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Most AI projects fail because they lack structure. Moving from tests to production requires a clear plan. Here’s how to do it:

\nStart with a problem: Define specific goals like automating 90% of billing queries or cutting response times by 50%.\n
\nPrepare your data: Use clean, well-organized sources like FAQs, helpdesk tickets, and internal documents.\n
\nSet up workflows: Map tasks, use LLMs for repetitive ones (e.g., password resets), and establish escalation rules for complex cases.\n
\nImplement governance: Create safeguards like approval workflows, content filters, and human oversight for high-risk tasks.\n
\nMeasure success: Track accuracy, deflection rates, and ROI. Adjust based on feedback.\n

LLMs work best for repetitive, low-risk tasks like FAQs or multilingual support but should avoid compliance-heavy or precision-critical areas. Starting small and scaling gradually ensures better results and minimizes risks.

\"5-Step — 5-Step Framework for Building an AI Strategy with LLMs
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What an AI Strategy with LLMs Actually Means

Experiments vs. Production Systems

It's easy for teams to mistake quick tests for fully operational systems. Experiments are short-term trials - like spending a couple of weeks testing a chatbot on sample data without involving real users. These tests are all about seeing if an idea has potential. Production systems, on the other hand, are much more complex. They need scalable infrastructure, continuous data pipelines, and clear governance. They’re integrated into tools like your support ticketing system and handle actual customer interactions.

While experiments might wrap up in a few weeks, production systems require a phased approach to implementation. Here’s the catch: 62% of AI projects fail during the transition from testing to full-scale deployment. Without a clear plan to move from experimentation to everyday use, projects often stall. The key to success lies in answering critical questions before development even begins.

Questions to Answer Before You Start

Before diving into development, you need to address some crucial questions: What specific problem are you solving? Saying "improve support" is too broad. Are you aiming to automate 90% of billing inquiries? Or maybe you want to cut response times for password resets? Where is your data coming from? Are you pulling from FAQs, helpdesk tickets, internal documentation, or a mix of these? How will you measure success? Define metrics like resolution accuracy, average handle time, or cost per ticket. Who owns this workflow? Assign clear responsibilities for things like escalation rules, quality assurance, and ongoing maintenance.

These questions form what’s known as the Phase 1 strategic foundation. This groundwork is essential before running any pilot. Skipping this phase often leads to scattered experiments that never scale. By addressing these foundational questions, you set the stage for understanding how LLMs can transform customer support.

Why LLMs Matter for Customer Support

LLMs are particularly effective in customer support because they excel at understanding and generating natural language, far outperforming traditional rule-based systems. When combined with your company’s resources - using techniques like retrieval-augmented generation (RAG) - LLMs can provide accurate answers in real time. They pull information from your help center, resolved tickets, and internal policies to handle a wide range of customer inquiries.

This isn’t about replacing your team. Instead, LLMs take over repetitive tasks like password resets, order status checks, and basic troubleshooting. This frees up your agents to focus on more complex issues that require human expertise.

The impact is clear: 87% of enterprises now have at least one AI project in production, and those with a well-defined strategy report an average ROI of 4.3x. Specifically in customer support, LLMs can automate up to 90% of queries when properly configured, with built-in escalation paths for cases they can’t handle. That’s the difference between having a solid strategy and just running an experiment - one delivers measurable results, while the other merely hints at what could be possible.

When LLMs Make Sense and When They Don't

Use Cases That Work Well

LLMs excel at managing language-heavy, repetitive tasks. In customer support, this means tackling FAQs, handling multilingual responses, and assisting agents with internal knowledge searches. These tasks are ideal because they involve high volumes, minimal risk, and clear patterns. For instance, deploying an LLM to address common issues like password resets or order tracking works seamlessly. The model uses structured data from help centers, resolved tickets, and documentation to provide accurate responses.

Multilingual support is another area where LLMs shine. Instead of relying on translators or additional tools, LLMs can handle language detection, translation, and even subtle cultural nuances in one go. Internally, they assist with searching through documentation, summarizing past tickets, and drafting replies - where speed often matters more than absolute perfection. Research from EY highlights that lower-level customer support, content creation, and document analysis are some of the best early applications for LLMs. These tasks are generally low-stakes, where even a slightly imperfect result can save time and boost efficiency. However, not all scenarios are a good fit for LLMs.

Use Cases That Don't Work or Carry High Risk

When it comes to compliance-related tasks, LLMs fall short. Providing legal advice, regulated disclosures, or any information tied to laws or contracts is risky. Ungrounded models can fail in up to 30% of compliance-related tasks, and a single incorrect response could lead to fines or lawsuits. Similarly, workflows that demand precise numerical accuracy - like financial calculations, inventory management, or pricing - are not suitable. LLMs operate on probabilities, which means they make educated guesses. While this might work for drafting an email, it’s unacceptable for tasks like processing refunds or handling payments.

High-risk areas like medical diagnostics, financial auditing, or safety-critical decisions are also problematic. Studies indicate hallucination rates of 10-20% in LLMs when factual recall is required without Retrieval-Augmented Generation (RAG). For tasks involving sensitive data, real-time decisions, or high-stakes outcomes, rule-based systems or hybrid models with human oversight are more reliable. In fact, 62% of AI failures stem from poor risk assessment during deployment, often because LLMs are used in scenarios they weren’t designed for. To navigate these challenges, a scoring method can help evaluate the feasibility of different use cases.

How to Decide If a Use Case Is Worth It

To assess whether a task is a good fit for LLMs, I use a straightforward scoring method. Factors like ticket volume (high = 3 points), risk level (low = 3, medium = 2, high = 1), and automation potential (high ROI = 3 points) are rated. A total score above 7 signals a viable opportunity. For example, handling FAQs scores 3 (volume) + 3 (low risk) + 3 (automation potential) = 9, making it a strong candidate. In contrast, compliance queries score 2 + 1 + 1 = 4, suggesting they should either be avoided or handled with significant human oversight.

This approach also considers historical ticket volume (ideally 500+ per month), accuracy targets (95% or higher), and the potential cost of errors. Organizations that evaluate their data readiness beforehand report 26% higher success rates in AI projects. For customer support, we monitor escalation rates post-LLM deployment (aiming for less than 10%) and track user satisfaction scores. If a use case doesn’t score well, we avoid deploying it prematurely, focusing instead on quick wins and gradually expanding from there.

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Criterion	Good Fit for LLMs	Poor Fit / High Risk
Task Type	FAQs, ticket summaries, reply drafts, knowledge search	Billing calculations, compliance checks, and tax computation
Risk Level	Low–medium; errors cause inconvenience but not serious harm	High errors trigger legal, financial, or safety consequences
Precision Required	Approximate, conversational answers are acceptable	Exact outputs required (numbers, terms, deadlines)
Support Examples	Tier‑1 FAQs, multilingual chat, agent reply suggestions	Regulatory guidance, financial commitments, and account takeover decisions

Core Components of a Practical LLM AI Strategy

Data Foundation and Retrieval-Augmented Generation (RAG)

The backbone of any effective LLM strategy lies in its data. To ensure accuracy, start by cataloging all available sources - like documentation, FAQs, helpdesk tickets, internal runbooks, and chat logs. Then, evaluate their quality for completeness and accuracy. Once the data is gathered, clean and standardize it, adding metadata such as product category, language, and the last update date. This structured approach can lead to 40–60% improvements in response accuracy.

RAG works through a structured pipeline: content is ingested, split into segments of 500–1,500 tokens, and converted into embeddings, which are stored in a vector database. A hybrid search method - combining semantic and keyword approaches with reranking - is then used to retrieve the most relevant context for queries. For example, in customer support, Zendesk tickets and product documentation can be indexed to allow the LLM to reference real-time, resolved cases. Validation pipelines are also set up to catch outdated or duplicate content, ensuring that only reliable data feeds into your workflows.

Workflow Design and Escalation Rules

Designing workflows involves breaking down support processes into manageable tasks, such as triage, answer lookup, troubleshooting, and policy decisions. Low-risk queries - like password resets or order tracking - can be routed directly to the AI for instant responses. Escalation triggers, such as confidence scores below 75%, keywords like "refund dispute", or negative sentiment signals, ensure that complex cases are flagged. When triggered, the system can create a Zendesk ticket or send a Slack alert within 30 seconds.

Pilots have shown that when escalations are handled quickly, and agents are provided with full context - conversation history, user plans, and ticket metadata - deflection rates can reach 70%. Tools like Zendesk, Salesforce, Intercom, or Slack can be integrated via APIs to streamline this process. Standardized response templates and prompt instructions ensure the AI provides consistent answers, referencing documentation and aligning with your brand's tone. Platforms like CoSupport.ai can deploy agents capable of autonomously handling 90% of queries, escalating only the most complex cases to human agents.

Governance, Guardrails, and Safety Controls

A solid governance framework is essential for managing risks and maintaining trust. This includes establishing ethical guidelines, data privacy policies, and clear decision-making roles. To keep outputs safe and reliable, implement content filters for toxicity, rate limiting to prevent misuse, and validation processes to ensure compliance with business rules. Prompt engineering can also play a key role - for instance, templates can be designed to prevent the AI from fabricating pricing or policies. High-stakes decisions, like refund approvals or legal queries, should always involve human review.

Organizations with governance frameworks outperform others by 26%, while 62% of AI failures are linked to weak governance practices. In practice, this means using response moderation templates, maintaining audit logs of all interactions, and setting up monitoring dashboards to track issues like hallucination rates, escalations, and policy violations. CoSupport.ai, for example, meets enterprise-grade security standards, being ISO 27001 certified and compliant with GDPR and CCPA regulations. It also employs data anonymization and encryption to safeguard sensitive information.

Ownership, Metrics, and Feedback Loops

For long-term success, assign ownership of the AI strategy to a dedicated lead or cross-functional team that reports to executives. Define clear KPIs aligned with business goals. Key metrics might include deflection rates (targeting 70–90% of queries handled without human intervention), customer satisfaction (aiming for a CSAT score of 4.5+), first-response times (under 10 seconds), and ROI (averaging 4.3× from successful deployments). Dashboards integrated with tools like Zendesk can help track these metrics, with weekly reviews ensuring progress remains on track. Notably, 87% of enterprises with production AI monitor these metrics to scale effectively.

Feedback loops are critical for improving LLM performance over time. By gathering user reactions (thumbs up/down), agent escalations, and conversation logs, teams can refine prompts or retrain embeddings on a quarterly basis. Negative feedback is routed to data teams for updates to the RAG system, leading to 20–30% accuracy gains. In customer support, resolved ticket outcomes can be used to label data for continuous improvement. Pilots show that feedback-driven systems can reduce repeat queries by 35%. Automating these processes with tools that log interactions and trigger retraining when CSAT falls below thresholds ensures the system evolves with every interaction.

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Component	Key Actions	Success Indicators
Data & RAG	Catalog sources, clean data, segment into tokens, use hybrid search	40–60% accuracy improvement, low hallucination rate
Workflow & Escalation	Map tasks, set confidence thresholds (<75%), and integrate with Zendesk/Slack	70% deflection rate, escalation within 30 seconds
Governance & Safety	Define policies, implement content filters, and require human review for high-risk tasks	<1% error rate, 100% audit pass rate
Ownership & Metrics	Assign AI lead, track deflection/CSAT/ROI, conduct weekly reviews	4.3× ROI, 70–90% deflection, 4.5+ CSAT

Choosing the Right Use Cases for LLM Adoption

Customer Support Automation Examples

When it comes to automating customer support, tackling high-volume, repetitive tasks first often delivers the best results. Think password resets, order tracking, account status updates, and basic troubleshooting - these are the bread and butter of what large language models (LLMs) handle well. Take Softorino, for example. After implementing CoSupport AI to manage such queries, their resolution rates jumped from 69% to 82% in just three months

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Josh Brown, Softorino's CEO, shared, "Setup took one API key. In three months, resolution rates grew from 69 to 82 percent. We tested six other tools before. Nothing performed as well as CoSupport AI”.
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Another effective use case is ticket routing. LLMs can analyze incoming messages, categorize them by urgency or topic, and route them to the appropriate team without delay. Multilingual support automation is another game-changer, particularly for companies operating across time zones. For instance, ProjectFitter managed to slash its average resolution time from 2 hours to just 6 minutes by automating nearly 70% of its support tickets.

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Yaro Burgman, ProjectFitter's Project Manager, noted, "CoSupport AI takes care of nearly 70 percent of our support tickets and turns tasks that once took hours into minutes. It honestly made our support process so much faster and easier".
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These examples highlight how quickly automation can enhance customer support metrics. But the benefits don’t stop there - similar strategies can also transform internal workflows.

Internal Knowledge and Workflow Automation

LLMs aren’t just for external customer support; they can also streamline internal operations. Instead of combing through endless Confluence pages or Slack threads, support agents can rely on AI to instantly retrieve relevant documentation, past ticket resolutions, or product updates. Features like conversation summaries boil down lengthy chats into key points, making it easier for managers to review cases. Additionally, action item extraction identifies tasks - like "follow up with customer on refund" or "escalate to engineering" - directly from conversations and integrates them into project management tools.

The key to success here is a well-organized knowledge base and clean, structured conversation data. For example, SupportYourApp automated roughly 80% of its support chats and emails using CoSupport AI, saving $14,000 each month

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Axel Barrionuevo, SupportYourApp's Account Manager, explained, "We automated the majority of support chats and emails with CoSupport AI. About 80 percent resolve automatically now, cutting costs and keeping our quality high. Great experience"
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Starting with departments that handle a high volume of conversations is a smart way to build momentum. From there, these solutions can be scaled across teams, provided enterprise-level security and integration needs are addressed.

What to Consider for Enterprise Deployment

Security and compliance are non-negotiable for enterprise-level LLM deployments. Your setup should adhere to ISO 27001 standards, comply with GDPR and CCPA, and use data anonymization and encryption to protect sensitive information. Tools like CoSupport AI are designed for these demands, offering dedicated servers for enterprises that require complete control over their data. Seamless integration with platforms like Zendesk, Salesforce, Intercom, or Slack is equally critical, so it’s important to conduct thorough API assessments and monitoring before going live.

Risk management is another essential piece of the puzzle. Establish governance frameworks to catch issues like hallucinations, biases, or policy violations early on. It’s worth noting that 62% of AI failures have been linked to poor governance.

To minimize risk, start with low-stakes tasks like FAQ automation, and closely track metrics such as ticket deflection and customer satisfaction before rolling out to more complex workflows.

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3 Common Mistakes When Building an AI Strategy With LLMs

Starting With Technology Instead of Problems

Many companies jump straight into selecting a technology without first identifying the specific problems they need to solve. Unsurprisingly, this approach often leads to failure - 62% of bots fail to address real customer needs effectively. Take, for example, a SaaS company that implemented a well-known model to handle all customer inquiries without first analyzing its ticket data. The result? A solution that couldn’t meet actual customer demands.

The smarter move? Start by analyzing your recent support tickets to pinpoint recurring issues. Then, organize a team workshop to evaluate these challenges based on their impact and how ready your data is to address them. Focus your efforts on a high-volume problem that directly affects your operations rather than chasing after trendy technologies. And don’t forget: overlooking the quality of your data can make matters worse.

Ignoring Data Quality and Maintenance

Outdated or poor-quality data can wreak havoc on your AI’s performance. In fact, data that isn’t regularly updated leads to inaccuracies in 30–40% of responses. This can result in incorrect answers, frustrated customers, and a loss of trust.

To avoid this, set up regular data pipelines to pull updated content from your helpdesk. Validate this data for accuracy and remove duplicates. For instance, if your FAQs still reference old product details, your AI could confidently deliver outdated or wrong advice. Keeping your data fresh and accurate is essential for ensuring reliable responses. Without consistent updates, even the best AI strategies can fail.

Lack of Governance or Oversight

Without proper oversight, LLMs can create serious problems - exposing sensitive data, giving unsafe advice, or violating compliance rules. In some test cases, unchecked systems escalated harmful recommendations simply because there were no review thresholds or audit processes in place.

To prevent this, establish a governance committee before rolling out your AI. Define clear escalation rules, such as flagging any response that involves refunds over $500 for human review. Set high accuracy targets - aim for above 95% - and log all interactions for regular audits. Companies that skip governance see 40% more risk incidents compared to those that prioritize oversight from the start.

Implementing Private Large Language Models for In-House AI Solutions

Key Takeaways

Refining your AI strategy with large language models (LLMs) requires a structured approach. Here are some key points to guide you.

Focus on Solving Specific Problems

Start by addressing a clear, measurable challenge - like significantly reducing first-response times or managing a high volume of routine inquiries. Diving into LLMs without a concrete goal often leads to wasted effort. Identify your top 10 ticket categories by volume and prioritize the ones with the most impact. Evaluate which issues can be tackled using existing content, then select one or two critical problems with specific targets, such as cutting the cost per ticket by 20% within six months. Avoid treating this as a tech demo; instead, concentrate on outcomes that matter most to your business goals.

This focused approach helps build a foundation for consistent, trackable progress in your LLM initiatives.

Emphasize Data Quality and Governance

Once you've pinpointed the key challenges, the quality of your data becomes essential. An LLM’s reliability depends entirely on the accuracy of the information it processes. If your FAQs are inconsistent, your product documentation is outdated, or critical policies are scattered across informal channels, the AI might deliver incorrect answers with confidence. To avoid this, create a centralized, up-to-date source of truth for policies and product information. Use timestamps to version your data, and regularly review sensitive topics like refunds or data deletion.

Establish clear boundaries for AI responses - such as avoiding legal advice or contract changes - and set escalation rules to flag high-risk cases for human intervention. Additionally, log all interactions for auditing purposes. Companies with robust data governance practices consistently achieve better results from their AI systems compared to those relying on ad-hoc methods.

Start Small and Scale Gradually

Adopt a phased approach over 90 days. Spend the first two weeks defining your use case and setting measurable success criteria. Use weeks three and four to connect content sources and pilot the system on low-risk topics. In weeks five through eight, roll out the system to a limited group with human oversight. Finally, in weeks nine to twelve, refine the process and expand only if the metrics remain strong.

Scale up when you meet key benchmarks - such as 90–95% accuracy, no critical errors for 30 days, and a 15–25% reduction in handling time or ticket volume.For example, once your AI can reliably handle 40% of "how-to" questions in chat while maintaining customer satisfaction, you can extend its use to other areas like email suggestions or additional product categories. Organizations that follow a phased rollout and have a clear AI strategy often achieve significant improvements in ROI and operational efficiency.

FAQs

How do I know if a large language model (LLM) is right for my customer support team?

To determine if a large language model (LLM) is suitable for your customer support needs, start by analyzing your typical support queries. Look at factors like how many tickets you handle, the complexity of the questions customers ask, and whether the LLM can seamlessly work with your current knowledge base and support tools.

Put the LLM to the test with real-life examples. Check if it delivers accurate responses, resolves issues quickly, and matches the tone your team uses. If it can significantly cut down response times and handle a large chunk of common requests without making errors or generating irrelevant information, it’s probably a strong match for your team.

What are the steps to move from testing AI to using it in production?

To move from testing AI solutions to full-scale implementation, consider these practical steps:

\nConnect your data: Link your knowledge base, helpdesk, or internal documents to provide the AI with the information it needs to perform effectively.\n
\nSimulate and refine: Run realistic scenarios to uncover any gaps in the AI’s responses and make necessary adjustments.\n
\nEstablish clear guidelines: Define the AI’s conversational style, logic, and escalation protocols to ensure it behaves consistently and meets your standards.\n
\nRoll out in phases: Begin with specific channels or teams, observe how the system performs, and tweak it based on user feedback.\n
\nContinuously monitor and adjust: Keep an eye on metrics like response accuracy and resolution times, and use this data to fine-tune the AI as it’s used in real-world situations.\n

Taking these steps helps ensure a controlled and efficient transition, keeping performance on track as the system expands.

What’s the best way to manage data quality and governance when using LLMs?

To get dependable results from LLMs, it's crucial to keep your knowledge base accurate, complete, and regularly updated. Poor-quality data can lead to errors in outputs and erode trust in the system. Equally important is governance - assign clear responsibility for data management, control who has access, and conduct regular reviews to monitor AI performance.

Always connect your LLMs to verified, trustworthy sources to minimize errors or fabricated information. Enforce strict access controls and conduct routine audits to ensure the system aligns with your business goals. These practices not only maintain consistency but also help prevent misuse and ensure your AI remains dependable over time.

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How to Build an AI Strategy With LLMs

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