The critical period to showcase the significance of relational AI is likely to be in 2024. While some individuals might perceive conceptual AI as a recent advancement, the reality is that it was initially utilized in AI back in the 1960s. However, the introduction of generative adversarial networks (GANs) in 2014, a type of machine learning algorithm capable of generating precise images, videos, and audio, marked a significant acceleration in this technology, sparking considerable interest.
The surge in interest surrounding generative AI, particularly among major technology firms towards the end of last year, coupled with the current frenzy in the consulting market, has led to the bursting of the artificial intelligence (AI) bubble. Consulting firms that had not prioritized AI in their service portfolios are now scrambling to do so.
Deloitte recently released an insightful research report on relational AI, incorporating comprehensive market research and educational content. What sets this report apart is its application of psychological sentiment analysis to gauge respondents’ perceptions of conceptual AI, with Eureka emerging as a prominent aspect worth exploring.
Deloitte’s identification of key application areas for generative AI aligns with McKinsey’s previous research, which highlighted cybersecurity as a top investment opportunity. This recognition is crucial given the necessity for advancements like Troj to mitigate risks associated with deep fakes and AI data manipulation. Relational AI has shown promising value in various domains such as sales, customer service, product development, and supply chain management.
The study also underscores the ongoing concerns regarding talent, governance, and risk management, with a significant portion of leaders expressing varying levels of preparedness to address these issues in the context of generative AI adoption. Organizational readiness to tackle talent-related challenges stands at 22% for highly or very prepared entities, contrasting with 41% reporting only slight or no readiness. Similarly, governance and risk readiness levels reveal a similar pattern, with 25% highly prepared and 41% minimally prepared.
Embracing AI for exploration, experimentation, and fostering maturity is imperative for organizations. Drawing from over 15 years of experience in developing AI software solutions across diverse industries, it is evident that sustained support is paramount for AI systems. Like nurturing a child, the performance of AI models heavily relies on the quality of information and the nurturing environment. Despite the common practice of discontinuing AI models, it is crucial for leaders to sustain and nurture them effectively.
The quest for AI return on investment (ROI) prompts reflection on whether we have entered the disillusionment phase as per Gartner’s model. Success in significant transformation initiatives hinges on executive sponsorship, leadership, and the enhancement of comprehensive capabilities. In cases where skill gaps are apparent, partnering with established talent becomes essential. Recent instances, such as Purolator outsourcing AI Analytics to Deloitte and Canada Post transferring its Innovapost (IT function) to the same company, highlight the expanding trend of AI outsourcing.
In conclusion, while the Deloitte report offers valuable insights, it falls short in addressing the persistent challenges related to data integrity, data wrangling complexities, and inefficiencies that pervade client practices. Establishing robust data foundations is imperative to fully leverage the potential of generative AI and enhance its practical applications.
