Introduction
Taking a step back, AI adoption mirrors the same challenges enterprises have faced with major transformation initiatives over the past decade. Disagree? Let’s dive into the evidence below.
A Matter Beyond Technology
AI adoption is not a technical issue. Sure, the tech and platforms are in the early stage but gaining robustness and maturity. It is comparable to the early stage of Mega-Clouds and their progress made over the recent years. Similarly, not all Business or Use Cases make sense, yet. Nonetheless, AI tech matures with much larger steps compared to anything else before.
Most AI cases intend to drive optimization in the organization. Here the main issue is to change the routines (processes) of the organization. With this, the routines of the user. Approaching the users with immature solutions ends in pushback and resistance. Ultimately, we ask our employees to get a job done, so we must accept, that if we propose something better, it must be better.
Bridging IT and Business: A Critical Gap
IT is owning the job. For the tech teams, literally anything is possible to build. Aligning tech investments with business needs and outcomes though, requires strong collaboration with the business. Sprawling IT spending was the consequence and enterprises paid a solid tuition fee. At large, the investments didn’t necessarily monetize.
Silos, lack of standards, and governance often prevented faster digitization, making it difficult to overcome challenges along the way. Without proper feasibility studies upfront, use cases frequently collapse under their own weight. The effort often exceeded the benefit, and without a solid agenda and backing to drive initiatives across all levels of the organization, they were impossible to do.
A Reward for Digital Readiness
There are more arguments why the AI transformation for enterprises is not a significant challenge for those who did their homework in recent years.
Companies that invested in digital competency enablement within the business units, established an aligned enterprise portfolio management to synchronize transformational initiatives and modernized their IT stack are leading the AI adoption race.
This is the foundation to continue building for the AI era. In the digital transformation era, limitations were tackled with intermediate steps and third-party tools, or a missing security and governance concept was solved by limiting access.
The AI race will crystallize those who already invested in data more broadly—meaning companies that have at least worked on Data Platforms and Integration.
The Evolving Challenges of AI Adoption
AI holds significant new challenges to master for enterprises. While technical and data-related issues such as data quality, data integration, data privacy, and security continue to exist and gain importance, there are new topics to consider:
Ethical
Data Scientists shout for as much data as possible, which makes sense to have a statistically reliable output. Nonetheless, processing data also means understanding its limitations, especially when AI models impact human lives.
“AI gone wrong” can lead, in the worst case, to harassment of employees, and in a less dramatic way, to outputs that potentially harm ESG initiatives of a company—especially when the AI model is presented to the outside.
Regulatory and Legal
AI is on the agenda of governments, and keeping up with regulations is a challenge for business leaders to stay compliant.
Furthermore, especially when consuming external data or collaborating with partners, there are intellectual property, ownership, or copyright issues. Mitigating these disputes requires addressing these topics upfront.
Understanding the legal implications of deploying AI systems and maintaining compliance with future regulations is critical. Ensuring AI systems are not used to discriminate against a particular group or individual, invade privacy, or cause harm is essential.
Bias
AI models reflect historical prejudices and social imbalances. Furthermore, AI can learn from preferences, which is a well-known use case in social media. It is also possible to build bias into the model by the engineer.
Being aware of and mitigating the risk of building business decisions based on biased models is critical. A good example is an AI-based hiring process. The risk of getting more of the same, instead of building a diverse team, might be the outcome.
Continuous monitoring is critical to detect and mitigate bias within the AI model.
Transparency
Building trust is critical as business leaders must rely on the outcomes of AI systems. These systems must be transparent and explainable; capabilities must be communicated clearly, and engagement with stakeholders must be proactive to address their concerns transparently.
Culture of Constant Skill Development
Technology is evolving fast, and AI even faster. Businesses require a mindset of continuous learning to stay at the forefront of development.
Strong organizations prioritize learning and skill development across all business units. It is critical to develop the business units on the capabilities and the technical units on the emerging technologies and practices.
These five points represent the challenges businesses face with AI adoption. Staying ahead means that a certain organizational shift has already occurred. Companies that see potential in AI systems must start—or continue—taking bold steps toward AI transformation to leverage the potential more broadly.
As with the digital transformation, the critical question is how to bring the user along to be able to proactively support the transformation and help address the challenges outlined above.
Are you ready?
Throughout my professional career, I have been driven by technology’s capabilities and how to bring benefits to enterprises. Everything in IT comes down to data and its use. This is where I dedicate my time, and I keep learning!
IVAN JELIC
Group CEO and General Manager CH
The post Challenges Enterprises Face When Adopting AI appeared first on Joyful Craftsmen.