Adam Etherington, Senior Principal Analyst, Digital Enterprise Services
Artificial intelligence is in vogue. Some say it’s the new cloud. But are we destined to repeat the same mistakes in our rapid pursuit of AI’s bold promises?
Despite the hype surrounding AI’s potential to revolutionize IT, business processes, and customer experiences, major challenges threaten its success.
According to Omdia’s IT Enterprise Insights survey, only 11% of firms are optimizing AI for business outcomes, while nearly 90% remain in early stages of adoption. Without addressing key blind spots, most AI projects will fail to scale and become irrelevant.
Hybrid cloud is the reality for mature, complex enterprise systems. There was a time when the mantra of ‘cloud first’, promised to be a single architecture and approach that would address scalability, performance, and cost challenges.
Upon implementation and management, issues like security concerns, vendor lock-in, sovereignty, performance and high data transfer costs led to the rise of hybrid cloud models.
AI is following a similar trajectory, with messy integrations across infrastructures, platforms, and locations. Api’s and MCP offer remediation but are not the only approach. Enterprises must learn from painful lessons with to avoid repeating the same mistakes with ‘AI first’.
Source: Omdia
AI success depends on addressing foundational issues in enterprise architecture, including data integration, hybrid cloud readiness, and security. Despite progress, only 63% of firms in North America are considered mature in digital transformation. AI adoption faces even greater foundational challenges, requiring significant investment in enterprise architecture across applications, data, and business processes.
Investing in AI without addressing underlying issues will lead to failure. Enterprises must focus on readiness across three dimensions: technology, people, and processes. Blindly scaling AI without considering these factors risks wasting resources and undermining business goals.
Technology & techniques: Effective AI requires robust data discovery, classification, and quality management. Enterprises must also establish security and privacy guardrails within governance frameworks before scaling AI.
People and impact: Resistance to change and concerns about job displacement must be managed carefully. Employees and customers need assurance that AI will enhance experiences, not replace them.
Systems and processes: After a period of rapid AI industry innovation, deployment options already vary widely; firms can choose proprietary systems (e.g., Salesforce, Oracle), APIs, or open-source solutions (e.g., TensorFlow, Hugging Face). Each approach has trade-offs in terms of flexibility, cost, and maintenance.
To succeed with AI, enterprises must learn from past technology deployments, address foundational challenges, and adopt a strategic approach. By focusing on readiness and integration, firms can avoid becoming part of the 90% of AI projects that fail.
In an increasingly complex world, clarity and decisiveness are just enough to compete. But to get ahead, you need a partner who sees your market, knows your customers, and understands the intricacies of your business.
At Omdia, we are different. We have the humility to listen. It’s what sharpens our intelligence and powers our partners into action.
Content in this e-book is derived from four Omdia intelligence services:
Workplace Transformation Intelligence Service
Advanced Computing Intelligence Service
AI Applications Intelligence Service
Managed Security Services Intelligence Service
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Real-time enterprise insight
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Market data
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