Redefining Level 1 and 2 application support

Is your team stuck firefighting technical issues instead of moving the business forward? Level 1 and Level 2 application support can be a black hole for time and resources, especially when it's reliant on outdated, manual processes. But AI is changing the game.

By applying AI to managed services, businesses are not just patching problems faster, but preventing them entirely. This shift allows them to cut costs, streamline operations and deliver the fast, reliable support that modern users expect. It isn't just a tech upgrade; it’s a strategic leap forward. 

Below, we explore the core benefits of AI in managed services and how you can start taking advantage of it sooner rather than later.

Why AI is essential for 21st century managed services  

Traditional support models lean heavily on human intervention. While necessary in the past, this approach is often reactive, slow to scale and costly. AI offers a smarter alternative — one that's automated, predictive and always-on.

Here’s what AI brings to the table:  

• Automation of repetitive tasks such as ticket classification and resolution.  

• Proactive incident prevention using predictive analytics.  

• Scalable solutions that grow with your business needs without proportionally increasing costs.  

• 24/7 availability, ensuring service continuity.  

• Year-over-year cost reduction through continuous improvement.

What this means for your company

But the real value lies in how these capabilities translate into lasting change for your business. You'll see that it's more about creating intelligent operations that advance your goals than about simply deploying another tool to your tech stack.

1. From firefighting to fine-tuning

Too often, support teams are stuck in reactive mode, scrambling to put out fires as they appear. AI flips the script. It automates repetitive tasks like password resets and incident triage. That means fewer tickets, fewer distractions and more time for your teams to focus on bigger-picture goals like performance optimization and root cause analysis.

Pro tip: Train your AI on historical incident data to identify recurring problems and recommend preventive fixes.

2. Reliability on autopilot

This goes beyond eliminating noise: it’s about engineering resilience directly into your systems. With AI continuously scanning logs and telemetry data, it empowers applications to self-heal. By triggering autonomous corrective actions, the system adapts to stress before a disruption can occur. 

It’s the difference between catching a server slowdown before peak traffic or after customers start complaining.

Example: A government agency used AI-based monitoring to detect potential bottlenecks in a citizen portal. The result was a 97% drop in escalation times, with smoother service during peak periods.

3. Enhanced decision-making  

AIOps dashboards offer a unified, bird’s-eye view of your entire application landscape. This provides instant visibility into system health, helps you spot inefficiencies and allows you to act quickly on correlated anomalies across your technology stack.

No more waiting for monthly reports. Your team is always in the know and empowered to make smarter decisions.

4. Continuous improvement

An AI-powered support system grows smarter with every interaction. It uses feedback loops to learn from the outcomes of its actions. When a human engineer resolves a novel issue, those resolution steps are fed back into the AI’s knowledge base, refining its ability to handle similar incidents in the future.

Impact in numbers

Companies across different industries are already seeing measurable gains in speed, efficiency and cost savings with this new approach to managed services. Here’s a look at the impact we've seen firsthand through our client work:

• Efficiency gains: A global beauty retailer used AI to automate routine Level 1 tasks, from initial ticket triage to full resolution of common requests. This resulted in a 72% reduction in manual effort, freeing up the support team to focus on proactive platform improvements and user training initiatives.

• Cost reduction: An e-commerce giant leveraged AI to optimize its cloud infrastructure usage and detect performance issues before they could cause a customer-facing outage. The payoff was $320,000 in annual savings from reduced cloud spend and prevented revenue loss. 

• Improved accuracy: Modern AI-powered ticket classification and routing systems are hitting 90%+ accuracy. This minimizes human error and eliminates the costly delays caused by misrouted tickets that bounce between teams.

• Enhanced user experience: An Australian SaaS company put AI at the heart of its support strategy, resulting in a 75% reduction in its Mean Time to Resolution (MTTR). With predictive analytics in place, the system now alerts the support team to potential disruptions before they happen, allowing for proactive communication with users and building a sense of trust and reliability.

• Scalability without proportional cost: A publishing company grew its active user base from 8,000 to 180,000 without hiring a single new support agent. AI-powered knowledge bases and virtual agents handled the increased volume, allowing the company to scale its business without scaling its support costs.

Steps to prepare for AI integration  

AI can deliver remarkable results, but only when it’s implemented with care. It’s not a plug-and-play fix. It’s a transformation that touches systems, data, processes and people. 

Below, we’ve highlighted some of the most important steps to lay the groundwork and set your support function up for lasting success:

1. Get your data in shape

AI thrives on clean, structured, reliable data. If your support systems are filled with inconsistent records, siloed logs or outdated formats, AI will struggle to deliver accurate insights or make the right decisions.

Start by auditing what you already have. Are support tickets tagged consistently? Are resolution notes complete? Are incident logs stored across five tools or one?

Do this: Establish centralized data repositories that consolidate information from across your support stack. Standardize formats so AI can parse them effectively and implement data-cleaning protocols to weed out duplicates and errors. The more disciplined your data, the more powerful your AI becomes.

2. Address team concerns

Introducing AI into support can spark anxiety. Some employees may worry that automation means their jobs are at risk, while others may feel overwhelmed by new tools and workflows.

Ignoring these concerns only slows adoption and creates friction. Instead, bring people into the process early.

Do this: Communicate clearly and consistently. Emphasize how AI is there to take over repetitive, low-value tasks, freeing teams to focus on meaningful, strategic work. Offer training, run pilots with champions from within the team and celebrate quick wins. When people understand the why, they’re far more likely to embrace the how.

3. Bridge the skills gap

Many organizations underestimate the level of technical knowledge required to implement and maintain AI systems. From understanding machine learning models to interpreting AI-generated insights, the skill set is specialized and often scarce.

Without the right capabilities in-house, even the best tools will fall short. Upskilling your team is essential.

Do this: Invest in ongoing training to build AI fluency across your support organization. Focus on practical skills in data science, machine learning and AI operations. Explore partnerships with online learning platforms or universities to accelerate knowledge-building. 

4. Tame your legacy tech

Even the smartest AI tool can’t work miracles if it’s bolted onto outdated or incompatible systems. Many enterprises operate with legacy platforms that weren’t built for integration, let alone automation.

You don’t need to replace everything overnight, but you do need to ensure that AI can access the right data and execute actions across your ecosystem.

Do this: Conduct a comprehensive readiness assessment. Identify integration points where AI can connect to your existing tools, flag systems that may require upgrades or API development and map out a phased rollout. Often, the right strategy is a hybrid one, where AI augments current systems while building a path toward modernization.

Future outlook

AI is only just beginning to reshape how organizations manage applications and infrastructure. As the technology matures, we expect it to play a deeper, more strategic role across IT operations. 

Here are four trends set to define the next chapter:

• Advanced machine learning for predictive accuracy: Future systems will anticipate issues with pinpoint precision.

   

• Emerging tech synergies: AI integration with IoT and blockchain will unlock new operational efficiencies.

   

• Ethical AI practices: Trust will be non-negotiable. Responsible AI practices, including transparency, compliance and bias mitigation, will be essential.

   

• Strengthened collaboration: A human-AI partnership will evolve, empowering teams to innovate faster and serve customers better.

Transform your operations with AI  

Want to see what AI can do for your business? Learn more about Thoughtworks’ DAMO™ Managed Services, where we help enterprises modernize IT operations with cutting-edge AI-driven solutions.   

Instead of simply maintaining outdated systems, DAMO™ helps you build a future-ready foundation, reducing maintenance costs by up to 40% and redirecting those savings into continuous innovation. It’s a smarter, phased approach to application management that delivers consistent, high-quality service and scales with your business.

Take the first step today. Contact us for a portfolio assessment.