Page 17 - AI for Good - Impact Report

AI for Good

Latin America

• Venture capital invested in AI startups in Latin America grew over the past years, raising
the numbers from US$29 million in 2019 to US$202 million in 2022 but declining in 2023
to US$110 million. 54

Following the overall trend, GenAI is a key driver for investments. In 2023, GenAI investment
increased nearly eightfold from the previous year to reach US$25.2 billion. This significant
55
increase highlights the growing interest and confidence in generative AI technologies. Major
players in the GenAI space reported substantial fundraising rounds, underscoring the sector's
potential and attractiveness to investors. Despite an overall decline in AI private investment,
GenAI managed to attract a significant portion of the funding, accounting for over a quarter of
all AI-related private investments. The prominence of GenAI is further reflected in corporate
56
activities and discussions. In 2022, GenAI was the most frequently cited theme in Fortune 500
earnings calls, appearing in 19.7% of all calls, a substantial increase from 0.31% in 2022. This
57
surge in mentions indicates that companies are increasingly recognizing the importance and
potential impact of GenAI on their operations and strategies.

Barriers to wider adoption of AI

The adoption of AI in organizations faces barriers beyond financial investments. Deloitte United
States's Report on Generative AI in the Enterprise Q3 identifies the top global barriers as: worries
about regulatory compliance (36%), lack of technical talent (31%), and difficulty managing
risks (30%). The report, based on a survey of around 2,000 business and technology leaders,
58
reveals that while the excitement around GenAI remains high, there are challenges related
to trust, workforce adaptation, and the need for tangible results that will be discussed in the
following section.

Scaling GenAI from pilots and proofs of concept to large-scale deployment is a primary
challenge. It involves coordinated efforts across strategy, process, people, data, and technology.
Risk management, governance, workforce transformation, trust, and data management become
even more critical during this phase. What worked well in the past might not be as effective with
this new technology. The scaling phase is where potential benefits are converted into real-world
value, but also where potential challenges become real-world barriers. 59

Legacy operational structures present a significant barrier to integrating GenAI effectively. These
structures often do not support AI's dynamic needs, hindering collaboration and innovation.
Fear of the unknown, reluctance to experiment with new technologies, and a tendency to
maintain the status quo impede adoption. Ethical considerations and governance concerns also
play a critical role. Clear guidelines and governance protocols are needed to ensure responsible
use of GenAI. Leaders must address concerns, engage in open dialogues, and break down
barriers of fear or misunderstanding to build trust and align the entire organization with the
GenAI mission. 60

Trust is a significant barrier to large-scale GenAI adoption and deployment. Organizations
need to build trust in the quality and reliability of GenAI's output, supported by improved
transparency and explainability. Additionally, there is a need to build trust among workers that
GenAI will make their jobs easier and not replace them. Many organizations measure worker
trust and engagement as part of their talent strategies to address this matter. Greater exposure

7