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Why CIOs need AI fix-engineers for chatbot success
Enterprise chatbots can break under real-world use. IT executives need strategies and AI fix-engineers to maintain performance and trust.
Chatbots are often an organization's entry point into the world of GenAI.
A chatbot provides users with an AI-powered assistant that responds to queries, offers information, and ideally directs them to the resources they need. Chatbots typically perform flawlessly in demos and impress executives. When users test them on carefully curated questions, they respond as expected. The challenge arises after the proof of concept and early deployment successes, when the technology is broadly deployed and the number of users and unexpected queries increases.
Chatbot failure sometimes results in serious consequences. For example, in August 2025, the Commonwealth Bank of Australia fired employees after installing a chatbot, believing it would reduce the need for humans; instead, chatbot failure increased call volume – and the need for more human assistance. In 2024, Air Canada's recently deployed chatbot provided incorrect fare information that a customer exploited to their advantage, resulting in a financial loss for the airline.
Companies invest heavily in custom chatbots, then let them degrade after deployment. The problem isn't the initial build. It's what happens – or doesn't – next.
"What I see across most enterprises is that early GenAI adoption created a kind of 'Let a thousand flowers bloom' moment," said David Guarrera, principal with EY Americas Technology Consulting. "Every team built its own chatbot using different tools, different prompts, different data sources and no shared patterns. These systems often looked great in demos because they were tested on small, curated data sets. But once they were exposed to the broader messiness of enterprise data and real user behavior, the brittleness became obvious."
Why chatbots fail
"Enterprise chatbots can degrade due to both technical issues and organizational barriers," said Baris Sarer, global AI leader for technology, media and telecom at Deloitte Consulting. "The potential technical issues – context and goal drift, hallucinations, suboptimal selection of tools and integration challenges – lead to inaccurate responses from the chatbot and a loss of trust and adoption."
In general, failures fit into one of the following categories that IT leaders must understand and, when necessary, mitigate:
Context drift and technical degradation
Context drift occurs when a bot loses track of business-specific meanings or relationships between concepts. Integration gaps emerge when the chatbot can't reliably access or interpret data from enterprise systems. User expectations shift as employees discover edge cases the developers never considered.
"Context and concept drift are a serious problem that's very hard to pin down within these highly probabilistic systems, especially with use cases where specific business context comes into play," said Brad Shimmin, vice president and practice lead, Data Intelligence, Analytics, & Infrastructure at Futurum Group. "That's why we're seeing a lot of effort going into concepts like building semantic layers, knowledge graphs and even rules engines into these agentic processes. Those can help with model consistency."
The ownership gap
Curtis Hughes, managing director of Vaco by Highspring, said most chatbot failures aren't technical; they're human.
"Too often, once the chatbot goes live, no one really owns it," he said.
This ownership gap creates systems that degrade unnoticed. While technical challenges – once they're discovered – prove solvable, businesses struggle with human and organizational aspects that support effective chatbot performance.
Amplification in agentic workflows
Problems multiply when organizations deploy agentic AI workflows that link multiple model calls to automate complex tasks.
"Enterprises are chaining together dozens or hundreds of model calls to automate a task," said Guarrera. "A tiny error that would've gone unnoticed in a simple chatbot suddenly gets amplified across a multi-step workflow."
Organizational barriers
Sarer emphasized organizational challenges, particularly the need to build trust in the process.
"Before implementing enterprise AI solutions, an organization needs to clearly articulate the business case and ensure change management systems are in place to facilitate adoption," Sarer said. "When chatbots fail to deliver on their promise, they erode user trust and discourage further AI adoption."
External model instability
From a technical perspective, the models themselves lack consistency, particularly those accessed using an application programming interface (API).
"As we've seen with frontier models like OpenAI GPT, Google Gemini and others, model makers do not sit still," Shimmin said. "New model checkpoints, versions (and) features are introduced and deprecated over time, making it hard for agentic AI builders to debug sudden inconsistencies that might arise because a new model may demonstrate unexpected behavior."
The new role: Chatbot fix-engineer
The AI fix-engineer, also known as a forward-deployed engineer, emerged to address these challenges. These professionals maintain conversational AI engineering systems after deployment, focusing on AI model tuning, chatbot reliability and AI workflow optimization.
"(This) is the person who keeps these systems healthy once they're deployed," said Guarrera, "the forward-deployed problem solver who can debug a hallucination, fix a broken RAG (retrieval-augmented generation) pipeline, tighten a prompt, repair a flaky integration or spot when an agent has drifted into a loop."
This role differs fundamentally from traditional software maintenance. Hughes described AI fix-engineers as the modern equivalent of a DevOps engineer for the conversational era, analyzing where a bot fails conversationally with real people, then making adjustments that help it learn and improve.
"The best ones don't just fix code; they also understand context. They can tell when the system is confusing, off topic or even tone deaf," Hughes said.
The skillset is hybrid by necessity. Sarer said forward-deployed engineers display both a deep software engineering background and proven experience in leading product platforms and delivering real-world outcomes.
Demand is surging for several reasons. For Sarer, the increasing gap between AI investments and tangible returns forces organizations to re-evaluate current staffing and delivery mechanisms. Guarrera points to the rise of agentic workflows.
"Organizations are realizing they need someone who understands the whole stack: the model, the data, the prompts, the guardrails and the enterprise systems behind the scenes," Guarrera said.
Why IT executives should care
The business case for investing in AI fix-engineers centers on a handful of strategic facets, including:
- ROI. Without proper chatbot maintenance, investments fail to deliver a good return on investment (ROI). "Having someone who can diagnose issues, adjust prompts, update retrieval logic and keep the system aligned with business intent is often the difference between a prototype that quietly dies and a tool that generates sustained ROI," Guarrera said.
- Talent pipeline. "Many organizations already have these people on their teams; they just need reskilling, empowerment and a clear mission," Hughes said. The fix-engineer role creates a career path for existing technical staff to evolve with AI.
- Vendor strategy. AI fix-engineers help organizations hold vendors accountable and avoid relationships without clear maintenance commitments.
- Risk management. When businesses deploy agentic workflows that involve models making decisions, calling APIs, and moving data, any errors increase the risk of widespread damage. "As enterprises start to rely on agentic workflows, the cost of an unmitigated error compounds fast," Guarrera said.
- User trust. Hughes said forward-thinking CIOs treat AI as they did cybersecurity a decade ago: not as a project to finish, but as an ongoing discipline that protects the business and strengthens trust over time.
How to respond strategically
IT leaders must develop a structured approach to assess organizational readiness for AI maintenance and build the required capabilities. Steps include:
Start with an honest assessment
"Organizations need to look at whether they truly understand how their AI systems behave day to day," said Guarrera. "Do they know when accuracy drifts? Do they have visibility into prompts and outputs over time? Many discover they're essentially flying blind."
Identify and develop hybrid talent
"Organizations need engineers who are comfortable living in that messy intersection of LLM behavior, data engineering and enterprise integration," Guarrera said. "People who can work hands-on with the real systems, not just prototypes."
Build cross-functional pods
Sarer said he recommends forming small, cross-functional pods.
"For example, product owner, FDE lead, data engineer, prompt engineer, QA/SRE and a risk and compliance partner, embedded with business lines," Sarer suggested. "Give pods a charter to diagnose, fix and ship. Own a backlog, SLAs and on-call."
Restructure vendor contracts
Ensure vendor contracts specify continuous performance monitoring, incident escalation paths and shared accountability for reliability and data protection.
"Most companies never define who's responsible for retraining, model drift or performance metrics over time, and that's often where risk hides," Hughes said.
Establish central controls
Shimmin said he recommends that any company investing in AI first consider forming a board or other control mechanism that approves all deployments, best practices, and consistent and unified IT investments, as well as the necessary skills.
AI fix-engineer best practices
Leading organizations recognize several methods to improve AI fix-engineer outcomes:
- Create clear ownership structures. Assign responsibility for ongoing chatbot performance to specific teams or individuals. Don't orphan systems after deployment.
- Establish observability from day one. Build monitoring, logging and feedback systems into initial deployments. Track when accuracy drifts and understand how RAG pipelines source information.
- Define shared standards. "Enterprises need clear standards for prompts, retrieval logic, safety policies and model updates because, without those, every small team rebuilds the world in its own image," said Guarrera.
- Enable governance that moves fast. "Good governance isn't about control, it's about being able to learn faster than the problems pile up," said Hughes. Build frameworks that allow rapid iteration while maintaining safety and compliance.
Common pitfalls to avoid
Best practices include recognizing and avoiding typical hazards such as:
- Treating deployment as a finish line rather than a starting point.
- Failing to establish feedback loops with actual users.
- Approving isolated resolutions without shared standards.
- Underestimating ongoing investment requirements.
- Neglecting to account for model provider changes.
"The companies that succeed in GenAI aren't the ones with the most experiments," Guarrera said. "They're the ones that treat AI as a living system that requires care, discipline and a real maintenance strategy."
Sean Michael Kerner is an IT consultant, technology enthusiast and tinkerer. He has pulled Token Ring, configured NetWare and been known to compile his own Linux kernel. He consults with industry and media organizations on technology issues.
