Revenue From Additional Business Opportunities

Evidence and data. Generative AI on AWS enabled the interviewees’ organizations to generate additional revenue through sales process automation, generative AI-augmented sales assistance, and advanced analytics. Interviewees said these capabilities improved win rates and deal sizes by reducing quotation times, expanding customer coverage, and providing content and insights that empowered sales teams to convey value, negotiate, and build stronger relationships. Additionally, AI-powered platform enhancements boosted conversions, while analytics connected disparate data for better organizationwide sales strategy. According to interviewees, these benefits substantially increased revenue by tens of millions of dollars at some of their organizations. Among those interviewees and survey respondents who quantified the impact, affected revenue streams typically increased by about 10%, with multiple organizations seeing 20% increases. 

• Revenue generation was one of the most common business benefits of implementing generative AI solutions on AWS according to interviewees and survey respondents. In the survey, 69% of respondents reported increased revenue.³ Of these, 54% experienced improvements to existing revenue streams and 45% saw the creation of new revenue streams. These gains were driven by increased lead generation, improved conversion rates, and larger average order values.⁴

• The CIO said their healthcare organization deployed an intelligent sales assistant on AWS that dramatically increased quote speed and customer coverage, resulting in tens of millions of dollars in incremental revenue. The solution categorized inbound customer requests, parsed attachments of various formats, determined buying intent, evaluated inventory, and provided optimal pricing.

• The same interviewee noted their organization also deployed an internal, sales-focused chatbot on AWS to help sales tailor messaging and negotiate. This resulted in higher win rates, improved upselling, and increased the sales team’s capacity for client relationship development.

• The CIO added: “[By leveraging generative AI solutions on AWS, our salesforce has gained] speed, scale, and precision. We reduced quotation times by over 90%, increased customer coverage from 30% to over 90%, and enabled our salesforce to focus on value and negotiating with clients instead of repetitive tasks.”

• The same interviewee noted the importance of their AWS Partner to achieving success: “Our AWS Partner played a key role in shaping the use cases, accelerating development, and deploying generative AI in production. Their expertise helped us unlock the full stack of AWS services and establish a secure, scalable foundation.”

• A survey respondent who served as a manager in materials reported substantial revenue gains with generative AI on AWS and explained, “AI-based chatbots [on AWS] are now handling customer inquiries and actively selling additional services.”

• The director of finance and IT detailed how their manufacturing organization used generative AI solutions on AWS to analyze customer transcripts and emails to identify new opportunities, which expanded the pipeline and uncovered potential revenue that would have otherwise been missed. These new leads and upselling targets were automatically routed into their organization’s CRM and service platforms.

• The revenue-generating use cases at the interviewees’ and survey respondents’ organizations were often centered on empowering sales with content augmented by generative AI. Examples included clearer and more tailored proposals, stronger pitch decks with effective visuals, and optimized bundling recommendations, which all contributed to higher close rates or deal sizes.

• Generative AI solutions on AWS were also used to improve websites and platforms at multiple interviewees’ organizations, creating additional revenue streams. For example, the director of engineering said their food and beverage company enhanced search and discovery on its online ordering platform to boost conversions, while machine learning lead noted their e-commerce organization quickly generated visually compelling product pages for thousands of niche items. Several survey respondents said their organizations also expanded with multilingual content.

• The director of product reported that their professional services organization incorporated generative AI-powered analytics into its offerings, which delivered richer insights and cost savings for clients. This interviewee explained how this in turn created a stronger value proposition and significantly increased sales opportunities: “Our pipeline has doubled. It has never been bigger. [Our generative AI use case on AWS] has a serious impact with higher-level economic buyers, who also like that we’re leveraging AI at scale.”

• Survey respondents also reported net-new revenue that stemmed from their generative AI solutions. For example, a director in energy and utilities said, “[Generative AI on AWS] enabled tailored financial reports [that are] attracting more premium consulting clients.” Additionally, a manager explained that their logistics organization was able to monetize AI dashboards as a new offering.  

• Beyond sales enablement, generative AI solutions on AWS supported innovation and better strategy at the interviewees’ and survey respondents’ organizations. In the survey, 60% of respondents said this enabled innovation.⁵ Among them, 57% discovered new customer needs or behaviors and 46% identified new business insights.⁶

• Interviewees described using generative AI-augmented insights and analysis to spot market shifts and accelerate ideation. A survey respondent serving as a manager in the automotive industry added: “The ability to connect disparate thoughts and strategies is mind-blowing. [Investing in generative AI on AWS] has been a game-changing decision for us.”

• Interviewees and survey respondents repeatedly highlighted several specific features of AWS — such as easy integration with existing systems, security and governance, scalability, and model flexibility — that allowed their organizations to be successful with their revenue-generating use cases. The machine learning lead in e-commerce added: “We chose AWS because of the model flexibility and the ease of model switching. For an exploratory approach and developing proofs of concept, the flexibility Amazon Bedrock provides is the best.”

Modeling and assumptions. Based on the interviews and survey, Forrester assumes the following about the composite organization:

• Total annual revenue is $1 billion, $200 million of which is impacted by additional business opportunities with generative AI on AWS in Year 1. This expands to $240 million in Year 2 and $288 million in Year 3. 

• This impacted revenue increases by 10% due to additional business opportunities with generative AI on AWS.

• The operating margin is 12%.

Risks. The benefit of revenue from additional business opportunities will vary based on:

• Pursuing achievable use cases. Interviewees and interviewed partner representatives emphasized starting with feasible use cases. Greater complexity can be layered on over time as the organization builds confidence and capability.

• Exploring multiple proofs of concept. Interviewees noted the importance of experimentation. An interviewed partner representative recommended experimenting and failing fast (sometimes within an hour) in order to move on to use cases that will succeed.

• A focus on business value. Interviewees said that, ultimately, business value motivated their generative AI strategy and maintaining this focus helped ensure long-term success.

• Defining business value. Interviewed partner representatives stressed the importance of grounding generative AI initiatives in a clear understanding of what actually constitutes business value, as this can vary by organization.

• A culture of innovation. Interviewees and interviewed partner representatives said that embracing innovation and all parties remaining flexible and open to incremental change helped ensure long term success.

• Aligning technical and business teams. Interviewees said that collaboration across teams helped accelerate deployment and noted that business user involvement was key to defining success and securing organizationwide buy-in.

• Choosing the right model. Interviewees and interviewed partner representatives emphasized selecting models that fit the use case, balancing factors such as speed, cost, and performance.

• Refining for scalability. Interviewees and interviewed partner representatives noted the importance of refining models to meet business needs before scaling across the organization.

Additional risks include:

• The solutions and processes to drive revenue that are in place prior to implementing generative AI solutions on AWS.

• The size and skill set of the sales team.

• Annual revenue and the number of revenue streams.

• Operating margin.

Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $6.1 million.

Revenue From Improved Customer Retention

Evidence and data. Interviewees and survey respondents successfully leveraged generative AI solutions on AWS to improve customer experience and, ultimately, customer retention. Use cases centered on improving products and services through personalization, new generative AI features, and deeper analytics, along with faster and more insightful customer support. Both B2B and B2C segments benefited, and 75% of survey respondents reported improved customer satisfaction.⁷ Among interviewees who could quantify the impact, customer churn decreased by approximately 50% for affected products and services.

• Improving websites and platforms with generative AI-augmented features was a common use case at interviewees’ organizations. For example, the director of engineering in food and beverage said, “Generative AI has the power to help with a good customer experience and enhance features, so users come back again for repeat orders.”

• The same interviewee explained that generative AI was built on AWS to improve their organization’s online ordering platform with more personalized results: “We have dedicated personalization and recommendation teams. We went from traditional machine learning to generative AI [in order] to turbocharge these models. … Improving our platform holistically is what we are thinking about as we leverage generative AI.”

• The director of product said their professional services organization leveraged generative AI solutions on AWS to process millions of data points daily for thousands of customer assets. This generated detailed recommendations and action plans that saved their customers from costs and business disruptions.

• The same interviewee reported a 50% reduction in churn due to generative AI on AWS and added: “[Leveraging generative AI on AWS with an AWS Partner] provides a huge competitive advantage. There’s massive opportunity.”

• This interviewee also explained that generative AI-powered analytics provided their customer success team with five times more insights and empowered them to better convey the value current customers have seen: “[By implementing generative AI solutions on AWS], a customer success manager can get ready in 10 minutes instead of 2 hours. Customer insights are readily available and documented for them. They can build the customer relationship instead of spending time on data analysis. … [Our customers are] seeing greater ROI.”

• The AI platform lead in software noted the improvements to their offerings with generative AI built on AWS and highlighted the value of AWS’s flexibility and scalability: “Using AI in good ways prevents user churn. There’s extensive use of AI within our products and it’s growing every day. … In our preproduction environment, the model flexibility [with AWS] is a huge benefit. Then in our production environment, the scalability is a huge benefit.”

• Interviewees and survey respondents commonly leveraged generative AI solutions on AWS for more effective customer support, which drove retention and increased the probability of repeat business. Examples included customer support automation for speed and consistency, proactive issue detection in customer support transcripts, and interactive customer onboarding to accelerate time to value and reduce early churn.

Modeling and assumptions. Based on the interviews and survey, Forrester assumes the following about the composite organization:

• Total annual revenue is $1 billion, $100 million of which is impacted by improved customer retention with generative AI on AWS in Year 1. This expands to $120 million in Year 2 and $144 million in Year 3.

• This impacted revenue increase sees a 50% reduction in customer churn due to implementing generative AI solutions on AWS.

• The operating margin is 12%.

Risks. The benefit of revenue from improved customer retention will vary based on:

• The solutions and processes to retain customers that are in place prior to generative AI on AWS.

• The size and skill set of internal teams, such as sales and customer success.

• Annual revenue and the number of products and services.

• Churn rate prior to implementing generative AI solutions on AWS.

• Operating margin.

• The scope of the generative AI on AWS deployment.

Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $3.2 million.

Revenue From More Effective Marketing Campaigns

Evidence and data. Interviewees and survey respondents reported that B2C marketing campaigns built with generative AI solutions on AWS were more successful than traditional campaigns. Organizations securely leveraged their own data to create campaigns at scale that were more personalized and engaging. Many also automated the creation of emails, ads, and landing pages to reduce costs and deliver timely, high-performing campaigns, while others used generative AI-enhanced analytics to identify top-performing approaches and optimize spend. At interviewees’ organizations, targeted campaigns built on AWS with an AWS Partner saw up to 65% improvement in click-through rates and 85% improvement in conversion rates.

• The CIO reported that their public sector organization leveraged generative AI solutions on AWS to create a highly personalized email fundraising campaign using its proprietary data that saw double digit improvements to open, click-through, and conversion rates. The interviewee also noted the importance of their AWS Partner: “We didn’t have the skill set internally, but [our AWS Partner] is very knowledgeable and thoughtful and helps us through our AI journey. Everything has been high-quality, and they do the heavy lifting for us.”

• The associate director said their communications company launched a marketing campaign with interactive generative AI features that customers themselves could use. This reached a broad audience and successfully targeted specific customer segments, such as younger cohorts.

• The associate director added: “[The campaign we created with generative AI solutions on AWS] was great for our company and we won awards. We had very good reach and very good engagement on social media.”

• The machine learning lead said their e-commerce company leveraged generative AI solutions on AWS to quickly create compelling ads for thousands of niche, longtail products: “The advertising banners are tailor-made and with no human costs. It was a huge leap and a great success.”

• Survey respondents also reported that automated or AI-augmented content (e.g., ads, emails, and landing pages) reduced costs and increased conversions. Respondents often emphasized that the faster execution enabled timely, frequently refreshed campaigns that were more effective. Some of respondents’ organizations also leveraged generative AI solutions on AWS to create impactful localized content for regions lacking high-performing material.

• Utilizing generative AI solutions on AWS for enhanced analytics and insights was a common use case across interviewees’ and survey respondents’ organizations. Some teams applied these capabilities to better understand campaign performance and reallocate budget from underperforming efforts while doubling down on successful ones.

Modeling and assumptions. Based on the interviews and survey, Forrester assumes the following about the composite organization:

• Four marketing campaigns leverage generative AI on AWS in Year 1. This expands to six in Year 2 and eight in Year 3. The composite leverages generative AI on AWS for key, high-visibility campaigns.

• The number of messages sent per campaign is 900,000.

• The average click-through rate of these campaigns without generative AI on AWS is 6%.

• Click-through rates increase by 65% due to implementing generative AI solutions on AWS.

• The average conversion-to-sale rate of these campaigns without implementing generative AI solutions on AWS is 4%.

• Conversion to sale rates increase by 85% due to implementation of generative AI solutions on AWS.

• Average order value is $100.

• Operating margin is 12%.

Risks. The benefit of revenue from more effective marketing campaigns will vary based on:

• The solutions and strategies for marketing prior to implementing generative AI solutions on AWS.

• The size and skill set of the marketing team.

• The number of marketing campaigns leveraging generative AI solutions on AWS.

• Click-through and conversion rates prior to implementing generative AI solutions on AWS.

• Operating margin.

• The scope of the generative AI on AWS deployment.

Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $699,000.

Enhanced Employee Efficiency With AI-Augmented Assistance

Evidence and data. Interviewees and survey respondents reported that generative AI on AWS improved employee efficiency across multiple roles and departments. Impacted employees typically saw time savings of 25%, with some realizing as much as 70% time savings. These gains were driven by a range of use cases, including assistive chatbots, content summarization, fraud detection, and tailored analytics for customer-facing teams.

• Sixty-four percent of survey respondents said their organizations improved employee efficiency with generative AI on AWS.⁸ According to these respondents, the average time savings for impacted employees was 26%.⁹

• Interviewees and survey respondents identified several roles experiencing notable efficiency gains with generative AI on AWS. These varied by organization and deployment strategy, but included data and analytics, customer support, sales, legal, HR, marketing, operations, administrative support, IT, and engineering and software development. 

• A common use case was assistive chatbots across the workforce, often fine-tuned for specific departments. For example, sales teams used them to generate quotes and improve negotiation tactics, marketers for content creation, educators for qualitative feedback, and customer success teams for robust client insights. The CIO in healthcare reported, “[With AWS and our partner], we easily scaled AI agents to thousands of users across sales, customer experience, and [other] internal teams.”

• Additional tasks that interviewees said were streamlined with generative AI on AWS included contract creation and review for legal, onboarding and expense report approvals for HR, querying dashboards and generating insights for business analytics, ticket triage and resolution for customer service, coding assistance for developers, and large-scale document analysis for finance and operations. 

• The head of product analytics said their financial services organization deployed a generative AI-powered fraud detection solution built on AWS, which reduced false positives by 90% and, in turn, resulted in a 70% time savings for a 20-person team dedicated to fraud prevention. The same organization rolled out a chatbot for its entire workforce to drive broader efficiency gains.

• The CIO in healthcare shared that workflows were augmented: “We’ve redesigned several end-to-end workflows [with generative AI on AWS]. For instance, in HR, onboarding and training are now guided [by AI agents]. Integrating company policies, systems navigation, and knowledge base access are all powered by generative AI.”

• Multiple interviewees expected efficiency gains to scale to more and more employees over time. The director of finance and IT in manufacturing believed that every employee would see substantial time savings: “There’s been a 25% increase in the efficiency of our customer service team [due to leveraging generative AI solutions on AWS]. And 20% more efficiency for every single department and employee is a conservative goal.”

• The same interviewee discussed how employees can now focus on higher-impact business priorities: “That time savings [with generative AI on AWS] can be contributed back to more valuable work. Sales has time for more sales calls, accounting can focus more on cash flow, [and] the warehouse can deal with urgent orders.”

Modeling and assumptions. Based on the interviews and survey, Forrester assumes the following about the composite organization:

• There are 3,500 employees, 200 of which see improved efficiency by leveraging generative AI on AWS in Year 1. This increases to 240 employees in Year 2 and 300 employees in Year 3.

• Due to implementing generative AI solutions on AWS, these employees save 25% of their time.

• The fully burdened hourly rate for employees is $40.

• The productivity recapture rate is 50%. This means employees convert 50% of their time saved to productive time.

Risks. The benefit of enhanced employee efficiency with AI-augmented assistance will vary based on:

• The number of employees.

• Productivity tools in place for employees prior to implementing generative AI solutions on AWS.

• The average fully burdened salary of employees.

• The scope of the generative AI on AWS deployment.

Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $5.7 million.

Operational Efficiency With Process Automation

Evidence and data. Interviewees and survey respondents reported that, beyond improving employee efficiency, building generative AI solutions on AWS allowed their organizations to fully automate some processes, which eliminated manual work and reduced costs. Unlike efficiency gains that sped up human work, these automations removed manual steps entirely and, in some cases, saved more than 3,000 hours annually per process. Examples included intelligent document processing, data entry and validation, and multistep agentic workflows.

• Automation was a common use case among interviewees and survey respondents. In the survey, 76% of respondents reported leveraging generative AI built on AWS for automation, and 79% said generative AI on AWS decreased the cost of doing business.¹⁰

• Intelligent document processing that removes manual steps completely was a common automation scenario, often saving thousands of hours annually for the interviewees’ organizations. For example, the head of product analytics said their financial services organization automated document analysis for insurance appraisals and the director of finance and IT said their manufacturing business used it to process purchase orders.

• The CIO in healthcare added, “Tasks such as classifying incoming customer emails, document summarization, and knowledge search are now fully automated through intelligent agents.”

• Some interviewees’ organizations deployed agentic workflows to automate multistep processes, such as purchase order approvals and sales outreach. These workflows connected multiple AI agents — each responsible for a specific task — to enable end-to-end automation.

• The director of finance and IT in manufacturing explained that agentic workflows made their generative AI deployment more successful: “Agentic workflows connect agents to job roles. It makes embracing AI more fun and native to our way of thinking about our organization. And that agent leverages another, and it becomes a workflow and the agents become part of our organization.”

• The same interviewee noted that their AWS Partner helped their organization identify use cases and develop multiple agentic workflows cost efficiently and at a pace three times faster than traditional projects.

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• There are two processes that can be automated with generative AI solutions on AWS in Year 1. This increases to four processes in Year 2 and six processes in Year 3.

• Each automated process saves 3,000 manual hours annually.

• The fully burdened hourly rate for employees manually completing this work is $30.

Risks. The benefit of operational efficiency with process automation will vary based on:

• Automation tools in place prior to implementing generative AI solutions on AWS.

• The number of processes that can be automated.

• The average fully burdened salary of employees.

• The scope of the generative AI on AWS deployment.

• Successful deployment of agentic workflows. Interviewees and partner representatives noted the importance of starting with small, easily tractable agentic workflows and layering on top of them over time.  

Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $780,000.

Unquantified Benefits

Interviewees and survey respondents mentioned the following additional benefits that their organizations experienced but were not able to quantify:

• Accelerated time to value. Interviewees said they leveraged AWS’s model flexibility and integrated services to experiment, select the best models, develop effective pilots, and scale with safeguards in place. Broad access to foundation models through Amazon Bedrock and comprehensive capabilities for customization, training, and deployment through Amazon SageMaker allowed the interviewees’ organizations to move quickly from pilot to production. In the survey, 64% of respondents’ organizations saw measurable business value with generative AI on AWS within six months and 92% saw value within a year.¹¹
  Interviewees explained that implementing generative AI solutions on AWS was an ideal choice for large enterprises and startups alike. The director of product in professional services said: “The concentration of all the services in AWS makes time to value easy for us a startup. We don’t have to think about external data pipelines and the security around that.” The CIO in the public sector added, “I think [generative AI on AWS] models and tools are probably the best commercially available, especially for enterprise use cases.”

• Cost efficiency. Interviewees said that generative AI on AWS enabled their organizations to balance speed, performance, and cost across diverse use cases. By offering a broad choice of models and services, the interviewees’ organizations could optimize for cost efficiency while scaling securely and maintaining high-performing, enterprisewide deployments. Of survey respondents, 82% agreed that AWS offered cost efficiency.¹² 

• AWS Partner Network. Interviewees and survey respondents said their AWS Partners played a key role in developing, implementing, and scaling their customized generative AI use cases. Among survey respondents, 95% characterized their organization’s partner as either “Important,” “Very important,” or “Essential.”¹³ The machine learning lead in e-commerce said: “Our partners have the expertise to develop the approach we want to take. They first do a proof of concept given our infrastructure, show us the value, and develop from there. Our partner provided broad knowledge and expertise and also speed.”
  A majority of survey respondents reported that their AWS Partners guided their organizations in developing best practices (77%), helped identify the most feasible use cases (74%), maximized the value of AWS capabilities (71%), and provided technical expertise (68%) (see Figure 1).

The associate director in communications said: “[Our AWS Partner’s] team was very good, and they refined [so that our content generated by generative AI] was perfect. They trained the tool, and they were very effective and motivated by the project.” The AI platform lead in software added: “Our partner takes a bunch of complexities of distributed training [training AI models across multiple machines] off our plate. They’re very good at supporting us and are considered part of the team.”

Ultimately, AWS Partners helped interviewees’ and survey respondents’ organizations achieve greater business value. Sixty-four percent of survey respondents reported that their AWS Partners accelerated the time to production, 55% said partners helped optimize costs, and 50% stated partners helped further increase the ROI of their generative AI solutions.¹⁵

Flexibility

The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement generative AI built on AWS with an AWS Partner and later realize additional uses and business opportunities, including:

• Generative AI leadership into the future. Interviewees said that given the business impact of their current use cases, their adoption of generative AI on AWS will continue to grow over time; some interviewees said it will likely touch all aspects of their business. They expressed high confidence in AWS’s ability to support future needs, noting their pace of innovation and breadth of services. The director of finance and IT in manufacturing said, “I’m 100% confident in AWS’s ability to meet our needs as generative AI evolves.”
  The CEO in marketing added: “We’re constantly learning about new AWS features and services to help us be more efficient, reduce our costs, and improve everything we are doing. They have everything we’re going to need to support our growth.”

Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Total Economic Impact Approach).