New Technology: The Projected Total Economic Impact™ Of Adobe Acrobat AI Assistant
Cost Savings And Business Benefits Enabled By AI Assistant
A Forrester New Technology Projected Total Economic Impact™ Study Commissioned By Adobe, January 2025
Generative AI (genAI) has quickly emerged as a transformative force, enabling companies to improve productivity, innovation, cost efficiency, and revenue.1 Users of Adobe’s AI Assistant for Acrobat may achieve significant time savings in document-centric work and processes. Based on pilot testing, AI Assistant can significantly improve employee productivity and drive operational efficiency gains.
Acrobat AI Assistant is a conversational genAI feature that enables users to interact with their documents, including PDFs, meeting transcripts, scans, contracts, slide presentations, and documents to quickly generate comprehensive summaries, insights, and content. Integrated into Adobe Acrobat, AI Assistant can empower teams to transform document-based processes and improve productivity. With attribution features, data controls, and ease of deployment and management, AI Assistant can enable organizations to unlock the potential of genAI within their documents to enhance time to insight and creation of new content based on source documents. AI Assistant supports a range of file types, including PDFs, DOCX, PPTX, TXT, and RTF.
Adobe commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying Acrobat AI Assistant.2 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of AI Assistant on their organizations.
Projected return on investment (ROI)
176% - 415%176% - 415%
Projected net present value (NPV)
$930K - $2.2M$930K - $2.2M
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed eight representatives of six organizations with experience using AI Assistant. For the purposes of this study, Forrester aggregated the interviewees’ experiences and combined the results into a single composite organization that is a global organization with 5,000 employees.
Interviewees said that prior to using AI Assistant, employees at their organizations struggled with time-consuming digital document workflows. They often had to manually summarize lengthy documents, extract key insights, and repurpose them into other content formats. As a result, employees experienced productivity limitations that diminished their capacity and ability to engage in strategic initiatives.
Based on pilot programs with AI Assistant, interviewees noted that users achieved time savings for document-related work, enabling their organizations to streamline relevant processes and improve operational capacity. Key results from the investment include better productivity, operational efficiency gains, and improved employee experience (EX).
Key Findings
Quantified projected benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
- Document summarization and analysis efficiency improvement of up to 45%. The composite organization improves employee productivity with AI Assistant as users experience a reduction in the time required to summarize and review documents. Over three years, the improved productivity is worth between $1.1 million and $2.1 million.
For , this benefit could be worth between and .
- Content generation efficiency increase of up to 30%. The composite organization enhances operational efficiency as employees leverage AI Assistant to streamline document creation workflows. With AI Assistant, users produce document-driven deliverables and complete associated workflows with greater speed. Over three years, the efficiency improvement is worth between $359,000 and $569,000 to the composite organization.
For , this benefit could be worth between and .
Unquantified benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
- Data security. AI Assistant addresses the composite organization’s data governance and security requirements as customer data is not used to train its model, documents are not indexed, and the tool only accesses documents opened in Acrobat.
- Increased accuracy. AI Assistant provides users with attribution information alongside responses, enabling them to verify the accuracy and ensure that generated content and insights are reliable to use. Additionally, responses are generated based solely on uploaded documents rather than internet sources, offering additional layers of accuracy and trustworthiness.
- Improved EX. AI Assistant helps users reduce tedious document review and analysis work and improve content development efficiency, freeing them to focus on more valuable work.
- Faster time to value. Access to AI Assistant is easily enabled through the Acrobat management console, enabling users at the composite organization to quickly start leveraging the tool.
Flexibility. In the long term, the composite organization can achieve business outcomes, including:
- Revenue growth. AI Assistant contributes to revenue growth by helping the composite organization’s sales teams leverage background documents to better understand prospects and deliver more personalized content, messaging, and proposals. Document analysis and content generation efficiencies, free up more time for sellers to directly engage with buyers.
- Long-term business value. The composite organization realize greater business value over time as users develop new use cases for AI Assistant and new product enhancements and improvements are released.
Costs. Three-year, risk-adjusted PV costs for the composite organization include:
- AI Assistant subscription costs. The composite organization pays a monthly subscription cost of $4.99 per user, totaling $188,000 over three years.
For , this cost could total over three years.
- Implementation, training, and ongoing management labor. The composite organization incurs labor costs associated with a pilot program, user administration, training, and ongoing management totaling $339,000 over three years.
For , this cost could total over three years.
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 net present value (NPV) for each scenario by enabling Adobe AI Assistant:
- Projected high impact of a $2.2 million NPV and projected ROI of 415%.
For , the projected high impact could have an NPV of and PROI of 0%.
- Projected medium impact of a $1.6 million NPV and projected ROI of 296%.
For , the projected medium impact could have an NPV of and PROI of 0%.
- Projected low impact of a $930,000 NPV and projected ROI of 176%.
For , the projected low impact could have an NPV of and PROI of 0%.
Key Statistics
-
Projected return on investment (PROI):
176% - 415%176% - 415%
-
Projected benefits PV:
$1.5 - 2.7M$1.5 - 2.7M
-
Projected net present value (PNPV):
$930K - $2.2M$930K - $2.2M
-
Total costs:
$527K$527K
Three-Year Projected Financial Analysis For The Composite Organization
Initial Year 1 Year 2 Year 3 High impact NPV Mid impact NPV Low impact NPV PROI ofNew Tech TEI Framework And Methodology
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 AI Assistant.
The objective of the framework is to identify the potential cost, benefit, flexibility, and risk factors that affect the investment decision. Forrester took a multistep approach to evaluate the projected impact that AI Assistant can have on an organization.
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Due Diligence
Interviewed Adobe stakeholders and Forrester analysts to gather data relative to AI Assistant.
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Early-Implementation Interviews
Interviewed eight representatives at six organizations using AI Assistant in a pilot or beta stage to obtain data about projected costs, benefits, and risks.
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Composite Organization
Designed a composite organization based on characteristics of the interviewees’ organizations.
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Projected Financial Model Framework
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.
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Case Study
Employed four fundamental elements of New Tech TEI in modeling the investment’s potential impact: benefits, costs, flexibility, and risks. Given the increasing sophistication of ROI 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.
Disclosures
Readers should be aware of the following:
This study is commissioned by Adobe and delivered by Forrester Consulting. It is not meant to be used as a competitive analysis.
Forrester makes no assumptions as to the potential ROI 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 AI Assistant.
Adobe 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.
Adobe provided the customer names for the interviews but did not participate in the interviews.
Consulting Team:
Kara Luk
Drivers leading to the AI Assistant investment
Interviews
| Role | Industry | Region | Employees |
|---|---|---|---|
| Business manager | Legal | APAC HQ, national operations | 4 |
| VP of financial operations and digital initiatives | Financial services | US HQ, multistate operations | 200 |
| Global head of data and analytics | Financial services | EMEA HQ, global operations | 360 |
| Chief digital officer | Legal | APAC HQ, national operations | 450 |
|
Chief technology officer IT desktop manager Chief AI strategy and transformation officer |
Government | US HQ, local operations | 1,800 |
| Global Adobe technical lead | Professional services | US HQ, global operations | 57,000 |
Key Challenges
Interviewees noted that before piloting AI Assistant, their organizations struggled with common challenges, including:
-
Document overload. Interviewees reported that many employees at their organizations were required to review and analyze large volumes of documents as part of their roles. These documents necessitated users to thoroughly read and understand the content, summarize key details for other stakeholders, search for specific information, and repurpose the content into various forms of communication, deliverables, and presentations. Interviewees shared that these documents could sometimes be hundreds of pages long and that it could take several hours to read, summarize, and search through them to find specific information.
The global head of data and analytics at a financial services organization said: “We receive large documents like prospectuses from fund managers and quarterly reports from our managers. People need to understand and go through them in detail.” The high volume of documents reduced time available for other tasks, increased the risk of oversight, and made it difficult for team members to stay aligned on document insights. - Limited time available for heads-down work due to meetings. Interviewees shared that meetings reduced time available for focused, document-centric work. A global Adobe technical lead at a professional services organization shared that many employees spent significant portions of their workdays in meetings, which decreased their capacity for day-to-day work and led to multitasking in some instances. They mentioned that this issue was even more severe with executives who often handled even greater volumes of meetings and documents to read and analyze.
- Capacity impacted by resource constraints. Several interviewees shared that their organizations faced challenges with resource constraints and operational efficiency because employees were at the maximum capacity of their workloads. This made it difficult to grow operations without impact on quality and employee experience. For example, the VP of financial operations and digital initiatives at a financial services organization shared that their company struggled with resource constraints that reduced the ability to perform various tasks (e.g., member risk evaluations) with greater frequency.
Composite Organization
Based on the interviews, Forrester constructed a TEI framework, a composite company, and an ROI analysis that illustrates the areas financially affected. The composite organization is representative of the eight interviewees, 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 organization is a global organization with 5,000 employees. A majority of the employees have access to Adobe Acrobat, and PDFs play a critical role in operations across departments by driving workflows and facilitating communication.
Deployment characteristics. The composite organization rolls out AI Assistant over three years, with 10% of employees receiving access in Year 1, 25% receiving access in Year 2, and 40% receiving access in Year 3. In Year 1, the composite primarily makes AI Assistant available to employees with roles that involve significant review and analysis of PDFs (e.g., those in legal and finance departments), enabling the composite organization to concentrate on high-value use cases.
Quantified benefit data as applied to the composite
Total Projected Benefits
| Projected Benefits | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|
| Total projected benefits (low) | $224,900$224,900 | $555,760$555,760 | $1,055,600$1,055,600 | $1,836,260$1,836,260 | $1,456,849$1,456,849 |
| Total projected benefits (mid) | $369,860$369,860 | $812,500$812,500 | $1,432,600$1,432,600 | $2,614,960$2,614,960 | $2,084,058$2,084,058 |
| Total projected benefits (high) | $514,800$514,800 | $1,069,260$1,069,260 | $1,809,600$1,809,600 | $3,393,660$3,393,660 | $2,711,265$2,711,265 |
Document Summarization And Analysis Efficiency
Evidence and data. Interviewees highlighted that AI Assistant significantly decreased the amount of time required to review and analyze PDFs, improving user productivity. Interviewees shared the following experiences:
- The interviewees at a government organization shared that AI Assistant had an impact on pilot users that spanned several departments (e.g., legal, HR, administration, and IT) for workflows involving document review and analysis. The organization’s chief AI strategy and transformation officer shared that AI Assistant improved productivity for legal personnel by helping them review, interrogate, and summarize contracts and other legal agreements. Based on the pilot experiences, the interviewees estimated a 50% time savings for document review and summarization efforts across departments, and they noted the time savings could be greater depending on the task.
- The chief digital officer at a legal organization noted that AI Assistant enhanced productivity across various teams, including legal, IT, and executive leadership. They shared that it enabled their legal team to quickly review detailed documents (e.g., legislation changes) and summarize key information for distribution among team members. Executive team members benefitted from concise summaries of various documents, enabling them to stay informed despite their busy schedules. Additionally, IT personnel used AI Assistant to review and extract insights from complex technical documents. The interviewee estimated that it could take 1 hour to read an average document and an additional hour to summarize it without AI Assistant. But they said AI Assistant allowed for the completion of both tasks in 1 hour, equating to a 50% productivity improvement for users.
- Similarly, the business manager at a legal organization estimated that the time required to review and summarize documents (e.g., legal briefs) reduced from between 30 and 45 minutes down to just 5 minutes.
- A global Adobe technical lead at a professional services organization shared that pilot users had experienced an estimated 50% to 75% reduction in time spent on document summarization work. For instance, they said the organization’s client delivery team utilized AI Assistant to digest and summarize client briefs and slide decks. Additionally, the organization’s executive team gained the ability to quickly absorb critical information from various documents and slide decks, which proved especially helpful given the high number of meetings and documents the team was required to manage.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite has 500 AI Assistant users in Year 1, 1,250 in Year 2, and 2,000 in Year 3.
might have 0 AI Assistant users in Year 1, 0 in Year 2, and 0 in Year 3.
- These AI Assistant users reduce the time it takes to summarize and analyze PDFs by 15% to 35% in Year 1 of the investment. As users increase the frequency with which they use the tool, improve prompt writing skills, and reduce the need to refine prompts, time savings increase to between 25% and 45% by Year 3.
- In Year 1, 30% of the composite’s AI Assistant users are in roles that require heavy use of PDFs, and they spend 6 hours per week summarizing and analyzing the documents. As AI Assistant is democratized to a broader range of users across roles in the following years, the percentage of heavy users decreases to 13% in Year 2 and to 9% in Year 3.
For , the percentage of AI Assistant users in roles that require heavy use of PDFs might be 0% in Year 1, 0% in Year 2, and 0% in Year 3.
- The percentage of AI Assistant users who manage typical volumes of PDFs in their workflows is 70% in Year 1, 87% in Year 2, and 91% in Year 3. These users spend 1 hour per week reviewing and analyzing PDFs.
For , the percentage of AI Assistant users in roles that require typical volumes of PDFs might be 0% in Year 1, 0% in Year 2, and 0% in Year 3.
- The average fully burdened salary for an AI Assistant user is $40 per hour.
At , the average fully burdened salary for an AI Assistant user might be $0 per hour.
- Each user recaptures 50% of time savings towards productive tasks.
Results. This yields a three-year projected PV ranging from $1.1 million (low) to $2.1 million (high).
For , the three-year projected PV could range from (low) to (high).
Employee Productivity Module: Range Of Three-Year Cumulative Impact
High projection Mid projection Low projectionDocument Summarization And Analysis Efficiency
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| A1 | AI Assistant users | C3 | 500500 | 1,2501,250 | 2,0002,000 | |
| A2low | 15%15% | 20%20% | 25%25% | |||
| A2mid | Time savings for PDF summarization and analysis | InterviewsInterviews | 25%25% | 30%30% | 35%35% | |
| A2high | 35%35% | 40%40% | 45%45% | |||
| A3 | Percent of AI Assistant users who utilize large volumes of PDFs | CompositeComposite | 30%30% | 13%13% | 9%9% | |
| A4 | Time large-volume users spent per week summarizing and analyzing PDFs before AI Assistant (hours) | CompositeComposite | 66 | 66 | 66 | |
| A5low | 7,0207,020 | 10,14010,140 | 14,04014,040 | |||
| A5mid | Time large-volume users save on summarization and analysis efforts (hours) | A1*A2*A3*A4*52 weeks | 11,70011,700 | 15,21015,210 | 19,65619,656 | |
| A5high | 16,38016,380 | 20,28020,280 | 25,27225,272 | |||
| A6 | Percent of AI Assistant users who utilize typical volumes of PDFs | 100%-A3 | 70%70% | 87%87% | 91%91% | |
| A7 | Time typical-volume users spent per week summarizing and analyzing PDFs before AI Assistant (hours) | CompositeComposite | 11 | 11 | 11 | |
| A8low | 2,7302,730 | 11,31011,310 | 23,66023,660 | |||
| A8mid | Time typical-volume users save on summarization and analysis efforts (hours) | A1*A2*A6*A7*52 weeks | 4,5504,550 | 16,96516,965 | 33,12433,124 | |
| A8high | 6,3706,370 | 22,62022,620 | 42,58842,588 | |||
| A9 | Average fully burdened hourly salary for a user | CompositeComposite | $40$40 | $40$40 | $40$40 | |
| A10 | Productivity recapture rate | TEI standard | 50%50% | 50%50% | 50%50% | |
| Atlow | $195,000$195,000 | $429,000$429,000 | $754,000$754,000 | |||
| Atmid | Document summarization and analysis efficiency | (A5+A8)*A9*A10 | $325,000$325,000 | $643,500$643,500 | $1,055,600$1,055,600 | |
| Athigh | $455,000$455,000 | $858,000$858,000 | $1,357,200$1,357,200 | |||
| Three-year projected total: $1,378,000$1,378,000 to $2,670,200$2,670,200 | Three-year projected present value: $1,098,310$1,098,310 to $2,142,412$2,142,412 | |||||
Content Development Efficiency
Evidence and data. Based on pilot testing, interviewees said they saw opportunities to streamline processes and improve capacity through content-generation efficiencies. Interviewees shared the following:
- The VP of financial operations and digital initiatives at a financial services organization explored several use cases aimed at improving capacity through faster content development. One such application involved using AI Assistant to help advisors review bond security prospectuses and generate reports on their suitability as collateral. Based on pilot testing, the interviewee estimated that it could reduce the typical review process from between 25 to 45 minutes down to just 5 minutes. Another tested use case involved member credit evaluation in which AI Assistant reviewed financial statements and credit reports and extracted relevant data to generate reports on the financial health of members. Additionally, the interviewee said they saw an opportunity to utilize AI Assistant alongside a comprehensive library of the organization’s regulations and policies and enable chat support and member services teams to more quickly and accurately craft responses to inquiries.
- A global Adobe technical lead at a professional services organization shared that AI Assistant helped teams produce client deliverables more efficiently. For instance, one team utilized AI Assistant to develop internal documentation, such as client brand strategy documents, pitches, and client content deliverables. The interviewee estimated a 50% to 75% reduction in time required for these tasks. The interviewee also noted that developers working on client websites and applications found efficiency in using AI Assistant to navigate brand guideline documents, which could often be up to 300 pages long. This helped them reduce the time spent searching for specific information and minimize rework.
- The chief AI strategy and transformation officer at a government organization shared that AI Assistant led to early operational efficiency gains, particularly within the HR department. For example, by leveraging AI Assistant to compare and benchmark job descriptions against peer organizations and help tailor the organization’s job descriptions to improve recruitment efforts, the HR team completed the activity in 5 hours when it was estimated to take a week without it. By streamlining processes with AI Assistant, the department gained greater capacity to work on other critical tasks and creative, strategic, and innovative work.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite has 500 AI Assistant users in Year 1, 1,250 in Year 2, and 2,000 in Year 3.
might have 0 AI Assistant users in Year 1, 0 in Year 2, and 0 in Year 3.
- With AI Assistant, the composite’s users experience a 10% to 20% reduction in the time it takes to develop content in Year 1 of the investment. As users improve the frequency and proficiency with which they utilize AI Assistant, the time savings increase to between 20% and 30% by Year 3.
- In Year 1, 5% of the composite’s AI Assistant users are involved in large volumes of content generation activities for job-specific tasks, and they spend 2 hours per week on content-development efforts in which PDFs are leveraged. As employees discover more opportunities to leverage AI Assistant for content creation, the percentage grows to 10% in Year 2 and to 15% in Year 3.
- The remaining population of users are involved in lower volumes of content development, and they spend 30 minutes per week developing content in which PDFs are leveraged. The percentage of AI Assistant users in this population is 95% in Year 1, 90% in Year 2, and 85% in Year 3.
- The average fully burdened salary for an AI Assistant user is $40 per hour.
At , the average fully burdened salary for an AI Assistant user might be $0 per hour.
- Each user recaptures 50% of time savings toward productive tasks.
Results. This yields a three-year projected PV ranging from $359,000 (low) to $569,000 (high).
For , the three-year projected PV could range from (low) to (high).
Operating Efficiency Module: Range Of Three-Year Cumulative Impact
Content Development Efficiency
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| B1 | AI Assistant users | C3 | 500500 | 1,2501,250 | 2,0002,000 | |
| B2low | 10%10% | 15%15% | 20%20% | |||
| B2mid | Content development time savings with AI Assistant | InterviewsInterviews | 15%15% | 20%20% | 25%25% | |
| B2high | 20%20% | 25%25% | 30%30% | |||
| B3 | Percent of AI Assistant users involved in large volumes of content development | CompositeComposite | 5%5% | 10%10% | 15%15% | |
| B4 | Time large-volume users spend per week on content development leveraging PDFs (hours) | CompositeComposite | 22 | 22 | 22 | |
| B5low | 260260 | 1,9501,950 | 6,2406,240 | |||
| B5mid | Time large-volume users save on content development efforts (hours) | B1*B2*B3*B4*52 weeks | 390390 | 2,6002,600 | 7,8007,800 | |
| B5high | 520520 | 3,2503,250 | 9,3609,360 | |||
| B6 | Percent of AI Assistant users with typical content development needs | 100%-B3 | 95%95% | 90%90% | 85%85% | |
| B7 | Time typical users spend per week on content development leveraging PDFs (hours) | CompositeComposite | 0.50.5 | 0.50.5 | 0.50.5 | |
| B8low | 1,2351,235 | 4,3884,388 | 8,8408,840 | |||
| B8mid | Time typical users save on content development efforts (hours) | B1*B2*B6*B7*52 weeks | 1,8531,853 | 5,8505,850 | 11,05011,050 | |
| B8high | 2,4702,470 | 7,3137,313 | 13,26013,260 | |||
| B9 | Average fully burdened hourly salary for a user | A9 | $40$40 | $40$40 | $40$40 | |
| B10 | Productivity recapture rate | TEI standard | 50%50% | 50%50% | 50%50% | |
| Btlow | $29,900$29,900 | $126,760$126,760 | $301,600$301,600 | |||
| Btmid | Content development efficiency | (B5+B8)*B9*B10 | $44,860$44,860 | $169,000$169,000 | $377,000$377,000 | |
| Bthigh | $59,800$59,800 | $211,260$211,260 | $452,400$452,400 | |||
| Three-year projected total: $458,260$458,260 to $723,460$723,460 | Three-year projected present value: $358,539$358,539 to $568,853$568,853 | |||||
Unquantified Benefits
Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:
- Data security. The global Adobe technical lead at a professional services organization said: “One of the key benefits of AI Assistant is that data isn’t stored or used to train the model. It is private to our company and private to my identity within the company. The information is not retained once it’s used for the process. So, that’s a key benefit for me. It follows under our data security and privacy policies.”
- Increased accuracy. Interviewees said AI Assistant provided users at their organizations with attribution information alongside generated responses, highlighting specific sections of a document from which answers were derived. This enabled users to verify the accuracy and origins of information to ensure insights and generated content were reliable.
- Improved EX. Interviewees shared that AI Assistant helped pilot users reduce tedious PDF-related work and improve content-development efficiency, which freed them to focus on more enjoyable or strategic tasks. The chief technology officer at a government organization said: “That mundane work can now be done with AI Assistant. Who wants to read a 150-page document when you can summarize it?? If you do it with AI instead, that allows you to do something else with it that you may enjoy more.”
- Faster time to value. Interviewees said user access to AI Assistant was easily enabled through the Acrobat management console, which enabled their organizations to quickly leverage the tool and achieve time to value during pilot testing.
Flexibility
Flexibility represents business outcomes, unique use cases, and opportunities that a customer may realize in the future after implementing AI Assistant. The value of flexibility is unique to each customer and may require additional investment on top of the initial investment already made. These flexibilities can include:
- Revenue growth. Forrester research finds that genAI can contribute to revenue growth in several ways, such as by enabling sales teams.5 For example, sellers can use genAI to analyze and improve their understanding of prospects and deliver highly personalized content, messaging, and proposals. Additionally, genAI can help reduce time spent on administrative tasks, such as searching for sales content, researching products and solutions, and preparing presentations and assisting with email generation, RFP response, and proposal configuration. This can enable sellers to spend more time directly engaging with buyers. While many of the interviewees’ organizations were early in the process of exploring such use cases, the global Adobe technical lead at a professional services organization highlighted that a sales team had used AI Assistance to analyze background briefs and respond to RFPs for new business opportunities.
- Long-term business value. Interviewees said their organizations will realize greater business value over time as users develop new use cases and as new AI Assistant capabilities are released. The chief technology officer at a government organization said: “If we democratize access to everyone, I think it will help build more use cases and creativity. Until you get it into the hands of the people who are doing the work, there may be some ideas that we may not even be aware of at the moment. I think we are going to find more efficiencies and create some really interesting use cases that are going to drive long-term value for our organization. And we’re going to see a lot of ROI by doing that.”
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Appendix A).
Quantified cost data as applied to the composite
Total Costs
| Ref. | Cost | Initial | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|---|
| Ctr | Acrobat AI Assistant subscription | $0$0 | $31,437$31,437 | $78,593$78,593 | $125,748$125,748 | $235,778$235,778 | $188,008$188,008 |
| Dtr | Implementation, training, and ongoing management labor | $31,504$31,504 | $85,259$85,259 | $133,659$133,659 | $133,659$133,659 | $384,080$384,080 | $319,894$319,894 |
| Total costs (risk adjusted) | $31,504$31,504 | $116,696$116,696 | $212,251$212,251 | $259,407$259,407 | $619,858$619,858 | $507,902$507,902 | |
Acrobat AI Assistant Subscription
Evidence and data. Interviewees said their organizations pay a monthly subscription cost of $4.99 per user for AI Assistant. Pricing may vary. Contact Adobe for additional details.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite organization has 5,000 total employees.
has 0 total employees.
- In Year 1, 10% of employees begin using AI Assistant. By Year 3, this number increases to 40%.
- The composite organization incurs a monthly subscription price of $4.99 per user.
Risks. Results may not be representative of all experiences and the cost may vary between organizations depending on the following factors:
- The size of the organization and its adoption rate for AI Assistant.
- Future adjustments to pricing mechanics.
Results. To account for these risks, Forrester adjusted this cost upward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $188,000.
For , the three-year, risk-adjusted total PV could be .
Acrobat AI Assistant Subscription
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| C1 | Employees | CompositeComposite | 5,0005,000 | 5,0005,000 | 5,0005,000 | |
| C2 | Percent of employees who receive AI Assistant | CompositeScaled for | 10%10% | 25%25% | 40%40% | |
| C3 | Users | C1*C2 | 500500 | 1,2501,250 | 2,0002,000 | |
| C4 | Price per month | CompositeTEI case study | $4.99$4.99 | $4.99$4.99 | $4.99$4.99 | |
| Ct | Acrobat AI Assistant subscription | C3*C4*12 months | $29,940$29,940 | $74,850$74,850 | $119,760$119,760 | |
| Risk adjustment | ↑5% | |||||
| Ctr | Acrobat AI Assistant subscription (risk-adjusted) | $0$0 | $31,437$31,437 | $78,593$78,593 | $125,748$125,748 | |
| Three-year total: $235,778$235,778 | Three-year present value: $188,008$188,008 | |||||
Implementation, Training, And Ongoing Management Labor
Evidence and data. Interviewees shared that their organizations incurred initial testing and use-case discovery costs and that they expect minimal administration and ongoing management labor in a full deployment scenario.
- Pilot testing. Several interviewees shared that their organization engaged in pilot testing by organizing small committees of users from different departments. These committee members conducted initial testing, documented use cases and prompts, and shared ideas and feedback. The chief AI strategy and transformation officer in government said: “We officially launched the beta program to a group of about 30 within the organization. They were people who were of high interest and people who were power users, spanning all departments, including legal, HR, clerks, administration, and IT. It was an opportunity to champion the cause and really test the product.”
- User administration and ongoing management. Interviewees reported that the IT effort required to administer access to pilot users was minimal, therefore they anticipate that larger-scale deployments in the future will be equally straightforward. The global Adobe technical lead at a professional services organization shared that access to AI Assistant was easily provisioned through the Adobe admin console. And interviewees said that due to the simplicity of the tool, they expect minimal needs for ongoing management.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- During the initial period of the investment, the composite organization organizes a champion program initiative involving 25 employees across various departments to test AI Assistant, document use cases, develop prompts, and share ideas and feedback. Each member of the champion program dedicates 8 hours to the initiative.
At , the champion program initiative might involve 0 employees.
- During the initial period, two director-level resources lead the champion program initiative, build the business case for the investment, secure buy-in and budget for the investment, and help produce documentation and communications around the rollout. Each resource dedicates 80 hours to the effort and has an hourly fully burdened salary of $100.
At , 0 director-level resources might lead the champion program initiative, and each resource might have an hourly fully burdened salary of $0.
- One IT resource dedicates 80 hours to the implementation process, administers user access to AI Assistant, develops documentation and governance policies, and rolls out communications.
At , 0 IT resource hours might be dedicated to implementation.
- Each new user participates in training and discovery on AI Assistant: This includes 475 new users in Year 1, 750 new users in Year 2, and 750 new users in Year 3. During Year 1, users participate in 5 hours of training. As use cases and common prompts become well-documented and guidance is disseminated by peers, the required training drops to 4 hours in Year 2 and Year 3.
At , 0 new AI Assistant users might participate in training in Year 1, 0 new users might participate in training in Year 2, and 0 new users might participate in training in Year 3. At , all new users participate in 4 hours of training.
- The average hourly burdened salary for an AI Assistant user is $40.
At , the average fully burdened salary for an AI Assistant user might be $0 per hour.
- One IT resource dedicates 26 hours per year (30 minutes per week) to ongoing management activities, including administration for new users and troubleshooting.
At , 0 IT resource hours might be dedicated to ongoing management.
- The hourly fully burdened salary for an IT resource is $58.
At , the hourly fully burdened salary for an IT resource might be $0.
Risks. Results may not be representative of all experiences, and the cost may vary between organizations depending on the following factors:
- The organization’s adoption rate for AI Assistant.
- Pilot program efforts and the number of participating employees.
- Training time for new users and the amount of discovery time required for users to gain proficiency.
- Actual time required for IT to implement and manage AI Assistant.
Results. To account for these risks, Forrester adjusted this cost upward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $339,000.
For , the three-year, risk-adjusted total PV could be .
Implementation, Training, And Ongoing Management Labor
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| D1 | Champion program members | CompositeScaled for | 2525 | |||
| D2 | Testing and discovery time per member (hours) | InterviewsTEI case study | 88 | |||
| D3 | Average fully burdened hourly salary for a user | A9 | $40$40 | $40$40 | $40$40 | $40$40 |
| D4 | Champion program leaders | CompositeScaled for | 22 | |||
| D5 | Time dedicated to champion program per leader (hours) | CompositeTEI case study | 8080 | |||
| D6 | Average fully burdened hourly salary for a champion program leader | CompositeTEI standard | $100$100 | $100$100 | $100$100 | $100$100 |
| D7 | Subtotal: Champion program labor costs | D1*D2*D3+D4*D5*D6 | $24,000$24,000 | |||
| D8 | IT resource time dedicated to implementation (hours) | InterviewsScaled for | 8080 | |||
| D9 | Average fully burdened hourly salary for an IT administrator | CompositeTEI standard | $58$58 | $58$58 | $58$58 | $58$58 |
| D10 | Subtotal: Implementation labor costs | D8*D9 | $4,640$4,640 | |||
| D11 | Users who participate in training | Y1: Y1: C3-D1PY; Y2 and Y3: C3-C3PY | 475475 | 750750 | 750750 | |
| D12 | Training and discovery time per new user (hours) | InterviewsTEI case study | 55 | 44 | 44 | |
| D13 | Subtotal: Training costs | D11*D12*D3 | $76,000$76,000 | $120,000$120,000 | $120,000$120,000 | |
| D14 | IT time dedicated to ongoing management (hours) | InterviewsScaled for | 2626 | 2626 | 2626 | |
| D15 | Subtotal: Ongoing management labor | D9*D14 | $1,508$1,508 | $1,508$1,508 | $1,508$1,508 | |
| Dt | Implementation, training, and ongoing management labor | D7+D10+D13+D15 | $28,640$28,640 | $77,508$77,508 | $121,508$121,508 | $121,508$121,508 |
| Risk adjustment | ↑10% | |||||
| Dtr | Implementation, training, and ongoing management labor (risk-adjusted) | $31,504$31,504 | $85,259$85,259 | $133,659$133,659 | $133,659$133,659 | |
| Three-year total: $404,980$404,980 | Three-year present value: $338,894$338,894 | |||||
Consolidated Three-Year, Risk-Adjusted Metrics
Three-Year Projected Financial Analysis For The Composite Organization
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The financial results calculated in the Benefits and Costs sections can be used to determine the PROI and projected NPV for the composite organization’s investment. Forrester assumes a yearly discount rate of 10% for this analysis.
These risk-adjusted PROI and projected NPV values are determined by applying risk-adjustment factors to the unadjusted results in each Benefit and Cost section.
Cash Flow Analysis (Risk-Adjusted)
| Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
|---|---|---|---|---|---|---|
| Total costs | ($31,504)($31,504) | ($137,596)($137,596) | ($212,251)($212,251) | ($259,407)($259,407) | ($640,758)($640,758) | ($526,902)($526,902) |
| Total benefits (low) | $0$0 | $224,900$224,900 | $555,760$555,760 | $1,055,600$1,055,600 | $1,836,260$1,836,260 | $1,456,849$1,456,849 |
| Total benefits (mid) | $0$0 | $369,860$369,860 | $812,500$812,500 | $1,432,600$1,432,600 | $2,614,960$2,614,960 | $2,084,058$2,084,058 |
| Total benefits (high) | $0$0 | $514,800$514,800 | $1,069,260$1,069,260 | $1,809,600$1,809,600 | $3,393,660$3,393,660 | $2,711,265$2,711,265 |
| Net benefits (low) | ($31,504)($31,504) | $87,304$87,304 | $343,509$343,509 | $796,193$796,193 | $1,195,502$1,195,502 | $929,947$929,947 |
| Net benefits (mid) | ($31,504)($31,504) | $232,264$232,264 | $600,249$600,249 | $1,173,193$1,173,193 | $1,974,202$1,974,202 | $1,557,156$1,557,156 |
| Net benefits (high) | ($31,504)($31,504) | $377,204$377,204 | $857,009$857,009 | $1,550,193$1,550,193 | $2,752,902$2,752,902 | $2,184,363$2,184,363 |
| PROI (low) | 176%176% | |||||
| PROI (mid) | 296%296% | |||||
| PROI (high) | 415%415% | |||||
Appendix A: New Technology: Projected Total Economic Impact
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.
Total Economic Impact Approach
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Projected Benefits represent the projected value to be delivered to the business by the product. The New Tech TEI methodology places equal weight on the measure of projected benefits and the measure of projected costs, allowing for a full examination of the effect of the technology on the entire organization.
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Projected Costs consider all expenses necessary to deliver the proposed value, or benefits, of the product. The projected cost category within New Tech TEI captures incremental costs over the existing environment for ongoing costs associated with the solution.
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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.
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Risks measure the uncertainty of benefit and cost 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 initial investment column contains costs incurred at “time 0” or at the beginning of Year 1 that are not discounted. All other cash flows are discounted using the discount rate at the end of the year. PV calculations are calculated for each total cost and benefit estimate. NPV calculations in the summary tables are the sum of the initial investment and the discounted cash flows in each year. Sums and present value calculations of the Total Benefits, Total Costs, and Cash Flow tables may not exactly add up, as some rounding may occur.
Appendix B: Endnotes
1 Source: September 2023 Artificial Intelligence Pulse Survey, Forrester Research Inc., October 2023
2 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.
3 Source: Your Employees Aren’t Ready For Generative AI Tools, Forrester Research, Inc., November 21, 2024
4 Source: Prepare Your Entire Workforce For AI Now, Forrester Research, Inc., November 20, 2024
5 Source: Generative AI: What It Means For B2B Sales, Forrester Research, Inc., September 14, 2023
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