Future of generative AI

 This is the future of generative AI, according to generative AI

According to 2023 research, most people are concerned about the implications of generative AI on data security, ethics and bias. In fact, 81% of customers want a human to be in the loop, reviewing and validating generative AI outputs. A mere 37% of customers trust AI's outputs to be as accurate as those of an employee. The research found that the trust gap widens as AI goes mainstream. Brands are turning to generative AI to boost efficiency while improving customer engagement. Customers, wary of the technology risks, demand a thoughtful approach built on trust. 80 % of customers say it's important for humans to validate AI's outputs.

Navigating the future of Generative AI

Generative Artificial Intelligence (AI) has emerged as a powerful force, reshaping the technological landscape with its ability to create content autonomously. From language models like GPT-3 to image-generating algorithms, generative AI holds immense promise for the future. However, this promising future is not without its challenges. Here we will explore the potential negative outcomes and the most exciting possibilities of generative AI, aiming to strike a balance between optimism and caution. 

Lessons in ethical AI

Recently, the AI community witnessed a significant leadership shift at OpenAI involving Sam Altman. The CEO, known for his influential role in steering OpenAI's initiatives, faced a period of controversy surrounding his firing and subsequent re-hiring. This incident underscored the challenges associated with ethical considerations in AI development and management. Addressing ethical concerns: Sam Altman's leadership shift prompted a re-evaluation of ethical considerations in AI development and organizational decision-making. The incident raised questions about transparency, accountability, and the need for robust ethical frameworks to guide the development and deployment of AI technologies.

Transparency and accountability: The leadership transition emphasized the importance of transparency in organizational decision-making, especially in contexts where AI technologies with widespread implications are involved. It highlighted the necessity of holding leaders and organizations accountable for their actions and ensuring that ethical guidelines are followed. 

Community involvement: The controversy surrounding Sam Altman's leadership shift also brought to light the significance of involving the wider community in decisions related to AI development. The call for more inclusive decision-making processes gained momentum, reinforcing the idea that diverse perspectives are crucial in navigating the ethical challenges associated with AI technologies.

Demystifying AI could significantly reduce the fear surrounding it. If we can move AI from an opaque black box to a transparent glass cube, we can recalibrate how we adopt the technology. A strong argument can be made that every AI foundational model must have a FICO score. The latest State of IT 2023 Report by Sales force, a survey of 4,300 IT decision-makers and leaders, found that 9 out of 10 CIOs believe generative AI has gone mainstream. The report found that AI and automation underpin efficiency and innovation. Process automation is on the rise as businesses tighten their belts and seek efficiency boosts, while advances in AI prompt IT to determine how to responsibly propel their organizations forward. 86% of IT leaders believe generative AI will have a prominent role in their organizations in the near future. 

McKinsey's latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases analysed by McKinsey -- by comparison, the UK's entire GDP in 2021 was $3.1 trillion. According to McKinsey, 50% of organizations used AI in 2022. IDC is forecasting global AI spending to increase a staggering 26.9% in 2023 alone. A recent survey of customer service professionals found adoption of AI had risen by 88% between 2020 and 2022.  McKinsey's latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases. Generative AI will revolutionize the way we work. 

AI is the electricity of the 21st century. Ignore it and your business will be left in the dark. After all, we already know many ways that generative AI will shape how we work.  Research on generative AI's impact on the future of work reveals that AI has the potential to automate 40% of the average workday. Productivity gains from generative AI in marketing sees marketers saving one month a year. A survey suggests AI has the potential to automate 40% of the average work day. The widespread use of generative artificial intelligence has raised public awareness of its ability to increase productivity and efficiency, as well as its risks. 

AI and automation propel efficiency and innovation.

So, what are the largest and most influential technology analyst firms saying about the impact of generative AI on the future of work and the enterprise? According to Gartner, generative AI will make an increasingly strong impact on enterprises over the next five years. Gartner predictions are given below:-

By 2024, 40% of enterprise applications will have embedded conversational AI, up from less than 5% in 2020.

By 2025, 30% of enterprises will have implemented an AI-augmented development and testing strategy, up from 5% in 2021.

By 2026, generative design AI will automate 60% of the design effort for new websites and mobile apps. 

By 2026, over 100 million humans will engage robocolleagues to contribute to their work.

By 2027, nearly 15% of new applications will be automatically generated by AI without a human in the loop, which is not happening at all today.

More than 55% of all data analysis by deep neural networks will occur at the point of capture in an edge system by 2025, up from less than 10% in 2021.

The primary focus 

Generative AI is positioned on the Peak of Inflated Expectations on the Gartner Hype Cycle for Emerging Technologies, 2023. Here are Gartner's top 10 strategic predictions:- 

By 2027, the productivity value of AI will be recognized as a primary economic indicator of national power.

By 2027, Gen AI tools will be used to explain legacy business applications and create appropriate replacements, reducing modernization costs by 70%.

By 2028, enterprise spending on battling malinformation will surpass $30 billion, cannibalizing 10% of marketing and cyber security budgets to combat a multiform threat.

By 2027, 45% of chief information security officers (CISOs) will expand their remit beyond cyber security, due to increasing regulatory pressure and attack surface expansion.

By 2028, the rate of unionization among knowledge workers will increase by 1,000%, motivated by the adoption of Gen AI.

In 2026, 30% of workers will leverage digital charisma filters to achieve previously unattainable advances in their careers.

By 2027, 25% of Fortune 500 companies will actively recruit neurodivergent talent across conditions like autism, ADHD, and dyslexia to improve business performance.

By 2028, there will be more smart robots than frontline workers in manufacturing, retail, and logistics due to labor shortages.

By 2026, 50% of G20 members will experience monthly electricity rationing, turning energy-aware operations into either a competitive advantage or a major failure risk.

By 2026, generative AI will significantly alter 70% of the design and development effort for new web applications and mobile apps.

Generative AI is positioned on the Peak of Inflated Expectations on the Gartner, Inc. Hype Cycle for Emerging Technologies, 2023.

IDC believes that the tech industry is at a seminal moment. Never have we seen a technology emerge with this much executive support, clearly defined business outcomes, and rapid adoption.  IDC has identified three broad types of generative AI use cases that need to be assessed that are industry specific, business function and productivity-related. 

The path to impact

IDC notes that the landscape of business may be seeing a seismic shift with the rise of generative AI. IDC advises business leaders to start with the following solid foundation: 

Responsible AI policy: A well-defined AI policy that outlines principles of fairness, transparency, accountability, and data protection is paramount.

AI strategy and roadmap and the role of the proof of concept: The AI strategy should include the rules or guidelines for generative AI proofs of concept (POCs), and it should incorporate the results of the POCs to recursively improve the strategy.

Intelligent architecture: Data privacy, security, and intellectual property protection must also be embedded within this platform architecture.

Reskilling and training: Most organizations do not have mature skill sets (prompt engineering, data science, data analysis, AI ethics, modelling) required to take full advantage of generative AI. 

Revolutionizing Healthcare

The future of generative AI promises breakthroughs in healthcare, from drug discovery to personalized medicine. AI models can analyse vast datasets, identify patterns and propose novel solutions, significantly accelerating the pace of medical research and improving patient outcomes.

Innovations in Art and Entertainment

Generative AI is already making waves in the art world, creating unique pieces that challenge traditional notions of creativity. In the entertainment industry, AI-driven content creation can open new avenues for storytelling, virtual worlds, and interactive experiences, pushing the boundaries of what is possible in these fields.

Human-AI Collaboration

Instead of replacing human roles, generative AI is likely to enhance collaboration between humans and machines. AI tools can serve as creative partners, aiding professionals in various fields to achieve outcomes that wouldn't be possible with traditional methods alone. This collaborative approach can lead to unprecedented levels of innovation.

Enhanced Creativity and Productivity

Generative AI has the potential to amplify human creativity and productivity by automating mundane tasks, allowing individuals to focus on more complex and strategic aspects of their work. In fields like content creation, design and marketing, AI can assist and inspire, leading to a surge in innovation and efficiency.

Customized User Experiences

As generative AI continues to evolve, it can provide highly personalized and tailored experiences for users. From adaptive learning platforms to content recommendations, AI systems can understand individual preferences and behaviour's, offering a more seamless and engaging user experience.

Constellation Research believes generative AI will supercharge the future of work. The research notes that many work tasks will benefit from between a 1.3x to 5x gain in speed alone. Constellation research advice on the adoption of generative AI in the digital workplace is to have the following:

Clear AI guidelines and policies

Education and training

AI governance structures

Oversight and monitoring

Collaboration and feedback

Create clear ethical guidelines

Conduct ethical impact assessments

Monitor for AI bias

Provide transparency

Ensure compliance with regulations

Constellation also provides a list of the leading generative AI enterprise grade solutions. Constellation also breaks down the impact of generative AI by industry. For the education industry, Constellation notes that Generative AI, which appears to be initially loved by students and loathed by educators, is coming to education as its embedded in courseware as well as learning-management systems. The overall forecasts are optimistic, but there are cautionary notes about guardrails for AI. 

Expected negative Results

Ethical concerns and bias

One of the primary concerns with generative AI lies in its susceptibility to biases present in training data. If the data used to train these models reflects societal biases, the AI may inadvertently perpetuate and amplify those biases in its generated content. Addressing this issue is crucial to prevent AI systems from unintentionally reinforcing and spreading harmful stereotypes.

Security threats 

The ability of generative AI to produce highly realistic and convincing content raises serious security concerns. Deep fakes are AI-generated images or videos which manipulate and superimpose content onto real footage. This technology can be exploited for malicious purposes, such as creating fake news, impersonating individuals or spreading misinformation. As generative AI becomes more sophisticated, the challenge of distinguishing between real and fake content becomes increasingly difficult.

Privacy invasion

The advancements in generative AI also raise concerns about privacy invasion. The ability to generate realistic images and videos of individuals who never participated in such content creation poses a risk to personal privacy. Protecting individuals from the unauthorized use of their likeness in AI-generated content will be a pressing issue in the future.

Unemployment and Economic Disruption

The automation capabilities of generative AI may lead to workforce displacement in certain industries. Jobs that involve routine and repetitive tasks, such as content creation, could be at risk. Striking a balance between technological advancement and societal well-being will be a challenge to ensure that AI complements human labour rather than replaces it.

Moral and Ethical Dilemmas

As AI systems become more proficient in generating content that mimics human creativity, ethical questions arise. For instance, who owns the rights to AI-generated art or literature? Determining the legal and moral implications of creations produced by non-human entities challenges our conventional understanding of authorship and intellectual property.

IDC also notes that data serves as the foundation for generative AI. When IDC surveyed clients about their data, troubling results were revealed.

82% of organizations report siloed data 

41% cite that data is changing faster than they can keep up with 

24% do not trust their data 

29% have issues with data quality 

Conclusion

The future of generative AI is a double-edged sword, offering both exciting possibilities and potential pitfalls. As we navigate this technological landscape, it is crucial to acknowledge and address the ethical concerns and negative outcomes associated with generative AI. Striking a balance between innovation and responsible development is imperative to harness the full potential of AI while mitigating its risks.

By 2025, according to IDC, organizations will allocate over 40% of their core IT spend to AI-related initiatives, leading to a double-digit increase in the rate of product and process innovations. AI will radically reshape IT, according to IDC. Code generation, enterprise content management, marketing, and customer experience applications are some of the key areas for generative AI use cases in the enterprise. IDC forecasts enterprise spending on GenAI services, software and infrastructure will grow from $16 billion in 2023 to $143 billion in 2027. Spending on generative AI over the four-year period to 2027 is expected to reach a compound annual growth rate (CAGR) of 73.3%.

By fostering interdisciplinary collaboration, implementing ethical guidelines, and investing in research to address biases and security threats, we can pave the way for a future where generative AI enhances human creativity, productivity and well-being. The recent leadership shift serves as a reminder of the importance of ethical considerations in AI development, urging the industry to learn from past mistakes and collectively shape a future where AI benefits everyone around the world.