Over the past couple of years, irrespective of the industry, every corporate boardroom discussion has revolved mostly around the topic of AI and how every facet of the business can be transformed with the technology. Gartner estimates that enterprise AI spending will surpass USD 3.3 trillion by 2029. But despite the push to integrate AI deeper into every layer of the business, there are resounding challenges that enterprise leaders are facing with the rapid proliferation of AI.
Are AI initiatives moving beyond the pilot stage?
AI adoption is not really a big problem for most enterprises. But the real issue is scaling AI initiatives from their pilot stage to fully-fledged business assets and deriving tangible value and ROI from the heavy investments made for the same. Studies show that nearly 95% of all generative AI pilot projects fail to realize value for enterprises. Cracking the code to scale AI experiences is becoming a major bottleneck for leaders.
Why are AI projects failing to deliver value beyond the pilot phase?
In several instances, enterprises often get stuck at the pilot stage with their AI initiatives largely because of a lack of attention towards some of the key elements that are necessary for an AI cultural shift. Some of the most prolific misses include:
Low data readiness
Without a clear and defined data infrastructure in place, no AI model would live up to its expectations. Across the business, there is a critical need for an integrated, visible, accurate, and strategically relevant stream of data being readily available for consumption by AI services that an enterprise builds. From formatting and syntax irregularities to a lack of standardization among different data logs and metrics being tracked, there may be several discrepancies present in the data ecosystem.
If left unattended, they can obstruct the scaling of pilot AI programs as inaccuracies and inefficiencies will plague growth cycles.
Presence of legacy systems
We have already seen how poor and unclean data can wreck the progress of AI projects. Similarly, adding more complexity is the notorious presence of legacy systems within an enterprise technology ecosystem. They may be siloed, running old technologies incapable of supporting AI-friendly data generation, and pose significant security vulnerabilities.
This prevents the enterprise from going beyond a certain level of growth for its pilot AI initiatives. They are faced with failure prospects either at the data-level or system integration level, or at the security level.
Wrong cultural integration
One of the biggest mistakes that an enterprise could make in its AI journey is perceiving AI as a side project and then claiming it to be its core growth engine. Leaders must understand that AI doesn’t just happen at a tool level. Rather, it should be woven into the very process fabric of the organization. This includes building a roadmap that integrates AI into every key business process and workflow. IT, operations, finance, and other key business departments must collaborate without friction at the process level to prevent automated and autonomous AI workflows from getting stuck in bureaucratic or approval delays.
For example, when the CFO wants an AI tool to provide them a snapshot of the business’s financial performance, the AI tool must be able to leverage data freely from across all relevant departments, crunch and process them accurately without any errors, delays, or missing entities. Such a free-flow execution happens when AI programs are embedded as a cultural trait and not just a new tool for employees. When done so, every department will strive to complete its end of the puzzle and contribute relevant data and insights for a collaborative AI experience.
Miscalculated cost overruns
Making baby steps into the AI paradigm isn’t a concern for most enterprises financially. However, scaling AI programs from their pilot stage is a different journey and one that requires significant investments into infrastructure across multiple disciplines like computing power, data, talent, security, etc. The costs balloon as the scope of coverage of AI initiatives increases. In large companies, there may be processes or systems being managed by external consultants and contractors. When an enterprise AI pilot program is directed towards such systems for adoption, there will be a critical need for overhauling the entire practice, which requires investments across vendor capabilities as well.
Another major challenge is the misplaced investment priorities. To make quick wins, AI initiatives are often piloted across customer-facing avenues first. Budgets are allocated disproportionately towards customer experience-driven AI projects, but what leaders often ignore is the fact that such avenues have the liberty to leverage AI tools and workflows that have already proven their mettle across other sectors as well. The less glamorous areas on the operational front of the business, however, hold immense potential for transformation with scaled AI initiatives. For example, accelerating production or generation cycles, eliminating process inefficiencies, streamlining financial operations, enhancing security and governance, etc., can offer significantly higher ROI for the business when scaled.
What should enterprises do for the successful scaling of pilot AI programs?
The successful scaling of pilot AI initiatives depends greatly on how organizations can propagate true awareness of the essential success elements of AI – getting data ready, becoming flexible in processes and technology, growing a culture of AI-first collaboration, and prioritizing long-term returns over short-term visibility. Getting the roadmap for AI success correct requires not just a typical working model. Leaders must bring everyone into agreement over shared autonomous workflows, break down barriers in management and flexibility, adopt proven technology stacks, and be open to continuous feedback-driven evolution of AI experiences.
The best way to transform AI from the pilot phase into a true enterprise asset is to strategically evaluate the business's priorities and focus on investing to adopt the most tailored AI experiences for both customers and staff. There is no need for the business to always start from scratch and spend considerable time building the basics on its own. They can leverage partnerships with experienced players in the domain, like Wissen, to bring in proven solutions, success frameworks, and transformation guidance to help nurture a successful AI culture across the length and breadth of the organization. This will help in building AI as a core business growth engine and not a side project.
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FAQ
What are AI pilot programs?
It is a small-scale implementation of an AI capability within an enterprise to check for feasibility and adaptability before going for full-scale implementation.
How does data readiness impact AI scalability?
AI initiatives reap success only when enterprises can guarantee them with an adequate supply of relevant data insights with high accuracy.
How can legacy applications disrupt the scaling of pilot AI initiatives?
Legacy applications can prevent AI services from accessing the right data, create process and compliance hurdles, and prevent end-to-end autonomous workflow execution.
