While AI gains traction as a core business enabler for organizations across different industries, a few barriers to AI adoption have slowed down the mainstream adoption and full-fledged applications of this technology. With time, investment, and continued experimentation, these obstacles will eventually be overcome, giving rise to a whole new generation of advanced AI applications.
While the concept and its applications have been around for a while now, the field of AI still hasn’t stopped surprising us with new innovations and milestones. In recent years we’ve been treated to numerous stunning applications of the technology, from AI machines that can engage in logical debates with humans to those that can detect cancer and other diseases better than human physicians. Businesses worldwide have lined up even more ambitious and revolutionary applications of AI to improve their products as well as their processes. However, adopting this technology, regardless of how beneficial they might have been, have also raised a few challenges. These barriers to AI adoption, mostly stemming from the rapid and exponential growth of AI capabilities combined with the lack of preparedness of businesses as well as governments, must be addressed before we transition to an AI-driven future.
Barriers to AI adoption and their solutions
1. Gaining organization-wide buy-in
The foremost and the earliest challenge to AI adoption for any business, especially if it’s a traditional, established organization, is to get buy-in from stakeholders. These stakeholders include everyone involved in operating the business — from the directors and investors all the way down to the last employee. It is vital to convince each of these stakeholders of the value of using AI applications not only to the organization but also to them individually.
While the buzz surrounding the technology in the mainstream and the numerous cases of successful AI implementation may influence most investors and leaders to commit to AI, the same may not necessarily work for everyone at the organization. Even then many among the leadership and management may see AI as an extravagance. They can adopt the “If it ain’t broke, don’t fix it” attitude towards driving organizational change. Most employees may feel threatened by the idea of incorporating AI into different business processes. They may feel control slipping out of their hands and may fear becoming redundant due to AI.
CIOs and technology leaders must recognize these resistances as natural human responses and therefore, commit to educating their employees and the C-suite leaders about the need for AI. More importantly, they must focus on making their employees aware of the fact that AI will only make life easier for them by assisting them instead of replacing them altogether. They must also implement appropriate change management strategies, as people’s inherent resistance to change may also make them averse to any transformative efforts, especially something as disruptive as AI.
2. Developing a clear AI strategy
Any major investment in technology by enterprises should follow a thorough strategy phase where the expectations, objectives, and plans are clearly laid out. However, aiming to exploit the early adopter advantage, businesses may want to rush into implementation by choosing the seemingly most obvious applications areas. And although such implementations may deliver desirable results, they may not align well with the organization’s broader, long-term directives. Thus, considering how new AI initiatives align with organizational goals is key to ensuring that they are in perfect alignment with each other, enabling a smoother transition towards AI-driven processes. More importantly, having a clear AI strategy can enable businesses to avoid the “analysis paralysis” trap, where, due to the numerous benefits and newly emerging applications of AI, businesses struggle to prioritize what they want to achieve with AI. They then end up investing in initiatives that may not necessarily align with their long-term objectives. While this may not necessarily be disadvantageous, but can lead to hefty opportunity costs which may, at least temporarily, derail them from their desired direction. However, smaller organizations that are constrained for resources may feel the impact of such initiatives in the worst possible way, leaving them with no fallback options and resources.
To create a clear AI implementation plan, businesses must draw up all the possible use cases of AI that can benefit their organization. Then they can assess the feasibility of individual cases and their expected impact on the business’s mission and long-term strategy. They can prioritize the different use cases by starting with simpler applications and pilot projects and incrementally transitioning to larger, overarching ones. Doing so will ensure that businesses can accelerate AI adoption and minimize the time to value.
3. Finding the right talent
Among the biggest barriers to AI adoption for most organizations is finding the right AI talent. Being an AI researcher requires a specific set of skills, knowledge, and attributes that is currently in rare supply across the world. Most of the existing global AI talent pool is currently engaged with the top international corporations, as they are the only ones who can financially afford them. Thus, most of the remaining organizations are not able to attract AI talent as easily.
However, this problem will be alleviated with time, as more and more people are pursuing an education and a career in AI research, driven by the abundance of opportunity and the high valuation in the job market. This will lead to a greater influx of new AI talent, filling the current talent void. However, businesses should look for ways to identify potential AI talent within their existing talent pool. They can identify those with the aptitude and the willingness to pursue AI research and development and build their own internal AI teams. These teams can be augmented by hiring AI expertise from established AI research organizations. These businesses should also look to partner with AI development firms and educational institutions to not just gain implementation ideas in the short term but also to build a deep AI knowledge base for the long run. Partnering with educational institutions can also help businesses acquire AI talent easily.
4. Overhauling existing systems
Many businesses, especially the established enterprises that have built their business processes, technological systems, and the overall enterprise architecture over multiple decades, will find that AI may not be as easy to incorporate with these systems. Advanced AI applications require newer hardware and supporting platforms, making legacy systems redundant. Using AI may mean completely overhauling their existing systems, procedures, and even strategies and policies. This may come with a lot of associated costs both in terms of money and time, which can potentially cause them to lose their footing in the market and fall behind their competitors. Thus, AI, although necessary for their sustainability, may seem like too big a step for these enterprises.
Such organizations must adopt a multi-phased approach toward adopting AI. They must start small on projects that are highly likely to succeed. They must incrementally scale up their AI implementation by capitalizing on the small wins and later perform exploratory research to eventually achieve disruptive transformation. In addition to these barriers to AI adoption, there are numerous other challenges that must be addressed before embarking on the ambitious AI projects envisioned for the future. These barriers include those involving the quality of data used with AI, safety concerns, regulatory uncertainty, ethical implications, and a host of other related issues. Overcoming these challenges will require the collective effort of the global tech and AI community, regulators, and the enterprises adopting the technology, which can be motivated by the potentially groundbreaking applications and benefits of AI.