According to a study conducted by McKinsey in 2022, “the adoption of AI has more than doubled in the last five years” and “the level of investment in AI has increased in parallel with its increasing adoption.”

While previous business initiatives in artificial intelligence (AI) have mostly relied on analytical AI or symbolic AI capabilities, the latest development in generative AI has taken the world by storm. Unlike its big sisters, the latter is characterized by its ability to generate text, images or other media in response to a request made by the user and based on a gigantic database.

The unlimited potential of generative AI

Current generative AI systems include the famous GPT chat (and its Bing Chat variant), a chatbot (programmable conversational agent) built by OpenAI, and BARD, the recent chatbot developed by Google.

Business leaders increasingly see generative AI as a powerful tool for innovation and problem solving. Indeed, it can be used to automate complex processes, create personalized experiences for customers, and even generate new ideas and designs. In fact, there are use cases in sectors such as fashion, design, media and entertainment, where creating new works of art, music, and assets by AI was previously unimaginable.

More than a new innovation, generative AI is proving to be completely complementary to automation. It works like the “brain“of the digital transformation ecosystem, while AI-powered automation serves as “muscle” necessary to act on the knowledge generated.

A redesigned value proposition

While it's easy to get distracted by the hustle and bustle surrounding generative AI, it's essential to focus on the value proposition that it underlies.

While customer service is one of the key areas being explored for deploying generative AI, there are other opportunities that leaders need to consider.

For example, using automated workflows and generative AI,Financial institutions can automatically report suspicious transactions and analyze patterns to identify potential fraud.. This can save analysts valuable time and increase fraud detection accuracy. Once the analysis is complete, the software robots deployed by our technology partner UiPath can help automate the population of the respective flags in the underlying applications.

Generative AI can also be used to analyze historical sales data and other relevant factors, such as weather conditions, in order to generate more accurate demand forecasts. This can help businesses optimize inventory levels and reduce waste, while automation workflows can be used to automatically adjust stock levels and reorder products if necessary.

Building your organization to welcome the generative AI revolution

It is vital to ensure that your organization is at the maturity level needed to adopt generative AI. This includes assessing whether the underlying systems are scalable, secure, and can be integrated with AI and automation capabilities. In addition, The role of managers is based in particular on the establishment of processes for managing the necessary data. They also need to ensure the quality and security of data, and establish governance processes to ensure that data is managed effectively.

Ultimately, businesses must also establish clear governance and change management processes to ensure that automation and generative AI initiatives align with business goals and that stakeholders are prepared for change. This includes establishing roles and responsibilities, creating communication plans, and providing training and support to employees.

In the same way, risks associated with generative AI need to be identified and managed effectively. This includes establishing clear policies and procedures for managing data, ensuring that technology complies with relevant regulations, and creating contingency plans for unexpected events. To achieve this, business leaders will have to work closely with legal and compliance teams to ensure that all stakeholders are aware of the risks and can manage them effectively when adopting generative AI.

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Sources: StoryShaper, UiPath, Wikipedia