As AI increasingly manages routine, simple matters, some unintended consequences can emerge:

1. Customers can feel devalued when forced to deal with an AI agent that doesn’t meet their needs. They vent their anger at human agents when they are finally able to reach one.
2. The learning curve for human agents is disrupted. Junior agents don’t gain the benefit of handling the more routine interactions and the learning that occurs at that stage.
3. Human agents end up handling only the complex and emotionally demanding interactions, leading to agent burnout and turnover.

It doesn’t have to be that way. These issues can be minimized or avoided with thoughtful use of AI that can both enhance human performance and improve customer experience.

Design for Resolution, Not Deflection

Protect your agents from the “angry handoff” by designing AI experiences that prioritize resolution over deflection. When the goal of automation is genuine assistance rather than simple deflection, the human agent inherits a manageable task instead of a frustrated caller.

Design the experience to include:

• An “escape hatch”: A human agent should be easily accessible to customers. If an AI agent senses repetitive questioning or sentiment-based frustration, it should proactively offer a transfer.
• Contextual handoffs: There’s nothing more aggravating than reaching a human and having to repeat everything you just told the bot. A well-designed system passes the full transcript and intent to the agent instantly.

The Paradox of Automation: Protecting the Human Talent Pipeline

By delegating all simple queries to AI, we inadvertently disrupt the traditional human learning curve. Historically, agents learned by handling routine interactions. These low-stakes tasks provided the foundational knowledge required for them to become senior agents needed to      manage complex cases requiring empathy and judgment.

When AI oversees a very simple task, new human agents are thrust into complex situations without the benefit of a foundational learning phase. To prevent this, management must be intentional about developing the skills of human agents by:

• Identifying which “routine” interactions are truly vital for long-term proficiency and ensuring that beginning agents have full exposure to them.
• Protecting introductory roles where human agents are permitted to be slower and more expensive than AI. This isn’t a cost—it’s an investment in developing the seasoned experts of tomorrow.
• Creating explicit career progression. If a career path isn’t articulated, it doesn’t exist. Progression from junior to senior roles must be mapped out with clear milestones.

Strategic Augmentation

When used strategically, AI can be used to resolve this paradox. AI can collaborate with agents to augment their capabilities and capitalize on the human skills that provide customers with empathy and answers that feel human.

However, a one-size-fits-all approach to AI augmentation fails both the agent and the customer. Instead, AI augmentation should be applied dynamically based on the agent’s tenure and the complexity of the task.

Junior or trainee agents need a coach that provides prompts, suggests resources and explains why a step is taken. This allows them to build foundational knowledge and judgment.

Intermediate agents benefit from AI that automates data entry and suggests next best actions, while allowing the agent to lead the interaction. With AI as an assistant, agents can improve efficiency and increase speed while maintaining quality.

Experienced agents who oversee complex cases can use AI as a research tool that allows them to focus on empathy and strategy. This enables them to more quickly solve the most difficult “edge cases” while ensuring that customers’ emotional needs are met.

The golden rule: In the early stages of a career, AI should be used as a training tool to teach humans how to do the work, rather than simply doing the work for them.

Strategically varying the level of AI augmentation ensures that technology elevates the human agent rather than making them obsolete.

Managing the Complex-Only Queue: Mitigating Human Agent Burnout

When AI successfully manages all simple, transactional inquiries, the human agent’s workload becomes entirely composed of the most complex, ambiguous, high-stakes or emotionally charged interactions.

This creates a complex-only queue, eliminating the psychological benefit of “easy wins.” Continuous exposure to high-intensity customer issues accelerates mental and emotional fatigue, primarily manifesting as:

• Compassion fatigue: The constant need for deep emotional labor when dealing with distressed, angry or desperate customers rapidly drains mental reserves.
• Increased cognitive load strain: Sustained complex problem-solving requires significantly more mental energy than following routine processes. Agents must constantly apply nuanced judgment, leading to mental fatigue without respite.
• Loss of control and motivation: A perpetual struggle against difficult challenges erodes the feeling of mastery. Consistent stress can severely decrease job satisfaction, leading to withdrawal and burnout.

Solutions for Agent Well-Being

Organizations must address factors that could degrade agent well-being by incorporating tactical and strategic processes designed to support human agents.

Tactical Solutions

These immediate solutions focus on providing support and necessary mental breaks:

1. Mandated decompression time: Implement formal, scheduled breaks between complex interactions that agents cannot skip. This time must be reserved strictly for a mental reset, not for administrative work. Software tools are available that recognize stressful interactions and provide agents with short mental breaks when needed. Metrics show that these tools improve agent productivity.
2. Peer support and mental health resources: Provide accessible and destigmatized training and tools focused on managing compassion fatigue.
3. Complexity rotation: Structure shifts to allow agents to rotate between various levels of task complexity (e.g., integrating time for less complicated tasks or internal support) rather than permanently assigning them to the most difficult queue.
4. Training in strategic empathy: Coach agents on how to use empathy effectively, teaching them to set necessary emotional boundaries to protect themselves while still providing genuine connection.
5. Continual evaluation and improvement of the customer journey: Analyze points that create friction and make proactive design improvements to reduce customer frustration. It’s much easier for agents to handle a customer with a question, instead of an angry, frustrated escapee of a bad experience.

Strategic Solutions

The stress from the complex-only queue can create high turnover. Reducing turnover requires mitigating burnout and focusing on recognition, compensation, and career development to match the increased specialization of the role:

1. Elevate compensation and title: Recognize that the agent is now performing a highly specialized role. Adjust compensation and job titles to reflect the higher level of cognitive labor, emotional intelligence, and critical decision-making authority required.
2. Invest in soft skill development: Provide advanced training that goes beyond product knowledge focusing on advanced “soft” skills such de-escalation techniques, and complex case management. Investing in agent growth increases job commitment and perceived value.
3. Formalize recognition of “human wins”: Implement a formal recognition system that specifically rewards outstanding empathy, and successful complex problem resolution, rather than just speed or volume.
4. Demonstrate concern: Leadership must actively monitor the effective utilization of mandated decompression time and caseloads. Agent well-being must be viewed as a core business priority.

Conclusion

It can be tempting to use AI to gain quick wins by automating routine tasks. However, taking a long-term view will help to avoid customer frustration, agent burnout and turnover. And it will help ensure there’s a pipeline for building human expertise.

Human empathy and critical thinking skills are still essential. AI augmentation that enhances human capabilities takes advantage of the strengths of both AI and humans, making them better together.

Global Tech specializes in helping contact leaders to identify AI technology solutions that deliver measurable results. Further resources on AI best practices can be found here.

This article was originally published on the Genesys blog here.

And agentic AI is poised to revolutionize personalization – taking user experiences across various industries to new levels. This article explores the goals of personalization, how agentic AI can transform personalization in CX, and some examples of how agentic AI can improve personalization across several use cases.

Goals of Personalization

Personalization enhances interactions by tailoring responses and actions for individual interactions, rather than using a one-size-fits-all approach. Personalized experiences make customers feel seen and understood, which can increase trust and can lead to repeat business positive referrals.

Without personalization, interactions are necessarily generic and, therefore, limited to standardized responses that may not fit the situation.

How Agentic AI Improves Personalization

Unlike traditional, script-driven AI models that passively wait for user commands, agentic AI can make decisions and execute complex tasks with minimal human intervention. This allows for personalization at an unprecedented speed and scale, acting as an always-on customer relationship strategist.

Artificial intelligence can automate tasks necessary to gain information about each customer, individually. By analyzing and incorporating data, such as browsing history, account information, purchase patterns and location information, a customer profile can be built.

Even more powerful is the agentic AI’s ability to remember and build upon past interactions. This combination makes interactions with AI agents feel more like a conversation with someone who understands your specific context and can make customers think, “They know me.”

A personalized approach transcends simple interactions, such as “Our address and hours are…” Instead, it anticipates needs, offering solutions, such as, “I see you have oral surgery scheduled for Thursday at noon. Would you like to reschedule? We have availability next Tuesday at 9 AM. Will that work for you? Please remember to refrain from eating prior to the procedure.”

Common Applications of Personalization Using Agentic AI

While the uses of agentic AI are potentially unlimited, here are a few examples of how organizations can use it to enhance customer experience – and the benefits that can be realized.

Proactive Problem Solving

Airlines

Agentic AI can determine if a passenger on a delayed flight will be at risk of missing their connecting flight. If so, it can look up the passenger’s destination and airline status and automatically book a replacement flight with the correct cabin class and seat preference.

Upon landing, the app can greet the passenger with information about the new flight and directions to the new gate.

Benefits:

• Ability to handle spikes during weather events and other widespread conditions
• Smoother, less stressful experiences for passengers
• Less manual effort for staff
• Improved efficiency
• Reduced complexity
• Happier passengers
• Reduced staffing requirements
• Less stress for staff and airports as a whole

Insurance

After a regional hurricane, some carriers integrated agentic First Notice of Loss (FNOL) experiences into their mobile apps, allowing policyholders to report damage, share photos, and receive personalized updates and repair instructions from an AI agent.

Benefits:

• Policy holders don’t have to wait for a human adjuster in order to start the claim process
• Policyholders get tailored updates and instructions, streamlining a potentially difficult process
• Ability to handle spikes during weather events and other widespread conditions
• Reduced load for staff
• Improved efficiency
• Reduced complexity
• Happier policy holders

Utilities

During an outage, call volumes spike and wait times skyrocket. There is no way to anticipate when an outage will occur and staff is consequently overwhelmed. However, agentic AI can identify all homes and businesses within an outage area and provide proactive notification that an outage is occurring.

For those who are calling in to report an outage, the agentic agent (as opposed to a more traditional scripted agent) can identify the address associated with the calling number, check a GIS map for outages and inform the caller if their property is in a known outage state. It can potentially provide an estimated resolution time. And it can handle notifications when the outage is resolved.

Benefits:

Proactive notification deflects calls to contact center to report outages already known

Ability to handle traffic spikes during outage events and deflect callers in known outage areas, reducing call volume

Less stress for contact center staff

Improved efficiency

Happier customers

Healthcare

Agentic AI can be used for appointment scheduling and proactive outreach for:

• Appointment reminders
• Lab results
• Rescheduling
• Appointment follow-ups
• Confirmation and instructions for upcoming procedures
• Prescription refills
• Health tips

In addition, agentic AI can provide:

• 24/7 access to information and support through intelligent chatbots and virtual assistants for inquiries
• Personalized health insights through analysis of patient data for:
• Disease diagnosis
• Second opinions
• Early disease and risk identification
• Readmission risk
• Recommended proactive interventions

Benefits:       

• Reduced cost of care by ensuring patients are well-informed and adhere to care plans
• Improved health outcomes
• Smoother, less stressful experiences for patients
• Improved efficiency
• Happier, more well-informed patients
• Less stress for staff and patients

In essence, agentic AI transforms personalization from a rule-based, reactive process into a seamless, autonomous engine that can understand, predict, learn and adapt in real time. This allows organizations to create truly individualized experiences for millions of customer touchpoints.

The Future of Agentic AI in Personalization

During a recent trip, I encountered an unexpected fuel charge on my rental car bill after returning home. I logged into the rental agency’s online portal and initiated a chat, where an agentic AI responded.

I provided my rental agreement number and stated my intention to dispute the fuel charge. The AI immediately presented all rental costs within the chat window, formatted clearly and embedded directly, rather than as a separate attachment. In addition to the full breakdown, the AI specifically itemized the fuel charges.

Then I clarified that there should be no fuel charges, as I had returned the tank full and had a photo of the dashboard showing both mileage and a full tank at the time of hand-off. I reiterated my dispute of the charges.

The AI then seamlessly handed me to a live chat agent, who had access to the full transcript, eliminating the need to re-explain the situation. I again stated my dispute regarding the fuel charges, and the live agent confirmed that a credit would be issued. Aside from a 5-minute delay between each exchange with the live chat agent, the experience was relatively smooth, and the credit did, in fact, appear on my credit card statement.

Personalization allowed the AI agent to respond specifically based on both my account information and what I was requesting. It’s not a bad start, but I wasn’t thinking “They know me” afterward.

How could personalized agentic AI improve this experience in the future?    

Hyperpersonalization: Anticipating user needs before they arise

Because I’m an existing user of the rental agency app, the AI agent could have proactively identified my most recent rental based on available information, such as the date and location. This would have eliminated the need for me to manually provide the rental agreement number, streamlining the initial interaction. This simple type of anticipation should be easy, given the information is readily available.

Seamless integrations across multiple touchpoints

It’s easy to envision an AI agent managing this interaction entirely, rather than transferring it to a live agent. It could access the dashboard image I uploaded and scan it for verification. It could then issue the credit and send a confirmation email for my records.

Increased sophistication and learning capabilities increases proactive problem-solving

Going further down the line, a system integrated with the car itself would have seen the tank was full and not charged the fuel charge in the first place. One can dream…

This dream highlights the potential of truly integrated, agentic AI systems. Imagine a future where your car isn’t just a collection of disconnected features, but a cohesive entity aware of its own status and your interactions with it.

In this ideal scenario, the car’s integrated system would possess real-time knowledge of its fuel levels. Upon recognizing that the tank was already full, it would intelligently bypass any attempts to initiate a fuel charge, thereby preventing an erroneous charge on your statement. This would then eliminate the need to dispute the charge, deflecting an irritating interaction and negative customer experience.

Such a system wouldn’t merely react to explicit commands; it would anticipate needs and correct potential issues before they arise. This level of foresight and self-awareness is the hallmark of personalized, agentic AI – a system that acts autonomously and intelligently to optimize its own operations and, by extension, the user experience.

The implication is a seamless interaction, free from the minor annoyances and potential financial discrepancies that currently exist due to fragmented systems. It’s a vision of ultimate convenience and efficiency, where the technology fades into the background, working flawlessly to enhance daily life.

The Seamless Integration of Agentic Personalization

Personalized agentic AI offers a vision of ultimate convenience and efficiency, where the technology fades into the background, working flawlessly to enhance daily life.

At the same time, poor implementations or lax security have the potential to create a fiasco. So, it’s critical to ensure that your organization has a strong foundation from which to build AI capabilities.

Global Tech offers an online quiz that will provide a high-level score of your organization’s AI readiness. We also assist in identifying and assessing the viability of AI use cases and creating strategic plans for implanting AI.

This article was originally published on Genesys.com/blog here: http://bit.ly/4hhiXua

Cynically, this confusion could be seen as a mechanism used by vendors to set the stage to highlight capabilities that they provide, while downplaying those that are not offered.

Alternatively, confusion may be reduced as multiple “flavors” of AI agent emerge as technology matures, allowing for flexibly segmented development.

Is Agentic AI a breakthrough technology, or just the latest hype?  Let’s dig a little deeper and see if we can find some answers.

What is agentic AI?

According to this video from Google, “No one seems to agree on exactly what an agent is”.  It makes sense to think of agentic capabilities as a continuum of capability and complexity.  Not all agents will be equally complex; some will perform very simple tasks.  Others may have greater capabilities and the ability to handle more complexity.

For example, information summarization is typically low complexity and offers limited capabilities.  Self-driving cars, on the other hand, are highly complex and also offer a slew of capabilities.

However, there are some characteristics that are generally considered to be capabilities of Agentic AI:

1. Agents exist within a specific environment
2. They have one or more goals to accomplish
3. Agents are able to sense inputs or stimulus in their environment
4. They are able to reason about the things that they sense and decide on a course of action based on the stimulus
5. Agents can act autonomously to accomplish a specific goal. Actions can include:
   1. Providing notifications if specific conditions are met
   2. Reviewing documents and providing an answer to a question
   3. Handing off the interaction to another agent or a human
   4. Updating records, canceling orders, or kicking off other processes

In short: Agents Sense, Decide and Act within their Environment to fulfil a given agenda.

Additionally, they may be able to interact with other AI agents, analyze and improve their performance over time, have dialogue capabilities, act proactively, and/or affect the real world by taking actions such as preventing a financial transaction when fraud it detected, or placing orders to restock for low inventory.

According to Gemini:

Agentic AI refers to a class of artificial intelligence systems designed to operate autonomously, make decisions, and perform tasks without constant human intervention. The term “agentic” highlights their “agency” – their capacity to act independently and with purpose towards a goal.

How is Agentic AI different from traditional AI?

Gemini says “agentic AI represents a significant step forward from traditional AI, moving towards systems that are not just intelligent but also capable of independent action, planning, and continuous self-improvement in dynamic environments.”

Traditional applications rely on a set of predefined rules to make decisions or perform simple tasks, and are typically scripted. The rules and scripts control the actions. Researchers in the multi-agent systems field contend that legacy “agents” that follow pre-defined rules are really just programs, and are not truly agentic.

Agentic AI does not follow a script.  It is given a goal instead. It determines the best route to reach the goal. Typically, the agentic AI is given parameters such as a set of tools, instructions on how to use them, and a description of how to execute the action.

Agentic capabilities include the ability to evaluate different scenarios and predict the outcomes of various actions. They can even assign a value to each action based on how well it aligns with the agent’s goals. By doing so, the agent acts autonomously to choose the action that is most likely to achieve its goals.

Agentic AI uses LLMs (Large Language Models) to understand inputs they receive and to communicate via dialogue. They can provide responses based on the context of a conversation, including information previously provided. This greatly improves the perceived quality of the interaction.

For example, if a user asks, “Can I add my new Chevy to the policy?” the AI can respond with “Sure. To add your Chevy, I’ll need to know…” This confirms, in a fluid way, that the transaction is progressing properly.

Users can add follow on questions, with the agent “remembering” and incorporating previous inputs.   For example, “It’s a 1968 model. Is special insurance required?”  Here, the agent would “remember” that the vehicle is a Chevy and investigate to provide an answer.

“Can you also add my Nissan?” The agent recognizes that this is a new request to add an additional vehicle. Agentic AI can handle this smoothly, whereas a scripted interaction would require the user to return to an earlier part of the transaction to start down this new path.

“For the Nissan, I only need Collision and Liability, not Comprehensive.  How much will my rate increase?” The agent can respond to this request by looking up the information, obtaining additional information if needed, and calculating the amount to provide an answer.

How can it be used?

Agentic AI can be used in many ways, across functions and industries.  From a customer experience perspective, agentic AI can be used:

To extend the capabilities of traditional chatbots to provide true problem resolution:

• Retrieve customer data, history, and knowledge base information
• Deliver context-specific responses, or pass customers to the appropriate resources for further assistance
• Take action to resolve a problem, update records, provide status information, etc.

For determining customer intent, even in situations where the information provided is complex or vague:

• Ask follow up questions to gain more context, based on each individual situation
• Determine if there is a question to be answered or if action is required
• Retrieve information to answer the question or route to another agent/program/person to fulfill the action
• Communicate with requestor regarding status or to obtain additional info needed to progress through a workflow
• Select the right tool to perform the required action

To operate across channels (voice, email, chat, SMS, etc.):

• Access full customer history and context from multiple sources (order history, chat history, previous calls, emails, etc.)
• Provide follow up information via customer’s preferred channel (SMS, Chat, email)

To automate documentation of interactions, including:

• Interaction summarization
• Trend analysis

Proactive outbound contacts:

• Follow up information
• Status updates
• Reminders

What are the benefits?

At a high level, some of the benefits that agentic AI offers for customer experience include:

• Automation of more complex processes than simple scripting programs can handle
• Improved, nuanced dialogue that appears natural to customers/users
• Offloading of mundane tasks from humans, allowing them to handle more complex tasks or those requiring empathy or creative thinking
• Faster processes
• Consistent quality
• Ongoing improvement
• 24x7x365 operation
• Handling spikes in volume that would overload traditional operations

Is there a down side?    

As with everything, there are drawbacks with the use of agentic AI. It’s much like using a blowtorch.  In the right situation, a blowtorch is a fantastic tool.  But if used incorrectly, it can burn down a building.

Here are some of potential risks that must be mitigated when using this powerful technology.

Costs are hard to predict, especially at the outset

Agentic AI is typically priced via a consumption model; the more you use, the more you pay. Typically, consumption is measured via tokens, and each provider has a different definition and different way of calculating token use.

This makes it very difficult to forecast costs of an agentic AI solution. Past volume information is often in the form of a number of transactions by channel and/or average handle time. Tokens are calculated based on completely different information. Initial cost projections require many assumptions (that may not be accurate in the long run).

For example, Genesys token costs are based on many factors including the number of users (which can be named or concurrent), number of minutes, number of sessions, number of translations, and other factors. Fortunately, there are token calculators which help predict costs before deployment.

Garbage In, Garbage Out

Before you automate, it’s imperative that your existing processes are optimized and well documented. Underlying data must be clean, classified, and accessible. Optimizing your processes and data can be challenging.  Don’t underestimate the level of effort necessary.

Complexity

Supporting agentic AI requires staff with the skills necessary for managing and governing AI agents.  There can be many ongoing support challenges due to their technical complexity. They must be managed and optimized on an ongoing basis to ensure that they are operating as expected and don’t drift or experience downgraded quality.  Testing and monitoring can be challenging as agentic AI applications and LLMs do not provide the exact same output in every iteration.

Sabotage

Employees who are fearful of future job loss may sabotage AI rollouts.

The Usual Suspects

AI has other risks that must be mitigated, such as:

• Inherited bias
• Possibility of hallucinations
• Security issues including:
  • Risk of exposure of proprietary information
  • Susceptibility to external manipulation through malicious prompt injections

Conclusion

Agentic AI is a powerful technology that promises automation of increasingly complex workflows. At the same time, poor implementations or lax security have the potential to create a fiasco.  It’s important to ensure that your organization has a strong foundation from which to build AI capabilities.  Global Tech offers an online quiz that will provide a high-level score of your organization’s AI readiness. We also assist in identifying and assessing the viability of AI use cases and creating strategic plans for implanting AI.

This article was originally published on the Genesys.com/blog The rise, benefits and concerns around agentic AI

In the rapidly evolving landscape of artificial intelligence, deploying an AI application is just the beginning of the journey. The true power of AI solutions emerges through a deliberate, continuous cycle of analysis and improvement. This iterative approach ensures that AI applications not only meet current requirements but evolve to deliver increasing value over time.

The Analytics Foundation

Analytics form the cornerstone of any successful AI improvement strategy. Without robust measurement systems, organizations operate in the dark, unable to quantify performance or identify opportunities for enhancement. Effective analytics in AI applications track multiple dimensions:

• User/Customer engagement metrics: Where do people spend time? Where do they drop off?
• Accuracy measurements: How often does the AI provide correct or helpful responses?
• Performance indicators: Response times, processing efficiency, and resource utilization
• Business outcome metrics: Conversion rates, customer satisfaction, cost savings

As the saying goes, “You can’t improve what you don’t measure.” Analytics provide the visibility needed to transform subjective impressions into objective insights. 

The Iterative Improvement Cycle

The process of improving AI applications follows a cyclical pattern that might be familiar to those versed in agile methodologies:

1. Measure current performance through comprehensive analytics
2. Analyze the data to identify patterns, bottlenecks, and opportunities
3. Hypothesize potential improvements based on findings
4. Implement changes to address identified issues
5. Test the modifications against baseline performance
6. Evaluate results and begin the cycle again

This “measure, test, measure, test” rhythm creates a feedback loop that drives continuous improvement. For example, in a customer-facing chatbot, analytics might reveal that users frequently abandon conversations after certain types of questions. This insight leads to targeted improvements in those specific conversation flows, which are then measured to confirm effectiveness.

Beyond Initial Deployment

A common misconception is that AI applications reach a “finished” state – that they can be deployed and then left to run indefinitely. This static approach fails to leverage one of the greatest strengths of AI: its ability to learn and adapt over time.

The most successful AI implementations embrace the philosophy that the solution is never “gold” or complete. Instead, they recognize that each iteration provides valuable data that informs the next round of enhancements. This perspective transforms AI development from a project with a defined endpoint into an ongoing process of optimization.

Keys to Successful Iteration

For organizations embarking on this journey, several principles can guide effective iteration:

• Prioritize improvements based on impact: Focus on changes that address the most significant pain points or opportunities first
• Compare against baselines: Maintain clear records of performance before changes to accurately assess improvements
• Involve end-users: Complement quantitative analytics with qualitative feedback from actual users
• Balance quick wins with strategic enhancements: Mix easily implemented improvements with more fundamental adjustments

The cycle of analyzing and improving AI applications represents not just a technical process but a mindset – one that embraces continuous learning, adaptation, and refinement. Organizations that master this cycle transform their AI investments from static tools into dynamic, evolving assets that deliver increasing returns over time.

Begin With Small, Measurable Tasks

When approaching AI implementation, start small rather than attempting sweeping changes. Look for specific, measurable, and typically repetitive tasks that could benefit from automation. Focus on automating individual tasks rather than entire job roles.

 For example, in a contact center environment, agents perform numerous tasks daily. Instead of replacing human agents entirely, identify specific functions that AI can enhance.

One opportunity for significant, measurable results is the use of AI for call summarization.  This automates the documentation process agents must complete during or after calls.

Benefits include:

• • Reduces after-call handling time
  • Allows agents to focus on customer interaction rather than documentation
  • Increases agent satisfaction by eliminating tedious work
  • Improves overall efficiency
  • Reduction in time callers spend in queue

A less obvious example is background noise reduction in headsets. On the surface, this doesn’t seem like an area where there can be significant results.  After all, noise cancelling headsets have been around for a while.

However, AI-powered headsets can filter out non-voice sounds in real-time on both ends of the conversation.  This goes beyond basic noise cancellation, providing measurable benefits such as:

• • Enhances call transcription quality for better quality assurance
  • Enables accurate sentiment analysis
  • Reduces average handle time by minimizing repetition
  • Improves conversation quality

Building Momentum

Successfully implementing small AI projects with measurable results creates confidence and momentum for tackling more complex challenges later.

Before starting your AI project, we recommend that you start with an AI Readiness Assessment to ensure that the foundational building blocks for a successful project are in place.

Before implementing any AI solution, it’s essential to clearly define your objectives. Begin by asking:

• What specific business problem are you trying to solve?
• Is the investment financially viable?
• What measurable outcomes do you expect?
• How do you envision your organization will be different after implementing AI?

Your AI implementation should align with strategic goals such as cost reduction, operational efficiency, or enhanced information quality. This clarity forms the foundation of your business case. 

Select the Optimal Use Cases

Once you have established your goals, it’s time to focus on which areas offer the most viable opportunities for AI success.  Characteristics can include:

• Repetitive tasks
• High frequency activities
• Lack of automation
• Low complexity
• Data sources that have been normalized and categorized

Document the Current Processes

A thorough understanding of existing processes is crucial before any automation attempt. This often presents a significant challenge, but is an essential starting point.

To document processes effectively:

1. Schedule meetings with key stakeholders and process owners
2. Ensure team members can articulate their workflows in detail
3. Create comprehensive documentation of current processes, including edge cases and exceptions
4. Map dependencies between different processes and systems

This documentation not only supports your business case development but also becomes a blueprint for implementation. 

Build the Economic Case

Financial viability is essential for any AI business case. Develop a detailed cost-benefit analysis that includes:

• Initial implementation costs (software, hardware, integration)
• Ongoing maintenance expenses
• Expected ROI timeline
• Quantifiable benefits (cost savings, revenue increases, productivity gains)
• Intangible benefits (improved customer experience, competitive advantage)

Consider long-term value, scalability, and compounding returns from AI investments. 

Evaluate Operational Readiness

Successful AI implementation depends on operational preparedness:

• Assess your team’s current capabilities and identify skill gaps
• Create a communication plan to inform staff of upcoming changes and how their jobs will be impacted
• Develop training plans to ensure staff can support and maintain new AI systems
• Update or establish data governance protocols to maintain data quality and relevance
• Create standard operating procedures for AI system maintenance 

Understand Risks and Mitigation Strategies

A comprehensive business case acknowledges potential risks:

• Data quality or availability issues
• Integration challenges with existing systems
• Adoption resistance from staff
• Security and privacy concerns
• Regulatory compliance requirements

For each identified risk, develop mitigation strategies and contingency plans.

Make the Case to Decision Makers

When presenting your business case:

• Lead with the business problem and its impact on organizational goals
• Present clear, data-driven ROI projections
• Outline implementation timelines with key milestones
• Demonstrate how success will be measured
• Address potential objections proactively

Remember that non-technical stakeholders need to understand the business value without getting lost in technical details.

Run a Pilot for Proof of Concept

Consider recommending a pilot implementation to:

• Validate assumptions in a controlled environment
• Gather real-world performance data
• Refine the approach before full-scale deployment
• Build organizational confidence in the solution

A successful pilot provides compelling evidence for broader implementation.

Conclusion

Creating a strong business case for AI requires thorough analysis of problems, processes, economics, and operational factors. By systematically addressing each of these elements, you can build a compelling case that demonstrates clear value to your organization while acknowledging and mitigating potential risks.