To equate innovation with AI is to say that systems are being modified with automation, experience is augmented for users, and outdated features are being changed. That being said, it is becoming a notion to include AI in operations and user-focused assistance.
Since such is the premise, enterprises are associating themselves with the transition and taking a step towards revolution with legacy modernization services. You may ask why. Well, to say that adoption is imperative, it is not possible within the outdated legacy systems.
With old features, close to no updates, and issues in system integration, the target of reaching efficiency with just AI is not possible.
That is exactly why businesses are opting to optimize their systems with next-gen modernization and adapting to AI to uplift the integration outcome.
To believe that many decision-makers find this concept a little bit confusing is not unusual. While being straightforward, the combination can be tricky to understand. Since that is the notion in the AI adoption market, we came forward with a bifurcation to assist you with the Generative AI for enterprise adaptation concerns.
Why Modernization is the Key Enabler of AI
As we mentioned above, the existence of AI progresses on modified infrastructure ready to adapt, a foundation of quality data, and real-time processing for faster decision-making.
Legacy systems, however, are often siloed, rigid, and cost-heavy, making AI adoption inefficient or impossible.
In such cases, the legacy modernization services bridge this huge gap by transforming outdated systems into cloud-native environments that can fully leverage AI technologies and move towards business efficiency.
Not only that, but it also opens up the window for building new revenue streams to increase profits for the firm. Keeping that in mind, let us find out how modernization is considered a key enabler for AI when it comes to legacy systems.
Cloud-Native Infrastructure
Legacy systems are often dependent on on-premise systems that are heavily loaded, not likely to be scalable, and consume enormous cost investment. In this case, when businesses modernize, they bring in scalable infrastructure with a cloud-native environment that is elastic to new modifications.
While AI is among them, it provides the technology with a serverless and independent structure, which neither costs a lot nor has issues scaling as per the needs.
Data Modernization & Accessibility
The core of AI lies in the fact that the more data it’s trained on, the advanced it gets. However, this ideology goes down the drain if businesses try to train AI with legacy system-based data. They are often trapped in silos and do not have structures that create pipeline issues and an unclear interpretation of data.
In this case, when opting for legacy modernization services, businesses find their systems adaptable, offer unified data lakes and warehouses, build straightforward data pipelines for ongoing AI training, and offer better insights. This way, modernization clears the training pathway for AI.
Real-Time Processing Capabilities
From the above, we do get that once the data lakes are streamlined, only possible in modernized solutions, they offer AI with real-time data analysis. This changes how the AI functions all in all and allows it to interpret data just in time to locate patterns, flag the issues, mitigate risks, or predict upcoming maintenance dates.
Again, only modernized systems are viable to do so because of their clear process capabilities, while legacy systems might abort the entire process right from the data synchronization.
APIs, Microservices & Interoperability
Downtime is a concern that is raised with legacy systems and disrupts the operations workflow within an organization. Making all changes at once can do the same thing to a brand and dampen their workflow and revenue system.
Keeping that in mind, with legacy modernization services, businesses can ensure phased implementation for AI with third-party APIs and microservices to ensure no downtime and disruption. This way, phase AI implementation can be done and segment-based efficiency can be introduced within the system.
Automation & DevOps Alignment
As mentioned above, the thriving nature of AI depends on an agile environment, which is not a common trait among legacy systems. During modernization, businesses can automate their system with DevOps and enable streamlined deployment of AI models within the system.
This way, the modernization works as an enabler of AI within the system, creating an agile and automated system for monitoring, testing, and subsequent deployment.
Enhanced Security & Compliance Readiness
The main issue with legacy systems is that they are not a viable option for brands because they tend not to comply with the security-focused regulations. That being said, if an AI model is introduced into the system without a vulnerability fix, the sensitive data used in AI pipelines may face data breaches and penetration. To avoid such compliance-based issues, modernization with them, like HIPAA and GDPR, ensures data governance, fixes undetected vulnerabilities, and secures the data with a zero-trust architecture.
Cost Optimization for AI Scaling
As inferred above, implementing AI in outdated systems with an on-premise setup ends up being expensive. With heavy hardware, bought software, and resource consumption, it is necessary to sustain the heavy cost investment of AI models.
However, when businesses modernize with a cloud-native ecosystem, the premise changes and works on cost optimization while offering auto-scaling as per the needs for the AI models. That being the focus, it emphasizes cost optimization with pay-as-you-go and still brings enterprises innovation.
Talent & Ecosystem Advantage
AI adoption is not limited to just technologies but also the professionals who work on them. Oftentimes, when systems are old and cause issues in minute operations, such thing demotivates the employees and drive them away.
However, when the same employees are asked to make a massive change for adoption, with modernized systems, they feel compelled to innovate and bring a better version of what is present with AI.
Conclusion
While AI adoption and implementation are achievements, the drive towards it is your modernized enterprise solution that presents a scalable and flexible platform for change. That being said, it is always preferred to go ahead with legacy modernization services and make the pathway for AI adoption.