342%

AI-Driven Contact Resolution Efficiency

Evidence and data. Rigid routing, prescriptive IVR, and inefficient AHT characterized the interviewees’ organizations’ prior environments with legacy contact center solutions. The transition to Amazon Connect introduced a set of capabilities that fundamentally changed how these organizations managed contact flows, enabled self-service, and leveraged data for operational excellence. Interviewees said these enablers collectively improved routing accuracy, reduced AHT, and enhanced customer experience.

• Conversational self-service and agentic AI capabilities enabled the interviewees’ organizations to resolve routine inquiries without agent intervention, significantly improving containment rates and reducing inbound contact volumes. By replacing rigid menu-driven IVR with natural language understanding, the interviewees’ organizations handled high-frequency, low-complexity transactions, such as account updates or payment changes, through self-service. The head of delivery technology at the IT organization shared: “We saw a 10% to 15% increase in automation when we implemented Connect for self-service. Now that we’re doing the next generation with Connect AI agents, we’re going to see additional savings.” The senior director of contact center transformation at the financial services organization emphasized the strategic role of AI in transforming customer experience: “It’s no longer a pipe dream to have a voice bot that rivals an agent’s experience. … If you want an ID card or need to add a driver, you don’t really want to talk to a person. … Those things can be done with self-service AI today.”

• Interviewees spoke highly about Amazon Connect’s intelligent routing capabilities combined with AI-driven intent recognition, which delivered the customer to the right agent the first time, minimizing wasted time with customer transfers and improving first-contact resolution. The senior director of contact center transformation at the financial services organization highlighted the link between routing and AHT: “Routing is all about getting it to the right agent the first time, so you don’t have to transfer and so that works for sales and service. Misdirect rates and transfer rates last week got influenced by 200 basis points for sales and service within a week’s period of time, which would have taken me months on our legacy system before.”

• For interactions that require human involvement, interviewees’ organizations leveraged Connect AI agents with real-time prompts and knowledge recommendations, reducing cognitive load and accelerating resolution. Newer human agents were particularly impacted by this capability, shortening ramp-up time and improving first-contact resolution. By embedding AI-driven guidance into the agent desktop, interviewees’ organizations ensured consistency and accuracy while reducing time spent searching for answers.

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

• In the prior environment, the composite organization has 20 million annual contacts, of which 75% are calls. The number of annual contacts increases by 30% per year with the business expansion and merges and acquisitions. The AHT with legacy systems is 10 minutes.

• By deploying Amazon Connect, the composite organization is experiencing a 10% decrease in calls that are directed to chat/message channel. The AHT decreases by 12% in Year 1. With more calls handled by advanced routing and AI-powered self-service, the agents can help customers solve more complex issues, leading to an AHT increase in Years 2 and 3 compared to Year 1.

• Employees do not use all time savings to improve the business or reduce costs. The productivity recapture rate is 80% based on TEI standard.

• The average fully burdened annual salary for an agent is $45,760.

Risks. The expected financial impact is subject to risks and variations based on several factors that may reduce or slow the recognition of this benefit, including the following:

• The legacy solution’s performance.

• Actual implementation of Amazon Connect.

• The extent of process, contact flow, and IVR redesign.

• The degree of integration with other IT solutions.

• Business growth rate’s impact on contact volume.

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 $51.7 million.

AI-Powered Content And Sentiment Analysis Savings

Evidence and data. Interviewees reported savings from AI‑powered content and sentiment analysis in Amazon Connect that was realized through post contact summary automation, conversational analytics, sentiment analysis, call/screen recording, and automated quality evaluations.

• Interviewees noted that manual after-call work traditionally added 30 to 90 seconds per interaction, creating inefficiencies across thousands of contacts. Amazon Connect addressed this through AI-powered summarization, which automatically generated structured contact notes and pushed them into CRM systems. This capability eliminated manual note-taking, improved data accuracy, and accelerated wrap-up time for the interviewees’ organizations. The product manager at the telecommunications organization highlighted the tangible impact of this feature: “Auto summarization — we’ve seen massive savings [of] about 77 seconds in average handling time from that. The agent doesn’t have to type up the summaries anymore.” The director of contact center solution at the real estate services organization echoed this benefit, describing the feature as transformative: “A game changer for us was the implementation of post contact summaries. … They’re AI post contact summaries [and] that’s been phenomenal.” At scale, these time savings translated into agent hours reclaimed monthly, reducing labor costs and enabling agents to focus on complex, high-value interactions. Standardized summaries also enhanced downstream analytics and compliance reporting.

• Amazon Connect’s conversational analytics capabilities enabled the interviewees’ organizations to get full interaction capture, including audio recording, transcription, and sentiment scoring. Supervisors could pinpoint critical moments in customer interactions without manually reviewing entire recordings, accelerating coaching and improving resolution quality. Screen recording added workflow context, helping identify process bottlenecks. The senior director of contact center transformation at a financial services organization emphasized the operational shift, noting, “Now we’re doing 100% call recording and call transcription, and our supervisors [can] see every employee, their call, and their sentiment … and they can zoom into specific elements of the call.” These capabilities compressed supervisory analysis time, shortened feedback loops, and reduced repeat contacts caused by unresolved issues.

• Traditional QA processes relied on manual sampling, limiting visibility and slowing corrective action. Interviewees said Amazon Connect automated evaluations across 100% of interactions using AI-driven scorecards, ensuring compliance and surfacing systemic issues quickly. The director of contact center solution at the real estate services organization quantified the scale advantage: “We have two completely automated QA evaluations using conversational analytics and agent evaluations. … We get 60,000 to 70,000 completed evaluations in a week versus a cost-prohibitive group of 10 agents that were labeled quality assurance before.” The head of delivery technology at the IT organization reinforced the operational transformation: “By introducing Amazon Connect AI-powered QA, we immediately shifted from a range of 1% to 3% up to 100% of calls. … We remove human bias and fully take data-driven decisions.”

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

• With legacy solutions, agents spent 60 seconds on average summarizing a call and take notes after each call they handled. With Amazon Connect AI-powered post contact summaries, agents save this time and redirect it toward helping other customers.

• With legacy solutions, the supervisors listened to 1% of the calls for QA and sentiment analysis purposes. With Amazon Connect AI-powered conversational analytics, sentimental analysis, and automated agent evaluations, 80% of the time is saved. Instead of listening to the 1% of calls, supervisors read the AI-generated summaries from all calls.

• The fully burdened hourly rate for agents is $22. The average fully burdened annual salary for supervisors is $55,800. The productivity recapture rate is 80% based on the TEI standard.

Risks. The expected financial impact is subject to risks and variations based on several factors that may reduce or slow the recognition of this benefit, including the following:

• The company practice of post contact summary and sentimental analysis in the prior environment.

• How Amazon Connect is deployed and used.

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 $11.3 million.

AI-Enabled Forecasting And Agent Scheduling And Supervision

Evidence and data. Interviewees reported measurable workforce savings by adopting Amazon Connect’s workforce management (WFM) and conversational analytics capabilities. Enabled by machine learning (ML) and with tight integration with conversational analytics, these capabilities improved staffing accuracy, reduced over‑/under‑coverage, and expanded supervisor span of control.

• Interviewees noted that Amazon’s WFM (often referenced by interviewees as FCS, or forecasting, capacity planning, and scheduling) used ML‑driven demand curves rather than legacy approaches, allowing forecasts to reflect real interaction patterns and seasonality. ML‑informed forecasts reduced staffing variance and overtime, while automated schedule generation aligned agent rosters to true demand. The head of delivery technology at the IT organization explicitly contrasted WFM with traditional tools and linked it to measurable savings and data consolidation: “One of the key features with AWS is that innovation. [WFM] doesn’t do the regular standard functions to do calculations. They base this on their experience [and] on their own data. … Having data in one single platform helps the business case. … We cross‑reference the data from multiple areas so that we can get the benefit. Right now, what we have seen is improvements of 10%.”

• By consolidating interaction data (e.g., transcripts, categories, sentiment) into the same ecosystem, planners feed richer, real‑time signals into staffing models. The interviewees noted that it was a closed loop with automation, analytics, then workforce management. This loop tightened the latency between demand shifts (e.g., self-service changes, policy events) and schedule updates, reducing overstaffing in low‑volume hours and mitigating queue spikes, improving service levels without adding cost.

• By leveraging AI-driven monitoring and automation, the supervisors in the interviewees’ organizations increased their capacity, reduced administrative burden, and improved coaching effectiveness. This shift delivered measurable cost savings and operational agility, positioning enterprises to scale efficiently while maintaining high service quality. As one leader summarized, the real value lies in freeing supervisors to “spend more quality time with agents and help coach them to get to their next level,” turning oversight into a strategic lever for performance and retention.

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

• The composite organization has 2,000 agent FTEs in Year 1 with legacy solutions. With the business expanding by 30%, the number of agent FTEs is required to grow at 30% with legacy solutions to support the business.

• With Amazon Connect ML-driven workforce management, the composite organization saves 5% on agent FTEs.

• In the prior state, each supervisor manages 10 agents with full capacity. With Amazon Connect, the total number of agent FTEs decreases, and each supervisor has the capacity to manage 12 agents with decreased administrative workload.

• The productivity recapture rate is 80%. The average fully burdened annual salary for supervisors is $55,800.

Risks. The expected financial impact is subject to risks and variations based on several factors that may reduce or slow the recognition of this benefit, including the following:

• The previous workforce management system used by the organization and the integration of that WFM system with contact center solutions.

• The supervisors’ capacity and workload.

• The implementation of Amazon Connect Workforce Management.

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 $19.5 million.

Data-Driven Profit Lift With Increased Conversion

Evidence and data. Interviewees consistently highlighted higher conversion rates and faster, data-driven decision-making across departments due to Amazon Connect, resulting in measurable profit lift. This benefit stemmed from Amazon Connect’s ability to unify customer interaction data, apply AI-driven insights, and share actionable intelligence across sales, service, and marketing teams.

• Interviewees gave credit to Connect AI agents for the improvement of the conversion rate. Connect AI agents provided real-time prompts and contextual recommendations during customer interactions with agents. By surfacing relevant offers, compliance scripts, and next-best actions, Connect AI agents reduced cognitive load and ensured agents focused on persuasive engagement rather than searching for information. Real-time AI guidance ensured consistency across agents, reduced errors, and accelerated time to close, especially for new hires. The director of contact center solutions at the real estate services organization shared, “We’ve seen a 5% to 6% lift in conversion rates since implementing real-time prompts and guidance through Connect.”

• Interviewees’ experience illustrated how Connect’s conversational analytics capabilities transformed decision-making speed and accuracy. By capturing all customer interactions, Connect’s conversational analytics automatically transcribed conversations, applied sentiment scoring, and categorized calls based on intent. These structured insights were then pushed into centralized data platforms, creating a single source of truth accessible across departments. The director of contact center solutions emphasized the strategic advantage of this integration: “We’ve integrated Connect data into one centralized data platform. … Marketing and retention teams now have visibility into what drives cancellations or upsells. … We can make decisions in days instead of months.” Instead of relying on anecdotal feedback or delayed reporting, teams could act on real-time AI-driven insights, improving conversion rates.

• Furthermore, interviewees’ organizations could act on real-time signals, such as negative sentiment or cancellation intent, rather than waiting for lagging reports. By adjusting offers and scripts dynamically, they prevented churn before it happened. The head of delivery technology at the IT organization described using sentiment and categorization data to improve customer experience and loyalty: “We provide agents the last sentiment of the last interaction. … We have seen a 15% increase in customer satisfaction just by providing that sentiment to the agent. … They can proactively change [their] approach.”

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

• The composite organization has approximately $10 billion revenue in Year 1, which increases by 30% each year. Contact center outbound marketing campaigns drive 5% of this revenue.

• With the legacy solution, the conversion rate was 10% on average. By switching to Amazon Connect and implementing Connect’s AI agents and conversational analytics, the conversion rate improves by 20%.

• The operating profit margin of the composite organization is 12%.

Risks. The expected financial impact is subject to risks and variations based on several factors that may reduce or slow the recognition of this benefit, including the following:

• Business model, margin, and contact center scale and use case will affect the revenue impact of improved CX.

• The deployment of Amazon Connect and the integration with other existing IT systems.

Results. To account for these risks, Forrester adjusted this benefit downward by 20%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $3.1 million.

Legacy Solution Cost Savings

Evidence and data. Interviewees consistently reported substantial financial benefits from retiring legacy contact center platforms and consolidating fragmented systems into Amazon Connect. These savings were realized through the elimination of license-based pricing models, on-premises infrastructure, and multiple third-party integrations, while gaining advanced AI capabilities that legacy solutions could not deliver.

• Interviewees’ organizations’ traditional platforms often relied on license-based pricing and max concurrent seat models, which forced their companies to pay for peak capacity even during off-peak periods. Amazon Connect’s consumption-based pricing model allowed the interviewees’ organizations to align costs with actual usage reducing waste. The VP product for customer services at the media entertainment organization shared: “AWS charges by usage whereas our old solution charges by license. … It charges max concurrent. … We save roughly $2 million a year by switching just for that.”

• Beyond licensing, interviewees’ organizations eliminated hardware maintenance, dedicated circuits, and support contracts, further reducing operational expenses. The head of delivery technology at the IT organization highlighted the magnitude of these savings: “We started with that regional approach with Costa Rica. … The savings that we experienced were around 75%. … We then spent six months testing the platform. … It turned out that Amazon Connect was the one that would give us the best return on investment.”

• Interviewees noted their organizations’ legacy environments often required multiple vendors for IVR, QA, analytics, and workforce management, creating complexity and integration overhead. Amazon Connect consolidated these functions into a single ecosystem, enhanced by AI-driven capabilities. The VP product for customer services at the media entertainment organization shared: “It wasn’t just the price, although the price was attractive. … I wanted to be able to plug and play other things in here. … AWS is an awesome integration partner. … We do feel like we are sort of innovation partners with them.”

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

• Legacy solutions required year-round per-user licensing capacity for the peak number of agents and supervisors. The average monthly license cost is $180 per user.

• For the legacy solutions, additional contracted costs for recording, storage, integrations, and infrastructure were needed as well as costs for ongoing management. The average overhead is 30% above subscriptions.

Risks. The expected financial impact is subject to risks and variations based on several factors that may reduce or slow the recognition of this benefit, including the following:

• Actual cost savings will depend on the prior solution’s cost model and usage.

• The migration plan may require extending usage of legacy solutions.

Results. To account for these risks, Forrester adjusted this benefit downward by 20%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $16.1 million.

Unquantified Benefits

Interviewees mentioned the following additional benefits that their organizations experienced but could not quantify:

• Strategic partnership and innovation enablement. Interviewees consistently described Amazon Connect as an innovation platform and a collaborative partner rather than a point solution. The product manager at the telecommunications organization positioned Connect as a transformation layer: “You can see Amazon’s vision. … It becomes a business transformation tool instead of just being a contact center in the cloud.” The senior director of contact center transformation at the financial services organization had similar strategic rationale, tying selection to long‑term advantage: “AWS is investing heavily in Connect. … It was more of a strategic play to create a long‑term advantage.” The solution’s open integration posture (e.g., third‑party logs and APIs); structured engagement through product feature requests and account teams; and ecosystem breadth, serverless services, and native integrations with other enterprise platforms enabled these experiences.

• Agility and speed of change. Interviewees’ organizations highlighted the velocity of change on Connect compared to legacy environments, particularly in routing and IVR optimization. The interviewee at the financial services organization reported week‑over‑week KPI improvements immediately post‑go‑live despite launch constraints, noting, “KPIs improved within a week, which would have taken months on our legacy solution,” and, more broadly, “We’re making changes very, very rapidly.” The director of contact center solution at the real estate services organization contrasted their prior change cycle with the in‑house agility now achievable: “Anytime we requested changes, the standard answer was six months and a million dollars. … Now we do that in‑house.” This agility flowed from a workflow‑first model, usage‑based economics that removed license friction, and native features that can be activated quickly, such as courtesy callback, postcall surveys, conversational analytics, and AI agent assist.

• Data consolidation, compliance, and governance. Consolidating contact telemetry and analytics within Amazon Connect was described as pivotal for security, compliance, and decision‑making. The head of delivery technology at the IT organization emphasized GDPR‑aligned regional isolation and cross‑referencing operational data to right‑size resources: “Having data in one single platform … helps with GDPR. … Data residency end to end.” The director of contact center solution said their real estate services organization built an enterprise data lake linking summaries, sentiment, and outcomes to support marketing, retention, dynamic pricing, and legal readiness. By combining data from Amazon Connect's analytics data lake — including contact records and conversational analytics insights — with other business data sources, they created a comprehensive view, noting, “We ingest anything and everything … used for marketing, funnel yield, repeat engagement, legal.”

• Better coaching quality, training efficiency, and agent experience. Interviewees noted their teams reported qualitative improvements in coaching, training, and agent experience from automated analytics and unified workflows. The director of contact center transformation at the financial services organization described supervisor capacity gains from 100% transcription, sentiment, and self‑service quality forms. The director of contact center solution at a real estate services organization reduced training time materially by prompting edge cases at the point of need. The director of contact center solution reported: “Agents don’t have to know all the different products. … Connect prompts at point of need,” taking service training “from four weeks to just over two and a half” and inbound “from two weeks to one.” These results were enabled by automated QA coverage; conversational transcripts and sentiment scoring; Connect AI agents with embedded knowledge; agent workspaces and Customer Profiles for single‑pane workflows; and auto summarization plus screen recording for consistent interaction histories.

• Risk reduction with better compliance, legal exposure, and decision auditability. Amazon Connect’s AI and data fabric supported compliance and reduced legal exposure for the interviewees’ organizations through auditability and automation. The director of contact center solution said their real estate services organization replaced costly QA licensing and ensured scalable monitoring with PCI/PII safeguards: “We saved $660,000 … and we’re now covering our expense with Connect’s QA capabilities ... [while using] automated PCI‑PII redaction ... [and completing] 60,000 to 70,000 evaluations in a week [for 360‑degree quality].” The head of delivery technology at an IT organization implemented LLM‑based contract validation for authorization decisions, driving accuracy from about 55% when they first launched to 97% and enabling audit on the back end for 100% of their authorization decisions for regulatory compliance. The product manager at the telecommunications organization noted the need for CX reporting that advances from “This happened” to “Why did this happen?,” yet recognized improved data fidelity and governance with Amazon Connect compared to legacy. These gains were enabled by Connect redaction and retention policies; structured storage for multi-year access and audits; Bedrock‑driven validation workflows grounded in contract corpora and prompt engineering; and centralized data lineage connecting summaries, surveys, and case records.

Flexibility

The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement Amazon Connect and later realize additional uses and business opportunities, including:

• Strategic ecosystem leverage across AWS. Interviewees noted that Amazon Connect serves as a gateway to the broader AWS portfolio — compute, storage, analytics, and AI — reducing integration overhead and creating a coherent technical strategy. This ecosystem leverage is particularly important for large organizations seeking consistency in security, governance, and observability while scaling innovation. The head of delivery technology at the IT organization shared: “Amazon Connect was the first use case. … [Now Connect has] opened up the possibilities and we’re expanding into additional things with AWS … including network unification and other services.”

• Rapid test-and-learn cycles for new capabilities. Usage‑based economics and modular workflows allowed the interviewees’ organizations’ teams to pilot, prove, and scale features quickly, minimizing sunk cost and lock‑in. This radically shortened idea‑to‑impact cycles, letting the interviewees’ organizations respond to market changes or internal priorities in quarters rather than years. The product manager at the telecommunications organization commented: “In a year’s time, we might have consolidated another three big capabilities that are currently with other technology partners into Amazon Connect. … [Before Amazon Connect], that would have taken about five years and probably four times the price.”

• Dynamic architecture simplification. As the interviewees’ organizations explored and adopted Amazon Connect's expanding capabilities, they could phase out niche tools (e.g., credit‑card masking, call analytics/storage, surveying, WFM, cases) in favor of integrated capabilities. This reduced vendor risk, license overhead, integration fragility, and operational complexity, freeing teams to focus on differentiated experiences rather than plumbing. The product manager at the telecommunications organization shared: “We’re consolidating services to make sure that for the money we’re spending, we’re getting the best value. If Amazon can do something cheaper than another partner … take a slight hit on capability and bring it in, how much can we save and what can we get out of it.”

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