By Soum Paul, Founder & CTO, Superteams.ai
The enterprise AI gold rush narrative flooding social media over the past few years hides a dirty secret: most companies are struggling to extract real value from AI. According to recent BCG research, 74% of companies have yet to unlock meaningful value from AI. Meanwhile, 64% of organisations worry that they lack full visibility into their AI risks, creating dangerous security blind spots.
Part of the reason is how AI has been marketed. AI models built using advanced neural network architectures have been positioned by many as a cure-all for all workflow, data, and support challenges enterprises typically face. Pick a problem statement, train a model, and voila, you have a new model that can do what required full teams in the past. Reality, however, is very different: model training is a difficult and painstakingly statistical process, and few have the wherewithal to do it successfully.
The more feasible approach, instead, is to leverage pre-trained models, fine-tune them, and build workflows with them. This second approach is where enterprises can derive true value from modern AI.
Why AI Workflows are Important for Enterprises
Traditionally, such processes demand significant manual effort from teams — especially when dealing with unstructured data such as documents, invoices, audio, video streams, images, free-form text, or even high-volume structured data. These tasks, often central to an enterprise’s revenue cycle or day-to-day operations, are slow, error-prone, and ultimately degrade customer experience.
AI models change the equation. They have the remarkable ability to understand text and speech, interpret scanned images, analyse audio streams, segment video, and then convert all of this into structured formats such as SQL tables or JSON objects.
By converting unstructured data into structured, machine-readable formats, AI unlocks efficiency and scale that were previously impossible. Given that an estimated 80–90% of the world’s data is unstructured, the potential to build AI-powered workflows that extract insights, automate decisions, and accelerate processes is massive.
Why Enterprises Worry About AI Deployments
However, despite the powerful abilities that AI unlocks, businesses often hesitate. A common concern is that sharing enterprise data with external AI providers is risky, as that data may be used in training AI models. The question that often arises is: ‘How do we ensure that our data isn’t being used for training AI models?’.
Data privacy and sovereignty are valid concern indeed. Financial data, or data used for customer service, vendor management, or compliance, cannot simply be handed over to third parties who may use it to further train their models. For enterprises, this raises questions of privacy, competitive risk, and compliance.
In India, these worries are reinforced by regulations such as the Digital Personal Data Protection Act (DPDPA) and sector-specific guidelines from regulators like the RBI, SEBI, and IRDAI. Mishandling sensitive data can quickly become a legal liability. As a result, many companies choose to delay or limit AI adoption, even when they see the clear efficiency gains it promises.
Using Open-Source Models to Build Sovereign AI
So, what do you do when you need to control your AI stack? The answer is simple – build using open source and deploy in an environment you fully control. This is what Sovereign AI brings to the table. Rather than depending on external endpoints, you can deploy open-source AI models and AI agents within your permission boundaries, whether on-premises, on their cloud, or even hybrid cloud environments.
For most of the use cases that enterprises deal with, they don’t need platform models or platform APIs – an ensemble of specialised open-source models is more than sufficient to address the complex problem statements enterprises struggle with today. And we have a plethora of them to choose from (Hugging Face leaderboard is a great resource to use for this). By building AI workflows and stacks powered by these models, organisations gain full assurance on data residency and privacy while avoiding compliance risks.
This approach keeps data firmly under enterprise control, ensures that fine-tuning remains private, and allows model deployment to be monitored and audited in line with regulatory requirements. Just as importantly, it enables seamless integration of AI into existing ERP, CRM, and workflow systems without the uncertainty of relying on external providers whose terms can change without warning.
Sovereign AI is Cost-Effective
Another major concern that enterprises typically have is around the cost of AI. Astronomical training costs of AI models that are now public information give the impression that AI deployment is expensive. Additionally, due to the way commercial AI services are priced, usage-based billing can quickly spiral out of control, especially when workloads involve processing large volumes of documents, images, or customer interactions.
Sovereign AI, on the other hand, can be far more cost-effective in the long run. By deploying open-source models within their own infrastructure, enterprises convert unpredictable operating expenses into manageable capital and operational costs. Once the models are deployed, the marginal cost of running additional queries, fine-tuning, or integrating new workflows is significantly lower than relying on commercial APIs. Over time, the total cost of ownership for Sovereign AI is far more sustainable, especially for organisations that deal with high-volume data or mission-critical processes.
Last, but not least, enterprises that adopt Sovereign AI avoid vendor lock-in and maintain full control over their scaling decisions, and ensure that they have full control over the cost of their AI deployments.
Final Note
Due to AI’s ability to extract information from formats that once demanded laborious manual effort, it can unlock immense value for enterprises. From interpreting PDFs and complex images to pulling structured insights out of multi-language audio and video, and even enabling AI agents that drive growth, reducing customer churn, or accelerating internal processes — all of this is now within reach.
The right strategy for adoption, however, lies in building AI systems that are inherently sovereign, respect privacy, comply with regulatory requirements, and remain cost effective. By keeping data within enterprise boundaries, leveraging open-source models to power workflows, and retaining control over both costs and governance, organisations can realise the full potential of AI without sacrificing trust or compliance.
AI will define the workflows of tomorrow — but for enterprises to truly benefit, Sovereign AI is the only way forward.