You “lifted and shifted” to the cloud years ago. But today, your platform might well be constraining your business rather than enabling it.
Whether you are navigating multi-cloud strategies, stricter data sovereignty mandates, global expansion, or the need to align with a provider that better suits your current architecture, tech leaders face a new reality. On top of that, the AI revolution has completely rewritten the rules of data gravity: organisations must now seamlessly move data to where the best AI models are trained and deployed.
However, expansion cannot come at the expense of efficiency. With global cloud waste hovering around 30%, CTOs and engineering directors face intense pressure to control run rates. Any cloud migration or platform modernisation must be ruthlessly optimised from day one.
In Part 2 of our series, Beyond the Lift and Shift: The AI Acceleration Engine, Scale Factory Technical Director Mike Mead was joined by Principal Consultant Saborni Bhattacharya to break down how to harness AI-driven automation, build modern data strategies, and leverage vendor co-investment to build a future-ready platform.
Here are the strategic priorities for CTOs and tech leadership teams:
Let AI-Driven Automation Do the Heavy Lifting in Modernisation
Upgrading legacy codebases, refactoring monolithic services, and converting code between frameworks used to mean months of tedious, manual engineering effort. With modern AI services and specialised tooling (such as AWS generative AI capabilities and automated code transformation engines), that dynamic has shifted:
- Automated Code Conversion: AI tooling can now evaluate, translate, and modernise legacy application stacks significantly faster, cutting down discovery and refactoring cycles.
- Refactoring at Scale: Instead of static, manual rewrite projects, AI-driven automation accelerates workload modernisation, letting your senior engineers focus on high-level architecture and business logic.
- Continuous Optimisation: Machine learning models can analyse telemetry and infrastructure configurations to proactively identify performance bottlenecks and overprovisioned resources.
KEY TAKEAWAY
AI is no longer just an application feature you build for end users, it is a core tool in your software engineering and infrastructure delivery toolkit that dramatically shortens migration timelines.
Shift Your Data Strategy to Fit New AI Realities
The traditional model of locking data away in isolated silos or single-provider lock-ins no longer works when your business needs to leverage cutting-edge foundation models. Modern data strategy is dictated by AI requirements:
- Data Gravity vs. Model Capability: You must move data to where the best AI models operate, or build secure, low-latency pipelines that allow foundation models to interface with your data without risking exposure.
- Clean Foundations for Agentic AI: AI and agentic automation are only as good as the underlying data hygiene, governance, and quality engineering. If your data pipelines are brittle, your AI outcomes will be unreliable.
- Data Sovereignty and Compliance: Shifting data across platforms or region boundaries to feed models introduces strict regulatory checks around data residency and privacy, meaning governance must be built into the pipeline, not tagged on as an afterthought.
KEY TAKEAWAY
Modernising your platform isn’t just about compute; it’s about building a flexible, secure data architecture that allows you to plug into next-generation AI models without friction.
Tackle Cloud Waste Before Scaling Up
Expanding into new clouds or deploying resource-intensive AI workloads will quickly compound existing cost inefficiencies if left unchecked. With global cloud waste climbing near 30%, platform modernisation must double as a financial clean-up:
- Audit Before Moving: Eliminate zombie assets, unindexed log stores, and over-allocated instances prior to migrating, ensuring you don’t pay to shift waste across clouds.
- Match Architecture to Workload: Use serverless, dynamic auto-scaling, and container orchestration to match consumption strictly to demand.
- AI Cost Governance: AI workloads can quickly introduce unexpected spend. Establish clear guardrails, token limits, and continuous cost observability early in the implementation phase.
Capitalise on AWS Co-Investment for AI Modernisation
Just as with infrastructure migrations, major hyperscalers are eager to fund your AI transformation if you navigate their programs effectively.
- AWS Funding Programs: AWS offers specific co-investment and funding routes designed to offset the cost of workload modernisation, proof-of-concept builds, and AI adoption initiatives.
- Maximising ROI: Partnering with an accredited consultancy ensures you access the full spectrum of available funding, directly reducing the capital expenditure required to modernise your platform.
KEY TAKEAWAY
You don’t have to foot the entire bill for modernising your stack or implementing AI capabilities. Vendor funding programs can absorb a significant portion of the upfront investment.
