The enterprise landscape is on the cusp of a transformative era, with the emergence of gen AI (generative artificial intelligence).
This technology, capable of creating entirely new content, promises to revolutionize countless workflows and redefine enterprise operations.
Generative AI’s integration into platforms such as SAP, Oracle, Microsoft, Salesforce, and Pega is not merely a trend but a fundamental shift in how enterprises will innovate and operate.
Reach out to discuss this topic in depth.
The enterprise perspective
Enterprises today face a critical decision when considering generative AI adoption: whether to opt for point solutions or a platform-led approach. This decision is crucial as any such investment demands substantial investment.
While many enterprises initially gravitate towards point solutions, deploying isolated instances of large language models (LLMs) for specific features, this fragmented approach has limitations. Generative AI models are typically trained for broad, personal usage rather than enterprise-specific applications, which can limit their effectiveness in enterprise scenarios.
On the other hand, platform-embedded solutions such as SAP Joule, Microsoft Copilot, Oracle Digital Assistant, Salesforce Einstein and others, are not only more relevant but also easier to scale adopt. Think of it as having a mini-AI (artificial intelligence) assistant built right into your familiar software, empowering you to leverage its power without needing extensive technical expertise.
Our recent interactions with enterprises revealed that 70% of enterprises are prioritizing platform-embedded generative AI as a key strategy for digital transformation. This approach not only simplifies AI deployment, but also enhances productivity and operational efficiency, making it a compelling choice for forward-thinking organizations.
By integrating Gen AI capabilities directly into existing enterprise platforms, enterprises are benefiting from:
Integrated operational environment – Platform-embedded AI seamlessly integrates into existing business systems (enterprise resource planning (ERP), customer relationship management (CRM), human capital management (HCM), and others), ensuring consistent AI-driven insights across all functions. This integration reduces disruptions and fosters a cohesive operational environment, in which data flows effortlessly between applications, maximizing the utility of AI insights
Enhanced data utilization – Embedded AI has access to enterprise-wide data, generating more accurate and holistic insights. It ensures seamless data exchange and integration across applications, making AI insights more valuable and actionable compared to point solutions limited to specific data sets
Futureproofing innovation – Adopting platform-embedded AI aligns enterprises with the strategic roadmap of leading software providers, ensuring access to the latest AI advancements and innovations
Higher cost efficiency – Platform-embedded AI leverages existing infrastructure, reducing the need for additional hardware, software, and technical expertise, offering more cost-effective AI capabilities. This consolidation leads to a lower total cost of ownership (TCO), by avoiding the costs associated with deploying and maintaining multiple standalone AI solutions
Reduced complexity – Embedding generative AI within enterprise platforms simplifies deployment and usage. Unlike traditional AI implementations that require extensive setup, platform-embedded AI integrates into daily-use software, reducing the need for specialized technical expertise, accelerating implementation timelines, and minimizing workflow disruptions
Despite the enthusiasm, enterprises adopting the platform-embedded gen AI approach should take care of challenges associated such as:
Enterprise readiness – Integrating Gen AI into existing platforms can be complex and requires significant investment in technology and skills. Enterprises should conduct a thorough assessment of their current infrastructure and capabilities, and consider partnering with experienced AI vendors to streamline the integration process and mitigate risks
Skill gaps – There is a high shortage of professionals within the data, AI, ERP and CRM sector, with these workers needing the skills to develop and maintain gen AI solutions. Enterprises need to invest in training and development programs to upskill existing employees or can consider hiring new resources and collaborating with educational institutions to build talent
Ethical and regulatory compliance – Businesses must navigate the ethical implications of AI, such as bias and fairness, to build trust with their users. Establishing a dedicated ethics committee to oversee AI initiatives, performing regular audits and implementing bias detection algorithms are crucial ways to maintain fairness and transparency
Data security and privacy – Platform-embedded AI relies on vast amounts of data, raising concerns about data security and privacy. Enterprises must adopt robust data security measures such as encryption, access controls, and regular security audits and ensure compliance with data protection regulations such as general data protection regulation (GDPR) and California consumer privacy act (CCPA)
Change management and adoption – Ensuring that employees adapt to new AI-driven processes and tools can be difficult. Also, resistance to change and a lack of understanding of AI capabilities can impede successful adoption. Thus, implementing a comprehensive change management strategy that includes clear communication, training programs, and user support remains a must
Adoption trends and future outlook
While the adoption of platform-embedded generative AI is gaining momentum across various enterprises, solutions like Joule, Copilot, and Einstein are witnessing increased uptake, driven by their ability to enhance productivity, efficiency, and decision-making.
Enterprises are now tailoring these AI functionalities to their specific needs, integrating them seamlessly with existing business processes within platforms such as SAP BTP. This customization ensures that AI solutions are closely aligned with unique workflows, improving decision-making and automating routine tasks.
As businesses grow, the scalable infrastructure provided by platforms supports the expanding adoption of generative AI, allowing for increased data handling and more complex AI models. Future trends indicate even greater collaboration between AI developers and business units, driving innovation and creating new use cases. This will ensure that enterprises remain at the forefront of AI-driven transformation, leveraging advanced analytics and intuitive AI interfaces to maintain a competitive edge in their respective industries.
By understanding and harnessing the trends within platform landscape, enterprises can position themselves at the forefront of AI-driven transformation, reaping the benefits of enhanced productivity, efficiency, and strategic decision-making.
If you found this blog interesting, check out our recent blog focusing on What Recent Generative AI Updates And Announcements Signal For Some Industries | Blog – Everest Group (everestgrp.com)
At Everest Group, we are closely tracking the generative AI evolution in enterprise platforms. To discuss this topic more with our team, please reach out Abhishek Mundra or Vinisha Choudhary.
