Enterprises that are betting big on AI transformation must overcome the complex challenges of leveraging their data to achieve success.
For example, businesses using SAP technology to manage critical information can easily find their AI ambitions being stalled without addressing how their data can feed automation and still remain secure and keep corporate data private.
AI continues to dominate the typical CIO’s agenda. IDC projects worldwide spending on technology to support AI strategies to reach $337 billion in 2025, then more than double to $749 billion by 2028.[1]
They predict that 67% of projected AI spending in 2025 ($227bn) will come from businesses embedding AI capabilities into core business operations.[2]
But a recent Gartner study found more than 90% of CIOs found their ability to get value from AI was limited by the need to manage costs.[3]
For large organisations with existing SAP environments, there may be a more effective approach.
By integrating Amazon Bedrock into their SAP environment, they can embed a range of AI models and capabilities into their applications, without requiring specialist AI skills and expertise.
Scale automation
They can leverage the vast quantities of data already being generated by their SAP systems to scale automation and enhance decision-making. What’s more, with Amazon Bedrock based on AWS infrastructure, the platform can dynamically scale resources to match workload demands.
One of Bedrock’s strengths is that it’s a flexible and open architecture, supporting a range of large language models (LLMs) including Amazon Titan, Anthropic Claude 3 Opus, Mistral 7B and Meta’s Llama.
Organisations can use the best technology for a specific purpose. Compliance reports, audits and financial data summaries could all be generated automatically from the relevant SAP data, with issues pulled out for further inspection along with useful insights.
Not only would there be less scope for human error, but human workers would have more time to pursue higher value tasks.
They can harness Amazon SageMaker to develop custom ML models tailored to SAP workloads, then integrate those models directly into SAP for predictive analytics, process optimization or trend forecasting.
It’s a faster way for enterprises to apply generative AI and ML to their SAP core business data, in order to improve the key business functions already being handled within SAP.
Meanwhile, Amazon’s Q Sematic Query service enables enterprises to query SAP data using natural language processing, so that users can request real-time data without needing to know specific SAP report syntax.
Crucially, with Amazon CloudWatch in the platform, enterprises get full visibility into usage and performance, making it easier to optimise and control costs.
Bedrock is a natural choice for organisations working in highly regulated sectors, as the SAP integration means they’re already starting with high-quality data in a secure corporate environment, and it’s an easier shift than integration with a secondary AI platform and data layer.
By working with an established SAP partner like valantic, firms can further accelerate their AI journey by using their expertise to embed AI in business processes at pace.
Learn more about how valantic can support your cloud adoption journey.
[1] IDC Unveils 2025 FutureScapes: Worldwide IT Industry Predictions, IDC, 30/10/24, https://www.idc.com/getdoc.jsp?containerId=prUS52691924
[2] Ibid
[3] Gartner Says CIOs Need to Overcome Four Emerging Challenges to Deliver Value With AI, Gartner, 4/11/24, https://www.gartner.com/en/newsroom/press-releases/2024-11-05-gartner-says-cios-need-to-overcome-four-emerging-challenges-to-deliver-value-with-artificial-intelligence