)
Earlier this month, our CEO and Co-founder, Umesh Sachdev shared his top four AI predictions with Nasdaq’s Live from MarketSite. (You can view the discussion in its entirety on LinkedIn.) To summarize:
- AI proof-of-concepts will aggressively move into production among some of the biggest companies in the world
- There will be a strategic shift from point solutions to full enterprise AI architectures
- With AI, CIOs will retake front-and-center positions within the enterprise
- Regulation and policy will play an outsized role in the coming months
In addition to these macro trends, there are several micro trends currently reshaping the enterprise AI landscape. As organizations—led by CIO initiatives—embrace enterprise-wide platforms as a gateway to total AI transformation, these developments will define how we build, deploy and interact with AI in the months ahead. Here are the top four enterprise AI trends to watch for in 2025:
Data readiness will become the focus of enterprise AI transformations
AI-ready knowledge, or the lack thereof, may be the biggest barrier to widespread enterprise AI adoption. While this isn’t news to tech leaders, it’s been an ongoing source of confusion and frustration for C-Suite executives with big AI expectations. According to a recent Capital One AI readiness survey featured in CIO, 87% of business leaders see their data ecosystem as ready to build and deploy AI at scale; however, 70% of technical practitioners spend hours daily fixing data issues. Those issues range from poor data quality and unstructured formatting to a wide array of data silos and governance barriers.
Closing the gap between executive expectation and ground-level execution will require enterprises to prioritize data readiness above all else. This realization is driving tech leaders to explore—and consequently adopt—a new platform approach that underpins all AI initiatives to a broader, enterprise-wide data ecosystem. However, they’re also discovering that not all enterprise AI platforms are created equally.
On one end of the spectrum are generalized, “one-size-fits-all” platforms developed by big name LLM providers. Bloomberg predicts continued consolidation among these foundation model companies—a strategy that suggests ongoing model training and development for broad, nonspecific applications. On the other end are highly verticalized applications created by fast moving yet unproven startups. While capable of performing highly specialized tasks, these models are significantly more expensive to build and train than their off-the-shelf peers. To achieve enterprise-wide data readiness, organizations will have to balance the need for verticalized, “out-of-the-box” scalability and enterprise-specific AI readiness and enablement considerations.
Enter the Zero Data AI Cloud. Unveiled in late 2024, this unique, infrastructure-agnostic platform creates a seamless data fabric across all data sources, enterprise applications or cloud environments. This allows organizations to bypass their AI readiness barriers and fast-track their AI initiatives—delivering both broad scalability and organization-specific verticalization.
Multimodal AI will become the standard
The writing has been on the wall for some time now: multimodal AI will soon become the norm. And it’s looking like 2025 will be the year the scales really start to tip. According to research by Gartner, 40% of generative AI solutions will be multimodal by 2027, up from 1% in 2023. However, judging by the market, that number might be modest. Last year, OpenAI, IBM and Meta all introduced multimodal capabilities to their AI portfolios. At the same time, Google doubled down on its Gemini product, released in late 2023.
The technology itself, however, isn’t new. AI pioneers (including Uniphore) have been developing multimodal AI solutions for years. Some are quite sophisticated, integrating sentiment, tonal and emotion analysis with traditional communication modes, like voice and text. These next-gen applications have transformed Sales, Recruiting and CX, providing enterprises with more accurate understanding and richer customer insights.
So, why the sudden buzz around multimodal AI? It goes back to the rise in AI platforms. With the ability to synthesize data from anywhere and in any form, today’s leading platforms enable organizations to create unified AI models capable of processing text, images, speech and other data types simultaneously. And with reduced latency and improved contextual understanding, the results are quicker and more accurate than ever before.
Take call compliance for example. Voice-only solutions that process audio and don’t analyze images, videos or text create data siloes and blind spots that can jeopardize compliance efforts. This oversight can be particularly damaging for global banks and other heavily regulated industries.
Multimodality is also transforming how we buy, sell and even find employment. Multimodal CX enables customers to engage with brands in the method(s) they deem most convenient. On the Sales side, AI Agents, like Uniphore’s Q for Sales, turn data from meetings, CRM and more into actionable insights to guide sales engagement. Similarly, multimodal recruiting tools, like Q for Recruiting, apply the same principle to talent acquisition, empowering recruiters with rich insights gleaned from video interactions that interpret both verbal and nonverbal signals to improve recruiter performance and better understand candidates.
As more enterprises embrace multimodal AI in the coming months, those that leverage advanced solutions that utilize sentiment/tonal analysis and emotion AI will have a significant advantage over voice- or text-only incumbents.
Specialized Language Models will be the gateway to agentic AI and domain-specific models
The rise of generative AI has seen an increased interest in domain-specific applications. 2025 will see those aspirations become a reality—on a growing scale. However, building AI agents and SLMs in-house is often easier said than done. That’s because agentic architecture is complex, with extensive data retrieval and AI modeling needs. In fact, according to Forrester, three out of four firms that build aspirational agentic architectures on their own will fail. But that doesn’t mean that agentic AI is out of reach. It just means that enterprises will need help developing it.
Until now, that has been largely unattainable (or cost-prohibitive). Most large language models today rely on publicly available data, which lacks the domain-specific context needed for business applications. However, new developments are enabling businesses to convert, enrich and integrate their own data into their AI systems as usable, contextual knowledge. By training generative AI models with AI-ready knowledge that forms a composable enterprise knowledge layer, like Uniphore’s X-Stream, enterprises can tailor models and AI applications to specific domains of enterprise data (i.e., compliance controls, customer data or product knowledge).
Domain-specific models, like those mentioned above, are coming soon. Forbes predicts that more organizations will shift from generalized applications to highly focused solutions in the coming months, as business leaders double down on targeted use cases that deliver measurable results. Those with the best chances for success will be the ones who partner with experienced AI vendors to help develop these innovative models.
That help will come in the form of a model layer, housed within the larger AI platform. This model layer will allow enterprises to quickly build and deploy Specialized Language Models (SLMs) with the freedom to choose from pre-built industry models and LLMs. In fact, advanced SLM capabilities, like those built into Uniphore’s Zero Data AI Cloud, already enable organizations to tailor-make AI agents using pretrained industry and domain-specific models. This ability to fast-track SLM development will be a gateway to verticalized agentic solutions and custom AI models with enhanced contextual understanding, operational efficiency and scalability.
As demand for agentic AI grows in 2025 (and beyond), more enterprises will see the enterprise data and model layers as key differentiators among LLM providers—ones that provide flexibility and offsets prohibitive development costs while leveraging built-in domain expertise.
Enterprise AI platforms with low-code/no-code interfaces will fuel the democratization of AI agents
SLMs aren’t just a catalyst for domain-specific capabilities. They’re also rewriting the rules of who can develop, deploy and control AI agents. When built on a foundation of composable data and AI-ready knowledge, SLMs put the creative power in the hands of the user. As a result, enterprises can quickly develop AI agents leveraging pre-trained and production-ready components rather than having to build or buy verticalized and disconnected AI agents, which are often siloed across multiple enterprise software clouds, platforms and applications.
At the heart of this democratization is the no-code/low-code agentic builder. By creating an intuitive interface to data connectors, pipelines and models that anyone can use, the last barrier to AI entry—technical proficiency—will soon fall. Thanks to no-code/low-code agent builders and other tools, AI adoption will surge among non-technical users, empowering teams like marketing, sales, and HR to leverage AI without deep technical expertise.
Conclusion
As we begin 2025, enterprise AI is no longer a speculative frontier—it is a critical enabler of operational excellence, competitive differentiation and long-term resilience. For enterprises around the globe, the path to success lies not in chasing trends but in establishing an AI-native foundation to power business critical workflows, drive measurable outcomes and future-proof your organization.
To remain competitive in this fast-changing landscape, enterprise leaders must focus on the following imperatives:
Define your AI approach
Rather than chasing the latest trends, enterprises must align their AI strategies with specific, measurable outcomes. This means creating a roadmap that balances near-term wins with long-term value creation, addressing questions like:
- What operational inefficiencies can AI resolve immediately?
- How can AI enhance customer engagement or workforce productivity?
A well-defined strategy can also help guide critical decisions, whether to adopt modular AI solutions that integrate with existing enterprise software for incremental gains or transition to an AI-native infrastructure to enable comprehensive, long-term transformation.
Prioritize data readiness
AI success starts with a strong data foundation. CIOs must address fragmented silos, poor quality and governance gaps. Tools like AI data fabrics and Zero Data AI Cloud unify enterprise data, ensuring it’s AI-ready. Investing in data observability and synthetic data generation will maintain high-quality inputs for reliable AI outcomes.
Leverage partners for AI transformation
Strategic collaborations with experienced AI providers, industry-specific consultants and academic institutions will be critical. These partnerships enable businesses to access cutting-edge technologies, domain expertise and tailored solutions without overextending resources.
AI is not a one-size-fits-all solution; but with the right partnerships, a strong foundation and a long-term vision, it will become a catalyst for growth and innovation in the enterprise in 2025.
Learn More About Uniphore’s Zero AI Data Cloud
Eliminate months of data prep and integration for faster, smarter enterprise AI transformation.