A Forrester New Technology Projected Total Economic Impact™ Study Commissioned By Microsoft, July 2024
Understanding and engaging with people is core to the success of any organization, be that their customers or the public. The availability of generative AI (genAI) technology serves as an enabler for organizations — including for-profit businesses, schools, health care providers, and government agencies — to deliver smooth customer or constituent interactions in their physical locations and digital spaces, and it creates targeted content that drives better engagement with the intended audience. Combined, this has the potential to greatly enhance the efficiency and creativity of these content-intensive interactions, enabling organizations to achieve higher impact and improve stakeholder engagement that results in better service delivery and even potentially higher revenue growth for commercial organizations.
Microsoft Azure OpenAI Service is a fully managed service that allows developers to integrate OpenAI models into their applications. It is a key element to Copilot Stack, which is a collection of foundational elements needed to build transformative AI solutions. Copilot Stack brings together purpose-built AI infrastructure, foundational models, data platform, a collection of models and AI tooling, and other developer solutions that are all supported by Microsoft’s enterprise-grade commitments to ensure AI privacy, safety, and security. Advanced by Microsoft’s own experience building genAI applications, this stack powers Microsoft Copilot. Organizations can leverage this along with other solutions in Microsoft Cloud to reinvent customer or constituent engagement, enrich employee experience, reshape organizational processes, and better enable innovation.
Microsoft commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the projected financial benefits enterprises may realize by deploying Azure OpenAI Service, specifically for use cases that aim to reinvent customer or constituent engagement.1 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of Azure OpenAI Service on their organizations. This study is also supplemented by eight industry spotlights that demonstrate the impact of generative AI on industry specific KPIs.
Projected annual revenue growth from better engagement with existing customers
Up to 8.42% in Year 3
Projected annual revenue growth from better engagement with prospective customers
Up to 6.85% in Year 3
Projected improvement in chatbot resolution at contact centers
Up to 50% per year
Projected efficiency gain in content-generation activities
Up to 60% per FTE per year
To better understand the projected benefits associated with this investment, Forrester interviewed 20 representatives from 16 organizations with experience using Azure OpenAI Service. For the purposes of this study, Forrester aggregated the interviewees’ experiences and combined the results into a single composite organization that is an organization with an annual revenue of $10 billion and 10,000 employees.
Interviewees said that prior to using the solutions built on Azure Open AI Service, their organizations struggled with customer and constituent engagement. Creating personalized content was often a manual and time-intensive process, so those responsible for generating this content often resorted to creating generalized, impersonal materials. Additionally, contact centers were often overwhelmed with calls and inquiries, with contact center agents often ill-equipped to address specific questions or requests. These inefficiencies burdened content creators, customer service agents, and other internal teams while satisfaction of the engaged audience suffered. For commercial organizations, this potentially impacted metrics such as conversion rates, customer satisfaction scores, and customer retention numbers.
After the implementation of Azure OpenAI Service, the organizations were able to boost efficiency in both content creation and interactions with their targeted audiences. For commercial organizations, this improved engagement led to higher expectations of business growth as the organizations anticipate increases in conversion rates and the number of leads, improved customer retention, and higher revenue per customer. Even in industries not focused on revenue, such as government or public education, Azure OpenAI Service unlocked efficiencies that significantly increased access to public services and government programs.
Azure OpenAI Service’s customer engagement benefits, both realized and anticipated, are shown in the figure below. In addition to the examples of quantified benefits for the composite organization, this New Tech TEI also discusses drivers contributing to the benefits and how the benefits are expected to expand and evolve over time.
Quantified projected benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
Interviewees from public sector and education organizations told Forrester they saw the following benefits of Azure Open AI Service:
∙ Better engagement with existing recipients to improve service delivery. Interviewees from non-commercial organizations said using Azure OpenAI Service allowed them to enhance people’s access to specific public services. People have more channels to express their concerns and issues with particular programs, which allows the implementing organizations to use that feedback to improve the service quality.
∙ Improvement in effort to increase service awareness among the general public. These interviewees also said Azure OpenAI Service allows their organizations to expand public awareness of their programs to pockets of the population they previously could not engage with. They explained that this is a result of using genAI to better understand what messaging or framing attracts people to their services and using genAI again to repurpose the messaging — with the appropriate adjustments if needed — to other parts of the population.
∙ Improvement in contact center chatbot resolution rate. These interviewees said that because contact centers are key channels for public sector and education organizations to engage with their audiences, their organizations use AI-enabled chatbots to help their agents better engage callers. Similar to the commercial application, as chatbots handle the simpler queries, human agents can focus on more complex engagements.
∙ Productivity gains in generating public-facing content. Similar to the commercial application, interviewees from public sector and education organizations said they can benefit from being able to generate more personalized content while scaling the production of public-facing content. This allows their public service employees to shift focus to value-added activities.
Unquantified benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
Forrester modeled a range of projected low-, medium-, and high-impact outcomes based on evaluated risk. This financial analysis projects that the composite organization accrues the following three-year present value (PV) for each scenario by enabling Azure OpenAI Service:
“[This is the] key to understanding our customers better. [Azure OpenAI Service] can really transform companies across all different dimensions [and allow] the business to be more automated, [which leads to] quality improvement and better time to market.
Head of data, telecommunications
Improvement in sales conversion rate:
Increase in top-of-funnel prospects:
Improvement in annual customer retention:
Increase in revenue per customer:
Productivity gain in content generation:
Reduction in calls requiring human agent:
From the information provided in the interviews, Forrester constructed a New Technology: Projected Total Economic Impact™ (New Tech TEI) framework for those organizations considering an investment in Azure OpenAI Service.
The objective of the framework is to identify the benefits that affect the investment decision. Forrester took a multistep approach to evaluate the projected impact that Azure OpenAI Service can have on an organization.
Interviewed Microsoft stakeholders and Forrester analysts to gather data relative to Azure OpenAI Service
Interviewed 20 representatives at 16 organizations using Azure OpenAI Service in a pilot or beta stage to obtain data about projected financial benefits.
Designed a composite organization based on characteristics of the interviewees’ organizations.
Constructed a projected financial model representative of the interviews using the New Tech TEI methodology and risk-adjusted the financial model based on issues and concerns of the interviewees.
Employed fundamental elements of New Tech TEI to modeling the investment’s potential benefits. Given the increasing sophistication of financial analyses related to IT investments, Forrester’s TEI methodology provides a complete picture of the total economic impact of purchase decisions. Please see Appendix A for additional information on the TEI methodology.
Readers should be aware of the following:
This study is commissioned by Microsoft and delivered by Forrester Consulting. It is not meant to be used as a competitive analysis.
Forrester makes no assumptions as to the potential benefits that other organizations will receive. Forrester strongly advises that readers use their own estimates within the framework provided in the study to determine the appropriateness of an investment in Azure OpenAI Service.
Microsoft reviewed and provided feedback to Forrester, but Forrester maintains editorial control over the study and its findings and does not accept changes to the study that contradict Forrester’s findings or obscure the meaning of the study.
Microsoft provided the customer names for the interviews but did not participate in the interviews.
Consulting Team:
Adi Sarosa
Matt Dunham
Role | Industry | Region | Annual Revenue | Total Employees |
---|---|---|---|---|
EVP | Retail and consumer goods | Americas | $26B | 30K |
Head of data | Retail and consumer goods | EMEA | $1.6B | 4.5K |
Data lead | Retail and consumer goods | EMEA | $1.6B | 4.5K |
Senior director | Manufacturing and mobility | Americas | $3.7B | 7K |
Head of engineering | Manufacturing and mobility | Americas | $51B | 160K |
CIO | Education | Americas | $579M | 1.7K |
Head of AI | Education | APAC | $5B | 100K |
Head of data | Telecommunications | EMEA | $48B | 96K |
Head of digital | Telecommunications | EMEA | $48B | 96K |
SVP | Media | Americas | $10B | 1K |
Digital strategist | Healthcare and life sciences | APAC | $1B | 33K |
Data and analytics lead | Healthcare and life sciences | APAC | $1B | 33K |
Business analyst | Healthcare and life sciences | APAC | $1B | 33K |
VP of platform engineering | Healthcare and life sciences | APAC | $31B | 15K |
AI lead | Government | APAC | $3.2M | 15 |
Attorney | Government | Americas | $800M | 6K |
Chief architect | Financial services and insurance | APAC | $939M | 2K |
CTO | Financial services and insurance | EMEA | $2B | 6K |
Senior director | Energy | Americas | $28B | 18K |
Manager of enterprise automation | Energy | Americas | $11B | 10K |
Forrester interviewed 20 representatives from 16 organizations with experience using Azure OpenAI Service. They said that in trying to reinvent engagement with their customers or the general public, their organizations were faced with the fact that people increasingly prefer to engage with content and messaging that is personalized to them at the individual level. To meet that demand would mean creating millions of variations of the same content.
The interviewees noted that prior to using Azure OpenAI Service, this would have been nearly impossible due to:
Interviewees in the public sector said their organizations were faced with similar demand related to needing to create more personalized content to generate better engagement from their constituents. They said their organizations were hindered by:
∙Operational inefficiencies. Interviewees from public sector organizations shared similar challenges related to operational inefficiencies. An interviewee from a government agency indicated that before using Azure OpenAI Service, their organization’s lawyers were manually reviewing high volumes of long and complicated documents, which resulted in a time sink for these highly valuable resources: “It doesn’t make sense to have our lawyers with expensive rates do this type of task. We want them to do more complex analysis.” The AI lead supporting government agencies said the delayed delivery times resulted in delayed legal processes for constituents.
∙Inconsistencies in data analysis. Manual processes often resulted in data analysis errors, which led to confusion and productivity losses. An interviewee at a government agency indicated that two different employees could come up with different analyses when reviewing similar cases with prior review methods.
∙Technology infrastructure gaps. Without proper technology to support complex government environments, the interviewees’ organizations often resorted to applying more internal employees to the problem, which further compounded both the operational inefficiencies as well as the data inconsistencies. The AI lead supporting government agencies described how the technology gaps directly impacted constituents’ abilities to understand schemes and receive legal support: “[Our country’s judgement] is notoriously long and hard to understand. You would need an army of lawyers to understand. However, 99% of [our constituents] cannot afford a lawyer.”
To address the challenges related to their customer and public engagement, the interviewees’ organizations searched for a solution that could:
“Azure OpenAI Service will improve our business process from hours to minutes and from minutes to seconds. We can then turn this business process efficiency into customer satisfaction.”
Digital strategist, health and life sciences
“Our genAI mandate is to get our students career-ready. We believe that when they graduate, they will be walking into a world full of genAI, so it’s our responsibility to provide them with sufficient opportunities to critically engage with these technologies.”
CIO, education
Based on the interviews, Forrester constructed a TEI framework, a composite company, and a benefit analysis that illustrates the areas financially affected. The composite organization is representative of the 20 interviewees from 16 organizations, and it is used to present the aggregate financial analysis in the next section. The composite organization has the following characteristics:
Description of composite. The composite is a global organization with $10 billion in annual revenue and an operating margin of 10%, and it has a 2.6% annual growth rate. The organization employs 10,000 total employees, and 500 do significant content generation work. The contact center organization handles 4 million calls each year, and a percentage is already handled by a frontline chatbot.
Deployment characteristics. The organization gradually deploys Azure OpenAI Service use cases. In the low-case scenario, 5% of the organization is affected by Azure OpenAI use cases in Year 1. This grows to 20% in Year 2 and to 30% in Year 3. In the mid-case scenario, adoption starts at 7% in Year 1, 25% in Year 2, and 50% in Year 3. In the best-case scenario, adoption starts at 10% in Year 2, 40% in Year 2, and 80% in Year 3.
Projected Benefits | Year 1 | Year 2 | Year 3 | Total | Present Value |
---|---|---|---|---|---|
Total projected benefits (low) | $4,976,500 | $18,213,500 | $35,096,844 | $58,286,844 | $45,945,349 |
Total projected benefits (mid) | $8,683,725 | $35,948,750 | $80,897,392 | $125,529,867 | $98,383,415 |
Total projected benefits (high) | $15,032,000 | $68,410,000 | $169,284,840 | $252,726,840 | $197,388,850 |
Evidence and data. Interviewees shared that with Azure OpenAI Service applied at their organization’s contact centers and support organizations, agents can more easily pull up information while on the call, which allows them to quickly and better address the issue at hand. This can impact customer satisfaction levels and, by extension, the level of customer churn.
Interviewees reported that in addition to creating personalized content for prospective customers, their organizations were able to more effectively personalize materials for their existing customer bases. By being able to create tailored materials for specific customers, the organizations uncovered cross-sell and upsell opportunities, increasing the average revenue per customer.
Interviewees from the public sector shared examples of how their organizations were looking to use Azure OpenAI Service to deliver better outcomes to their patients, students, and constituents.
∙The digital strategist at a healthcare and life sciences organization reported that using Azure OpenAI Service helped their company provide health information to patients more quickly than it was able to previously: “For a health issue where 80% to 90% of cases are actually normal, having to wait days or even a week later to get the result can be frustrating. With AI, you can just wait an hour, go get coffee, and the result is ready. There is no need to make another appointment.”
∙The CIO in higher education reported their organization was hoping to use Azure OpenAI Service to personalize learning, identify struggling students, and optimize resources to improve the most important success metrics: “We will want to see if there is an increase in the students’ job employment rate and graduation rate. We have struggled to increase our graduation rate for many years. This is the biggest initiative that we are doing to improve it.”
∙An attorney at a government agency described how their firm intends to use Azure OpenAI Service to modernize legal processes — including the analysis of legal documents and precedents — to give citizens faster and more accessible legal assistance: “Think about a citizen. If they need to spend hours, days, months, or years to fight for a right they have, that hurts them, and that hurts us as a county. If we can avoid cases that require years of fighting for a citizen’s right, that would be a big societal win.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
Results. This yields a three-year projected PV ranging from $18.7 million (low) to $93.3 million (high).
Base: 20 representatives of organizations
with
experience using Azure OpenAI Service
Source: A
study
conducted by Forrester Consulting on behalf of Microsoft, July
2024
Improvement in annual customer retention
10% – 30%
Increase in revenue per customer
1% – 7%
“Sentiment analysis can uncover pain points or challenges that cause churn so we can take action on it.”
Head of engineering, manufacturing and mobility
Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | ||
---|---|---|---|---|---|---|---|
A1 | Revenue | Composite | $10,000,000,000 | $10,260,000,000 | $10,526,760,000 | ||
A2Low | 5% | 20% | 30% | ||||
A2Mid | Percentage of organization impacted by AOAI use cases | Composite | 7% | 25% | 50% | ||
A2High | 10% | 40% | 80% | ||||
A3 | Annual churn rate before AOAI | Composite | 10% | 10% | 10% | ||
A4Low | 10% | 15% | 20% | ||||
A4Mid | Avoided annual churn | Interviews | 15% | 20% | 25% | ||
A4High | 20% | 25% | 30% | ||||
A5 | Operating margin | Composite | 10.00% | 10.00% | 10.00% | ||
A6Low | $500,000 | $3,078,000 | $6,316,056 | ||||
A6Mid | Subtotal: Profit gain from better retention | A1*A2*A3*A4*A5 | $1,050,000 | $5,130,000 | $13,158,450 | ||
A6High | $2,000,000 | $10,260,000 | $25,264,224 | ||||
A7Low | 1% | 2% | 3% | ||||
A7Mid | Percentage increase in revenue per customer | Interviews | 2% | 3% | 5% | ||
A7High | 3% | 5% | 7% | ||||
A8Low | $500,000 | $4,104,000 | $9,474,084 | ||||
A8Mid | Subtotal: Profit gain from increased revenue per customer | A1*A2*A5*A7 | $1,050,000 | $7,695,000 | $23,685,210 | ||
A8High | $3,000,000 | $20,520,000 | $58,949,856 | ||||
AtLow | $1,000,000 | $7,182,000 | $15,790,140 | ||||
AtMid | Profit gain from better engagement with existing customers | A6+A8 | $2,100,000 | $12,825,000 | $36,843,660 | ||
AtHigh | $5,000,000 | $30,780,000 | $84,214,080 | ||||
Three-year projected total: $23,972,140 to $1119,994,080 | Three-year projected present value: $18,707,994 to $93,254,756 |
Evidence and data. Interviewees shared that by using Azure OpenAI Service, their organizations were able to more easily generate personalized content, ensuring that marketing materials resonated with individual prospects. Additionally, the organizations were able to rely on AI-powered knowledge bases to better respond to prospects’ inquiries, which helped them avoid lost sales. Collectively, the increased personalization and the improved response cycles led to more prospects converting into sales.
Additionally, interviewees shared that AI-generated content is more optimized for search engines, which drives more prospects to their webpages. With the improvement in personalization, prospects who were directed to websites were also more likely to engage with the organizations’ sales teams.
Even in the public sector where financial revenue is not the main measurement of success, interviewees noted similar examples of how their organization’s use of Azure OpenAI Service proved crucial in driving interest towards their services.
∙The AI lead supporting government agencies noted, “You can talk to our chatbot in any language with both text and voice options, and it gives you the description of the situation and a response that is applicable to you.”
∙The head of AI in the education space explained how using Azure OpenAI Service enabled their organization to identify exactly where students were disengaging in the digital support process, which allowed it to determine which steps needed improvement: “We see in our digital support services the dropout rates are high. Now, we can analyze our interaction to understand why they dropped.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
Results. This yields a three-year projected PV ranging from $14.9 million (low) to $75.8 million (high).
Base: 20 representatives of organizations
with
experience using Azure OpenAI Service
Source: A
study
conducted by Forrester Consulting on behalf of Microsoft, July
2024
Improvement in sales conversion rate
10% – 40%
Increase in top-of-funnel prospects
5% – 20%
“Having reviews written, curated, and organized from actual, real-time reviews helps us with customer experience because [customers] now have all the information they need to evaluate their purchase decisions.”
EVP, retail and consumer goods
Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
---|---|---|---|---|---|---|
B1 | Top-of-funnel prospects | Composite | 4,200,000 | 4,200,000 | 4,200,000 | |
B2Low | 5% | 20% | 30% | |||
B2Mid | Percentage of overall funnel impacted | Composite | 7% | 25% | 50% | |
B2High | 10% | 40% | 80% | |||
B3 | Sales conversion rate in legacy environment | Composite | 2% | 2% | 2% | |
B4Low | 10% | 15% | 20% | |||
B4Mid | Improvement in sales conversion rate | Interviews | 15% | 25% | 30% | |
B4High | 20% | 30% | 40% | |||
B5 | Revenue per customer | Composite | $15,000 | $15,000 | $15,000 | |
B6 | Operating margin | Composite | 10% | 10% | 10% | |
B7Low | $630,000 | $3,780,000 | $7,560,000 | |||
B7Mid | Subtotal: Profit gain due to better conversion rate | B1*B2*B3*B4*B5*B6 | $1,323,000 | $7,875,000 | $18,900,000 | |
B7High | $2,520,000 | $15,120,000 | $40,320,000 | |||
B8Low | 2.20% | 2.30% | 2.40% | |||
B8Mid | New sales conversion rate | B3*(1+B4) | 2.30% | 2.50% | 2.60% | |
B8High | 2.40% | 2.60% | 2.80% | |||
B9Low | 5% | 8% | 10% | |||
B9Mid | Increase in top-of-funnel prospects | Interviews | 8% | 13% | 15% | |
B9High | 10% | 15% | 20% | |||
B10Low | $346,500 | $2,173,500 | $4,536,000 | |||
B10Mid | Subtotal: Profit gain from top-of-funnel growth | B1*B2*B5*B6*B8*B9 | $760,725 | $5,118,750 | $12,285,000 | |
B10High | $1,512,000 | $9,828,000 | $28,224,000 | |||
BtLow | $976,500 | $5,953,500 | $12,096,000 | |||
BtMid | Profit gain from better engagement with prospective customers | B7+B10 | $2,083,725 | $12,993,750 | $31,185,000 | |
BtHigh | $4,032,000 | $24,948,000 | $68,544,000 | |||
Three-year projected total: $19,026,000 to $97,524,000 | Three-year projected present value: $14,895,879 to $75,781,758 |
Evidence and data. Interviewees shared how using Azure OpenAI Service enabled their organizations to deflect more contact center requests with their chatbots. This allowed human agents to focus on more complex engagements in which human intervention is crucial without overwhelming them with simple queries now handled by the chatbot. Having the AI-enabled chatbot also empowers agents to have all up-to-date information at their fingertips, which they can use to seamlessly address the issue at hand. Forrester research found that while contact center hosts massive amounts of unstructured customer and interaction data, they rarely have access to customer and interaction insights.4 This gap is easily filled by having AI-enabled chatbots that can access those data points and information as needed.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
Results. This yields a three-year projected PV ranging from $7.5 million (low) to $17.5 million (high).
Base: 20 representatives of organizations
with
experience using Azure OpenAI Service
Source: A
study
conducted by Forrester Consulting on behalf of Microsoft, July
2024
Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
---|---|---|---|---|---|---|
C1 | Calls handled | Composite | 4,000,000 | 4,104,000 | 4,210,704 | |
C2 | Percentage of calls that are routed to a human agent | Composite | 50% | 50% | 50% | |
C3Low | 10% | 15% | 20% | |||
C3Mid | Percentage reduction in calls that require a human agent | Interviews | 15% | 25% | 35% | |
C3High | 20% | 35% | 50% | |||
C4 | Cost per contact at contact center | Forrester research | $10 | $10 | $10 | |
CtLow | $2,000,000 | $3,078,000 | $4,210,704 | |||
CtMid | Improvement in contact center chatbot resolution | C1*C2*C3*C4 | $3,000,000 | $5,130,000 | $7,368,732 | |
CtHigh | $4,000,000 | $7,182,000 | $10,526,760 | |||
Three-year projected total: $9,288,704 to $21,708,760 | Three-year projected present value: $7,525,548 to $17,480,811 |
Reduction in calls requiring human agent
10% – 50%
“Once we improve chatbot accuracy, we should be able to reduce 20% to 30% of the agents’ and associates’ time.”
EVP, retail and consumer goods
Evidence and data. Interviewees noted that the amount of content their organizations can create with Azure OpenAI Service is now significantly higher than it was before. In most cases, this meant their teams of content creators could essentially become content reviewers. Beyond creating more content, the organizations could now ask their generative AI tools to personalize short- or long-form content, ensuring brand consistency and achieving personalization at scale.5
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
Results. This yields a three-year projected PV ranging from $4.8 million (low) to $10.9 million (high).
Base: 20 representatives of organizations
with
experience using Azure OpenAI Service
Source: A
study
conducted by Forrester Consulting on behalf of Microsoft, July
2024
Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
---|---|---|---|---|---|---|
D1 | FTEs involved in content generation | Composite | 500 | 500 | 500 | |
D2 | Average percentage of time related to content generation | Composite | 50% | 50% | 50% | |
D3 | 10% | 20% | 30% | |||
D3 | Percentage of time savings in content generation using Azure OpenAI Service | Interviews | 15% | 50% | 55% | |
D3 | 20% | 55% | 60% | |||
D4 | Fully burdened salary for an FTE involved in content generation | TEI standard | $80,000 | $80,000 | $80,000 | |
D5 | Productivity recapture | TEI standard | 50% | 50% | 50% | |
DtLow | $1,000,000 | $2,000,000 | $3,000,000 | |||
DtMid | Go-to-market content generation efficiency | D1*D2*D3*D4*D5 | $1,500,000 | $5,000,000 | $5,500,000 | |
DtHigh | $2,000,000 | $5,500,000 | $6,000,000 | |||
Three-year projected total: $6,000,000 to $13,500,000 | Three-year projected present value: $4,815,928 to $10,871,525 |
Productivity gain in content generation:
10% – 60%
“We can now do 1,000-plus product descriptions per month, whereas previously, we could only do 10% of that.”
Data lead, retail and consumer goods
Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:
Efficiency and productivity gains in product development and management. Interviewees also shared that using Azure OpenAI Service enabled faster connection between customer input and product development. This means customer feedback received by call centers or store associates could be incorporated into future product updates sooner. The chatbots could support engineers and other employees involved in building the product to access the latest customer feedback. The EVP at a retail and consumer goods company said, “It opens up a huge backlog of work that my engineers needed to do. I’m freeing up a significant portion of my engineers' time to focus on the next innovative idea.”
The CTO at an investment management company added a similar sentiment of how having genAI capabilities through Azure AI Service allowed different roles in different departments pull insights themselves if needed. They noted: “People can now go further and do something themselves. If I have a great idea that requires some data engineering or manipulation, genAI can analyze that for me.”
“We want our lawyers to feel happier because we are supporting them [by] making it easier to do their jobs. Giving them Azure OpenAI to empower their work is one way to do this.”
Attorney, government
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement Azure OpenAI Service and later realize additional uses and business opportunities, including:
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Appendix A).
“[We do] business with various state [government agencies]. As we try to enter new states, we go through an RFP process. These efficiency gains [from using Azure OpenAI Service] will be part of our pitch and competitive advantage, showing how we are doing something different to competitors.”
VP, healthcare
Proper AI governance. Understanding the level of personal and private data that can be involved, genAI systems can generate a wide range of risks, including ethical ones. By establishing clear guidance and standards for implementing genAI, organizations can mitigate those risks and ensure their application and use cases are done responsibly.
The CTO at a financial services and insurance company said: “[Our success with genAI] is reliant on the data we have as well as our people’s understanding of the data. A good relationship with our data and making sure we can make the best of it can hugely impact the variable outcome that we get from our investment.”
Change management. GenAI is an ever-evolving technology that is rapidly changing and increasingly becoming more integrated in everyday work. It is essential that employees across the organization understand how to use these tools effectively, ethically, and securely. Without change management, user training, guardrails, and guidance, adoption can prove to be slow or haphazard. The head of data at a telecommunications organization shared: “Right now, in the pilot, we’re still seeing challenges related to agent behavior. They have a hard time letting go of past behaviors [that we are trying to change].”
Data quality. Generative AI models rely on high-quality data to produce accurate and relevant outputs. Poor data quality can lead to inaccurate or biased results, which can negatively impact decision-making and business outcomes. The AI lead supporting government agencies shared: “We are data-rich, but not very data-organized. This might take us seven or eight months [or] even a year to get access to data, clean it up, and make it ready for the bots.”
Regulation. Externally, regulation is key. If regulation can keep pace with technology, there should be better adoption and movement. Alternatively, if organizations face challenges in understanding and complying with new rules, this can lead to delays in implementing genAI-related projects.
“Having the genAI model itself is not the true competitive advantage because the barrier to entry in adopting this technology is so low. The difference is how you can adopt it at scale. How well can you integrate it to your date. How well do you democratize that insight and capability so the right people in your organization can use it in the best way in their customer interaction.”
CTO, investment management
Forrester assumes a yearly discount rate of 10% for this analysis.
These risk-adjusted present value numbers are determined by applying risk-adjustment factors to the unadjusted results in each Benefit section.
New Technology: Projected Total Economic Impact (New Tech TEI) is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists vendors in communicating the value proposition of their products and services to clients. The New Tech TEI methodology helps companies demonstrate and justify the projected tangible value of IT initiatives to both senior management and other key business stakeholders.
Projected Benefits represent the projected value to be delivered to the business by the product.
Flexibility represents the strategic value that can be obtained for some future additional investment building on top of the initial investment already made. Having the ability to capture that benefit has a PV that can be estimated.
Risks measure the uncertainty of benefit estimates given: 1) the likelihood that estimates will meet original projections and 2) the likelihood that estimates will be tracked over time. TEI risk factors are based on “triangular distribution.”
The present or current value of (discounted) benefit estimates given at an interest rate (the discount rate).
The interest rate used in cash flow analysis to take into account the time value of money. Organizations typically use discount rates between 8% and 16%.
Cash flows are discounted using the discount rate at the end of the year. PV calculations are calculated for each total benefit estimate. Sums and present value calculations of the Total Benefits tables may not exactly add up, as some rounding may occur.
1 Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists vendors in communicating the value proposition of their products and services to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of IT initiatives to both senior management and other key business stakeholders.
2 Source: Forrester’s Q2 2023 B2B Customer Engagement Value Realization Survey.
3 Source: Generative AI: What It Means For Knowledge Management, Forrester Research, Inc., July 7, 2023.
4 Source: Design An Insights-Driven Contact Center, Forrester Research, Inc., January 5, 2023.
5 Source: Generative AI Brings Superpowers To Portfolio Marketers, Forrester Research, Inc., March 5, 2024.
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