Intelligence at Scale
SAP’s AI platform integrates predictive models, automated insights, and closed-loop feedback into core workflows, enabling faster, more confident decisions. This approach shortens decision cycles, reduces wasteful spending, and uncovers new revenue opportunities, driving accelerated business growth and sustainable strategic differentiation. Download the DocumentIntelligence at Scale: How
SAP’s AI Platform Delivers
Enterprise-Wide Impact
OCTOBER 2025
SAP’s AI Platform Delivers
Enterprise-Wide Impact
OCTOBER 2025
Intelligence at Scale: How SAP’s AI Platform Delivers Enterprise-Wide Impact1
AI-driven intelligence is no longer a luxury, but a necessity for modern businesses. As organizations continue to integrate
data at scale, they have a massive opportunity to harness the varied insights contained within operational, product, and
customer data, and leverage it to make faster, more confident decisions. Leaders can significantly shorten decision cycles,
reduce wasteful spending, and uncover new revenue opportunities by integrating predictive models, automated insights,
and closed-loop feedback into core workflows.
Instead of treating AI as a separate tool, AI becomes a seamless part of the flow of work. For example, instead of agents
manually reading support tickets and deciding on responses, an embedded AI agent can triage tickets, suggest responses,
and even automate routine resolutions while the human agent handles more complex cases. Indeed, the business impact
of AI only emerges when it is embedded directly into everyday operations and workflows—from sales forecasting and
fraud detection to demand sensing—which leads to accelerated business growth and sustainable strategic differentiation.
Indeed, AI is quickly becoming a key priority for enterprise buyers, falling just behind overall software features and
capabilities, according to Futurum’s 2H 2025 Enterprise Software & Digital Workflows Decision Maker Survey. Both
generative AI and agentic AI capabilities are being cited as key decision criteria when evaluating enterprise software.
Introduction: Why AI-Driven Intelligence
Matters for the Enterprise Today
Features/Functionality
Generative AI Capabilities
Flexibility/Customization
Cost/TCO
Agentic AI Capabilities*
Pricing Model
Implementation
Speed/Time to Value
Promised Overall ROI
Interface/Ease of Use
Breadth of Integrations with
other Apps/Data Sources
Support/Service Offerings
In 2025, agentic AI
exploded as a new
vendor selection
powerhouse, eclipsing
drops in ease of use
and support, as buyers
ditch usability for
autonomous AI edge.
Generative AI and
ROI gained ground,
but flexibility and
implementation speed
slid, marking a ruthless
pivot to AI innovation
over practical basics.
Figure 1: 1H 2025 Vendor Selection Criteria, 2024 vs 2025
*n=895 No data available for 2024 Survey Source: Futurum Research, June 2025
0% 5% 15%10% 20%
19%
18.8%
13%
13.2%
10%
8.9%
8.9%
8.8%
8%
8.2%
9%
9%
7.4%
6%
6%
7.1%
6.8%
6.8%
2024
2025
6.8%
6%
0%
9%
AI-driven intelligence is no longer a luxury, but a necessity for modern businesses. As organizations continue to integrate
data at scale, they have a massive opportunity to harness the varied insights contained within operational, product, and
customer data, and leverage it to make faster, more confident decisions. Leaders can significantly shorten decision cycles,
reduce wasteful spending, and uncover new revenue opportunities by integrating predictive models, automated insights,
and closed-loop feedback into core workflows.
Instead of treating AI as a separate tool, AI becomes a seamless part of the flow of work. For example, instead of agents
manually reading support tickets and deciding on responses, an embedded AI agent can triage tickets, suggest responses,
and even automate routine resolutions while the human agent handles more complex cases. Indeed, the business impact
of AI only emerges when it is embedded directly into everyday operations and workflows—from sales forecasting and
fraud detection to demand sensing—which leads to accelerated business growth and sustainable strategic differentiation.
Indeed, AI is quickly becoming a key priority for enterprise buyers, falling just behind overall software features and
capabilities, according to Futurum’s 2H 2025 Enterprise Software & Digital Workflows Decision Maker Survey. Both
generative AI and agentic AI capabilities are being cited as key decision criteria when evaluating enterprise software.
Introduction: Why AI-Driven Intelligence
Matters for the Enterprise Today
Features/Functionality
Generative AI Capabilities
Flexibility/Customization
Cost/TCO
Agentic AI Capabilities*
Pricing Model
Implementation
Speed/Time to Value
Promised Overall ROI
Interface/Ease of Use
Breadth of Integrations with
other Apps/Data Sources
Support/Service Offerings
In 2025, agentic AI
exploded as a new
vendor selection
powerhouse, eclipsing
drops in ease of use
and support, as buyers
ditch usability for
autonomous AI edge.
Generative AI and
ROI gained ground,
but flexibility and
implementation speed
slid, marking a ruthless
pivot to AI innovation
over practical basics.
Figure 1: 1H 2025 Vendor Selection Criteria, 2024 vs 2025
*n=895 No data available for 2024 Survey Source: Futurum Research, June 2025
0% 5% 15%10% 20%
19%
18.8%
13%
13.2%
10%
8.9%
8.9%
8.8%
8%
8.2%
9%
9%
7.4%
6%
6%
7.1%
6.8%
6.8%
2024
2025
6.8%
6%
0%
9%
Intelligence at Scale: How SAP’s AI Platform Delivers Enterprise-Wide Impact2
Despite the potential of AI to transform all facets of a business, practical challenges can create significant hurdles to its
successful implementation and utilization. Brittle data, which fails with minor changes to format, context, or use, and siloed
data, which can only be accessed and used within a single system or application, can limit the ability of AI to be applied
effectively to relevant company data.
Furthermore, fragmented systems, overly ambitious timelines, and resistance to change can impede or inhibit the rapid
progress needed to not only implement AI, but quickly and sustainably generate business benefits. To overcome these
hurdles, it’s crucial to prioritize robust data infrastructure, as well as enact strong data hygiene and governance before
focusing on model development or selection.
When deciding whether to buy or build AI capabilities, businesses must weigh tradeoffs in time-to-value, ongoing
complexity, and scalability. Purchasing pre-built AI services offers faster outcomes and lower initial risk, making it ideal
for standard needs and rapid pilots. Conversely, building in-house solutions allows for tailored differentiation, but requires
additional engineering resources, strong governance, and lifecycle maintenance.
As such, the most effective strategy for companies seeking to quickly realize value from AI is to take a hybrid approach,
which includes the selection of a robust commercially available software platform that embeds AI throughout applications
and workflows to handle common functionalities, but also enables the use of custom models, templates, and workflows
that can leverage unique domain expertise.
Indeed, data from Futurum Intelligence found that most organizations select a primary platform to run a majority of their
core functions, and then augments it with additional applications to address business-specific functions.
Conducting a Strategic Evaluation
of Buy vs. Build
Figure 2. How Organizations Approach Enterprise Software Acquisition
60.00%
Most applications are delivered as a part of
a comprehensive, single platform, with point
solutions to fill in functional gaps
Best-of-breed approach using a combination
of point solutions
All applications delivered as a part of a
comprehensive, single platform
24.30%
15.70%
Source: 2H Enterprise Applications IT Decision Maker Survey, Futurum Research, June 2025
Despite the potential of AI to transform all facets of a business, practical challenges can create significant hurdles to its
successful implementation and utilization. Brittle data, which fails with minor changes to format, context, or use, and siloed
data, which can only be accessed and used within a single system or application, can limit the ability of AI to be applied
effectively to relevant company data.
Furthermore, fragmented systems, overly ambitious timelines, and resistance to change can impede or inhibit the rapid
progress needed to not only implement AI, but quickly and sustainably generate business benefits. To overcome these
hurdles, it’s crucial to prioritize robust data infrastructure, as well as enact strong data hygiene and governance before
focusing on model development or selection.
When deciding whether to buy or build AI capabilities, businesses must weigh tradeoffs in time-to-value, ongoing
complexity, and scalability. Purchasing pre-built AI services offers faster outcomes and lower initial risk, making it ideal
for standard needs and rapid pilots. Conversely, building in-house solutions allows for tailored differentiation, but requires
additional engineering resources, strong governance, and lifecycle maintenance.
As such, the most effective strategy for companies seeking to quickly realize value from AI is to take a hybrid approach,
which includes the selection of a robust commercially available software platform that embeds AI throughout applications
and workflows to handle common functionalities, but also enables the use of custom models, templates, and workflows
that can leverage unique domain expertise.
Indeed, data from Futurum Intelligence found that most organizations select a primary platform to run a majority of their
core functions, and then augments it with additional applications to address business-specific functions.
Conducting a Strategic Evaluation
of Buy vs. Build
Figure 2. How Organizations Approach Enterprise Software Acquisition
60.00%
Most applications are delivered as a part of
a comprehensive, single platform, with point
solutions to fill in functional gaps
Best-of-breed approach using a combination
of point solutions
All applications delivered as a part of a
comprehensive, single platform
24.30%
15.70%
Source: 2H Enterprise Applications IT Decision Maker Survey, Futurum Research, June 2025
Intelligence at Scale: How SAP’s AI Platform Delivers Enterprise-Wide Impact3
AI-driven intelligence is no longer a luxury, but a necessity for modern businesses. As organizations continue to integrate
data at scale, they have a massive opportunity to harness the varied insights contained within operational, product, and
customer data, and leverage it to make faster, more confident decisions. Leaders can significantly shorten decision cycles,
reduce wasteful spending, and uncover new revenue opportunities by integrating predictive models, automated insights,
and closed-loop feedback into core workflows.
Instead of treating AI as a separate tool, AI becomes a seamless part of the flow of work. For example, instead of agents
manually reading support tickets and deciding on responses, an embedded AI agent can triage tickets, suggest responses,
and even automate routine resolutions while the human ageTnt handles more complex cases. Indeed, the business impact
of AI only emerges when it is embedded directly into everyday operations and workflows—from sales forecasting and
fraud detection to demand sensing—which leads to accelerated business growth and sustainable strategic differentiation.
Indeed, AI is quickly becoming a key priority for enterprise buyers, falling just behind overall software features and
capabilities, according to Futurum’s 2H 2025 Enterprise Software & Digital Workflows Decision Maker Survey. Both
generative AI and agentic AI capabilities are being cited as key decision criteria when evaluating enterprise software.
Unlocking AI’s Full Potential Through a
Platform Approach
SAP embeds AI directly into its core applications, ensuring intelligence appears in the flow of work, rather than through
a separate, tangentially connected tool. Across S/4HANA, SuccessFactors, and other line-of-business applications, SAP
surfaces contextual recommendations, predictive insights, and task automation within finance, supply chain, sales, service,
and HR experiences.
These in-app capabilities are augmented by Joule, SAP’s generative AI copilot and studio for building tailored skills.
Moreover, at the platform level, SAP also provides pre-built experiences and agent capabilities that are designed to work
across departments, and allow companies to ground embedded or custom AI in shared business context and data across
functions rather than stitching disparate applications together.
This embedded approach drives immediate value by reducing the time and effort required to turn insights into action.
Business users are served context-aware suggestions (e.g., cash-flow or demand-forecast adjustments) that can help
automate routine work, and multi-step Joule agents can be used to execute context-aware and coordinated workflows
across the business.
Most importantly, SAP Business Technology Platform (BTP) centrally monitors and governs the AI models, and the data
that powers them. Because these AI capabilities are delivered as part of the SAP application portfolio, including ongoing
product releases and hundreds of built-in AI scenarios and skills, customers can realize productivity gains and faster time-
to-value without custom AI projects.
How SAP’s Embedded AI Approach
Provides Scalability, Security, and Value
AI-driven intelligence is no longer a luxury, but a necessity for modern businesses. As organizations continue to integrate
data at scale, they have a massive opportunity to harness the varied insights contained within operational, product, and
customer data, and leverage it to make faster, more confident decisions. Leaders can significantly shorten decision cycles,
reduce wasteful spending, and uncover new revenue opportunities by integrating predictive models, automated insights,
and closed-loop feedback into core workflows.
Instead of treating AI as a separate tool, AI becomes a seamless part of the flow of work. For example, instead of agents
manually reading support tickets and deciding on responses, an embedded AI agent can triage tickets, suggest responses,
and even automate routine resolutions while the human ageTnt handles more complex cases. Indeed, the business impact
of AI only emerges when it is embedded directly into everyday operations and workflows—from sales forecasting and
fraud detection to demand sensing—which leads to accelerated business growth and sustainable strategic differentiation.
Indeed, AI is quickly becoming a key priority for enterprise buyers, falling just behind overall software features and
capabilities, according to Futurum’s 2H 2025 Enterprise Software & Digital Workflows Decision Maker Survey. Both
generative AI and agentic AI capabilities are being cited as key decision criteria when evaluating enterprise software.
Unlocking AI’s Full Potential Through a
Platform Approach
SAP embeds AI directly into its core applications, ensuring intelligence appears in the flow of work, rather than through
a separate, tangentially connected tool. Across S/4HANA, SuccessFactors, and other line-of-business applications, SAP
surfaces contextual recommendations, predictive insights, and task automation within finance, supply chain, sales, service,
and HR experiences.
These in-app capabilities are augmented by Joule, SAP’s generative AI copilot and studio for building tailored skills.
Moreover, at the platform level, SAP also provides pre-built experiences and agent capabilities that are designed to work
across departments, and allow companies to ground embedded or custom AI in shared business context and data across
functions rather than stitching disparate applications together.
This embedded approach drives immediate value by reducing the time and effort required to turn insights into action.
Business users are served context-aware suggestions (e.g., cash-flow or demand-forecast adjustments) that can help
automate routine work, and multi-step Joule agents can be used to execute context-aware and coordinated workflows
across the business.
Most importantly, SAP Business Technology Platform (BTP) centrally monitors and governs the AI models, and the data
that powers them. Because these AI capabilities are delivered as part of the SAP application portfolio, including ongoing
product releases and hundreds of built-in AI scenarios and skills, customers can realize productivity gains and faster time-
to-value without custom AI projects.
How SAP’s Embedded AI Approach
Provides Scalability, Security, and Value