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What you need to know about foundational AI platforms, and where to start.
November 18, 2025
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Artificial intelligence (AI) projects seem manageable at first glance. A model that automates a process here, a chatbot that improves customer interactions there. But as companies scale, the cracks start to show. Many organizations will spend the next few years patching together AI tools in an effort to cut costs, drive growth or outpace the competition.
The smart ones are taking a different approach. Instead of managing AI on a project-by-project basis, they’re investing in enterprise AI platforms—systems designed to integrate, automate and scale AI across the organization.
An enterprise AI platform is like a hyperscaler for every AI tool you’ll ever create. It’s the most important investment, but often gets less spotlight than it deserves.
In technical terms, an enterprise AI platform is a comprehensive software system that allows AI tools to realize their full potential across the company. It manages data, automates machine learning (ML) and DevOps and ensures security, so AI tools actually do what they promise instead of causing expensive chaos. Without this platform foundation, many businesses end up with costly, isolated AI tools that quickly become outdated in comparison to the latest public AI applications.
The reason we need them: AI tools like chatbots, coding assistants and generative AI news summarizers will fall apart in the enterprise environment without a foundation.
It’s like having the fastest EV car without the proper charging infrastructure, suitable roads and proper safety precautions. Sure, you can drive the car, but it can’t always get you where you need to go.
Right now, some companies are rolling out products and marketing them as “AI platforms.” Many are missing the difference between a comprehensive enterprise AI platform, an AI tool and products that have elements of AI in them.
Here are three things commonly thought of as comprehensive platforms, and why they might not be getting the job done:
1. AI chatbots and copilots ≠ Platform
Take ChatGPT Pro or Microsoft Copilot—they’re impressive, but they:
An enterprise AI platform, in contrast, runs within a company’s infrastructure—whether on-prem, private cloud or hybrid environments—and enforces strict access controls, encryption and auditability.
2. SaaS AI add-ons ≠ Platform
Most hyperscale and SaaS-embedded AI tools shine in their domains—Salesforce Einstein powers CRM workflows, Microsoft’s Copilot surfaces insights in Office, and ServiceNow AI accelerates ticket resolution. But organizations often discover three common gaps when they try to build a truly company-wide AI strategy:
A true enterprise AI platform acts as an orchestration layer, integrating with multiple SaaS tools, internal databases and AI models to create a companywide AI strategy.
3. Generic infrastructure providers ≠ Platform
Major cloud and infrastructure providers supply many of the key components for an enterprise AI platform. However, businesses and developers must still build an orchestration layer to seamlessly integrate these tools, enabling them to develop, train and deploy custom machine learning models while automating parts of the ML training process—without requiring deep AI expertise or coding skills.
Generic infrastructure providers are missing:
In contrast to a generic infrastructure provider, an enterprise AI platform can integrate with your legacy technology stack, new composable commerce platform and payments platform. It also centralizes your data and best practices in one repository, making it a foundation for continuous AI-driven operations rather than a single-use automation tool.
We’re still in the early stages of AI, especially agentic AI. It may seem like it’s prudent to wait a few years before investing in an enterprise platform.
However, your competitive edge in AI—and in your industry—revolves around two things:
1) Your proprietary data
and
2) Your speed to market
If you don’t lay the AI/ML foundation for your own data and AI capabilities, your employees are going to continue using public tools or building clunky one-off solutions. Why? Because they’re often cheap or free, they’re fast and you haven’t given them another option. They’re most likely pasting company info into ChatGPT, experimenting with AI-powered automation and sending sensitive business documents through tools that you don’t control—with no real long-term plan in mind.
If too many employees or business units are rolling out their own solutions, you speed-to-market will suffer because these ad hoc solutions won’t stand the test of time.
Data leakage: Once confidential business data is fed into public AI models, it’s out of your hands.
Compliance violations: If employees are using external AI tools without guardrails, they are most likely breaking GDPR, HIPAA or other regulations.
Dependence on external SaaS AI: If you let third-party AI providers and individual business units own bits and pieces of your AI tooling, their changes in pricing, policies or data rights could foil your future strategy.
Creating an enterprise AI platform is not an easy solution. Many organizations are interested in building an enterprise AI platform and immediately face some significant obstacles.
You’re lacking organized data
AI needs clean, structured and unified data, and most enterprises don’t have that. Data is stuck in silos, riddled with inconsistencies and locked away in legacy systems.
What you need to do: Start small. Map your data sources, establish governance rules and get a handle on what data is actually usable.
Security and compliance are an afterthought
Enterprise AI will most likely touch sensitive data, which means security and compliance aren’t optional.
What you need to do: Build AI governance before widespread adoption—encryption, role-based access, audit logs and compliance tracking need to be built in from day one.
Your legacy systems hold you back
You can’t just rip and replace your entire existing IT stack, so your enterprise AI has to work with what you already have.
What you need to do: Focus on hybrid integration—use APIs and middleware to bridge old systems with new AI capabilities.
Your team doesn’t trust AI solutions
If AI is an unexplainable black box, your employees won’t use it—and your leadership team won’t trust it.
What you need to do: Prioritize AI explainability and transparency. Deploy models that show their work, provide audit trails and require human oversight
AI costs unexpectedly build up
AI costs could spiral out of control without planning. A good enterprise AI platform allows you to dynamically manage costs by using the most optimal services for the task at hand. We have compared AI solutions with identical capabilities where one is one-tenth the cost of the other, due to a strategic approach to LLM size and usage.
What you need to do: Use FinOps methodologies to track spending and optimize AI workloads—otherwise, you’ll burn your budget before seeing real ROI.
For companies that feel like they’re late to AI: it is never too late. The beauty of this technology and this early-stage time period is that there’s still an opportunity to define your future with AI business transformation.
Whether you’re ready to invest in a full enterprise AI platform or not, these are the lower-cost enterprise AI projects you can tackle right now to prepare for AI transformation.
1. Find a low-risk, high-value AI use case—Look for AI applications that won’t break your systems but will show immediate impact:
a. AI-powered knowledge assistants to speed up internal research
b. AI-generated reports and data summaries for operations teams
c. AI-assisted code suggestions to enhance developer productivity
2. Set AI usage guidelines—Employees are already using AI tools, so put realistic policies in place now to prevent data risks.
3. Get your data in order—Even if you’re not ready for enterprise AI today, modernizing your data infrastructure will put you in a much better position for AI adoption.
4. Train your workforce on AI—AI is only useful if people know how to use it. Work with your employees to create tools and processes that are customized to their workflows, not the other way around.
After you’ve tackled these projects and are ready to build out your enterprise AI platform, it’s important to know what makes an AI platform succeed.
These are the bones of a good enterprise AI platform:
Bodhi, Publicis Sapient’s enterprise agentic AI platform, operates as a three-tiered system, each layer designed to handle distinct AI-related functions. The first layer of the architecture encompasses all of the key features listed above, opening the door for two more layers of pre-built capabilities, business solutions and custom AI workflows.
The foundational platform
Modular AI capabilities
Bodhi’s second layer houses pre-built AI capabilities that can be activated based on specific needs. Each of these pre-built AI capabilities were created in record time due the foundational platform. This layer can quickly aggregate and normalize data from a variety of internal systems, host multiple new LLMs and quickly check for security and compliance.
These capabilities include:
Each of these capabilities can function as a standalone AI tool or be combined to create a more complex solution tailored to enterprise needs.
Business solutions and custom AI workflows
The final layer of Bodhi is its solution-building capability. This layer allows businesses to build custom agentic AI workflows that integrate multiple pre-built AI capabilities. Instead of adopting a one-size-fits-all AI workflow, enterprises can:
Here are just a few of the ready-to-use solutions that we’ve built using the solution-building capability thus far:
Learn how our engineers built Sapient Slingshot, our AI software development platform, on the Bodhi infrastructure.
Building an enterprise AI platform is a bit like “building the plane as you fly it”—a cliché, sure, but fitting. As technology evolves by the minute, there’s no such thing as a fixed, one-and-done enterprise AI platform.
Whether you build it yourself, hire a third-party to help or even invest in third-party software, these are the key elements you should prioritize to make sure your platform is scalable:
1. Cloud-agnostic and multi-LLM capabilities
Many organizations start with a preferred cloud provider or model to move quickly—AWS, Azure or Google Cloud, for example. Over time, consider designing portability into your platform so you can maintain flexibility and optimize cost and performance as your AI landscape evolves.
Additionally, make sure your platform can use multiple large language and visual models (LLMs, VLMs) in parallel. You might prefer OpenAI’s GPT-5 for customer support but want a specialized locally-hosted financial model for risk assessment, and your platform should integrate with both seamlessly.
2. Enterprise security and compliance
Two of the biggest burdens of even singular AI tools in an enterprise setting are security and compliance—especially in industries dealing with personally identifiable information (PII). While we talk about the importance of compliance when it comes to data, we often don’t define what compliance means tactically.
A sturdy enterprise AI platform should have compliance-by-design. This means:
3. Context-aware AI
Why invest in an enterprise AI platform if it’s not going to be unique to your business? Using third-party AI tools is almost like hiring and training a brand-new employee every single day. Unlike ChatGPT, which has the limited memory of your last few chat conversations, your enterprise AI platform should remember all interactions, all enterprise workflows and all documentation so that it can generate domain-specific insights.
There are a variety of approaches to creating this context awareness, from real-time data streams to knowledge graphs to edge AI, that will make your platform much more useful for your employees, which is hugely important for long-term value.
Here are some examples of why context-aware AI is important:
Everyone, including those of us in the consulting industry, wants the magic of AI, but no one wants to talk about the invisible platforms that will make it real. AI tools don’t run in a vacuum; they require an actual system, an enterprise backbone that can ingest data, fine-tune models, enforce security policies and orchestrate decision-making at scale.
This is the paradox of enterprise AI today: Companies chase the latest applications while neglecting the architecture and systems integration that will help them thrive in the long run. It’s like trying to build the next great software product without an OS, without cloud storage, without an API strategy—just raw applications with nowhere to live. The real investment, the one no one is paying enough attention to, is the AI/ML platform itself—the Bodhis of the world, the unseen but essential layers that allow AI to function reliably, securely and at enterprise scale.
Leaders who understand this are playing the long game. They’re not just deploying the newest AI tool. They’re building the infrastructure that will let their company adapt to whatever comes next, seamlessly and on their own terms. Because in the end, your infrastructure will not end up in a press release or an earnings report, but it will be the hidden reason you’re the next market leader. Build the foundation to make AI work for you, instead of racing to keep up with everyone else.
