IT Expenditure in Businesses is Malfunctioning: Strategies for Authentic AI Benefits Gain
In the dizzying world of tech, Jabin Geevarghese George – globetrotting Fintech maverick and visionary at TCS – cuts through the noise as a champion of system modernization, AI, and agile enterprise architecture.
Enterprise IT’s current state can be compared to a chaotic construction site, where billions are spent on AI, cloud, and modernization projects, but real value remains elusive. The $5.26 trillion expected by 2024 per Gartner doesn't seem to help much, as 70% of digital transformations reportedly fail, as per BCG.
The recent union of SAP and Databricks via the launch of the SAP Business Data Cloud is a game-changer. What’s the takeaway? Simple: If organisations don'tgit their data in order – structured, unified data is essential for successful AI adoption – they're doomed to fail.
Cloud Dilemmas: More Problems than Savings
With 90% of companies having migrated to the cloud, the issue is not adoption but mismanagement. The cloud serves as a mere storage solution instead of an AI-optimized ecosystem, causing costs to spin out of control and leaving enterprises struggling to manage workloads efficiently across AWS, Azure, and Google Cloud.
It’s time for executives to step up and make the cloud work for AI-optimized actual transformation. This means shifting from traditional lift-and-shift cloud migrations and adopting cutting-edge technologies like serverless AI, AI data centers, and real-time edge AI processing to make a real difference.
The AI Goldmine: Where Many Get It Wrong
Gartner has shared that 85% of AI projects falter due to poor data quality and fragmentation. Most businesses operate on separated, scattered data lakes, outdated apps, and a multitude of overlapping tools and platforms. This disarray is particularly striking in the finance sector where ERP, CRM, and wealth management platforms function separately, leading to disjointed AI strategies. Other industries grapple with legacy systems unable to integrate with AI-driven decision making.
Mistakes to Avoid:
- Failing to embrace strategic alliances: Instead of relying on internal solutions, collaborate with innovative product and service providers.
- Neglecting data unification: Prioritize unifying data and implementing real-time analytics and AI governance before adopting AI.
- Persisting with outdated processes and technology debt: Brace for complete overhaul: automate end-to-end operations, instead of deploying AI in isolated pilots and relying on extensive manual workflows.
To avoid overpaying for an AI initiative that yields little business value, enterprises need to reassess their cloud strategy, fix data issues first, and rethink business models, IT architecture, and AI integration. It’s crucial to transition from AI experiments to full-scale implementation, with AI driving business processes from the ground up.
The Enterprise Action Plan
- Sort your data: Implement a data fabric architecture to standardize AI-ready data models and establish enterprise-wide AI governance.
- Redefine cloud strategy: Move beyond the old, multi-cloud approach and adopt AI-native cloud infrastructure to drive real-time AI insights.
- Adapt to outcome-based consulting: Eschew traditional consultation models and invest in AI-driven solutions that deliver results instead of billable hours.
- Refocus IT spending: Focus on intelligent, adaptive technologies like AI-driven cybersecurity, compliance automation, and AI-native governance.
The era of fragmented, disjointed, and suboptimal AI adoption must come to a halt. Forward-thinking enterprises will recognize the importance of structured, unified data, AI-native cloud infrastructure, and hyper-automation to fuel exceptional efficiency, clear decision-making, and sustained growth.
Embracing an AI-first mindset is no longer optional; it’s the point of difference between merely spending on AI vs. undertaking a comprehensive transformation of one’s business model. Are you ready to take the plunge?
And if you’re a dynamic CIO, CTO, or tech leader, don’t miss the opportunity to join the exclusive Forbes Technology Council, where you can collaborate with the brightest minds in the industry! Do I qualify?
- To achieve success in AI adoption, it's crucial for enterprises to address data fragmentation and prioritize the unification of their data, much like Jabin Geevarghese George advocates for while championing system modernization.
- In order to disrupt the common pitfall of overpaying for AI initiatives yielding little business value, leaders should reassess their cloud strategy, address data issues first, and reevaluate their business models, IT architecture, and AI integration in line with Geevarghese's forward-thinking approach.
- As 85% of AI projects reportedly fail due to poor data quality and fragmentation, businesses must strive for strategic alliances with innovative product and service providers, focusing on data unification and implementing real-time analytics and AI governance before adopting AI – a lesson we can learn from Geevarghese Geevarghese's insights on the topic.
