AI Coaching is a solution many contact center leaders are turning to in hopes of supercharging agent performance at scale.
But does the AI coaching buzz live up to the hype?
The short answer is: it depends on how the AI is implemented in a coaching capacity.
Recent research, including a 2024 study on AI coaching, challenges the very concept of the effectiveness of an AI-Led Coach. Their findings suggest that what's marketed as "AI Coaching" (meaning AI does the coaching) doesn't meet the criteria of true coaching and is not an effective replacement for a human coach in complex scenarios.
In this article, we'll review the two types of AI Coaching, their use cases in a contact center, and demystify some of the hype.
Article topics:
What is AI Coaching?
AI Coaching describes the use of artificial intelligence in a coaching capacity to interact directly with people.
AI Coaching in a contact center
AI Coaching within a contact center uses AI to provide insights and actions to team leaders and frontline agents. This can include performance coaching, quality assurance and behavior modification.
2 Types of AI Coaching in a Contact Center
The two types of AI Coaching used in a contact center environment are AI-Led and AI-Enabled. Both types are built upon Large Language Models (LLM) and Generative AI (GenAI), but differ by use case within a contact center.
This type of AI Coaching (which we'll be referring to as AI-Led Coaching) is where AI directly does the coaching.
AI-Led Coaches are autonomous systems that directly interact with agents, providing 'coaching like' responses without human intervention.
This type of AI Coaching is also known as Real-Time Agent Assist.
While marketed as "AI Coaches", they function more as intelligent assistants.
.png)
Key features
- Real-time assistance during customer interactions
- Automated performance analysis
- Immediate feedback
- Available 24/7
How AI-Led Coaching works
- Utilizes natural language processing to analyze agent-customer interactions in real-time
- Applies machine learning algorithms to identify areas for improvement
- Delivers instant feedback and suggestions to agents during or immediately after customer interactions
AI-Led Coaching considerations: "AI Coaching" has become a catchall term being used to describe any AI-powered software that offers guidance or feedback to agents. This broad use of the term AI Coaching is a bit misleading because it doesn't necessarily align with the true nature of coaching.
If you're in need of an AI-Led Coach solution to help call center agents with prompts in real time, we wrote an article on the pros and cons of real-time agent assist.
AI-Enabled Coaching refers to coaching led by people who are supported by AI. Coaching is person-to-person, but the bulk of the data analysis, pre and post work are handled by AI.
AI is used to enable team leaders, supervisors, managers, quality trainers, and executives with the data, insights, tools, and actions to make informed decisions and deliver effective coaching at scale.
Simply put, AI augments the human coach's natural capabilities rather than attempting to replace them.
Key features
- Comprehensive data aggregation and analysis
- AI-powered performance insights and coaching recommendations
- Predictive analytics for proactive coaching
- Integration with existing coaching workflows
How AI-Enabled Coaching works
- Aggregates data from all sources (call recordings, chat logs, CRM data)
- Uses AI to analyze performance trends, real-time KPI's, and identify coaching opportunities
- Generates insights, next best actions, and recommendations for human coaches to use in their sessions with agents (and also during customer calls)
AI-Enabled Coaching considerations: If you're looking to remove the team leader/coach from the equation completely (we don't recommend this), AI-Led coaching would be your best option.
The Difference Between AI-Led and AI-Enabled Coaching
While AI-Led Coaching provides immediate, automated guidance directly to agents during calls, AI-Enabled Coaching removes the barriers to effective coaching, improving the coaching effectiveness of team leaders both pre and post call.
Another way to put it would be 'AI-centric coaching' vs 'employee-centric coaching'.
The differences between these two use cases of AI have important implications for the effectiveness, scalability, and long-term impact of coaching within your contact center.
Why is AI Coaching So Popular?
AI coaching has gained popularity in modern contact centers due to its potential to address the longstanding challenges faced by executives, call center managers, quality trainers, and team leaders.
Let's explore the top 5 challenges that have made AI coaching an attractive solution:
1. Time constraints
Contact center leaders juggle multiple responsibilities. AI coaching promises to automate time-consuming tasks, allowing managers to focus on high-value activities and provide timely feedback to their teams.
2. Data overload
With the abundance of call center analytics, it's challenging for team leaders to sift through data and identify key coaching opportunities. AI coaching offers to process this data quickly, highlighting areas that need attention.
3. Inconsistent coaching
Different coaching styles and personal biases can lead to inconsistent results across teams. AI coaching aims to standardize the coaching process, ensuring all agents receive consistent, high-quality feedback.
4. Lack of real-time insights
Traditional coaching relies on historical data, missing opportunities for immediate intervention. AI coaching promises real-time analysis of agent-customer interactions, enabling instant feedback and course correction.
5. Personalization at scale
Tailoring coaching to each agent's unique needs is crucial but the prep-work for coaching sessions is very time-consuming. AI coaching offers the promise to analyze individual agent performance and provide personalized coaching recommendations at scale.
Although these challenges have made AI coaching a popular solution for many contact centers, as we're about to discuss, AI isn't a magic bullet that can replace human coaches entirely.
Instead, the real potential lies in how AI can augment and support human coaches.
Limitations of AI-Led Coaching Compared to AI-Enabled Coaching
It might seem like a great idea to remove the human element of coaching and have AI directly coach agents. The AI-Led Coach would never get tired or 'have a bad day', and what about the money that could be saved in human resources?
The problem with this idea is, it doesn't work.
The studies conducted on the idea of removing the human element from coaching yielded negative results.
"(Human) Coaching became an intervention of choice in organizations and continues to be as such according to its essentially human elements that can be eroded in AI-Led coaching. No amount of technical sophistication could change that."
Source: AI coaching: democratizing coaching service or offering an ersatz?
While an AI-Led Coach has some advantageous use cases in the contact center, there are limitations that need to be considered before implementing it in a frontline coaching capacity.
The chart below shows the common use cases/features of AI in Coaching and how AI-Led stacks up against AI-Enabled Coaching.
"We believe that the essential features of professional organizational coaching cannot be replicated by AI-Led Coaching in principle. Even in the future, when as argued by some, it will pass the Turing test by exhibiting intelligent behaviour indistinguishable from that of a human, these criteria could not be met."
Source: AI coaching: democratizing coaching service or offering an ersatz?
These limitations paint a clear picture: AI cannot replace the complex, nuanced, and deeply human practice of coaching real-life contact center agents. It's simply not equipped to handle the intricacies of human interaction and development that make coaching effective.
Example of AI-Enabled Coaching
A leading US financial services company was experiencing long average handle times reducing the efficiency of their contact center. To mitigate this, leaders tried to keep up with manual coaching, but the coaching prep time was too time-consuming.
How AI Enabled the Coaching Process
- Data Consolidation: AI was leveraged to centralize all contact center data into a centralized location, providing a single source of truth with real-time metrics.
- Performance Transparency: Executives, managers, team leaders, and agents gained role-based access to clear real-time performance intelligence data.
- Next Best Coaching Action: AI was used to analyzed agent performance and provided data-driven recommendations to team leaders, including; pinpointing specific goals to prioritize. recognizing and rewarding wins, and addressing performance gaps.
- Coaching Effectiveness: AI was used to track the percentage of coaching sessions leading to measurable agent improvement, and isolate team leader effectiveness to replicate actions at scale.
The Results
- Reduced average handle time by 14 seconds
- Team Leaders saved an average of 15 minutes per-coaching session, resulting in an estimated 52,956 additional calls handled that year
- Cost savings worth $380k across 300 call center agents
Choosing the Right AI Coaching Software for Your Contact Center
As the AI coaching market expands, it's important to distinguish between the AI-Led and the truly transformative AI-Enabled Coaching.
While AI-Led Coaching (Real-Time Agent Assist) has its place in contact centers, it doesn't fully address the fundamental challenges that drive leaders to seek AI coaching software in the first place.
AmplifAI's AI-Enabled Coaching software is the only option purpose-built to tackle the core burdens faced by contact center leaders, offering the perfect balance of AI-powered insights, quality assurance, gamification, and human-led coaching, to create a synergy that drives real, sustainable results.
Don't settle for AI that only assists – invest in AI that truly enables your team to reach its full potential.
Ready to see how AI-Enabled Coaching can transform your contact center?
Book a demo with our team and experience the difference firsthand.
.png)
.svg)
