According to recent trends, Enterprise AI is shifting towards specialised models rather than a single generalised model.
Recent reports have highlighted that an overwhelming majority (96%) of global executives are actively engaging in discussions about generative AI within their organisations. However, despite the potential benefits, publicly available large models of generative AI pose substantial challenges for businesses. These models often lack alignment with practical business needs, exhibit limited controllability, carry data and privacy risks, and present difficulties in implementation and scalability.
Generative AI adoption should be achieved in a manner that prioritises safety, security, and control, while also tailoring the technology to align seamlessly with the unique realities of their businesses.
Central to this transformation is also the role of enterprise software vendors, which are actively integrating generative AI capabilities into their offerings.
Specialised Models for Specific Tasks
Salesforce, for instance, has introduced Einstein Studio, aiming to empower enterprises to train and operate generative AI models using customer data stored in its Data Cloud. Einstein Studio connects Salesforce data to AI models like Llama 2 and OpenAI’s GPT-4, boosting AI app development. It uses zero-ETL for data, saving time and costs, simplifies data, and offers real-time updates for training.
The tool lets enterprises monitor and serve models and link to AI models on platforms like Amazon SageMaker and Google’s Vertex AI. It includes a control panel for data exposure management. The launch of Einstein Studio addresses users who create custom models and those refining existing ones using Salesforce data, streamlining AI development in enterprises.
ServiceNow’s offering Now Assist, a virtual assistant, that is integrating generative AI features, facilitates processes through text-to-code functionality and streamlines interactions with clients using AI-generated case summaries. Powered by ServiceNow’s proprietary LLMs, it aims to reduce repetitive tasks and enhance productivity across various workflow offerings.
Oracle’s generative AI initiatives encompass hardware enhancements, cloud services, and integration with applications like Oracle Fusion Cloud Human Capital Management, offering assistance with content creation, summarization, and recommendations. These capabilities are embedded within existing HR processes to enhance business value, improve productivity, streamline HR tasks, and enhance the employee and candidate experience.
Similarly, Splunk is leveraging generative AI to enhance its observability tools through the use of Google’s T5 text-to-text transfer transformer model. The aim is to improve security and observability features in identifying threats using generative AI. Robert Pizzari, VP of Security at Splunk, discussed cybersecurity challenges and trends in the current landscape. He highlighted the rise of ransomware attacks, cloud security breaches, and supply chain attacks.
These developments collectively underscore the integration of generative AI to enhance various aspects of data analysis, user engagement, and automation within diverse industry contexts.
Why AI Caution is Necessary
Nevertheless, while enterprise software vendors are fervently driving generative AI innovation, CIOs are proceeding with caution. CIOs are carefully assessing how to effectively deploy generative AI within their organisations, with a strong emphasis on establishing robust safeguards.
“These models will be trained on local data, my data, and it can decide on my network or my cloud or my hardware. That is the point where of course you need something which is residing in your own network. And we believe we are on that side of the story, and believe that AI has to be specialised to solve a specific problem.” Atul Rai, CEO and co-founder, Staqu, said.
The implementation of generative AI can sometimes occur through unstructured individual or departmental initiatives, inadvertently entering the enterprise ecosystem. Moreover, it’s becoming increasingly common for generative AI capabilities to be bundled with existing enterprise applications by vendors, facilitating its adoption within established workflows.
The integration of generative AI capabilities by enterprise software vendors underscores the importance of AI in modern business operations.
Rai also believes that specialised models are the future. “The future of course belongs to personalised models. Generalised models can solve a broader range of queries. ……but the problem with generalised space is that you do not get ROI.”
He concurs with the scepticism enterprises have about the safety of APIs and larger models and is of the opinion that localised models are preferred. “Every industry and every human needs a specialised model which can solve a specific problem, and it must be something which adapts to my data and gives insight on that,” he said.