GenAI and outsiders

Generative artificial intelligence (GenAI) is an advanced technology capable of transforming not only business processes but also the world around us. For full implementation of GenAI, it is necessary to create AI Governance, or generally recognized approaches and practices concerning this technology.

Implementation challenges

The benefits of GenAI solutions for businesses are becoming obvious despite being relatively new. Using smart tools of the new generation will make work even easier, faster and more efficient than at the current digitalization level.

Everything has a price though. Adapting GenAI will require more organizational and managerial efforts. Challenges are similar to the recent developments with Big Data and analytics.

Only yesterday, controlling the lifecycle of data, from collecting and storing to retrieving upon request, transmitting, processing, analyzing and interpreting results, was one of the main tasks of digital transformation. All these tasks were incorporated into the Data Governance concept as an aggregate of policies, methods and technologies for operating data. It should continue as the general approach to AI in AI Governance.

One of the strategic challenges of AI Governance today is ensuring that machine-generated solutions do not contradict each other and are trustworthy, to build general trust in machine’s output.

Another major problem is ensuring privacy and data security. Leaks and ambiguous use of AI-generated results may undermine trust in smart new-generation tools.

And this is just the tip of the iceberg.

Why now

If we do not begin establishing AI governance now, it will become increasingly challenging in the future, as AI continues to integrate into more processes and areas of activity and its complexity grows. Currently, artificial intelligence is in the early stages of progressing from highly specialized (“weak”) AI to more general (“strong”) AI.

“Weak” AI is designed to perform specific, highly specialized tasks for which it has been previously trained, such as recognizing a certain set of images.

“Strong” AI will be capable of performing intellectual tasks even without prior training. Similar to humans, it will be able to generalize input data based on general knowledge, such as understanding that any object has weight, size, and color. The transition to “strong” AI will represent a significant advancement in the potential applications of this technology.

However, evolution will not stop there. It is possible that Super AI could be developed in the future – an even more advanced level where AI surpasses human abilities in many aspects. Although, this is not expected to happen in the near future.

“Strong” and “super” AI possess the potential to manage clusters of systems and processes not only within entire industries but also across multiple industries linked by single value chains.

This capability allows AI to seamlessly integrate into complex ecosystems, enhancing interactions between different sectors and production stages. Consequently, this integration can lead to substantial improvements in efficiency and productivity.

However, the risks and consequences of disruptions and failures in the operation, development, and management of next-generation tools will increase proportionally. Therefore, it is crucial to engage in research and development in the field of AI Governance now to ensure that the growth of AI potential is matched by the ability to manage and mitigate the associated “growing pains.”

Practical aspects and recommendations

The following fundamental aspects should form the foundation of any AI Governance approach for a business implementing AI or GenAI:

Employee training and adaptation

It is essential to provide training and explanatory sessions for staff to alleviate fears, enhance trust, and increase awareness of the risks associated with the conscious use of AI. Involving all employees in the GenAI implementation process and considering their opinions will help reduce resistance to change.

Safety first

Establishing and enforcing stringent data protection policies and procedures is crucial to preventing leaks and bolstering confidence in the safety of using AI and GenAI.

Pilot projects and gradual integration

Starting with small pilot projects for GenAI allows for a better assessment of effectiveness and the early identification of potential issues.

Testing before launching GenAI

AI may struggle to understand the broader context of tasks, leading to errors and incorrect decisions. Insufficient testing, evaluation, and validation of models exacerbate this problem. It is vital to thoroughly test and evaluate AI systems before implementation to ensure their reliability and the soundness of their decisions.

Defining responsibility for AI decisions

The ambiguity surrounding accountability when AI makes mistakes poses significant implementation challenges. Establishing clear mechanisms to determine responsibility is essential to address issues related to proving errors and linking AI decisions directly to any resulting harm.

Ethics iceberg

The future of AI, particularly as regards its generative capabilities, raises many open questions that concern not solely its technical aspects but also fundamental changes in the economy, society and even philosophy.

Integrating AI into the market nature of production

The essential question is how AI is going to integrate into the existing market nature of the economy. Conventional competition models suggest human involvement at all levels, from concept to reality. The advancement of AI may have a drastic effect on these processes. The search for optimal approaches will be made through precise calculations that involve thousands of parameters to increase efficiency and reduce costs, rather than as a competition of ideas. This will inevitably affect the structure of the market as well as the distribution of roles.

Competition in the era of Super AI

How will AI-based optimization of entire industries and sectors affect competition? Traditional competitive advantages, such as exclusive technologies or skilled workforce, may lose their relevance; instead, competitiveness may depend on the ability to efficiently integrate and use AI.

Ownership model

Who will own AI systems and their outputs? Will we see new emerging forms of ownership, such as collective or distributed ownership of AI and its outputs? As technology advances, the question as to who controls data and algorithms is becoming increasingly relevant.

Strategic goal-setting hub

Who or what will be responsible for strategic decision-making? At traditional companies, this task is normally performed by executives and business owners. The introduction of AI may shift this function to automated systems capable of analyzing large amounts of data to offer optimal strategies.

Can it benefit everyone?

The most essential question concerns optimization goals. What it is that we are going to optimize by using AI? Various scenarios may include:

owner profits: focus on profit maximization might remain a core factor; however, questions would arise as regards social responsibility and sustainable society;
satisfaction of the end consumer’s needs: this shift in focus could create more personalized products and services of greater quality – however, infinite consumption cannot be a goal in itself;
balance of production and environmental conservation, with optimization aimed to achieve sustainable development, curb environmental issues, and achieve harmony with nature.

Open questions regarding the future of AI only stress the complexity of intricate changes we will be facing. The answers will depend on many factors, such as technological advances, regulatory framework, social values, and economic environment.

Society should extensively engage in the efforts to discuss and build the future of AI for this powerful technology to benefit everyone, not just a select few.