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The State of Salesforce 2024–2025
3 priorities for driving an AI advantage
From ROI to reinvention
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Foreword
Analysts, venture capitalists, and trend watchers may predict that AI will consume software—that CRM as we know it could disappear. But we see a more optimistic future.
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Those that rely on Salesforce as a trusted partner suddenly find themselves in a whole new world: generative AI has ignited a revolution. The focus has shifted from the ROI of an individual technology to how technology comes together to reinvent the business in the age of AI.
Recent research from the IBM Institute for Business Value (IBM IBV) revealed that top global executives are increasingly focused on enterprise-wide business value. In fact, 72% of leaders said that improving ROI of the IT investment portfolio by at least 25% is a critical business objective.
At the same time, the Salesforce platform is changing, with generative AI capabilities consolidating under Einstein and Agentforce. This means users must be more intentional and forward-looking in their approach to technology—and their data—or risk getting left behind.
Re-creating the business to support these changes is no longer an option—it’s an imperative. Organizations looking to gain the most value from Salesforce must consider whether they are ready for what lies ahead: can they take advantage of enterprise-wide AI and improve their technology ROI?
Last year’s State of Salesforce highlighted top companies that were leveraging Salesforce to drive value. They were bold in their AI ambitions, and they used industry clouds to establish a competitive baseline for performance. This year, we surveyed more than 1,100 Salesforce customers to identify which practices and perspectives are driving business value with data and AI in 2024.
The results are compelling and actionable. Our research reveals three key priorities that differentiate leading companies—around data, AI, and partnerships. In each of the three sections that follow, we offer a strategic roadmap for how organizations—whatever their level of AI maturity—can best harness the opportunities ahead. Each section offers targeted analysis and insights, plus a specific action guide for advancing a Salesforce-powered transformation in the age of generative AI.
Foreword
Analysts, venture capitalists, and trend watchers may predict that AI will consume software—that CRM as we know it could disappear. But we see a more optimistic future.
Continue reading
Priority 1:
Data dominance
The data disconnect
Innovation with AI relies on data—yet data remains one of the top barriers to innovation, according to the CIOs, CTOs, and CDOs in our 2024 technology leaders study.
The obstacle is not the lack of data; most organizations already have the data they need. Instead, the challenge lies in having the capabilities to effectively exploit that data to drive business outcomes while managing cost and scalability. Indeed, our State of Salesforce 2024 research reveals that while 97% of Salesforce customers collect diverse types of data, only 24% are leveraging it to transform customer experiences.
The power of “data diligence”
That 24%—a group we call Data Pioneers—is reaping the rewards of effective data usage to gain more value from their Salesforce investment (see Figure 1). By turning data into a competitive catalyst, they’re outpacing rivals across the board. Sixty percent of Data Pioneers report outperforming peers in revenue growth, 51% outperformed in profitability, and 61% say Salesforce helped them achieve faster time to market. These results apply cross-industry: roughly 15%-30% of every industry surveyed belonged to the Data Pioneer group.
Figure 1.
An organization’s data prowess is key to unlocking the full potential of Salesforce in the AI era.
Organizations reporting positive impact of Salesforce investments on the organization
The Data Pioneers are cultivating “data diligence”—a fusion of high-quality data, access, governance, and connectivity—so they can make data-driven decisions that transform customer and employee experiences. Imagine retailers being able to orchestrate every in-store experience. With data mastery, they can tap into generative tools to harmonize their understanding of customer behavior and preferences in real time, fine-tuning interactions with employees and even store layouts to create a seamless customer journey (see case study, “Financial services organization targets personalized customer experiences with Salesforce”).
Figure 2.
Mature data capabilities position organizations to anticipate customer needs more successfully.
Ability to anticipate customer expectations
Data Pioneers are blazing this trail, leveraging their advanced data capabilities to reinvent their businesses. They are 81% better than their peers at predicting price expectations and 66% better at anticipating their customers’ convenience needs (see Figure 2). Our research reveals that this data maturity is the cornerstone of business growth, eclipsing other critical capabilities such as change management and innovation.
Enterprise scale means more opportunity—and more complexity
Expanding data capabilities is a tall—and ongoing—order, and even more so for large enterprises. Often, trying to store, sanitize, and process large amounts of data within a single platform can be resource-intensive and difficult to scale.
Large enterprises must scrutinize their data landscape and assess its readiness for AI-driven initiatives. Salesforce customers are on the cusp of a seismic shift in how they capitalize on data across the enterprise, especially with Data Cloud now underpinning Einstein and Agentforce. They face complex decisions on how to best consolidate, move, and manage their data in a way that accounts for their unique volume, cost, accuracy, and security needs.
This includes optimizing the flow of large volumes of data while maintaining quality and integrity. In fact, our analysis shows mastery of managing and optimizing data flow is the top driver of business growth, followed by a strong command of access and connectivity.
Yet, optimizing data flow is only part of the equation for delivering AI at scale. High-quality, proprietary data is a prerequisite. Imagine the cost, effort, and inaccuracy that would result from moving and managing multiple variations of a customer’s name and address into AI models. Data tools that collect, reconcile, and unify customer data into a single accurate customer profile are essential to reliable and effective AI models outputs. Combining optimized data flow with robust data management, enterprises can provide AI models with the accurate, consolidated 360-degree view of the customer needed to inspire meaningful customer experiences.
A Fortune 100 financial services organization embarked on a multiyear marketing transformation journey with a goal of delivering personalized customer experiences driven by data and leveraging their investments in Salesforce Marketing Cloud and Salesforce Data Cloud. The organization partnered with IBM Consulting to define a holistic customer-focused data and marketing technology strategy and solution that would enable 1:1 marketing personalization.
IBM used the data foundation of the client’s Salesforce Marketing Cloud instance to establish a new data model compatible with Salesforce Data Cloud. Enabled by this virtually seamless data connectivity across the enterprise, IBM helped the client build a new operating model and process across marketing technology, operations, and IT. These enhancements aided governance, accelerated product implementation, and deepened business value and measurement capabilities.
The client experienced significant benefits, including:- Activated six always-on personalization journeys and 11 seasonal campaigns
- Simplified data ingestion into Salesforce Data Cloud, reducing delivery timelines by 25%
- Enabled 50% consolidation of technical debt and systems needed to operate marketing operations
- Reduced time to manually manage and coordinate campaigns by 30%-50%
- Improved target conversion by 3%-5% through increased segmentation and personalization.
Based on IBM internal client information.
What to do now
Define data needs for target use cases.
Identify and prioritize the most impactful innovative use cases aligned with your strategy. Then decisively assess and address the data, tools, and data landscape changes necessary to power them.
Conduct a readiness assessment for your data.
Pioneers face the same unpredictability as everyone else, but they don’t let it become a barrier to the potential value of integrated generative AI.
Focus on pilots with quick ROI.
Start with a few simple use cases. Prove the value of your investment in data and AI before moving to more complicated use cases.
Priority 2:
AI-driven differentiation
Tailoring AI for impact
Many Salesforce customers (69%) are using the embedded AI capabilities of the platform to gain value, but savvy organizations are taking it to the next level for an edge in innovation. For example, in predictive analytics, 71% of organizations are enhancing Salesforce AI with additional AI capabilities to augment the platform’s native functionality. For the Data Pioneers, that number jumps to 83%.
What sets leaders apart is how they strategically balance platform-native AI and custom AI capabilities. Indeed, augmenting Salesforce with the right mix of custom AI workflows and large language models (LLMs) has become a fast track to competitive advantage. Augmented AI workflows can connect the front and back office, for instance, allowing organizations to tap into actionable insights from customer order statuses, transactional histories, or billing details to differentiate with more personalized experiences.
Around two-thirds of executives who strategically augmented their Salesforce AI capabilities report an advantage over peers in brand value, time to market, customer loyalty, innovation, and profitability. And four times as many say they anticipate customer needs more effectively than their peers in terms of design and experience, and three times more say the same for service, personalization, and convenience.
Deciding where to customize AI workflows starts by zeroing in on high-value use cases that are grounded in specific pain points or opportunities (see Figure 3). A telecom company, for instance, might decide to leverage AI to deliver self-service options for customers affected by an outage. Augmented workflows must be intentional and targeted to yield positive results.
Figure 3.
Organizations are focused on augmenting Salesforce with AI use cases that impact the customer experience.
A process revolution
Unlocking the full potential of AI with Salesforce requires a keen eye on process transformation. Customization of the Salesforce platform— extending the boundaries of existing features by adding or extending code—has become a double-edged sword. It offers flexibility in adapting to specific business, regulatory, or compliance needs, but when not implemented strategically, excessive customizations can create mountains of technical debt and/or high maintenance costs downstream. This leaves many Salesforce users caught in a tangled web of past customizations that limit the flexibility to innovate.
The rise of generative AI coding use cases offers a glimmer of hope, promising to automate customizations and avoid technical debt. But organizations can’t lose sight of the root problem: rigid, legacy business processes. Executives cite the need to accommodate inflexible business processes as the top reason for customizing their Salesforce solutions extensively. Generative AI capabilities such as automatic case summarization or enabling dynamic asynchronous messaging across channels require flexible processes to drive impact. Before jumping into additional customizations, organizations must reassess and modify business processes to support flexible, intelligent workflows for the future. Without updated business processes, gen AI could add to the mounting pile of technical debt.
AI delivers the most value when designed into processes—not when added as an afterthought (see case study “Telecom reimagines customer support with Salesforce solution”). Data Pioneers, for example, have prioritized advancing their business process modification capabilities and are much better equipped than their peers to integrate intelligence and automation into their workflows. More than two-thirds of them can implement tailored AI workflows across a variety of functions in the organization.
In an industry marked by intense competition, a telecom giant had little room for error. But the company realized its customer support operation was falling dangerously behind competitors. With customers spread across 185 countries demanding modern digital experiences, the company was struggling to keep pace using a system designed for internal needs—not customer expectations.
Its systems were fragmented, inefficient, and plagued by high resolution times, redundant manual efforts, and increasing maintenance costs. And a complex IT landscape involving many legacy systems and partners made integration and change management an enormous challenge. Automated workflows were out of the question, and the company lacked true insights into customer interactions with support agents, limiting its potential to monetize support services.
The telecom turned to its transformation partner IBM to design and implement a tailored, flexible customer support platform using Salesforce Experience Cloud and Service Cloud deployments. The resulting digital workspace platform provides customers with a single digital interface to open support tickets, have live chats, search a knowledge base, collaborate on user forums, and view network data—all backed by AI.
Benefits include:- 900 institutional customer accounts are empowered with self-service capabilities.
- Customers can expect up to a 50% reduction in issue resolution time for cases raised.
- 7,000 software-support agents across 100+ countries have been empowered with new tools and capabilities.
- Five legacy systems have already been decommissioned, reducing the need for engineers to visit customer sites by 25%. The future roadmap includes modernization of additional legacy systems.
Based on IBM internal client information.
What to do now
Focus on outcomes
Start with out-of-the-box processes on the Salesforce platform and adapt from there. Focus on the desired outcome rather than the existing process flow. Short-term change management will cost less than a heavily customized platform in the long run.
Leverage native capabilities and augment for tailored impact.
Train developers on Prompt Builder and start AI pilots. Identify how to further enhance differentiation, connect the front and back office more seamlessly, and augment with custom AI workflows accordingly.
Rally advocates around AI
Bring in leaders from every level of the organization to commit to redesigning legacy processes and removing blockages. These process champions will become change leaders for the new ways of working. Impacts to the business and how work gets done using AI must be the leading driver of projects.
Priority 3:
Ecosystem orchestration
A crisis of confidence
Organizations face a “Great Tech Reset”—rethinking how technology adds value to the business and the growth potential of leveraging AI with Salesforce. AI is more than a technology change; it is transforming how organizations operate—from data collection and integration to workflows to skills and processes. It’s no surprise then that the complexities and unknowns make many Salesforce users cautious: only 16% say they are confident in their AI capabilities. Nearly half (45%) admit to uncertainty about where to start or concern about the implications of AI.
Figure 4.
Salesforce customers have apprehensions when it comes to AI.
To overcome this hesitation, executives point to expanding their expertise as a top priority, alongside business results and risk mitigation (see Figure 5). This is consistent with other IBM IBV research: 63% of technology executives (CIOs, CTOs, and CDOs) agreed their competitiveness hinges on their ability to attract, develop, and retain top talent. But only half said their tech teams have the knowledge and skills to incorporate new technologies such as generative AI. Organizations need the support of trusted partners to help steer their organizations through the AI-driven business transformation (see case study, “Healthcare product manufacturer transforms customer service platform”).
Figure 5.
Salesforce customers want guarantees to move ahead confidently with AI.
Top guarantees needed
The new role of the Salesforce ecosystem
A robust ecosystem has always been a key component of the Salesforce community, encompassing both technology and consulting partners. Nearly all (97%) Salesforce users in our study say they rely on the technology and expertise of partners to support their implementations in areas such as data integration and experience design. At the same time, the rise of generative AI has raised the bar for partners: more than half of CEOs reported ending a partnership or supplier relationship due to a failure to meet standards.
As Salesforce customers embark on their AI journey, they must put their ecosystem under a microscope to see which partners can support the business changes needed to unleash the platform’s value. Optimal ecosystem orchestration will look different for each organization, but one thing is clear: the right mix of technology and expertise partners is essential to re-creating the business. The question for organizations is: which technology and consulting partners can meet their standards for security and trust while charting the AI journey ahead?
MOL Group is repositioning itself as more than a place to refuel. The pioneering oil and gas company is harnessing the power of CRM and AI to transform traditional fuel stations into vibrant retail destinations that deliver personalized customer experiences. An AI-powered loyalty program lies at the heart of the transformation.
The ultimate goal was to shift to a behavior-based model, synthesizing customer signals into hyper-personalized offers. For that, MOL Group needed generative AI. Partnering with IBM, the company integrated Salesforce Loyalty, Experience and Marketing Cloud with Data Cloud and IBM watsonx™ to leverage its vast amount of customer data—more than 5 million loyalty shopping transactions per month. Salesforce Data Cloud helped the team start fast, connecting and harmonizing data from other Salesforce clouds and third parties into customer profiles.
As part of a generative AI pilot, the MOL team then created a hyper-personalization engine with watsonx to tackle the marketing needs: different languages for prompts, new data sets to enhance customer profiles, and the ability to dynamically A/B test content on auto-generated personas by country.
The loyalty program expanded on 34 strategic segments to include 15 detailed microsegments with different personas based on specific measures such as coffee consumption or frequency of buying groceries. Using the gen AI tool, campaign managers could then rapidly draft messages in local languages for microsegment personas with a single click.
The gen AI hyper-personalization pilot has delivered impressive results:- Increased sales by 24%
- Improved marketer efficiency managing additional microsegments by 10 times
- Provided the foundation for future scalability into more countries.
MOL Group hopes to onboard 5 million customers onto the platform by 2025 and to deliver personalized messaging tailored by country and individual preferences, resulting in hundreds or thousands of unique segments.
What to do now
Determine your partners’ propensity to handle change.
Assess your partner’s ability to navigate change, including experience with large-scale transformations, adaptability to new technologies, and willingness to learn and evolve.
Develop a clear understanding of your partner’s AI expertise.
Evaluate each partner’s level of experience in AI and the types of AI projects they have worked on. Move on from those who don’t meet your standards and prioritize those that align with your approach to AI and ethics.
Decide what a best-of-breed or a best-of-suite ecosystem looks like for your organization.
Don’t continue to invest in partnerships that no longer produce results. Consider factors such as scalability, integration, and user adoption to orchestrate the ideal ecosystem for business growth.
Study approach and methodology
The IBM Institute for Business Value (IBV) partnered with Oxford Economics to conduct executive surveys between Q4 of 2022 and Q3 of 2023 on the use of Salesforce, extent of Salesforce integration, and overall approach to generative AI.
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Going the distance
The AI race is a marathon, not a sprint. The winners will be those who dare to reimagine their business with truly differentiated, AI-powered experiences.
As organizations consider how to leverage AI with Salesforce, the secret lies in the transformative power of data. It’s the key to taking advantage of both Salesforce native AI capabilities and additional augmented AI options for more tailored competitive results.
At the same time, process upgrades will be just as important as technology upgrades. Applying AI to bad processes won’t improve the end results. And organizations don’t need to navigate AI transformation alone. Well-orchestrated ecosystems and collaborative innovation will be essential for success.
Ultimately, it’s not just about the technology. It’s about having the courage to radically reinvent the future of the business.
IBM faced complexity and fragmentation in its client service and sales operations. Multiple channels and systems were used to support clients, leading to a lack of transparency and personalized service. IBM’s Client Service and Sales teams lacked insights into client data and activity history, making it challenging to deliver personalized customer service and sales approaches.
IBM turned to Salesforce to implement a digitized, streamlined, service-focused platform. Salesforce created an integrated CRM platform called Customer 360, which unified all client, client service, and seller experiences under a single source of truth. IBM used Salesforce products and cloud experts from within IBM Consulting to consolidate phone, email, and chat support tools onto Salesforce Service Cloud. Additionally, IBM deployed Sales Cloud, which is now used by tens of thousands of sellers, sales managers, and partners to build stronger relationships, manage workflows, and accelerate deals.
IBM brings Customer 360 to life, infusing the AI capabilities of IBM watsonx™ through Salesforce’s open API architecture. Virtual assistants answer client queries and streamline communication between agents in different departments. Intelligent case routing pairs client cases with the appropriate seller, and robotic process automation frees agents to personalize client communications.
The implementation of Salesforce Customer 360 has brought numerous benefits to IBM, including:- A 26% decrease in time to resolution (TTR) in Service Cloud
- A 25-point increase in the Net Promoter Score for case support
- Resolutions of customer questions in 12% of cases through self-service virtual agents.
- Saving each agent up to 45 minutes a day through case prioritization.
How do top Salesforce platform users optimize value?
Download The State of Salesforce 2024–25 summary and full report for insights gleaned from thousands of executive users.
Study approach and methodology
The IBM Institute for Business Value (IBV) partnered with Oxford Economics to conduct executive surveys between Q4 of 2022 and Q3 of 2023 on the use of Salesforce, extent of Salesforce integration, and overall approach to generative AI.
Continue reading
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