As the cloud ecosystem continues to evolve at a rapid pace, the role of support services is being redefined. No longer just a reactive function, support is becoming a strategic cornerstone in how IT service providers deliver value, especially amid increasingly complex and hybrid cloud environments. At the heart of this shift is the rise of AI-powered tools that promise faster, more intuitive, and scalable assistance.
In this interview, we speak with Nora Chazan, Senior Manager of Support Services at BitTitan, to explore how AI is transforming the customer support experience across IT services and cloud migration. With the recent introduction of Ask MigrationWiz, BitTitan is doubling down on intelligent support that empowers users while complementing human expertise. Nora shares her insights on the broader trends driving AI adoption, the evolving expectations of IT professionals, and the new standard of personalized, always-on support.
Nora, with BitTitan recently introducing Ask MigrationWiz as part of its support services, what larger trends do you see driving the adoption of AI-powered assistance across the IT services and cloud migration space?
As cloud environments become more complex and hybrid by nature, IT professionals are under increasing pressure to deliver faster, more efficient migrations with limited resources. AI tools like Ask MigrationWiz play a critical role in this landscape by offering real-time, contextual guidance that helps users troubleshoot and resolve issues without waiting for human support.
At the same time, customer expectations are evolving. End users want answers immediately and prefer self-service options that don’t compromise on accuracy or depth. AI meets these expectations by surfacing relevant, high-quality information instantly, reducing support friction and improving the overall user experience.
We’re also seeing a shift in how companies view support. It’s no longer just a reactive function; it’s becoming a strategic differentiator. With AI, support becomes more proactive, personalized, and scalable. At BitTitan, Ask MigrationWiz represents our investment in intelligent tools that augment, not replace, our human support team, helping us deliver smarter, faster assistance while continuing to learn from every interaction.
Customer expectations around support have evolved rapidly in recent years. How do you see AI reshaping what customers will expect from support teams, both in terms of speed and personalization?
AI is fundamentally shifting the way customers engage with support and what they expect in return. Speed has become table stakes. Customers now anticipate immediate responses, 24/7 availability, and fast resolution times. AI-powered tools are making that possible by automating routine inquiries, surfacing relevant knowledge instantly, and streamlining triage so support teams can focus on more complex issues.
But beyond speed, personalization is becoming just as important. Customers don’t want generic answers; they want support that understands their context, past interactions, and technical environment. AI can help tailor responses by learning from previous cases and adapting recommendations to individual needs. Over time, this creates a more proactive and intuitive experience, where customers feel seen, understood, and supported, without needing to repeat themselves.
AI is enabling support to become faster, smarter, and more customer-centric. As these capabilities evolve, expectations will continue to rise, and organizations that invest in AI-driven support will be better positioned to meet them.
Some argue that AI support is mainly about cost reduction, while others see it as an opportunity to enhance the customer experience. Where do you believe the real long-term value lies?
Cost efficiency is undoubtedly one of the immediate benefits of AI in support, automating common tasks and reducing response times can significantly streamline operations. However, the real long-term value lies in how AI elevates the customer experience.
AI allows support teams to move beyond reactive issue resolution and toward proactive, personalized engagement. By learning from past interactions, AI tools can anticipate needs, surface relevant insights in real time, and deliver faster, more accurate answers. This transforms support from a transactional function into a strategic touchpoint that builds trust and loyalty.
Ultimately, AI’s greatest potential is not just in reducing costs but in unlocking scale without sacrificing quality. That means being able to serve more customers more effectively and with greater consistency while still leaving room for the human touch when it matters most.
As AI assistants become more capable, how do you envision the role of human support professionals evolving? Are we moving towards a more hybrid, high-skill model?
Absolutely. We’re already seeing the shift toward a hybrid, high-skill support model. As AI takes on more repetitive and transactional tasks, human support professionals are freed up to focus on higher-value work: complex troubleshooting, strategic customer engagement, and empathetic problem-solving that AI can’t replicate.
This evolution elevates the role of the support professional. It requires deeper product knowledge, stronger communication skills, and the ability to interpret nuanced customer needs. At the same time, support teams are becoming more consultative, guiding customers through best practices, migrations, or integrations that go beyond simple Q&A.
Rather than replacing people, AI is becoming a powerful sidekick. The most successful support teams will be those that embrace this partnership, leveraging AI to increase efficiency while doubling down on the human strengths that create truly exceptional customer experiences.
One concern often raised with AI is over-reliance or creating a “black box” that is hard to audit. How can companies balance automation with transparency and trust in their support operations?
That’s a valid concern, and one that responsible companies need to address head-on. Automation should never come at the expense of transparency or accountability. The key is building AI systems that are explainable, auditable, and grounded in real human oversight.
At BitTitan, for example, we see AI as a support enhancer, not a support replacement. We focus on giving users clear, contextual responses that are traceable to source content or logic, not opaque outputs. Internally, we monitor AI performance closely, using feedback loops and data transparency to ensure accuracy and fairness.
It’s also important to give customers choices. They should always know when they’re interacting with AI and be able to escalate to a human when needed. That balance between smart automation and human accountability is what ultimately builds trust.
Beyond the initial deployment of AI-powered chat or virtual agents, what areas of customer support do you believe are most ripe for disruption or innovation over the next three to five years?
The next wave of support innovation will move well beyond basic chatbots. One major area of disruption will be proactive support, using AI to anticipate issues before customers even reach out. By analyzing patterns across environments, usage, and past tickets, AI can flag risks early, recommend preventive actions, and reduce downtime.
Another area is intelligent case routing and triage. Rather than assigning tickets based on fixed rules or queues, AI will dynamically route issues to the right expert based on context, sentiment, and even historical outcomes, speeding up resolution and improving accuracy.
We’re also likely to see major advances in multimodal support, where AI can process and respond to not just text, but screenshots, logs, voice, or even video, making interactions far more efficient and natural.
Finally, support analytics and agent enablement will benefit enormously. AI can help teams learn faster by summarizing conversations, highlighting gaps in documentation, and even coaching agents in real time.
As more companies adopt multiple cloud environments and hybrid infrastructure, how do you see support services needing to adapt to increasingly complex migration and integration challenges?
As cloud strategies become more distributed, spanning multiple platforms and on-prem environments, support services need to evolve in two key ways: depth of technical expertise and breadth of contextual understanding.
First, migrations are no longer one-size-fits-all. They involve intricate dependencies across applications, networks, security protocols, and compliance requirements. Support teams must be equipped to handle this complexity, with specialized knowledge across a broader range of platforms, tools, and use cases.
Second, customers expect guidance tailored to their specific architecture and goals, not just generic troubleshooting. That means support needs to become more consultative, offering not just answers but strategic insights and best practices for navigating multi-cloud and hybrid setups.
AI can play a critical role here, helping support teams quickly surface relevant documentation, flag integration risks, and learn from prior complex cases. But just as importantly, the human side of support must be prepared to step in when nuance, judgment, and deep system knowledge are required.
In your view, what emerging technologies beyond generative AI, such as predictive analytics, RPA, or real-time monitoring, could have the biggest impact on support operations?
While generative AI is receiving a lot of attention, and rightfully so, several other technologies are poised to significantly transform support operations. Predictive analytics is at the top of that list. By analyzing usage patterns, historical tickets, and system telemetry, predictive tools can anticipate issues before they become problems. This enables a shift from reactive to proactive support, helping teams prevent downtime and reduce ticket volume.
Robotic Process Automation (RPA) also holds huge promise, especially in streamlining backend workflows. From automating routine tasks like account provisioning or data updates to accelerating case handoffs across systems, RPA can free up human agents for more complex, high-value interactions.
Real-time monitoring and observability tools are equally critical in hybrid and multi-cloud environments. When integrated with support systems, these tools can provide instant visibility into performance issues and trigger intelligent alerts or workflows. This shortens response times and improves first-contact resolution rates.
Combined with AI, these technologies create a powerful ecosystem: one that enables faster, smarter, and more resilient support operations—ultimately delivering a better experience for both customers and support teams.
With so many vendors rushing into AI-driven support solutions, what differentiates a successful implementation from one that creates more frustration for customers?
The difference comes down to intentional design, transparency, and integration with the human experience. A successful AI implementation starts with a clear understanding of customer needs—not just automating for the sake of efficiency, but solving real pain points. Frustration often arises when AI tools are bolted on without context, deliver generic or inaccurate answers, or make it hard to escalate to a human.
What sets great implementations apart is the balance between automation and empathy. The AI should be fast, accurate, and clearly explain what it’s doing, while also knowing when to step aside and hand off to a human. It should enhance, not block, the customer’s path to resolution.
Additionally, the most effective solutions are deeply integrated with product knowledge, historical data, and live systems. That’s what enables meaningful, personalized support instead of one-size-fits-all responses. AI support succeeds when it’s thoughtfully built around the customer journey, not just as a cost-saving tool, but as a way to deliver smarter, more intuitive, and more human support at scale.
Finally, looking toward the next wave of AI innovation, are there any disruptive trends or technologies you are personally watching that could fundamentally reshape IT support?
One area I’m especially excited about is the convergence of AI with real-time observability and autonomous operations. Imagine a support system that not only identifies issues the moment they occur, but takes action to resolve them autonomously or guides users step-by-step through resolution based on real-time context. That kind of intelligent automation could dramatically reduce downtime and change how we think about incident response.
I’m also watching developments in AI agents that can reason across systems, not just answer questions, but perform tasks, manage workflows, and collaborate with human agents. These agents will be able to learn from interactions, adapt to unique environments, and deliver more personalized, action-oriented support over time.
And while we often focus on technology, I think the next wave will also bring new expectations around trust, transparency, and data ethics in AI. The companies that lead will be those that pair technical innovation with thoughtful, customer-centric design.
We’re just beginning to see what’s possible. The next phase won’t just reshape support, it will redefine the relationship between users, systems, and the teams that manage them.
By Randy Ferguson