Salesforce’s new findings reveal a striking generational split in attitudes toward autonomous AI, with Gen Z leading the charge while businesses lag behind growing consumer expectations for intelligent, always-on assistance. As everyday tasks increasingly move into AI-driven management, a gap emerges between what consumers want and what organizations deliver. The Salesforce study underscores a shifting landscape where younger users embrace agentic AI—systems capable of performing tasks and making decisions without human oversight—while many companies hesitate to scale these capabilities at the pace demanded by today’s digital-savvy customers. The result is a looming challenge for enterprise software adoption and a pivotal opportunity for early movers to redefine customer experiences, operations, and competitive differentiation.
Gen Z leads AI agent adoption
The Salesforce study highlights a clear generational divide in the embrace of AI agents, with Generation Z at the forefront of enthusiasm and willingness to rely on autonomous assistants. Among Gen Z respondents, a substantial 70% indicated they would use AI agents as personal assistants. This figure reflects a broader shift toward delegating routine and time-consuming tasks to intelligent systems, enabling quicker decision-making, streamlined daily routines, and enhanced personal productivity. In addition, 68% of Gen Z participants expressed interest in employing AI agents for job searches, signaling a strong expectation that automation will facilitate career development, employment discovery, and professional mobility in an increasingly competitive job market. These percentages stand out when contrasted with older generations, underscoring a trend where the youngest cohorts are shaping the future of consumer technology through widespread acceptance and utilization of agentic AI.
The study also notes that many consumers across demographics remain unfamiliar with the full spectrum of capabilities offered by agentic AI, a term used to describe systems capable of independent task execution and autonomous decision-making. Yet, even with gaps in broad awareness, interest spikes in domains where the tangible benefits are most evident. Employment searches, healthcare management, and retail transactions emerge as top areas where consumers perceive meaningful value from AI-driven services. This suggests that the demand curve for agentic AI is not limited to novelty; it is anchored in practical, outcome-oriented use cases that improve efficiency, accuracy, and convenience in daily life. With Gen Z leading the charge, the adoption momentum in consumer spaces could create a tipping point for enterprise deployment as organizations observe demand patterns and customer expectations migrating toward autonomous support.
The national and demographic data further illuminate how engagement varies across activities. In the realm of personal productivity, 70% of Gen Z consumers expressed willingness to deploy AI agents as personal assistants, signaling a readiness to offload scheduling, information gathering, reminders, and routine tasks to automated agents. For job searches, 68% of Gen Z participants were open to AI-assisted approaches, indicating confidence in AI’s ability to parse opportunities, tailor applications, and expedite qualifying experiences without extensive human intervention. These adoption rates reflect a broader belief that intelligent automation can help Gen Z navigate the modern job landscape more effectively, reducing time to find opportunities and increasing access to relevant roles.
However, awareness remains a challenge. A significant portion of consumers across generations still lacks deep familiarity with agentic AI’s broader capabilities, which may slow widespread adoption in certain sectors until educational and onboarding efforts catch up. The Salesforce data suggest that the most compelling value propositions for AI agents are those with clear, tangible outcomes—faster job searches, streamlined scheduling, improved access to services, and additional convenience in everyday life. As consumer expectations shift toward smarter, faster, and more seamless interactions, the expectation is that familiarity with agentic AI will grow, supported by real-world demonstrations of reliability, security, and privacy protections.
The implications for marketers, product managers, and customer experience leaders are clear. First, outreach and onboarding strategies should emphasize practical demonstrations of AI agent capabilities in everyday tasks—scheduling, information synthesis, and decision support—paired with transparent explanations of how the systems operate, what data they use, and how users retain control. Second, product ecosystems should be designed to integrate AI agents into familiar touchpoints—messaging apps, mobile interfaces, web portals, and customer service channels—so Gen Z users can seamlessly transition between human-assisted and AI-assisted interactions without friction. Third, education and user empowerment will be essential to broaden awareness of agentic AI’s full potential, especially in areas like healthcare management and grocery planning where privacy, accuracy, and safety are paramount.
In summary, Gen Z’s readiness to embrace AI agents positions this generation as a primary driver of demand, with implications for consumer-facing experiences across fields such as employment services, scheduling, and everyday shopping. The enthusiasm demonstrated by 70% of Gen Z for personal assistants and 68% for job searches signals a future where autonomous agents are a common component of daily life. As adoption expands, businesses that align with these expectations—by offering reliable, privacy-conscious, and user-centric AI agents—will be well positioned to capture loyalty and redefine convenience in the digital economy.
The breadth of AI agent applications across daily life
Beyond personal assistance and employment navigation, AI agents are seen as valuable tools in health, wellness, and transactional experiences. The Salesforce study captures a broad spectrum of anticipated use cases where agentic AI can make a meaningful difference, illustrating a path for consumer-centric AI that spans multiple sectors and activities. The results reveal that consumers are even more inclined to consider AI-enabled automation for tasks such as planning meals, ordering groceries, and supporting fitness goals, demonstrating that the appeal of AI agents extends far beyond simple scheduling to holistic lifestyle optimization.
A notable takeaway is that 43% of respondents would use AI agents to plan meals and place grocery orders. Among Gen Z, the interest is even higher, with 61% supporting this use case. This suggests that AI agents could become integral to meal planning and grocery procurement, enabling personalized nutrition recommendations, automated grocery lists, and streamlined purchasing workflows that align with dietary preferences, budget constraints, and time considerations. The potential impact on households includes reduced time spent on meal planning, improved adherence to nutritional goals, and more efficient supply chain interactions with grocery retailers and marketplaces.
Fitness tracking and goal setting also emerge as strong use cases for AI agents. Overall, 43% of respondents expressed interest, while Gen Z’s engagement climbs to 62%. This indicates that agents could be deployed to monitor activity data, offer insights, and motivate behavior change through personalized coaching and reminders. The overlap with healthcare and wellness domains underscores the growing convergence of consumer health management with autonomous digital assistants, creating opportunities for partnerships with fitness apps, wearables, and healthcare providers to deliver integrated, AI-powered wellness experiences.
Employment remains a central function in the AI agent value proposition. The study reveals that 44% of Americans would use AI agents for job matching, rising to 68% among Gen Z. The ability to scan, filter, and compare opportunities—leveraging AI to identify suitable roles, tailor applications, and optimize job search processes—could significantly reduce the effort required to navigate the labor market. Similarly, the concept of writing CVs and cover letters with AI is attractive to 45% of all participants, and to 65% of Gen Z. This demonstrates that AI agents are perceived as capable of enhancing professional presentation, ensuring alignment with job requirements, and accelerating career progression for younger workers who are building their early resumes.
Demographic variations in these preferences underscore the importance of personalization and targeted UX design in AI agent platforms. For younger users, the appeal lies in speed, convenience, and tailored guidance across a broader spectrum of life activities, from employment to wellness to daily routines. For older generations or less tech-savvy users, education and guided onboarding become critical to unlock the benefits of agentic AI and to dispel concerns about complexity, accuracy, and data privacy. Businesses that recognize these nuances can craft tiered experiences and adaptive interfaces that adjust to user capability, context, and preferences, thereby enhancing acceptance and long-term engagement.
In addition to consumer-facing use cases, the Salesforce study highlights the market momentum for AI agents in areas such as retail transactions and automated purchasing. The data point that 36% of consumers prefer automated digital purchasing over human interaction signals a notable shift toward self-service, frictionless commerce where AI agents can handle product discovery, price comparison, order placement, and post-purchase support. This trend aligns with broader e-commerce experiences, where customers increasingly expect fast, accurate, and autonomous assistance at every stage of the buying journey. The AI agent advantage in purchasing not only improves convenience but also has the potential to increase conversion rates, reduce cart abandonment, and deliver personalized shopping experiences at scale.
In health and wellness contexts, the 43% overall adoption and 61% Gen Z support for meal planning and grocery ordering illustrate a convergence of nutrition, activity, and shopping into an integrated AI-powered solution. Agent-based systems could analyze dietary restrictions, calorie targets, budget constraints, and local grocery options to propose meal plans and automatically generate shopping lists. This could be complemented by partnerships with grocery retailers and meal kit providers to streamline fulfillment and delivery. The wellness use case also intersects with fitness tracking, providing motivation, progress monitoring, and data-driven recommendations for exercise and recovery. As with other domains, privacy, consent, and data security will be central to user trust.
For employers and service providers, the breadth of potential applications means that AI agents can serve as a central layer that unifies customer interactions, operations, and decision support. By automating routine tasks, agents can reduce response times, enable 24/7 availability, and deliver consistent, high-quality interactions. This alignment with customer expectations—especially for younger, tech-savvy users who demand speed and reliability—positions AI agents as a strategic differentiator in competitive markets. However, realizing these benefits requires careful design, governance, and investment to ensure agents operate with transparency, accountability, and alignment with business goals and regulatory requirements.
In summary, the Salesforce data reveal that consumers are envisioning AI agents as versatile tools that can support messy, multi-faceted aspects of daily life—from nutrition and health to shopping and job hunting. Gen Z’s heightened interest across these dimensions suggests a future in which autonomous agents are embedded across the consumer journey, enabling faster, more personalized, and more convenient experiences. For businesses, the breadth of use cases signals opportunities to shape end-to-end experiences that anticipate consumer needs, streamline operations, and create new value propositions anchored in intelligent automation.
Enterprise adoption forecast: a tectonic shift on the horizon
Forecasts from industry analysts and technology researchers point to a major evolution in how enterprises will deploy agentic AI in the coming years. Gartner projects that by 2028, one-third of enterprise software applications will include agentic AI capabilities, a dramatic increase from the current level of less than 1%. This forecast signals a fundamental rethinking of software architecture, product design, and deployment strategies across industries. The expectation is that agentic AI will become an integrated component of enterprise workflows, assisting with decision-making, automating routine tasks, and enabling more agile, data-driven operations. The forecast also suggests that agentic AI systems will handle an estimated 15% of routine business decisions, representing a substantial augmentation of human decision-making rather than a complete replacement. The implications for CIOs, CTOs, and technology leaders are profound: enterprises will need to plan for increased AI-enabled capabilities, redefine governance and risk management, and invest in the necessary data, security, and governance infrastructure to support widespread autonomous functioning.
This forecast carries several practical implications for organizations considering AI agent adoption. First, the integration of agentic AI into enterprise software will necessitate a reevaluation of software procurement and deployment models. Rather than evaluating standalone AI tools, organizations will increasingly adopt platform-based approaches that embed autonomous capabilities into core applications. This shift will likely accelerate the commoditization of agentic AI features, pushing vendors to offer scalable, secure, and interoperable components that can be layered across multiple systems. Second, the expectation of 15% of routine business decisions being automated by AI agents will require a robust governance framework to manage risk, accountability, and compliance. Organizations will need to establish clear policies around data usage, decision transparency, fallback mechanisms, and human oversight where appropriate. Third, the forecast implies a heightened emphasis on data quality and accessibility. Autonomous agents rely on accurate, timely data to generate reliable recommendations and actions; as a result, data governance, data lineage, and data privacy protections will become strategic priorities, not afterthoughts. Fourth, security considerations will become central to adoption strategies. Autonomous systems that operate with a high degree of independence can introduce new attack surfaces and risk vectors. Enterprises must implement rigorous security controls, auditing capabilities, and anomaly detection to detect and mitigate potential threats. Fifth, talent and workforce planning will need to adapt. As AI agents take on more routine tasks, the nature of work for employees will evolve toward higher-order activities such as oversight, decision review, and complex problem-solving. Training, change management, and reskilling initiatives will be essential to ensuring that staff can collaborate effectively with autonomous systems and maximize the value of AI-enabled processes.
From an organizational perspective, the most successful adopters are likely to be those that embed agentic AI across the customer journey, operations, and product development. Agents can support customer service with intelligent routing, proactive issue resolution, and personalized recommendations; they can streamline procurement, finance, and supply chain activities by automating routine tasks and ensuring consistency in decision-making; and they can accelerate product development by analyzing market signals, coordinating cross-functional workflows, and generating data-driven insights. The ability of AI agents to operate across multiple domains and touchpoints makes them a versatile instrument for digital transformation, enabling enterprises to deliver faster, better, and more consistent outcomes at scale.
The practical expectations for businesses are clear. Companies will need to invest in platform capabilities that support agent orchestration, monitoring, and governance; implement strong data protection and privacy controls; build trust through explainability and transparency in agent actions; and design user experiences that enable both effortless agent interaction and clear human oversight where required. As adoption unfolds, the market will likely see a progression from pilot projects to broader deployments, with early successes creating a ripple effect across industries. In sectors with high customer interaction, such as retail, hospitality, and healthcare, the impetus to adopt AI agents will be particularly strong, given the potential to reduce friction, improve satisfaction, and drive loyalty. In more regulated or complex environments—such as financial services, healthcare, and public sector—enterprises will need to navigate additional compliance hurdles and ensure that autonomous decision-making adheres to policy and governance standards.
Strategically, leaders should view agentic AI as a platform investment rather than a one-off technology deployment. A platform-centric approach enables scalable integration across systems and processes, reduces siloed deployments, and fosters a consistent user experience. It also provides a framework for ongoing enhancements, risk management, and governance as the technology matures and as the business context evolves. When designing this strategy, organizations should prioritize interoperability with existing tools, ensure robust data governance and security, and establish clear metrics to assess impact—such as improvements in cycle time, accuracy, customer satisfaction, and cost-to-serve. The Gartner projection also hints at the democratization of AI capabilities, with many enterprise software suites incorporating agentic AI as a standard feature. This means that AI-enabled decision support and automation could become an embedded expectation rather than a specialized add-on, raising the bar for all vendors to deliver reliable, secure, and user-friendly agent-powered solutions.
In sum, the enterprise adoption forecast signals a tectonic shift in how organizations deploy software and manage decision-making. The anticipated proliferation of agentic AI in 2028 and beyond reflects a broader movement toward autonomous, intelligent systems that complement human capabilities. Businesses that act now—by embracing platform-based architectures, investing in data governance and security, and aligning AI initiatives with clear customer value—stand to gain a decisive competitive advantage as consumer demand for autonomous agents continues to grow and evolve.
The growing expectation gap: consumers demand, businesses lag
The Salesforce study underscores a widening gap between consumer expectations and the readiness of businesses to deliver AI-driven experiences. As AI agents become more capable and more integrated into daily life, consumers are increasingly prepared to engage with intelligent systems for routine tasks, personalized recommendations, and autonomous scheduling. Yet many organizations have not yet fully operationalized these capabilities at the scale and with the reliability that contemporary users demand. The divergence between demand and delivery is a critical challenge that providers must address to maintain customer trust and competitiveness in an increasingly automated landscape.
Vala Afshar, Chief Digital Evangelist at Salesforce, emphasizes the consumer shift and its implications for customer service: “Consumers are ready for AI agents to simplify their lives, and by delivering the benefits of this technology, businesses can strengthen customer service, satisfaction and loyalty.” This sentiment captures the essence of the expectation gap: when consumers experience the streamlined, proactive, and responsive service enabled by AI agents, they anticipate similar performance from every interaction with a brand. When that expectation is unmet, satisfaction and loyalty can waver, even among otherwise satisfied customers. The challenge for organizations is to translate the potential of AI agents into reliable, consistent experiences across channels, use cases, and contexts. This requires not only technology but also thoughtful design, governance, and workforce alignment to ensure that AI agents augment human capabilities rather than create friction or confusion.
Key consumer attitudes illustrating the gap include the following: nearly half of U.S. consumers (44%) say they would use an AI agent as a personal assistant. Among Gen Z, the willingness is even higher, at 70%. The high interest among younger consumers suggests that these capabilities are no longer optional add-ons; they are expected features of modern digital interactions. The study also finds that around 39% of people feel comfortable scheduling appointments through agents, while 34% would prefer using an agent over a human simply to avoid repeating information. These figures point to a desire for frictionless, memory-aware interactions that can remember preferences, maintain context, and complete tasks across sessions without repeated input. The 34% preference for agent-only interactions to avoid repeating information illustrates the demand for continuity in conversations—a key capability of well-designed agentic AI systems.
The expectation gap extends into purchasing behaviors. 36% of consumers prefer automated digital purchasing over human interaction, indicating a trend toward self-serve experiences powered by AI agents. This preference aligns with Gen Z’s comfort with automation and digital-first processes, signaling a future where many purchasing decisions are mediated by intelligent automation rather than direct human involvement. In addition to consumer services, there is a broader expectation that AI agents will play an integrated role across enterprise workflows, including job matching and resume preparation. The data indicate that 44% of Americans would use AI agents for job matching, rising to 68% among Gen Z, while 45% of all participants (and 65% of Gen Z) would consider AI-assisted CVs and cover letters. Such numbers reflect a strong desire for AI-driven support in career development, which has implications for human resources, recruiting, and education platforms.
Addressing the gap requires a multi-pronged approach. At the consumer level, brands must deliver reliable, transparent, and privacy-conscious AI experiences that genuinely reduce effort and save time. This means robust onboarding processes, clear explanations of how AI makes decisions, and explicit control mechanisms for users who want to override or customize AI actions. It also means designing interfaces that preserve context across channels and sessions, enabling agents to remember user preferences and past interactions while maintaining strict data protection standards. From a business perspective, the gap calls for a strategic shift in operations. Companies must align AI agent capabilities with core customer journeys, ensuring that the technology supports real business outcomes—improved satisfaction, higher retention, increased lifetime value, and more efficient service delivery. This involves rethinking workflows, integrating agentic AI into essential processes, and planning for governance, security, and risk management as central elements of the deployment strategy.
Overall, the expectation gap highlights the urgency for organizations to act. Consumer demand for AI agents is rising rapidly, driven by Gen Z and other tech-savvy cohorts, while businesses must accelerate their adoption, governance, and integration efforts to avoid lagging behind. The potential payoff—improved customer satisfaction, loyalty, and competitive advantage—depends on delivering consistent, secure, and intuitive AI-based experiences that meet users where they are and adapt to their needs over time. As the market evolves, companies that invest in agentic AI thoughtfully and strategically will be well-positioned to close the gap and capitalize on the growing demand for intelligent, autonomous support.
Key consumer trends driving the gap
- High willingness to adopt AI personal assistant capabilities among Gen Z and a broad cross-section of the population.
- Strong interest in AI-enabled job searching and resume enhancement, signaling demand for automated career-support tools.
- Positive attitudes toward scheduling, appointment management, and proactive assistance from AI agents.
- Preference for automated digital purchasing, reflecting a shift toward frictionless, self-service experiences.
- Growing comfort with AI for health, nutrition, fitness, and daily wellness tasks, expanding the scope of AI agent interactions beyond mere convenience.
Businesses that prioritize human-centered AI design—ensuring transparency, trust, and privacy—will be better positioned to meet these expectations and preserve customer loyalty as autonomous agents become more commonplace across consumer and enterprise domains.
Real-world deployments: how companies are already responding
A number of organizations are already taking steps to align with rising consumer expectations by deploying Salesforce’s Agentforce platform to deliver AI-driven experiences. Two notable early adopters are OpenTable and Secret Escapes, which have integrated agentic AI into their customer service workflows to address common requests, automate routine interactions, and provide faster, more convenient self-service options. These deployments illustrate how AI agents can be leveraged to handle customer inquiries, automate tasks such as itinerary adjustments and information retrieval, and support travel-related transactions with greater speed and precision.
In the travel sector, Secret Escapes uses AI agents to respond to customer inquiries, including itinerary changes, fee checks, and location-based information. The objective is to provide faster, more convenient self-service options that align with the expectations of today’s tech-savvy users who demand immediate assistance and seamless experiences. By automating routine tasks and enabling self-service capabilities, Secret Escapes aims to reduce friction in the customer journey while maintaining a high level of service quality. OpenTable’s use of Agentforce similarly focuses on enabling responsive, intelligent support that can assist diners with reservations, changes, and related inquiries, helping to streamline operations and enhance guest experiences. These deployments demonstrate the practical benefits of agentic AI in customer service contexts, including improved response times, consistency in service delivery, and the potential for personalized interactions that reflect individual preferences and past interactions.
Beyond these case studies, the broader implication for businesses is clear. As AI agents become more capable and reliable, companies across industries—retail, hospitality, healthcare, financial services, and beyond—can leverage agentforce-powered solutions to expand self-service capabilities, improve operational efficiency, and elevate customer satisfaction. The ability of AI agents to handle routine requests, provide timely information, and assist with complex tasks such as appointment scheduling and product recommendations empowers organizations to deliver more consistent, high-quality experiences across channels. However, success depends on thoughtful implementation that accounts for data privacy, security, governance, and user education to ensure that agents operate with transparency and accountability.
The overarching takeaway is that real-world deployments are moving from pilot experiments to scalable, enterprise-grade solutions that impact the end-to-end customer journey. As more organizations adopt agentic AI and embed it into their core workflows, consumers will increasingly expect this level of intelligent support as a baseline capability. The market momentum created by early adopters and enterprise platforms is likely to accelerate adoption across sectors, driving a broader shift toward autonomous customer service and automated decision support as a standard feature of modern business operations.
Health, wellness, and personal productivity: a deeper look
The Salesforce study’s health and wellness dimensions reveal a notable alignment between consumer goals and AI-driven capabilities. The data indicate that AI agents are poised to play a meaningful role in nutrition, fitness, and overall well-being, reinforcing the idea that autonomous systems can support long-term health and lifestyle management in practical, user-friendly ways. In nutrition and meal planning, 43% of respondents would use AI agents to plan meals and order groceries, with Gen Z leading the charge at 61%. This suggests a future in which AI agents assist with dietary planning, shopping lists, and grocery procurement, potentially reducing waste, optimizing nutrient intake, and aligning meal choices with budget and schedule constraints. The implications extend to grocery retailers and meal-kit providers, which could integrate with AI agents to deliver more personalized, efficient ordering experiences and timely delivery options.
Fitness tracking and goal setting also resonate strongly with consumers. Overall interest stands at 43%, with Gen Z showing higher engagement at 62%. AI agents could interpret wearable data, set personalized fitness goals, schedule workouts, and provide real-time feedback and encouragement. Such capabilities could complement existing fitness apps and wearables by offering a unified coaching experience that leverages cross-device data, aligns with individual preferences, and adapts as users progress. The ability to synthesize activity data, nutritional information, and habit-tracking metrics into actionable recommendations could help users stay motivated, adhere to plans, and achieve health outcomes more effectively than standalone apps.
The study also highlights job-seeking and career support as a core area of interest for AI agents. Employment-related use cases—specifically job matching (44% among Americans, 68% among Gen Z) and CV/cover letter generation (45% overall, 65% Gen Z)—underscore the potential for AI agents to become trusted assistants in the labor market. AI-enabled recruitment and resume optimization could streamline the candidate experience, reduce time-to-hire, and improve candidate fit. For employers, AI agents can assist with initial screening, interview coordination, and candidate outreach, enabling human recruiters to focus on higher-value tasks such as rapport-building, strategic sourcing, and cultural fit assessment. However, as with all data-driven hiring tools, careful attention to bias mitigation, fairness, and regulatory compliance is essential to ensure ethical and compliant use of AI in talent acquisition.
In health and wellness contexts, the adoption of AI agents also raises considerations about data privacy, patient consent, and the accuracy of medical information. While AI can support health planning, nutrition, and fitness management, it must operate within appropriate clinical boundaries and with safeguards to protect sensitive health data. Partnerships with healthcare providers, insurers, and wellness platforms can help ensure that AI-driven recommendations are evidence-based, personalized, and aligned with medical guidance. At the same time, user trust hinges on transparent data handling practices, visible consent mechanisms, and the ability to review and modify data-sharing preferences.
From a product and technology perspective, these health-oriented use cases suggest opportunities for cross-industry collaboration. AI agents could integrate with electronic health records (where permitted by privacy and regulatory constraints), fitness trackers, meal delivery services, grocery platforms, and telemedicine services to deliver a cohesive, end-to-end experience. The resulting ecosystem would enable users to manage health, nutrition, and fitness holistically, with AI agents acting as intelligent coordinators that gather information, propose plans, and execute tasks across multiple platforms. This kind of integration requires robust interoperability standards, clear governance around data usage, and a user-centric approach that prioritizes consent, control, and explainability.
As adoption expands, consumers’ reliance on AI agents for health and wellness tasks will likely grow, potentially enabling new business models in the wellness economy. Companies could offer AI-powered health coaching subscriptions, groceries and meal kits tailored to dietary goals, and integrated fitness programs supported by agent-based optimization. The key to success will be maintaining trust and ensuring safety, accuracy, and privacy in every interaction. For users, this means a convenient, personalized, and proactive health-and-wellness experience that aligns with their goals and preferences, delivered through AI agents that act as reliable partners in daily life.
Workforce implications and strategic governance for businesses
The rise of agentic AI at scale raises important questions about workforce planning, governance, risk management, and strategic alignment. As AI agents take on more routine tasks and decision-support functions, the composition of the modern workforce will shift toward roles that emphasize oversight, interpretation, and governance of autonomous systems. This transition presents opportunities for upskilling and redeploying talent into higher-value tasks that require judgment, empathy, and strategic thinking. At the same time, organizations must manage potential job displacement concerns and ensure a fair, inclusive transition for employees who are affected by automation. Proactive reskilling programs, clear career pathways, and transparent communication about the role of AI in the workplace can help ease the transition and maintain morale.
Governance frameworks will be essential to ensure that AI agents operate responsibly and in alignment with organizational values, regulatory requirements, and ethical norms. This includes establishing policies around data handling, consent, privacy, and security; implementing explainability and auditability so that AI decisions can be understood and challenged if needed; and defining accountability structures that assign responsibility for agent actions and outcomes. Organizations must also implement robust risk management practices to address potential failures, biases, or malicious use of AI agents, including testing, monitoring, red-teaming, and incident response protocols.
Security considerations are paramount when deploying autonomous agents across enterprise environments. Autonomous systems present unique risk profiles that require layered protections, including access controls, encryption, secure integration patterns, and continuous monitoring for anomalous behavior. Regular security assessments and penetration testing should be incorporated into deployment plans, along with incident response playbooks that specify how to detect, respond to, and recover from AI-driven security incidents. Data governance is equally crucial. With agents relying on data streams from multiple sources, organizations must ensure data quality, lineage, and provenance, along with strict adherence to data privacy regulations and policy requirements. Data minimization and purpose limitation principles should guide how agent systems collect, store, and use information.
From a strategic perspective, businesses should approach AI agent adoption as an enterprise-wide transformation rather than a series of isolated pilots. A platform-based strategy that embeds agentic AI capabilities into core processes and workflows will enable scalable, consistent experiences across functions. This approach fosters cross-functional collaboration, reduces siloes, and supports the consistent deployment of autonomous capabilities across customer service, operations, product development, and business enablement. It also creates a foundation for measuring impact through a standardized set of metrics—such as cycle time improvements, accuracy of AI-driven recommendations, customer satisfaction scores, and cost-to-serve reductions—enabling leaders to track progress and adjust strategies as needed.
Upskilling and talent strategy will be central to realizing AI’s potential. Companies should identify the roles most likely to be augmented by automation and design training programs that help employees collaborate effectively with AI agents. This includes teaching employees how to interpret AI-generated insights, how to validate autonomous decisions, and how to manage exceptions and escalation when human intervention is necessary. Encouraging a culture of continuous learning will be critical as AI technology evolves and expands its capabilities. Leadership should also address ethical considerations and establish a code of conduct for the responsible use of AI in business processes, ensuring that agents operate with integrity, fairness, and respect for user privacy.
The enterprise future will be defined by how well organizations integrate AI agents into their operating models while maintaining governance, security, and trust. Those that invest in robust data governance, clear policies, and ongoing workforce development will be better prepared to realize the benefits of agentic AI, including improved customer experiences, enhanced operational efficiency, and the ability to scale intelligent automation across the enterprise. As consumer demand for autonomous agents continues to grow, the organizations that embrace these technologies in a careful, deliberately governed manner will be positioned to enhance loyalty, differentiate themselves through smarter service experiences, and participate in the evolving digital labor landscape.
The digital-labor revolution: aligning business strategy with consumer expectations
The Salesforce study frames AI agents as a central component of a broader digital-labor shift—an evolution in which autonomous systems perform substantial portions of routine, decision-based work, enabling humans to focus on higher-value tasks such as strategy, creativity, empathy, and complex problem-solving. This paradigm shift has the potential to transform how organizations operate, how they interact with customers, and how work is organized within teams. The integration of agentic AI into enterprise software can drive dramatic improvements in speed, accuracy, and consistency, while also enabling personalized, on-demand services that align with the preferences and behaviors of individual users. The “digital labor revolution” implies a reorganization of workflows, the redefinition of job roles, and the creation of new capabilities that augment human expertise rather than simply replacing it.
As adoption accelerates, the market is likely to see several key trends emerge. First, there will be a higher emphasis on seamless orchestration of autonomous capabilities across diverse systems and touchpoints. This means that agents will operate not in isolation but as integrated components of a cohesive ecosystem that includes customer relationship management, supply chain tools, procurement systems, financial platforms, and content-management environments. The goal is to create a unified experience for users, where agents can move fluidly across channels, access the right data, and coordinate actions with minimal friction. Second, the emphasis on data governance and security will intensify as agents operate at scale and handle sensitive information. Organizations will invest in data quality initiatives, privacy-preserving techniques, and robust security controls to protect user data and prevent misuse. Third, the emphasis on trust and transparency will shape how agents communicate with users and how decisions are explained. Users will expect clear indications of when an action is AI-driven, why a recommendation was made, and what data informed the decision. This demand for explainability will influence the design of agent interfaces, feedback mechanisms, and governance practices, ensuring that autonomy does not come at the expense of accountability.
The consumer perspective is equally important. As AI agents become embedded in daily life—planning meals, managing health, coordinating shopping, and assisting with job search—consumers will come to expect consistent, high-quality experiences across brands and touchpoints. Brands that deliver reliable, secure, and user-friendly agent-powered interactions will differentiate themselves through superior experiences, increased efficiency, and heightened loyalty. Conversely, those that fail to meet these expectations risk eroding trust and losing customers to competitors that offer more seamless, intelligent support.
In the long run, the AI agent landscape will be shaped by ongoing innovation, governance, and collaboration among technology providers, businesses, and regulators. As agentic AI becomes more capable and integrated into essential processes, the pace of change will accelerate, with new use cases and enhanced capabilities continually emerging. The companies that stay ahead of the curve will be those that combine strategic investment in platform-based architectures, disciplined governance, and a strong focus on customer-centric design, ensuring that intelligent automation enhances the overall value proposition and strengthens relationships with customers across the digital economy.
Conclusion
The Salesforce findings illuminate a pivotal moment in which consumer demand for intelligent, autonomous AI agents is rising rapidly, led by Gen Z, while many businesses struggle to respond with equal speed and sophistication. The data reveal a clear preference for agentic AI in personal assistance, job search, health and wellness, meal planning, shopping, and career support, underscoring the broad potential of autonomous systems to transform daily life and professional workflows. Gartner’s forecast of one-third of enterprise software incorporating agentic AI by 2028, and the expectation that these systems will handle a meaningful share of routine decisions, signal a fundamental shift in how software is designed, delivered, and governed. As consumer expectations increase, the imperative for businesses is to move decisively from pilot programs to scalable, platform-based solutions that are secure, transparent, and aligned with customer needs.
Early deployments, such as those by OpenTable and Secret Escapes using Salesforce’s Agentforce platform, demonstrate the tangible benefits of AI agents in customer service and self-service contexts. These implementations illustrate how autonomous capabilities can streamline workflows, improve responsiveness, and deliver more convenient, personalized experiences that meet today’s demands for speed and efficiency. The growth of AI agents in health, wellness, and employment remains a promising avenue for further adoption, provided that privacy, safety, and governance considerations are addressed.
To close the gap between consumer expectations and enterprise delivery, organizations must adopt a holistic strategy that integrates agentic AI across customer journeys, operations, and product development. This approach requires robust data governance, strong security practices, and a clear framework for accountability and explainability. It also calls for strategic workforce planning, including reskilling initiatives and new roles that emphasize collaboration with autonomous systems. By prioritizing user-centric design, transparent governance, and scalable platform architectures, businesses can unlock the value of AI agents—delivering faster, smarter, and more reliable experiences that delight customers and create lasting loyalty in a digital-labor-driven economy.