Harry Chapman-Walker, CEO, Kallidus
The shine is fast coming off the AI bonanza. From fears that AI driven investment is overheating financial markets to news that upwards of 95% of AI projects fail, organisations are beginning to step back from the wholesale, somewhat gung-ho approach to AI led innovation. A reset is welcome. It should provide an opportunity to gain far more insight into what AI can actually deliver today – as opposed to the widespread vapourware. It is a chance to identify the operational outcomes that will provide tangible value – rather than vague concepts of generic efficiency gains. Critically, it should herald a commitment to understanding the implications of AI technologies for staff, skills and future career progression.
AI, or any other innovation, can and will deliver benefits with the right approach. It will improve productivity, transform customer engagement and drive new efficiencies. New operational models will not only demand new technology skills and a different management mindset but they will significantly alter the roles and experiences of millions of employees across the world. It is imperative therefore that training and learning must be embedded at every level of the business. It must be tightly aligned to both business and personal outcomes. And it must be considered the foundation for continual improvement.
As Harry Chapman-Walker, CEO, Kallidus, explains, the pace of AI adoption has amplified the decades long underinvestment in the people, skills and training required to successfully innovate. The companies that reverse that approach and actively invest in the skills required to deliver business outcomes will lead the way in achieving long term success with AI.
Technology First Failure
An estimated $30–40 billion has been spent by businesses in generative artificial intelligence, yet AI pilot failure has become the norm. A 95% failure rate is high, even in the technology industry littered with hype and failed promises. Replace AI with ERP, CRM, even digital transformation, and the story is disturbingly familiar: strategic IT projects have been failing for years. Why should AI be any different?
The extraordinary pace of AI awareness and adoption has simply amplified a long-term problem: organisations lack the right approach, capabilities and culture to successfully adopt new technologies. Yet the potential for change remains enormous. According to McKinsey the long-term AI opportunity could be worth $4.4 trillion in added productivity growth potential.
Moving from the current high level of failure towards successful, mature adoption represents a significant challenge. There is a recognition that the c-suite lacks clarity regarding the strategic direction of AI investment and, as a result, is failing to make the rapid changes to workforce skillsets, engagement and experience required for successful change. From defining the necessary additional skillsets for project delivery to assessing the implications for existing roles, organisations are simply not committing to the essential workforce rebalancing and training required.
Outcomes Led Model
Rather than looking at how to use an LLM or ‘Vibecode’, it is essential to take a step back and consider the desired business outcomes. What is the business looking to achieve? What skills will be required to reach those goals and once achieved, what are the implications for existing job roles? Can people be redeployed and, if so, what do they need to learn to be effective in the new role?
The endemic skills gap is a primary contributor to project failure. The result of layering AI over the top of the existing lack of knowledge and expertise is inevitable: faster failure. AI is not the starting point for innovation and change. Without the right foundation, including project management, communication and leadership skills, failure will continue. The starting point is to close the skills gap, and that requires not only a far stronger commitment to training and learning but one predicated on clearly defined business and personal outcomes.
Using a Learning Management System (LMS) that supports every facet of workplace learning, from creating and delivering training to tracking progress and reporting on results, supports the focus on measurable outcomes. A single platform allows learning and development teams to create and manage courses, enrol learners, and monitor training results. It provides employees with access to mandatory training and optional courses, while also tracking their own development journey.
Continual Commitment
Even more critically, a single LMS platform that maps training and learning to specific outcomes – at business, department, team and individual level – provides essential c-suite insight into the value of improving employee skillsets. From reducing recruitment costs by improving retention to achieving additional revenue through cross selling or customer engagement improvements, outcomes driven businesses rapidly move away from the incremental skills development often limited to onboarding (both people and technology) towards ‘everboarding’.
Realising the value of training, learning and development tied directly to specific outcomes builds a model of continual iteration and improvement. It also helps to create a culture where any innovation is considered on the basis of specific goals rather than generic promises. As such, it supports organisations in prioritising projects, robustly assessing technologies and, as a result, defining the next generation of skills, learning and retraining required.
This approach, however, is at odds with the current model. Rather than investing in skills, organisations are using AI as an excuse for job cuts, underlining the attitude that has prevailed for decades, where investment in training, learning and development has been continually sidelined by businesses. The result has not only been inevitable skills shortages that continue to undermine productivity and performance at both business and national level but expensive and disruptive employee turnover: 94% of workers said development opportunities would keep them in a role.
Conclusion
The pressure of AI-led innovation is now placing a spotlight on this failure. According to the World Economic Forum 1.1 billion jobs are likely to be radically transformed by technology in the next decade. Aneesh Raman, LinkedIn’s Chief Economic Opportunity Officer, maintains that by 2030, 70% of the skills required for the average job will have changed.
The implications for businesses of their failure to prepare for these changes are enormous. AI projects will continue to fail without a better approach, one based on building the skills required to deliver successful, outcomes led innovation. As a result, AI will not replace human jobs at the scale implied by many of the companies making cuts. Millions of highly intelligent and experienced individuals will have lost opportunities and been sidelined for no reason.
The entire innovation concept is back to front. It is by focusing on outcomes – not technology – and ensuring the right skills are being continuously developed to achieve those outcomes that organisations can safely and effectively hit their goals and, critically, ensure individuals across the business come with them on the journey.
