Artificial intelligence (AI) has become a significant technology tool and has created a dynamic landscape where organizations can embrace disruption, harness existing technologies and pursue other emerging trends. It’s an area of rapid advancement, widespread adoption and limitless opportunities.
According to HCLTech’s 2024 Tech Trends report, the landscape of AI will shift toward disruption, with generative AI (GenAI) making way for large-scale feasibility demos, driving rapid progress and new advancements in ethical AI. As organizations navigate the challenges and opportunities of advanced AI technologies, many are seeing its transformative and disruptive influence on industries.
Gauging the impact of AI
HCLTech is invested in building GenAI capabilities through co-creation with customers and leveraging engineering expertise. According to Alan Flower, EVP and Head of Cloud Native and GenAI Labs, HCLTech, and HCL CTO for Cloud Native, GenAI has garnered a considerable amount of attention.
“We expect these exciting times to continue and unleash a new frontier of possibilities,” says Flower. “The coming year is going to be a crucial in the establishment of new AI applications and the growth of existing ones.”
The current stage of AI, according to research conducted by HCLTech, is that of disruption. Organizations are evidently navigating the challenges and opportunities presented by advanced AI technologies, leading to a substantial percentage (41%) in the disruption category for organizations. Additionally, 32% are currently in the “Hype” stage and 27% in the “Adoption” stage.
Furthermore, 55% of respondents to HCLTech’s research believe that GenAI is leading the disruption in the overall AI segment and 28% believe that GenAI will play a transformational role for enterprises in the next few years.
The trending AI landscape
The current trending AI landscape can be segmented into three parts: ethical AI, generative AI and machine learning (ML). According to the research, ML has the highest adoptability factor and along with generative AI and ethical AI, both micro trends are primed to disrupt the market and generate hype in the next year, respectively.
What are these micro trends and what do they mean for the overall growing AI landscape? Ethical AI was designed and developed with the utmost regard for human values and ethics and has become a focal point where all AI advancements converge before getting adopted at scale. Ethical AI adheres to well-defined ethical guidelines, such as respecting individual rights, protecting privacy, avoiding discrimination and preventing manipulation.
GenAI is a type of AI that creates various types of content, such as text, images, audio or data, in response to prompts or inputs. GenAI uses algorithms to generate new content and recent advances have made it easier to use with user-friendly interfaces. Nearly half of respondents (48%) believe that content generation will be the most relevant use case for GenAI and a majority of respondents, 58%, also believe that machine learning is leading adoption in the overall AI segment.
Lastly, machine learning is a branch of AI that empowers computers to learn from data without explicit programming. Rather than follow rigid instructions, ML algorithms analyze massive datasets, uncover patterns and make predictions or decisions based on the knowledge acquired through this analysis.
Ethical AI seeing great potential
Ethical AI is front of mind for many organizations today to grow their businesses, but also due to legislation and regulations being implemented to address AI concerns. Recently, the EU announced a provisional agreement on an AI Act with the goal of paving a way for a global AI landscape that’s ethical, safe and trustworthy. In October, the Biden administration published a draft AI Bill of Rights that aims to guide the design, use and deployment of automated systems.
Enterprises implementing AI will need to consider the ethical component of the emerging technology, as well as being aware of increasing legislation in various regions around the world. Those who are already working on ethical AI frameworks will be the most capable of adapting to the shifting regulatory landscape.
The benefits ethical AI brings to society make it a good candidate for exploring new advancements within the AI landscape. The potential for ethical AI has great business value, including data privacy and security, bias mitigation and reliability and safety. Ethical AI systems are engineered to be impartial and avoid unfair discrimination. These systems provide equitable access and treatment, actively identifying and reducing biases related to factors like race, gender or nationality, which is important for organizations’ interaction with their communities.
Need for Ethical AI
Organizations will need a clear ethical AI strategy in 2024 as well, because without one, they will struggle with ineffective implementations. Developing a clear strategy will have to take into account the regulatory hurdles that vary by region and industry, presenting another challenge for adoption.
Ethical AI also places a strong emphasis on data security by establishing robust data governance and model management practices to safeguard sensitive information, ensuring user privacy, while adhering to AI principles. Ethical AI systems also operate reliably and safely, limiting their functions to their intended purposes. This approach reduces the likelihood of unexpected incidents or errors, which brings great business value to the landscape.
Ethical AI may be in the hype stage, but getting to the adoption stage will take overcoming certain challenges. Since ethical AI is new and complex, integration can be challenging due to limited familiarity. High-quality data for ethical AI systems is also essential, but this is hard to obtain and maintain, which risks biased AI outcomes.
Additionally, ethical AI introduces cybersecurity risks that need mitigation to protect data and systems.
GenAI in the disruption stage
GenAI is a disruptive tool with the highest impact on sociocultural environments, as well as in workplace environments. For example, Microsoft’s Copilot is an “everyday AI companion” that has made 88% of developers who use GitHub Copilot more productive, according to Microsoft, and 74% say they can focus on more satisfying work now. This represents a key driver for GenAI adoption: employee productivity.
“Generative AI is set to have a profound impact on numerous industries,” says Madhumit Dixit, EVP for Central Engineering and Technology. “It will augment core processes within these sectors by leveraging AI models, as well as influence supporting processes that span across organizations, affecting areas such as marketing, design, corporate communications, training and software engineering.”
GenAI can create creative content at scale and reduce the need for extensive manual work and through automation, while saving time and operational costs. It also creates business value through customized experiences by analyzing data to provide personalized recommendations and support in natural, human-like voices. GenAI automates complex processes, optimizes workflows, increases productivity and provides data-driven insights to help organizations be more efficient. But the true impact of its use cases are still uncertain. How far will GenAI take things like productivity? The industry will know more in 2024 as more organizations adopt the technology to drive transformative change.
Among the key drivers for adoption of GenAI includes how it empowers organizations to stand out by creating unique content and innovative solutions; increases customer loyalty, satisfaction and higher engagement rates through personalized GenAI content and GenAI’s support of rapid experimentation, product development and idea exploration.
Through responsible implementation, AI has the potential to drive change across numerous industries and empower businesses to thrive in a rapidly shifting landscape. By embracing these disruptions, organizations can create new business models and achieve limitless opportunities. We will continue to see innovation and adoption of AI technologies across industries and powerful new AI technologies, such as GenAI, ethical AI and machine learning will enable businesses and organizations to overcome the challenges facing their industries. Additionally, AI’s democratization will enable it to reach more employees, becoming a tool with expanded possibilities and resulting in businesses being even better prepared for an unpredictable future.
