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The Case for Adopting AI Gradually: A Roadmap for Tech Leadership

Manoj Chaudhary
Jitterbit

Artificial intelligence (AI) is rapidly reshaping industries around the world. From optimizing business processes to unlocking new levels of innovation, AI is a critical driver of success for modern enterprises. As a result, business leaders — from DevOps engineers to CTOs — are under pressure to incorporate AI into their workflows to stay competitive. But the question isn't whether AI should be adopted — it's how.

Instead of rushing into full-scale AI deployment, many organizations today are recognizing that a more evolutionary approach — one that integrates AI incrementally and strategically — will lead to more sustainable, long-term success. This gradual method not only minimizes disruption and reduces risks but also empowers organizations to learn and adapt, enabling them to fully harness the power of AI while maintaining business continuity.

The challenge is not just deploying AI but also aligning it with broader organizational goals. This alignment ensures that AI adoption is purposeful and focused, contributing directly to the organization's mission and vision. This article explains why a deliberate and thoughtful approach to AI adoption is critical and how it can be implemented effectively.

The Benefits of a Phased Approach to AI Adoption

Adopting AI in a phased manner allows organizations to gradually integrate it into existing infrastructure for specific use cases with challenges and pain points that AI tools could support. This approach allows organizations to pilot AI-infused automation in specific areas and ensures teams are aligned before scaling up across departments. By gradually introducing AI into workflows, businesses can start small and expand AI capabilities as teams become more skilled and familiar with using the technology. This minimizes risks, such as operational downtime or data security concerns. Additional benefits include:

Minimized Disruption: Introducing AI incrementally prevents implementation hurdles often associated with large-scale technology changes. AI can be introduced as a pilot program to automate business processes, allowing IT teams to test, learn and scale without affecting mission-critical systems.

Agility and Adaptability: AI technology is evolving rapidly, and a phased approach gives organizations the agility to adapt to new developments. IT and DevOps teams can iterate on their AI solutions, adjusting them as new algorithms, tools or use cases emerge.

Cost Control: Large-scale AI projects can come with substantial upfront costs for hardware and software. By taking a phased approach, organizations can spread these investments over time, mitigating the financial risk of AI adoption and allowing for more precise budget forecasting.

Improved Change Management: Resistance to change is a common barrier to new technology adoption. A gradual approach ensures better communication and collaboration across teams. For instance, early AI deployments can focus on optimizing routine and mundane tasks, demonstrating value without threatening job roles, which can lead to higher acceptance rates within an organization.

Integrating AI into Existing Workflows

While the benefits of AI are clear, its successful integration into an organization’s operational framework is often a significant hurdle. Here are key considerations for tech leaders to ensure AI solutions complement existing DevOps and IT workflows:

Piloting AI: A pilot phase allows businesses to evaluate the AI's performance in real-world scenarios, identify potential issues, and adjust the technology to meet specific operational needs. It also provides a controlled environment to test scalability, security, and compatibility with existing systems. By gaining insights from a pilot, organizations can optimize processes, enhance decision-making, and avoid costly disruptions when deploying AI across the enterprise.

Data Readiness: AI systems are only as effective as their data. Before rolling out AI solutions, organizations must ensure they have high-quality, well-organized datasets. IT teams will need to collaborate with data scientists to ensure data pipelines are optimized for AI processing, particularly when integrating AI into monitoring, security or software development workflows.

Modular Architecture: In a DevOps environment, a modular architecture allows for incremental AI integration. AI solutions can be designed as microservices or APIs, ensuring they can be scaled independently without requiring a complete system overhaul. This flexibility is crucial for tech teams adopting AI without disrupting the overall architecture.

Collaboration Between AI and Human Experts: AI adoption doesn’t mean human expertise becomes obsolete. Instead, it should be seen as a way to enhance human capabilities. For example, AI models can sift through vast amounts of operational data, identifying patterns and insights that might take engineers much longer to discover on their own. By implementing AI in this way, tech teams can augment their problem-solving skills and make more informed decisions.

Conclusion

Instead of viewing AI as a quick-fix solution, an evolutionary approach that aligns AI with existing operations and long-term business objectives will yield more sustainable success. By minimizing disruption and fostering a culture of innovation, tech teams can unlock AI's full potential while driving real business outcomes. The future of AI in business isn't about rushing forward — it's about strategic implementation, learning continuously and evolving over time.

Manoj Chaudhary is CTO of Jitterbit

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The Case for Adopting AI Gradually: A Roadmap for Tech Leadership

Manoj Chaudhary
Jitterbit

Artificial intelligence (AI) is rapidly reshaping industries around the world. From optimizing business processes to unlocking new levels of innovation, AI is a critical driver of success for modern enterprises. As a result, business leaders — from DevOps engineers to CTOs — are under pressure to incorporate AI into their workflows to stay competitive. But the question isn't whether AI should be adopted — it's how.

Instead of rushing into full-scale AI deployment, many organizations today are recognizing that a more evolutionary approach — one that integrates AI incrementally and strategically — will lead to more sustainable, long-term success. This gradual method not only minimizes disruption and reduces risks but also empowers organizations to learn and adapt, enabling them to fully harness the power of AI while maintaining business continuity.

The challenge is not just deploying AI but also aligning it with broader organizational goals. This alignment ensures that AI adoption is purposeful and focused, contributing directly to the organization's mission and vision. This article explains why a deliberate and thoughtful approach to AI adoption is critical and how it can be implemented effectively.

The Benefits of a Phased Approach to AI Adoption

Adopting AI in a phased manner allows organizations to gradually integrate it into existing infrastructure for specific use cases with challenges and pain points that AI tools could support. This approach allows organizations to pilot AI-infused automation in specific areas and ensures teams are aligned before scaling up across departments. By gradually introducing AI into workflows, businesses can start small and expand AI capabilities as teams become more skilled and familiar with using the technology. This minimizes risks, such as operational downtime or data security concerns. Additional benefits include:

Minimized Disruption: Introducing AI incrementally prevents implementation hurdles often associated with large-scale technology changes. AI can be introduced as a pilot program to automate business processes, allowing IT teams to test, learn and scale without affecting mission-critical systems.

Agility and Adaptability: AI technology is evolving rapidly, and a phased approach gives organizations the agility to adapt to new developments. IT and DevOps teams can iterate on their AI solutions, adjusting them as new algorithms, tools or use cases emerge.

Cost Control: Large-scale AI projects can come with substantial upfront costs for hardware and software. By taking a phased approach, organizations can spread these investments over time, mitigating the financial risk of AI adoption and allowing for more precise budget forecasting.

Improved Change Management: Resistance to change is a common barrier to new technology adoption. A gradual approach ensures better communication and collaboration across teams. For instance, early AI deployments can focus on optimizing routine and mundane tasks, demonstrating value without threatening job roles, which can lead to higher acceptance rates within an organization.

Integrating AI into Existing Workflows

While the benefits of AI are clear, its successful integration into an organization’s operational framework is often a significant hurdle. Here are key considerations for tech leaders to ensure AI solutions complement existing DevOps and IT workflows:

Piloting AI: A pilot phase allows businesses to evaluate the AI's performance in real-world scenarios, identify potential issues, and adjust the technology to meet specific operational needs. It also provides a controlled environment to test scalability, security, and compatibility with existing systems. By gaining insights from a pilot, organizations can optimize processes, enhance decision-making, and avoid costly disruptions when deploying AI across the enterprise.

Data Readiness: AI systems are only as effective as their data. Before rolling out AI solutions, organizations must ensure they have high-quality, well-organized datasets. IT teams will need to collaborate with data scientists to ensure data pipelines are optimized for AI processing, particularly when integrating AI into monitoring, security or software development workflows.

Modular Architecture: In a DevOps environment, a modular architecture allows for incremental AI integration. AI solutions can be designed as microservices or APIs, ensuring they can be scaled independently without requiring a complete system overhaul. This flexibility is crucial for tech teams adopting AI without disrupting the overall architecture.

Collaboration Between AI and Human Experts: AI adoption doesn’t mean human expertise becomes obsolete. Instead, it should be seen as a way to enhance human capabilities. For example, AI models can sift through vast amounts of operational data, identifying patterns and insights that might take engineers much longer to discover on their own. By implementing AI in this way, tech teams can augment their problem-solving skills and make more informed decisions.

Conclusion

Instead of viewing AI as a quick-fix solution, an evolutionary approach that aligns AI with existing operations and long-term business objectives will yield more sustainable success. By minimizing disruption and fostering a culture of innovation, tech teams can unlock AI's full potential while driving real business outcomes. The future of AI in business isn't about rushing forward — it's about strategic implementation, learning continuously and evolving over time.

Manoj Chaudhary is CTO of Jitterbit

Hot Topics

The Latest

According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Image
Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...