Barriers to Success: Why Are Companies Struggling to Innovate with AI?
Business leaders in 2024 are all excited about the opportunities of artificial intelligence (AI), whether they intend to use it or not. The immediate benefits are clear and, particularly in the recent economic climate where business resilience and profits are paramount.
As it stands, most organizations appear to have big plans for AI. 86% of senior business leaders globally have already deployed AI tools to enhance existing revenue streams or create new ones, according to our recent AI survey of over 1,272 companies. And in the UK, 92% have AI implementations planned, underway, or completed. But the level of innovation appears to be a stumbling block for companies incorporating AI into their business plans.
69% of business leaders said they are more focused on using AI to drive innovation and increase revenue than on improving productivity and optimizing costs. In practice, this simply isn’t the case. Only 4% of companies are currently using AI as a differentiator that will transform their business. This means there’s a huge gap between intent and implementation when it comes to innovating with AI. So why are companies facing challenges?
Head of Innovation, Tata Consultancy Services (TCS).
Risk aversion
Many businesses struggle with innovation due to a lack of robust financial models for digital transformation, a lack of leadership support, a culture that encourages innovation and structures, employee skills gaps and business leaders’ concerns about regulatory issues. But beyond these obstacles, when it comes to innovating with AI, risk aversion is often the biggest hurdle for business leaders. Only 23% of UK businesses have indicated a willingness to experiment and take risks with AI to maximise its benefits.
Any innovation involves trying new things, but with AI there are multiple types of risks. First, the technology is evolving extremely quickly, which means the risk of obsolescence is high and skills can be hard to acquire. The proliferation of platforms and tools can also create a risk of suboptimal choices. With so many options for business leaders to choose from, it can be difficult to know which one is right for your organization.
The cost behavior of AI applications at scale is not well understood, creating commercial risks that businesses must also contend with. And with governments still grappling with creating and rewriting regulations for emerging AI technologies, there is the specter of regulatory and compliance risk. Businesses can be held accountable for a lack of explainability or transparency in their use of AI, which in turn creates potential brand impact.
These risks are daunting because AI technology is new and the learning curve can be steep for many leaders. Yet, challenges can be mitigated. With a mature approach to innovation, experimentation, test-and-learn methodologies, and governance models, organizations can build safe environments to use and innovate with AI. If implementing these measures alone is complex or difficult, using AI to improve processes and create new revenue streams is also a popular option for building trust.
The complexity of AI
AI requires more data than traditional enterprise software to be effective, as it must be trained on large amounts of high-quality data that can be difficult to obtain. Additionally, algorithms are often quite complex and require specialists to maintain and develop, and legacy technology may need to be updated to support software. This is a large-scale change for leaders to manage if they want to be truly innovation-focused and AI-enabled. It can also be costly.
Some leaders may shy away from the cost without the ability to visualize the data, outcomes, and potential rewards, or simply not feel comfortable selling the cost to internal stakeholders. An additional concern for businesses is the changing capabilities of the tools themselves. AI is evolving rapidly, and the best model for the task one week may not be the best the next. Having an orchestration layer that can move applications between providers without impacting the business is therefore critical to building agility into AI enterprise offerings and processes. However, because AI is a new, prevalent technology with a wealth of information about it being published daily, not all businesses will be aware that offerings already exist.
Innovating in a safe environment
Many of these challenges will be addressed by businesses over time, but for those who already envision themselves ahead of their competitors and are struggling to do so in practice, having a safe environment to ‘test’ in is essential. Whether this means having the right employees with the skills to bounce ideas off of, or the experience to already advise on these new technologies, or even hiring an expert IT services partner who can provide stakeholders with a safe environment to innovate and implement effective change.
AI may be a widespread phenomenon, and there are certainly small-scale implementations taking place among UK businesses. However, it is clear that without further support and expertise, many businesses will not make the leap from implementation to innovation.
The biggest challenge:
There is a big difference between transformation and tactical use of AI. Everyone understands that AI can be transformational, but almost every use of AI today is tactical – in specific targeted projects that typically deliver cost reduction or marginal gains within 12-18 months. However, this can be the biggest risk for organizations in my experience – the inability to make bold and transformative decisions.
AI may feel like a known entity to IT professionals and innovation a tangible frontier of benefit, but AI transformation can often be an investment in an unknown to business leaders. Gaining leadership buy-in requires a broader perspective from IT leaders who want to convince stakeholders to invest in true innovation and transformation.
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