Your leaders want AI in service now. This self-paced practitioner course teaches the people who run service to brief and check AI, build knowledge a bot can answer from, use agent-assist safely, design chatbots and voicebots that hand off well, run AI quality checks fairly, read contact data and govern AI agents that issue refunds. You practise on Hearthwell Home, a fictional service operation, in sims, labs and an agent sandbox. About 14 hours over 6 weeks.
Bodhih Applied AI: Customer Service is a self-paced practitioner certification for service agents, team leads, QA analysts, knowledge managers and CX managers. In about 14 hours it teaches how to brief and check AI, build knowledge a bot can use, work safely with agent-assist, design chatbots and voicebots that hand off well, run AI-assisted quality checks fairly, read contact data, and govern AI agents that issue refunds. Learners practise in sims, labs and an agent sandbox, then complete a reviewed capstone.
Facts on this page last checked by Siddharthan VGJ, Director, Bodhih Training.
The help desk now drafts, summarises and translates, a bot answers from the knowledge base and an AI agent pilot is on the roadmap. Nobody decided what the agent may refund or change, who approves, or what agents may paste into which tool.
A session the bot kept is not a problem it solved, and neither is a “deflected” contact that returns by phone the next day. Without clear definitions and a repeat-contact check, a bot report tells leaders what they hoped to hear.
AI quality tools widen coverage from a few contacts to all of them, but a score with missing context, an unchecked sentiment label or no calibration damages trust fast, and some uses of emotion inference about staff are banned in the EU.
You check drafts, summaries and translations, take handoffs from bots and own knowledge articles. You will learn the checks that keep facts, promises and privacy right before anything reaches a customer or a record.
You run AI-assisted queues and coach with AI quality data. You will learn to calibrate people and AI, check scores for bias and missing context, and keep people accountable for decisions about staff.
Your articles are now what the bot says. You will learn the AI-ready article standard, knowledge health checks, bot intents and guardrails, handoff design and how to run a bot incident.
You choose and pilot AI uses and report results to leadership. Picture your next pilot review with containment, resolution and repeat contacts on separate lines, and an agent’s permissions written down before go-live.
This course is not for developers who need to build bots on one platform (a vendor academy fits better), or for people who first need the human skills of service, who should start with Bodhih Customer Service Excellence.
Four dated figures, each linked to its source.
Of customer service leaders surveyed report pressure from executive leadership to implement AI in 2026, and 58% aim to upskill agents into knowledge management specialists.
Gartner press release (survey of 321 customer service leaders), 18 February 2026Rise in AI spending by customer service leaders, while overall service and support budgets rose 2%.
Gartner press release (survey of 199 customer service leaders), 26 August 2026An AI assistant raised issues resolved per hour by 14% on average, and by 34% for novice and low-skilled agents, in a study of 5,179 customer support agents.
Brynjolfsson, Li and Raymond, “Generative AI at Work”, Quarterly Journal of Economics (NBER working paper 31161), 2025Of customers surveyed would prefer that companies didn’t use AI for customer service; their top concern is that it will become harder to reach a person.
Gartner press release (survey of 5,728 customers), 9 July 2024Imagine walking into Monday’s bot review knowing which of the contained sessions were really resolved, which customers came back, and which knowledge articles caused it. The course measures you at the start and at the end on parallel forms, and asks for a capstone (a real or desk pilot with controls, a test log and results) that a reviewer scores before you receive the credential.
Brief an approved AI assistant with the minimum case context and the right sources, keep customer data out of unapproved tools, and trace every claim to the account, policy or article.
Write and maintain articles with one source of truth, clear conditions, an owner and a review date, and find the stale or conflicting articles behind wrong answers.
Draft, summarise and translate with agent-assist, and check facts, promises, meaning and tone before anything goes to a customer or a record.
Set intents and guardrails on what a bot may say or do, disclose AI in every language and channel, and pass context so the customer never repeats themselves.
Separate the three measures, check repeat contacts and agent volumes, and never report a contained session as a saving it did not make.
Widen QA coverage with AI, calibrate people and AI against clear definitions, check for bias, and keep humans accountable for decisions about staff.
Use formulas and pivots on contact data to find drivers, repeat contacts and their causes, treat sentiment labels as data to check, and turn findings into fixes with an owner.
Set an AI agent’s goal, rules, tool permissions and limits for refunds and account changes, guard against prompt injection and over-reach, and decide when it is ready to scale.
The pathway runs in order: a baseline assessment, seven modules (briefing and checking AI; knowledge built for AI; agent-assist; chatbots and voicebots; AI-assisted quality; contact analytics; AI agents in service), each with a hands-on lab and boosters two and seven days later, a reviewed capstone on an AI-enabled service improvement, and a final assessment that unlocks once the capstone is accepted.
Measured before you start and again on the same instrument at the end, so the change is recorded rather than claimed.
The baseline assessment, Form A: 20 scenario and data questions on all seven competencies, about 25 minutes. It is not pass/fail; it sets your baseline, and you take the parallel form at the end.
What customers accept from AI, briefing AI with the minimum case context and checking every claim against the account, policy and knowledge. Brief Builder, then the Inspector Source Check.
Articles people and AI can retrieve safely, one source of truth per question, owners and review dates. Inspector: Article Inspector, then the Data Lab Knowledge Health.
Agent-assist for drafts, summaries, translation and knowledge, checked for facts, promises, meaning, tone and privacy. Scenario Sim: The Copilot Shift, then the Inspector Summary and Translation Check.
Intents, guardrails, AI disclosure, handoff with context and containment versus resolution for chatbots and voicebots. Scenario Sim: Helper Goes Viral, then the Bot Transcript Inspector.
AI quality scores for wider coverage and coaching, calibration against clear definitions, bias checks and limits on monitoring. Scenario Sim: The Calibration Meeting, then the AI Evaluation Inspector.
Contact drivers, repeat contacts and root cause with formulas and pivots, and sentiment treated as data to check. Data Lab: Contact Insight Lab.
An AI agent's goal, rules, permissions and limits for refunds and account changes, right-sized approval, prompt injection and measurement. Flight Deck mission: Desk Agent: refunds, accounts and escalation.
Design and pilot one AI-enabled improvement in your service operation. Choose one: a knowledge improvement for AI, a bot or voicebot flow, an agent-assist use in one queue, or an AI agent pilot for one ticket type. Hand in one document of 1,500-2,500 words plus tables, in five parts: (1) the problem and baseline, with each measure defined (keep containment, resolution and deflection apart); (2) the design with controls, including guardrails, limits, handoff, AI disclosure, privacy and who approves what; (3) a test log of at least 20 cases, with at least 5 adversarial or edge cases and a re-test after fixes; (4) pilot results over 2-6 weeks, including one result that went the wrong way or a limit of the data, and your decision to scale, change or stop; (5) governance and rollout: owner, thresholds, off switch, incident route and staff training. Remove names and customer details first. If you cannot use real data or tools, use the Hearthwell pack and run a desk pilot, and say so. A reviewer scores your work on five criteria (0-4 each). Met is 12 or more of 20, with every criterion at 2 or more and Design and controls at 3 or more. Feedback comes within 5 working days, and you may resubmit once.
The final assessment, loaded from Form B: 20 parallel scenario and data questions, about 25 minutes. Score 70% or more (3 attempts), with your capstone accepted, for the credential.
Finishing this pathway issues a credential backed by the evidence you produced along the way. It is verifiable by link and is also issued as an Open Badge 2.0, so an employer can check it without taking your word for it.
Drafts, contact summaries and translation checked for facts, promises and meaning, practised in The Copilot Shift and the Summary and Translation Check.
The AI-ready article standard, freshness and one source of truth, with the Article Inspector and the Knowledge Health lab on a knowledge base extract.
Intents, guardrails, AI disclosure in every language and a handoff packet, then a bot incident run end to end in Helper Goes Viral and the Bot Transcript Inspector.
Scoring every contact, calibration, fairness and appeals, practised in The Calibration Meeting and the AI Evaluation Inspector.
Contact drivers, first-contact resolution, repeat contacts and sentiment with care, in the eight tasks of the Contact Insight Lab.
Permissions, approval limits, prompt injection and an agent scorecard, flown in the Desk Agent mission on the Agent Flight Deck.
What comes with the course, and what makes the practice different.
Hearthwell Home (fictional) runs a bot, a voicebot pilot, agent-assist, an AI quality tool and an AI agent pilot. You work its knowledge base, its contact data and its incidents: Source Check, the Article Inspector, the Knowledge Health lab, the Summary and Translation Check, the Bot Transcript Inspector, the AI Evaluation Inspector and the Contact Insight Lab.
The Copilot Shift, Helper Goes Viral and The Calibration Meeting, then the Desk Agent mission on the Agent Flight Deck. Notice how a single setting, such as a refund limit or a source the bot may read, changes the whole outcome; the debrief shows which part of your set-up caused each result.
The Support Desk Set-Up Kit, The Call Quality Monitoring Kit, The Customer Feedback Programme Kit, The AI Use Policy and Governance Kit and The AI-Fluent Professional, each used in the modules where its files fit.
Company seats with reporting on each learner’s progress, before-and-after scores and capstone status; the sector lens lets one course serve several service teams.
Ask about team seats| What is compared | A typical AI customer service course | AI for Customer Service at Bodhih |
|---|---|---|
| Practice | Videos and tool demos, sometimes a quiz or a prompt template. | An agent sandbox, three branching sims, five Inspector labs and two Data Labs with real formulas and pivots, one on 2,000 contacts. |
| Scope | One product’s AI features, or strategy slides for managers. | The whole chain: knowledge, agent-assist, bots and voicebots, quality, analytics and AI agents. |
| Tools | Built around one vendor’s platform. | Tool-agnostic: any approved AI assistant and any help-desk or bot platform. |
| Measures | Containment and deflection read from a vendor dashboard. | Containment, resolution and deflection defined and calculated on data you analyse, with a repeat-contact check. |
| Governance | Light, or one session at the end. | AI disclosure, privacy, emotion inference at work and agent permissions built into every module, at principle level. |
| Measurement | A completion certificate. | A baseline and a final on parallel forms and a reviewed capstone; your before and after shows on the credential. |
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Straight answers on time, price, tools and the credential. If yours is not here, ask us and you will have a reply the same working day.
Service agents moving into AI-assisted work, team leads, QA analysts, knowledge managers, chatbot and conversational AI owners, and CX or contact-centre managers, in any sector. A sector lens with seven options (including retail, banking and insurance, telecom, healthcare, hospitality and B2B support) switches the examples to your setting.
No. You need experience in customer service or support. The course is tool-agnostic: it works with whatever AI assistant your employer approves (for example Copilot, ChatGPT, Gemini or Claude) and any help-desk or bot platform. Every lab, sim and the agent sandbox runs inside the course with fictional tools, so nothing needs installing.
About 14 hours: seven modules of about 75 minutes, a 25-minute assessment at the start and at the end, and a capstone of about four hours at work. Most people finish in about 6 weeks at two modules a week. Short booster questions arrive two and seven days after each module.
Containment counts bot sessions that end without a handoff to a person. Bot resolution asks whether the problem was actually solved: in the course, a contained session with no repeat contact on the same reason within seven days. Deflection is claimed only when agent contacts for that reason really fall. Module 4 teaches the three definitions, and a capstone that reports containment as a saving without a repeat-contact check is marked Not yet.
You design and pilot one AI-enabled service improvement (knowledge for AI, a bot or voicebot flow, an agent-assist use or an AI agent pilot) with a baseline, controls, a test log of at least 20 cases including adversarial ones, pilot results and a governance plan. A reviewer scores it on five criteria, and one resubmission is allowed. If you cannot use real data, you run a desk pilot on the Hearthwell data pack.
Customer Service Excellence teaches the human skills of service: listening, de-escalation, first-contact resolution and complaints, plus a frontline view of AI drafts and bot handoffs. Applied AI: Customer Service assumes those skills and teaches how AI is designed, run, checked and governed in service operations. Many teams take both; they share one fictional case company, Hearthwell Home.
Yes, at a practical level: telling customers they are talking to an AI (Article 50, which applies from 2 August 2026), the ban on inferring the emotions of staff at work (Article 5(1)(f)) and what it means for AI quality tools, and privacy basics under GDPR and India’s DPDP Rules. It is not legal advice.
Yes. When your capstone is accepted and you pass the final assessment (70%, with up to three attempts), you receive the Bodhih Applied AI: Customer Service credential with an Open Badge 2.0, a verify page and a PDF that shows your before-and-after result. It is a Bodhih credential, not a university qualification or an accreditation.
No. It is tool-agnostic and does not prepare you for any vendor exam (such as Zendesk, Salesforce or Cognigy certifications) or for a KCS certification. Module 2 draws on the KCS v6 Practices Guide to describe good knowledge practice; it is not a KCS course and is not endorsed by the Consortium for Service Innovation. KCS is a service mark of the Consortium for Service Innovation.
Yes. Company seats are $159 each, with reporting on progress, before-and-after scores and capstone status, and the sector lens lets one course serve several teams. Write to solutions@bodhih.com or call +91 99000 11601 for seats or a rollout.
Five complimentary Pro Kits (The Support Desk Set-Up Kit, The Call Quality Monitoring Kit, The Customer Feedback Programme Kit, The AI Use Policy and Governance Kit and The AI-Fluent Professional), a printable job aid for every module, booster questions and a workplace action log.
The course is $199 for an individual, or $159 per seat for companies. You can start today with the baseline assessment, then brief and check your first AI answer in Module 1. For a team, write to solutions@bodhih.com or call +91 99000 11601.
Starting records where you stand today. That baseline is what the re-measurement is compared against, and it is never rewritten afterwards.
Calm angry customers, solve on the first contact and handle complaints well, practised in six branching sims tailored to your sector.
Set up, supervise and test AI agents without code, in the hands-on Agent Flight Deck lab.
Four hours of hands-on AI literacy for every employee, with a measured before-and-after score.
Measure your team first, plan each person’s growth after, or bring a trainer or coach in. Bodhih’s sister companies cover the rest of the journey.