Generative AI is one of the most talked about and developed technologies of the past years. From local businesses to companies operating worldwide, most businesses are looking for a way to integrate AI into their workload and use it effectively. Although AI hype has been popularized with a chatbot that can generate human-like output, companies and users have seen higher potential in it. That leads to the use of AI in different aspects of companies in today's business world, from workflow streamlining to knowledge management. If you have limited knowledge about Gen AI and want to learn more, you are in the right place!

In this article, we will examine the development, future, and investments of Gen AI and dive into how to start using it!

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TL: DR

  • Gen AI Hype vs. Reality: Despite significant media attention and financial market reactions, the tangible impact of Generative AI on end-users and profit margins remains limited, raising questions about its translation into business value.
  • Uncertainty and Evolving Landscape: The unpredictability of technological evolution, shifting business landscapes, and concerns about data privacy and compliance make it challenging for businesses to commit to long-term Gen AI investments.
  • Cost and Integration Challenges: High initial investment costs, delayed ROI, and the complexity of integrating Gen AI with legacy systems present significant hurdles for adoption, often complicated by unclear funding responsibilities among executives.
  • Best Practices for Adoption: Companies should develop a methodical approach to overcome uncertainties and challenges, starting with a seamlessly integrating technology platform, focusing on small, tangible problems with tech-savvy users, and prioritizing fast, cheap feedback cycles to determine business value.

Hype around Generative AI

Gen AI has been the hype innovation topic for the last 1.5 years. The theme dominated stock markets, discussions within the boardroom, and management meetings. Major firms like Google and Meta are making unprecedented CAPEX bets in this space, alongside a surge of new startups and a growing pool of AI experts emerging. Despite the significant media attention and notable financial market reactions, the tangible impact on end-users and profit margins remains limited. It raises the critical question: why is the translation of Gen AI advancements into tangible business value lacking?

Future Direction of Generative AI

Certainly, the unpredictability of technological evolution plays a significant role in this dynamic landscape. As model development progresses and new applications emerge, the business environment continues to shift. Many companies (including Microsoft) have already invested heavily in their technology and infrastructure stack. Most have also experimented with the application layer, yet few scalable use cases have emerged that can be uniformly adopted across industries. This situation is further complicated by the influx of new potential alliance partners and the challenge of keeping pace with established vendors. The absence of clear market leaders makes it difficult for businesses to commit to long-term investments. Moreover, concerns and a lack of understanding regarding data privacy and compliance issues add another layer of uncertainty to the decision-making process.

Total Value of the Global Generative AI Markets
Total Value of the Global Generative AI Markets

Gen AI Investments

Gen AI technology is the rising star of today's world and the key to the beginning of a new era. Gen AI will make a significant development in the level of contribution to humanity that touch phones and home computers provided in the past. World-renowned companies such as Microsoft, Apple, and Nvidia are aware of these developments and are closely following the development of AI every day and adding it to their products. For example, Nvidia, a GPU manufacturer, has developed DLSS technology that generates frames using AI and Gen AI features.

EY AI Valuation
Generative AI Venture Capital Investment Globally

Companies are becoming more and more aware of the contribution that Gen AI technology will provide to their products and markets. According to a rumor that has recently spread, Nvidia, Microsoft, and Apple are planning to invest $100 Billion in OpenAI, the largest AI developer.

Gen AI Costs

Costs are a second significant hurdle. Gen AI adoption is associated with high initial investment, either to develop internal capabilities or to engage external expertise and technology solutions. This necessitates significant upfront expenditure with delayed return on investment, which can deter businesses from committing to such projects. The responsibility for funding Gen AI initiatives often falls into a grey area among top executives such as the CEO, CTO, or COO. This further complicates and slows down strategic decision-making.

Where to Start with Generative AI?

Further, the sheer complexity of integrating and overhauling legacy systems with new technology presents a huge challenge. Given this, many companies struggle to discern a clear starting point or pathway towards meaningful integration. A lack of essential knowledge, skills, and internal consensus further exacerbates this issue, leaving businesses vulnerable to inaction. The challenge is exacerbated by internal data issues, such as locked or siloed data. This hinders companies from scaling Gen AI and leaves them at risk of falling behind more agile and decisive competitors. 

Gen AI Best Practices

To maintain a competitive edge, companies must develop a methodical approach to overcome the uncertainties, high costs, and complex integration challenges posed by large-scale AI implementation.

This often involves leveraging a technology platform that seamlessly integrates with the existing tech stack and enables rapid deployment without significant upfront integration or consulting expenses. Companies should initiate their AI journey by experimenting with small, tangible, and well-defined problems, involving a select group of tech-savvy power users. This approach facilitates quick and cost-effective feedback cycles, allowing for clear verification of business value.

See the tangible results from one of our case studies:

  • TextCortex was implemented for Kemény Boehme Consultants as a solution to tackle these challenges and today employees report increased efficiency and productivity (saving 3 work days per month per employee on average).
  • AICX, an ecosystem partner of TextCortex, was integral to the onboarding and helped achieve a 70% activation rate of the team within the first weeks.
  • Employee confidence in using and working with AI increased by 60%.‍
  • The implementation results in a 28x return on investment (ROI).

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