Total Economic Impact
Cost Savings And Business Benefits Enabled By PolyAI
A FORRESTER TOTAL ECONOMIC IMPACT STUDY COMMISSIONED BY PolyAI, JUNE 2025
Total Economic Impact
A FORRESTER TOTAL ECONOMIC IMPACT STUDY COMMISSIONED BY PolyAI, JUNE 2025
Self-service applications for customers and employees have consistently featured poor user experiences that garner disappointing uptake. But large language models and generative AI now enable chatbots and interactive virtual assistants that are smarter, more useful, and more conversational than before.1
PolyAI aims to improve the user experience in the customer services space with a human-like, conversational AI agent. It enables organizations to increase their capacity for handling large volumes of customer inquiries without adding overhead — while improving the customer experience by ensuring immediate responses to customer contacts.
PolyAI commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying PolyAI.2 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of PolyAI on their organizations.
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed four decision-makers with experience using PolyAI. For the purposes of this study, Forrester aggregated the experiences of the interviewees and combined the results into a single composite organization, which is a US-based, multibillion-dollar organization with 4 million calls per year and 200 agents.
Interviewees said that prior to using PolyAI, their organizations relied on interactive voice response (IVR) systems and hiring agents to handle large call volumes. However, this yielded limited success, leading to high costs and budget pressures surrounding labor, hiring challenges from high attrition rates and seasonality, high abandonment rates leading to missed revenue opportunities, and long wait times leading to low customer satisfaction scores.
After the investment in PolyAI, the interviewees realized agent labor savings through the automation of queries; avoided agent hiring, which led to avoiding new hire recruitment and training costs; increased profit from recaptured calls; and improved their customer experience.
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
More than 4.2 million calls resolved by PolyAI by Year 3. The composite organization deploys PolyAI to resolve 25% of calls in Year 1, 35% in Year 2, and 40% in Year 3. With automation from PolyAI, the composite saves 133,333 live agent hours in Year 1, 168,000 live agent hours in Year 2, and 191,100 live agent hours in Year 3. Over three years, agent labor savings enabled by PolyAI are worth more than $10.2 million to the composite organization.
92 new agent hires avoided by Year 3. PolyAI handles up to 40% of calls by Year 3, allowing the composite to manage its call volume without hiring new agents from natural attrition. The composite avoids 25 agent hires in Year 1, 31 agent hires in Year 2, and 36 agent hires in Year 3. Over the course of three years, the agent recruitment and training costs avoided by using PolyAI are worth more than $1.0 million to the composite organization.
Increased profit as a result of a 50% reduction in abandonment rate. PolyAI’s 24/7 availability and its ability to handle multiple calls simultaneously enable the composite to reduce its abandonment rate by improving the customer experience. The organization recaptures more than 211,000 missed revenue opportunities with PolyAI. And with more experienced agents, upselling increases by 20%. Over the course of three years, the increased profit enabled by PolyAI is worth more than $2.8 million to the composite organization.
Unquantified benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
An improved customer experience. PolyAI increases the composite’s customer satisfaction scores as a result of customers receiving quick and personalized responses and 24/7 support.
Improved insights from greater data observability. The composite gains better insights than it did from its prior solutions, which allows it to make improvements to serve its customers better.
Costs. Three-year, risk-adjusted PV costs for the composite organization include:
PolyAI usage costs. Usage costs depend on the volume of calls and use cases for PolyAI. Over the course of three years, the composite pays approximately $2.6 million in PolyAI usage costs.
Implementation, deployment, and professional services. The composite spends 200 internal labor hours across four weeks on implementation and deployment; it also has several professional service engagements to expand its PolyAI use cases. Over the course of three years, the composite pays approximately $119,000 in implementation, deployment, and professional services fees.
Ongoing management. Ongoing management includes monitoring and optimizing the performance of PolyAI to ensure customer satisfaction and ongoing success for the composite. Over the course of three years, the composite spends approximately $96,000 on ongoing management.
The financial analysis that is based on the interviews found that a composite organization experiences benefits of $14.2M2 million over three years versus costs of $2.9M9 million, adding up to a net present value (NPV) of $11.3M3 million and an ROI of 391%.
Percentage of customer calls resolved by PolyAI by Year 3
Return on investment (ROI)
Benefits PV
Net present value (NPV)
Payback
| Role | Industry | Region | Number of calls per year | Number of agents | Annual revenue |
|---|---|---|---|---|---|
| Senior director | Energy | HQ in the US | 16,000,000 | 850 | $24.4 billion |
| Cofounder and CPO | Healthcare | HQ in the US | 4,200,000 | 150 | $26.0 million |
| Director, contact center operations | Hospitality | HQ in the US | 960,000 | 65 | $3.0 billion |
| Customer services director | Insurance | HQ in the UK | 480,000 | 150 | $294.7 million |
Prior to their investment in PolyAI, interviewees used IVR systems that had limited abilities to personalize conversations, had difficulty understanding natural language, and were hard to customize. In many cases, calls could not be fully resolved without a live agent.
Interviewees noted how their organizations struggled with common challenges as a result of this environment, including:
High costs and budget pressures for labor, equipment, and business process outsourcing. Interviewees discussed how in their prior environment, high costs and budget pressures impacted their contact center’s ability to scale as call volumes increased. The cofounder and CPO at a healthcare organization highlighted how they hadn’t had to backfill US-based roles after their investment in PolyAI, commenting, “We’ve been fortunate that we’re growing so quickly that we haven’t been eliminating roles more than just letting natural attrition happen and then kind of replacing offshore or [having] Poly take parts of it.”
Hiring challenges due to high attrition rates and seasonal workers. Interviewees discussed how their contact centers typically faced an influx of temporary employees hired for peak periods who often needed weeks of training; this affected operational efficiency and customer service quality during seasonal spikes in call volumes. The director, contact center operations at a hospitality organization noted a reduction in agent turnover: “There’s a bottom 25% of my staff that I needed to turn. Now, PolyAI handles roughly 30% of my call volume, which reduced my staffing by about 25%. That is the same piece that we kept turning over repeatedly, so my attrition dropped by more than half.”
High abandonment rates leading to missed revenue opportunities. Interviewees indicated higher abandonment rates in their prior environment, which led to dissatisfied customers and missed sales opportunities. The customer services director at an insurance organization highlighted a reduction in the abandonment rate thanks to PolyAI’s ability to answer and resolve calls immediately, commenting: “Three years ago, we were seeing abandonment rates of well above 10%. Now, we rarely see them above 3%. PolyAI is without a shadow of a doubt helping to answer all calls as it does, but it also makes sure that, even when we are busy, calls aren’t waiting too long and are not abandoned as often as they were.”
Long wait times leading to low customer experience (CX) scores. Interviewees noted that customers were impacted by long wait times in their prior environment; this led to frustration, dissatisfaction, and a negative perception of their brand, which was reflected in their CX scores, the director, contact center operations at a hospitality organization highlighted long wait times as a result of staffing challenges in handling the significant call volumes in their prior environment: “I had people waiting 45 minutes to an hour just to get transferred. It was a horrible experience before. Now, PolyAI answers immediately and takes care of their issue.”
The interviewees searched for a solution that could:
Be personalized and on-brand.
Have natural conversations via conversational AI with a human-like voice.
Provide better insights and visibility into customer interactions to make data-driven decisions.
Provide 24/7 availability.
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 interviewees’ 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 US-based, multibillion dollar organization handles an average of 4 million calls per year with 200 agents in Year 1. The composite’s call volume grows at an average rate of 5% per year. The organization faces challenges, including high agent turnover and an IVR system that is unable to resolve calls, leading to a high customer abandonment rate. The composite wants a solution that offers conversational AI to handle and resolve customer queries wherever possible.
Deployment characteristics. The composite invests in and deploys PolyAI to handle several use cases. PolyAI handles 25% of the organization’s calls by the end of Year 1, 35% by the end of Year 2, and 40% by the end of Year 3.
4.41 million total calls by Year 3
40% of calls are resolved with PolyAI by Year 3
221 agents would be needed by Year 3 without PolyAI
| Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|
| Atr | Agent labor savings enabled by PolyAI | $3,399,992 | $4,284,000 | $4,873,050 | $12,557,042 | $10,292,592 |
| Btr | Agent recruitment and training costs avoided with PolyAI | $337,500 | $418,500 | $486,000 | $1,242,000 | $1,017,825 |
| Ctr | Increased profit enabled by PolyAI | $856,800 | $1,208,088 | $1,430,428 | $3,495,316 | $2,852,030 |
| Total benefits (risk-adjusted) | $4,594,292 | $5,910,588 | $6,789,478 | $17,294,357 | $14,162,447 |
Evidence and data. Interviewees highlighted how PolyAI enabled labor savings by automating a wide range of customer queries and issues, reducing the need for live agents to handle repetitive and routine tasks. This freed up live agents to focus on more complex and high-value interactions with customers, leading to increased efficiency and productivity for the contact center.
The customer services director at an insurance organization discussed how PolyAI helps save live agent time by identifying and verifying customers as well as resolving certain queries. They said: “We’re identifying and verifying 90% of all the customers who contact us. That means there’s another 40 seconds of saving on each phone call: If the customer or patient needs to come through to a human, they’re already verified and it’s saving that time. Or they’re verified and PolyAI can answer the question for us because there’s a number of transactional queries, payment processes, and administrative issues that PolyAI is helping us with.”
The cofounder and CPO at a healthcare organization highlighted how automating ride scheduling with PolyAI reduced call volumes for their live agents while offering a seamless customer experience, commenting: “We rebalanced our call center to basically knock out 10% of the volume that we would see without PolyAI. They book rides, they cancel rides, they modify rides, they have the ability to tell members their benefit utilization and where the drivers are.”
The senior director at an energy organization commented on the high volume of calls managed each year and the impact of PolyAI taking up to 25% of those calls, which has allowed agents to focus on more complex calls. They commented: “PolyAI is handling some of the shorter, easier transactions, and customers find it easier to just be able to speak freely to get their transaction done versus having to touch buttons on their phone.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
It handles 4 million total calls in Year 1, 4.2 million in Year 2, and 4.41 million in Year 3, reflecting a 5% growth in call volume due to business growth.
25% of calls are resolved with PolyAI in Year 1, 35% in Year 2, and 40% in Year 3.
Prior to PolyAI, agents spend an average of 5 minutes on each call. The average fully burdened cost of a live agent is $30 per hour.
In addition to the fully automated calls resolved by PolyAI, live agents at the composite benefit from the partial automation of other calls routed by PolyAI. Prior to PolyAI, live agents spend 2 minutes verifying customers at the start of each call. With PolyAI, there is a 50% reduction in the time spent verifying customers.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this benefit will vary depending on:
The percentage of calls answered and resolved by PolyAI.
The average number of calls, time spent per call, and time spent verifying customers.
The geographical location of agents, which affects the fully burdened labor costs.
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 $10.3 million.
Reduction in time to verify customers with PolyAI
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| A1 | Total number of customer calls in prior environment | Composite | 4,000,000 | 4,200,000 | 4,410,000 | |
| A2 | Percentage of customer calls resolved by PolyAI | Composite | 25% | 35% | 40% | |
| A3 | Number of calls resolved with PolyAI | A1*A2 | 1,000,000 | 1,470,000 | 1,764,000 | |
| A4 | Average number of minutes per call with live agent in prior environment | Composite | 5 | 5 | 5 | |
| A5 | Total hours saved by live agents on calls resolved by PolyAI | (A3*A4)/60 minutes | 83,333 | 122,500 | 147,000 | |
| A6 | Average fully burdened cost of a live agent per hour | Composite | $30 | $30 | $30 | |
| A7 | Subtotal: avoided agent call savings | A5*A6 | $2,499,990 | $3,675,000 | $4,410,000 | |
| A8 | Number of customer calls resolved by live agents | A1-A3 | 3,000,000 | 2,730,000 | 2,646,000 | |
| A9 | Average number of minutes to verify customers with live agents in prior environment | Composite | 2 | 2 | 2 | |
| A10 | Percent reduction in time to verify customers with PolyAI | Interviews | 50% | 50% | 50% | |
| A11 | Total hours saved on live agent calls by verifying customers with PolyAI | (A8*A9*A10)/60 minutes | 50,000 | 45,500 | 44,100 | |
| A12 | Subtotal: automated customer verification efficiencies | A6*A11 | $1,500,000 | $1,365,000 | $1,323,000 | |
| At | Agent labor savings enabled by PolyAI | A7+A12 | $3,999,990 | $5,040,000 | $5,733,000 | |
| Risk adjustment | ↓15% | |||||
| Atr | Agent labor savings enabled by PolyAI (risk-adjusted) | $3,399,992 | $4,284,000 | $4,873,050 | ||
| Three-year total: $12,557,042 | Three-year present value: $10,292,592 | |||||
Evidence and data. Interviewees noted that PolyAI’s ability to handle multiple conversations simultaneously and provide round-the-clock support to customers allowed them to avoid hiring additional human resources, despite natural agent attrition. This scalability allowed organizations to meet fluctuating customer demand and peak call volumes without the time-consuming process of recruiting, hiring, and training new agents.
The cofounder and CPO at a healthcare organization discussed the impact of PolyAI in maintaining call volumes despite seasonality challenges, commenting: “We’re in healthcare, so we’re very strict around ‘average speed to answer whole‘ times. We staff to those levels, so the efficiency that we’ve gained is having 10% less staff because we are still meeting those levels with PolyAI.”
The director, contact center operations at a hospitality organization discussed how lower attrition has led to more experienced live agents who are well-trained to answer customer calls. They noted: “The majority of the callers that I have coming in are going to agents who have been here a little longer; when somebody calls in, they know the answers and they don’t have to spend as much time trying to look for them or ask questions to the supervisor because they know that.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
Without PolyAI, 200 agents would be needed to handle the call volume in Year 1, 210 in Year 2, and 221 in Year 3.
Annual agent attrition is 30%. New hire recruitment and onboarding costs are $15,000 per new hire.
With PolyAI, there is a 25% reduction in agent attrition.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this benefit will vary depending on:
The number of agents in the organization.
Agent attrition.
New hire recruitment and onboarding costs.
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 $1.0M0 million.
Decrease in agent attrition
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| B1 | Number of agents | Composite | 200 | 210 | 221 | |
| B2 | Agent attrition rate before PolyAI | Composite | 30% | 30% | 30% | |
| B3 | Expected agent hires per year without PolyAI | B1*B2 | 60 | 63 | 66 | |
| B4 | New hire recruitment and onboarding costs | Composite | $15,000 | $15,000 | $15,000 | |
| B5 | Subtotal: agent recruitment and training costs without PolyAI | B3*B4 | $900,000 | $945,000 | $990,000 | |
| B6 | Agents needed with PolyAI | B1-(B1*A2) | 150 | 137 | 132 | |
| B7 | Decrease in agent attrition with PolyAI | Interviews | 25% | 25% | 25% | |
| B8 | Agent attrition rate with PolyAI | B2-(B2*B7) | 23% | 23% | 23% | |
| B9 | Needed agent hires per year with PolyAI | B6*B8 | 35 | 32 | 30 | |
| B10 | Subtotal: new hire recruitment and onboarding training costs with PolyAI | B4*B9 | $525,000 | $480,000 | $450,000 | |
| Bt | Agent recruitment and training costs avoided with PolyAI | B5-B10 | $375,000 | $465,000 | $540,000 | |
| Risk adjustment | ↓10% | |||||
| Btr | Agent recruitment and training costs avoided with PolyAI (risk-adjusted) | $337,500 | $418,500 | $486,000 | ||
| Three-year total: $1,242,000 | Three-year present value: $1,017,825 | |||||
Evidence and data. Interviewees discussed how PolyAI’s scalability enabled them to handle significant call volumes and ensured they were answering and resolving more customers queries, which ultimately reduced abandonment rates. In some organizations, this led to the recapture of revenue opportunities that would have previously been lost. Furthermore, having more experienced agents allowed these organizations to increase their upselling when presented with these recaptured opportunities.
The director, contact center operations at a hospitality organization stated that the driving force for their organization to implement PolyAI was to recapture revenue from missed opportunities and abandoned calls. The lack of 24/7 availability and the lack of enough agents to handle the call volume in the prior environment led to thousands of missed potential revenue opportunities. With PolyAI, the interviewee was able to reduce their abandonment rate, which ensured more sales calls were answered and upsell opportunities increased. The director commented: “Our upsells went up significantly because we were abandoning a lot of calls. These abandoned calls were not only costing us missing revenue from a booking standpoint but also opportunities to upsell.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
Before PolyAI, the abandonment rate is 10%. With PolyAI, there is a 50% reduction in the abandonment rate.
30% of calls are sales calls, with an average order value of $500 per sales call.
5% of calls have an upsell opportunity. With PolyAI, there is a 20% increase in agent upselling. The average order value per upsell is $30.
The operating margin is 12%.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this benefit will vary depending on:
The abandonment rate.
The percentage of calls that are sales calls and the average order value of sales calls.
The percentage of calls with an upsell opportunity and the average order value of an upsell.
The 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 $2.9M9 million.
Reduction in abandonment rate
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| C1 | Number of PolyAI calls | A3 | 1,000,000 | 1,470,000 | 1,764,000 | |
| C2 | Abandonment rate in prior environment | Composite | 10% | 10% | 10% | |
| C3 | Reduction in abandonment rate with PolyAI | Interviews | 50% | 50% | 50% | |
| C4 | Recaptured calls with PolyAI | C1*C2*C3 | 50,000 | 73,500 | 88,200 | |
| C5 | Percentage of sales calls | Composite | 30% | 30% | 30% | |
| C6 | Average order value per sales call | Composite | $500 | $500 | $500 | |
| C7 | Subtotal: incremental revenue increase with PolyAI | C4*C5*C6 | $7,500,000 | $11,025,000 | $13,230,000 | |
| C8 | Number of live agent calls | A8 | 3,000,000 | 2,730,000 | 2,646,000 | |
| C9 | Percentage of calls with upsell opportunity | Composite | 5% | 5% | 5% | |
| C10 | Percentage increase in agent upselling after PolyAI | Interviews | 20% | 20% | 20% | |
| C11 | Average order value per upsell | Composite | $30 | $30 | $30 | |
| C12 | Subtotal: incremental revenue increase from upselling after PolyAI | C8*C9*C10*C11 | $900,000 | $819,000 | $793,800 | |
| C13 | Revenue captured as a result of PolyAI | C7+C12 | $8,400,000 | $11,844,000 | $14,023,800 | |
| C14 | Operating margin | TEI Standard | 12% | 12% | 12% | |
| Ct | Increased profit enabled by PolyAI | C13*C14 | $1,008,000 | $1,421,280 | $1,682,856 | |
| Risk adjustment | ↓15% | |||||
| Ctr | Increased profit enabled by PolyAI (risk-adjusted) | $856,800 | $1,208,088 | $1,430,428 | ||
| Three-year total: $3,495,316 | Three-year present value: $2,852,030 | |||||
Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:
Improved customer experience. Interviewees discussed the impact that PolyAI had on increasing their customer satisfaction scores as a result of customers receiving quick, personalized responses and 24/7 support. The customer services director at an insurance organization commented: “Before PolyAI, we were hovering in the late 60s for being very satisfied. Now we’re consistently meeting 72% very satisfied. If you add satisfaction on top of that very satisfied, we’re actually at 86% satisfaction. And that’s outstanding. If you compare that satisfaction level across any industry, that is well above and beyond. It’s right up there with some of the leading service providers in any industry.”
Improved insights from greater data observability. Interviewees noted that, compared to their prior solutions, the improved insights they’ve gained allow them to make enhancements to serve their customers better. The senior director at an energy organization commented: “The benefit that I have found internally is we now have all of our call recordings with PolyAI and we can go in and listen. It has been a big shift in the way my team is thinking about things and looking to make improvements; it’s giving them a lot more insight than we’ve had previously.”
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement PolyAI and later realize additional uses and business opportunities, including:
An improved employee experience. Interviewees highlighted the impact that PolyAI has had on their employees. In most cases, live agents have been freed from having to handle customers’ menial queries that PolyAI can resolve, allowing them to reallocate time toward their own career growth. The customer services director at an insurance organization commented: “We’ve redistributed some of the workforce to allow for a lot of multiskilling internally now. Live agents no longer just do phones, claims, administration. They also work through multiple channels, live chat, phone, email.”
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Total Economic Impact Approach).
| Ref. | Cost | Initial | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|---|
| Dtr | PolyAI usage costs | $0 | $997,500 | $1,081,500 | $1,155,000 | $3,234,000 | $2,668,388 |
| Etr | Implementation, deployment, and professional services | $37,800 | $52,500 | $21,000 | $21,000 | $132,300 | $118,660 |
| Ftr | Ongoing management costs | $0 | $25,740 | $41,184 | $51,480 | $118,404 | $96,114 |
| Total costs (risk-adjusted) | $37,800 | $1,075,740 | $1,143,684 | $1,227,480 | $3,484,704 | $2,883,162 |
Evidence and data. Interviewees noted how yearly usage costs depend on the volume of calls and the complexity of the use cases for which they deployed PolyAI. Pricing may vary. Contact PolyAI for additional details.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
It incurs usage costs for PolyAI of $950,000 in Year 1, $1.03 million in Year 2, and $1.1 million in Year 3.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this benefit will vary depending on:
The volume of PolyAI calls.
The complexity of the use cases for which PolyAI is deployed.
Results. To account for these risks, Forrester adjusted this cost upward by 5%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $2.7M7 million.
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| D1 | PolyAI usage costs | Composite | $950,000 | $1,030,000 | $1,100,000 | ||
| Dt | PolyAI usage costs | D1 | $950,000 | $1,030,000 | $1,100,000 | ||
| Risk adjustment | ↑5% | ||||||
| Dtr | PolyAI usage costs (risk-adjusted) | $0 | $997,500 | $1,081,500 | $1,155,000 | ||
| Three-year total: $3,234,000 | Three-year present value: $2,668,388 | ||||||
Evidence and data. Interviewees noted that their involvement in the implementation and deployment of PolyAI was more from an operational standpoint; they relied heavily on PolyAI for the technical setup of the solution. Interviewees described initial implementation taking about a month. The use of professional services in later years depended on the expansion of their use cases.
Professional services pricing may vary. Contact PolyAI for additional details.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
10 full-time employees (FTEs) — including telephony, networking, and digital service roles — spend 20 hours per week over four weeks on implementation. The average fully burdened hourly rate for an FTE is $45.
The composite pays $50,000 in PolyAI professional services fees in Year 1, $20,000 in Year 2, and $20,000 in Year 3 as it expands its usage of PolyAI. The composite contracts all ongoing platform build and maintenance services through PolyAI versus handling them in-house.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this benefit will vary depending on:
The number of FTEs involved and the percentage of their time spent on implementation and deployment.
Professional services fees.
Results. To account for these risks, Forrester adjusted this cost upward by 5%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $119,000.
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| E1 | Number of FTEs involved in implementation and deployment | Composite | 10 | |||
| E2 | Number of weeks for implementation | Composite | 4 | |||
| E3 | Internal hours spent on implementation per week | Composite | 20 | |||
| E4 | Average fully burdened FTE hourly rate | Composite | $45 | |||
| E5 | Internal labor implementation costs | E1E2*E3*E4 | $36,000 | |||
| E6 | PolyAI professional services fees | Composite | $50,000 | $20,000 | $20,000 | |
| Et | Implementation, deployment, and professional services | E5+E6 | $36,000 | $50,000 | $20,000 | $20,000 |
| Risk adjustment | ↑5% | |||||
| Etr | Implementation, deployment, and professional services (risk-adjusted) | $37,800 | $52,500 | $21,000 | $21,000 | |
| Three-year total: $132,300 | Three-year present value: $118,660 | |||||
Evidence and data. Interviewees discussed the minimal ongoing management needed for the solution. This included monitoring and optimizing the performance of PolyAI to ensure customer satisfaction and drive ongoing success for their organization.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
Five FTEs are involved in ongoing management in Year 1, eight FTEs are involved in Year 2, and 10 FTEs are involved in Year 3. Each FTE dedicates 5% of their time to ongoing management.
The average fully burdened salary per FTE is $93,600.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this benefit will vary depending on:
The number of FTEs.
The average fully burdened salary per FTE.
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 $96,000.
| ` | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| F1 | Number of FTEs involved in ongoing management | Composite | 5 | 8 | 10 | |
| F2 | Percent of FTE time involved in ongoing management | Composite | 5% | 5% | 5% | |
| F3 | Average fully burdened blended FTE salary | Composite | $93,600 | $93,600 | $93,600 | |
| Ft | Ongoing management costs | F1*F2*F3 | $23,400 | $37,440 | $46,800 | |
| Risk adjustment | ↑10% | |||||
| Ftr | Ongoing management costs (risk-adjusted) | $0 | $25,740 | $41,184 | $51,480 | |
| Three-year total: $118,404 | Three-year present value: $96,114 | |||||
| Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
|---|---|---|---|---|---|---|
| Total costs | ($37,800) | ($1,075,740) | ($1,143,684) | ($1,227,480) | ($3,484,704) | ($2,883,162) |
| Total benefits | $0 | $4,594,292 | $5,910,588 | $6,789,478 | $17,294,357 | $14,162,447 |
| Net benefits | ($37,800) | $3,518,552 | $4,766,904 | $5,561,998 | $13,809,653 | $11,279,285 |
| ROI | 391% | |||||
| Payback period (months) | <6 |
The financial results calculated in the Benefits and Costs sections can be used to determine the ROI, NPV, and payback period for the composite organization’s investment. Forrester assumes a yearly discount rate of 10% for this analysis.
These risk-adjusted ROI, NPV, and payback period values are determined by applying risk-adjustment factors to the unadjusted results in each Benefit and Cost section.
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.
From the information provided in the interviews, Forrester constructed a Total Economic Impact™ framework for those organizations considering an investment in PolyAI.
The objective of the framework is to identify the cost, benefit, flexibility, and risk factors that affect the investment decision. Forrester took a multistep approach to evaluate the impact that PolyAI can have on an organization.
Interviewed PolyAI stakeholders and Forrester analysts to gather data relative to PolyAI.
Interviewed four decision-makers at organizations using PolyAI to obtain data about costs, benefits, and risks.
Designed a composite organization based on characteristics of the interviewees’ organizations.
Constructed a financial model representative of the interviews using the TEI methodology and risk-adjusted the financial model based on issues and concerns of the interviewees.
Employed four fundamental elements of TEI in modeling the investment 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.
Benefits represent the value the solution delivers to the business. The TEI methodology places equal weight on the measure of benefits and costs, allowing for a full examination of the solution’s effect on the entire organization.
Costs comprise all expenses necessary to deliver the proposed value, or benefits, of the solution. The methodology captures implementation and ongoing costs associated with the solution.
Flexibility represents the strategic value that can be obtained for some future additional investment building on top of the initial investment already made. The ability to capture that benefit has a PV that can be estimated.
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 present or current value of (discounted) cost and benefit estimates given at an interest rate (the discount rate). The PV of costs and benefits feed into the total NPV of cash flows.
The present or current value of (discounted) future net cash flows given an interest rate (the discount rate). A positive project NPV normally indicates that the investment should be made unless other projects have higher NPVs.
A project’s expected return in percentage terms. ROI is calculated by dividing net benefits (benefits less costs) by costs.
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%.
The breakeven point for an investment. This is the point in time at which net benefits (benefits minus costs) equal initial investment or cost.
Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists solution providers in communicating their value proposition to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of business and technology initiatives to both senior management and other key stakeholders.
1 Source: The State Of Conversational AI, Forrester Research Inc., September 6, 2024.
2 Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists solution providers in communicating their value proposition to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of business and technology initiatives to both senior management and other key stakeholders.
Readers should be aware of the following:
This study is commissioned by PolyAI 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 PolyAI. For any interactive functionality, the intent is for the questions to solicit inputs specific to a prospect’s business. Forrester believes that this analysis is representative of what companies may achieve with PolyAI based on the inputs provided and any assumptions made. Forrester does not endorse PolyAI or its offerings. Although great care has been taken to ensure the accuracy and completeness of this model, PolyAI and Forrester Research are unable to accept any legal responsibility for any actions taken on the basis of the information contained herein.
PolyAI 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.
PolyAI provided the customer names for the interviews but did not participate in the interviews.
Nikoletta Stergiou
June 2025
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