement
AI done right starts with structure
AI is full of promise, but that promise is only realized when structure, governance, and strategy come together. The era of agentic AI is here, and it will transform how we work, think, and compete. Start preparing by getting your data AI-ready—because the future starts with the foundation you build today.
FAQs
Most AI projects fail not because of the AI model itself, but due to poor data quality, lack of integration, and unclear business goals. Without a structured AI platform and clean, accessible data, models can’t deliver value. Success depends on aligning strategy, technology, and governance before deploying AI tools.
An AI platform is a structured, layered architecture that integrates data, analytics, AI models, governance, and user enablement. Unlike isolated tools, a platform provides scalability, security, and alignment with business outcomes. It ensures AI is not only implemented—but also adopted, governed, and optimized for long-term success.
Start by identifying your key business goals and scoring high-impact use cases. Then, design a layered AI architecture—starting with a robust data foundation, analytics, AI models, and governance controls. Ensure teams are trained and empowered to use AI tools. Fortude recommends beginning with a detailed data readiness assessment.
Agentic AI refers to autonomous agents that plan, act, and learn on behalf of users. As AI evolves, these systems will transform business operations—from customer support to decision-making. However, agentic AI also demands stronger ethical frameworks, governance, and control. Building the right platform today prepares you for this autonomous AI future.
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