In the coming years, we can therefore expect a strong increase in the use of AI worldwide, with companies that already use AI reaping major benefits. But even for companies that do not yet use AI, it is becoming increasingly easier to access this technology thanks to the emergence of new applications such as apps, managed services and low-code environments. In this article, I’ll share my predictions about emerging technologies like AI and machine learning (ML), and discuss what companies can do to accelerate adoption of these technologies to increase and diversify their revenue streams.

Democratization

While we are not yet where we would like to be, the democratization of AI is expected to increase in the near future. Using apps, managed services and low-code environments will make it easier for everyone to use AI and make a quick difference in their business. We are expected to see more AI applications in organizations already this year, but it is important that people open up and are not afraid that AI will take over their work. Of course this will mean changes in skills and requirements, but if we understand how AI applications can empower people in their daily work, we need not fear these changes.


Faster adoption of new technologies

There are three essential steps in the process of accelerating the adoption of AI-based solutions. First, organizations need to understand how the enterprise-wide use of AI-based solutions can add value to the business. It is essential to find out how each department can use AI to find new ways to improve existing ones
to solve the problems. AI helps determine which problems to prioritize and the business reasons for solving them. Second, change is essential for better and smoother execution. As with any change process, the foundation lies in an inventory of existing skills, technologies and organizational structures. Companies should assess the customer’s existing AI and ML competencies, tools, processes and data platforms to generate a gap analysis. They can then develop an implementation plan to fill any identified gaps. After that, companies need to look at cultural changes, often through audience-tailored enablement sessions. Third, companies should appoint AI ambassadors, people who love technology. Instead of seeing AI just as a money-making investment, we need to intrinsically motivate people to work with AI. By engaging teams in transformational and futuristic projects, we encourage people to improve their AI skills and create communities around their enthusiasm, where they can share experiences and keep their enthusiasm alive.


Factors for responsible AI

While security is considered an absolute prerequisite for reliable AI solutions, other pillars of responsible AI, such as fairness, accountability and transparency, are often still seen as ‘nice perks’ or PR purposes. The return on investment of these aspects is still unclear, in contrast to the clear business value of safety. As a result, many companies are reluctant to focus on responsible AI processes and policies. However, Gartner predicts that organizations that incorporate AI transparency, trust and security into their business models will see a 50% improvement in terms of adoption, business objectives and user adoption by 2026. It is also predicted that by 2028, AI-powered machines will comprise 20% of the global workforce and 40% of all economic productivity. It is therefore important that companies rethink their mindset and align their business goals with responsible use of the AI ​​pillars to increase customer confidence, just as they did with security a decade ago. Organizations can achieve this by educating themselves and educating their customers about the business value of responsible AI. In this way, companies can begin to regard transparency, accountability and fairness as absolute conditions and, in combination with effective security, increase confidence in this new technology.


Conclusion

Companies need to connect their business strategy with objectives for developing AI solutions, starting small and thinking big. Finding the right use case and balancing data and AI is critical, as is aligning data and AI operations across the organization. Communication, education and user involvement are essential to success, as is the involvement of top management, owners and technical resources. When scaling AI efforts, it is important to look beyond the technology and also focus on processes, services, governance, finance and skills built on analytics and data platforms.

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