If you’re struggling to embrace AI because your tech stack is inadequate – you’re not alone.
Although you might be surviving in the digital age, are you thriving?
Legacy systems and outdated architecture can be the bane of any forward-looking organisation, especially when it comes to adopting artificial intelligence (AI).
Legacy Systems, Lack of Integration & The Challenge Ahead
These older systems not only lack scalability, but also hamper innovation.
In fact, they can become a severe liability in the race to stay competitive, bogging down your operational efficiency and increasing your cybersecurity risks.
And these systems pose a variety of challenges for businesses including:
- High maintenance costs
- Limited scalability
- Increased security risks
These systems often require significant resources to keep running, with 70% of IT budgets being spent on maintaining legacy systems. They also lack the flexibility to scale up as a business grows, which can restrict a company’s ability to process increasing amounts of data.
Lack of integration across channels, particularly between legacy and modern systems, can create further issues. This often requires extensive custom code, making the process time-consuming and expensive.
However, transitioning from legacy systems to modern data architectures is not without challenges. Legacy system architecture tends to create data silos, as many legacy software solutions were never designed to integrate and were built on frameworks that cannot integrate with more modern systems.
On top of that, radical modernisation approaches can present higher costs, risks, and increased disruption. So for those risk-averse organisations, a step-by-step or evolutionary modernisation approach is often a better option.
The Good News? There’s a Way Out
Luckily, reimagining your digital landscape is doable and essential for AI readiness.
Let’s explore the key components:
- Cloud Architecture
- Data Engineering
- Machine Learning Engineering
- UX Design
Cloud Architecture: The New Norm
Legacy, or traditional, models don’t just hamper your ability to scale. They also put a dent in your budget, require endless maintenance, and can’t adapt to new-age AI technologies as quickly as cloud services can.
Moving to the cloud offers elasticity, cost-efficiency, and most importantly, an environment that is tailored for AI implementations.
Companies like AWS, Azure, and Google Cloud offer robust platforms to migrate, store, and process data in a scalable manner.
Modernising data architectures and transitioning away from legacy systems can yield significant benefits.
For instance, when the InterContinental Hotels Group (IHG) launched a cloud-based guest reservation and revenue management system which not only simplified its data architecture and broke down data silos, but also enabled real-time use of structured and unstructured data.
In another example, the Databricks Lakehouse Platform has been successful in simplifying data architecture, breaking down data silos, and providing a single source of truth for analytics, data science, and machine learning.
Data Engineering: The Backbone of AI
If you thought working with data was simple, you thought wrong. Data isn’t useful unless it’s processed, cleaned, and ready for analysis.
Inconsistent or ‘dirty’ data leads to skewed insights, subpar machine learning models, and ultimately, flawed decision-making. A strong data engineering framework will ensure that your data is reliable, available, and ready to fuel your AI ambitions.
This involves everything from data cleansing to creating data pipelines that can feed real-time data into your AI models.
Businesses that rely on data engineering and ML engineering, are more likely to acquire and retain customers and be profitable. They are:
- 23x more likely to acquire customers
- 6x as likely to retain customers
- 19x as likely to be profitable
Data engineers play a central role in such organisations. They are responsible for transporting data, enriching data while building integrations between analytical and operational systems, and the deployment of models and other artefacts analysing or consuming data.
Machine Learning Engineering: Where the Magic Happens
With AI the centre of attention, it’s clear that machine learning is already reshaping industries and creating new opportunities.
And although the challenge isn’t in creating an ML model; it’s in deploying it effectively.
Machine learning engineering ensures that your models are not just theoretical exercises but fully-functioning features that bring real business value. For this, you’ll need a team that knows not just data science but also software engineering principles to seamlessly integrate these models into your existing systems.
UX Design: The Human Element
Is Your User Experience AI-Ready?
Don’t overlook UX Design. A poorly designed user interface can negate all your AI advancements.
The Stumbling Block
A clunky interface can deter users, leading to decreased engagement and reduced data points—which your AI models need to improve and adapt.
Design for Your Future
Good UX design ensures that the human-AI interaction is intuitive, engaging, and efficient,
allowing your technologies to truly shine and provide the user value you’re aiming for.
Wrapping It Up: The Time is Now
Ready to dive into the future?
Staying ahead in the AI race is not just a matter of technological readiness but strategic foresight.
Evaluate your legacy systems, embrace the cloud, build robust data and machine learning engineering frameworks, and don’t forget the importance of UX design. Your future, powered by AI, awaits.
