The Future Is AI: Predictions for Generative AI with a Fun Twist
Experts at Avena believe the rise of generative AI models is just the appetizer before the main course. Considering its immense potential, what new horizons could be opened up if these GenAI models became skilled, reliable partners in every field?
Generative AI is at the heart of the global tech fiesta, transforming almost all major industries. From content creation to software development to research, AI is unstoppable. From multi-modal AI to domain-specific models, it’s clear that AI has gone beyond its “buzz phase” and is now poised to be an integral part of our lives, leading us into an era of end-to-end intelligent automation.
Salesforce’s recent survey suggests that over 67% of IT leaders have already implemented generative AI in their business processes.
However, we’re yet to discover the true potential of this groundbreaking technology. Industry pundits say it’s just a matter of time. In this blog, Avena’s industry experts make predictions about the transformative role generative AI could play in the years ahead.
Top Generative AI Trends That Will Shape The Future
1. Augmenting Human Capabilities One of GenAI’s most profound impacts lies in its ability to supercharge human potential and efficiency. Instead of handling tedious tasks yourself, you can delegate them to GenAI, enabling you to get things done faster and direct your extra effort and time toward tasks requiring higher creativity, reasoning, and emotional intelligence, where humans outperform AI.
Avena has helped numerous healthcare professionals achieve their healthtech transformation ambitions. Here’s what they had to say about whether AI can augment the capabilities of medical professionals:
“AI is still quite underequipped to help doctors improve healthcare outcomes. Still, it can enable them to leverage valuable medical insights, visualize patient data, and simplify the patient journey and experience. That being said, the final decision always resides with the on-site medical professionals.”
Similarly, designers can take inspiration from AI-generated designs. AI can improve and streamline the brainstorming process and provide designers with comprehensive oversight of intended functionality, aesthetics, and brand values.
There are countless use cases where GenAI can augment your capabilities and help you achieve better results quickly. It all depends on how you choose to leverage it.
This trend is only expected to grow as we see AI labs globally pumping millions of dollars into their AI models and enhancing their capabilities. Technology experts also believe that in the near future, users will have their own personalized AI assistants with similar cognitive abilities, personality traits, and emotional quotients for more personalized AI experiences.
2. Streamlined Research Generative AI can potentially create a ripple effect in the research and development (R&D) industry.
Models can rapidly analyze massive datasets, uncover hidden patterns, and generate hypotheses humans would likely overlook. GenAI-powered research then sets the stage for experimental testing, enabling scientists and researchers to validate their findings.
As computational power and data availability grow exponentially, AI systems will seamlessly integrate into scientific workflows and streamline R&D cycles to unlock faster breakthroughs. These breakthroughs will translate into life-saving medicines, sustainable materials, cleaner energy, and impactful inventions that improve the quality of life globally.
3. Reimagined Robotic Process Automation (RPA) AI’s role in robotic process automation (RPA) is already well-known. It enables more intelligent, natural responses and enhances the overall functionality of physical robots. But GenAI can reimagine RPA and usher us into a new era of robotics where robots will become more collaborative, dextrous, safe, adaptive, and flexible over the coming decade.
With generative intelligence, we’ll see robots automate tasks that would’ve required human intervention and creativity in the past.
Augmented with computer vision, voice recognition, image processing, language comprehension, and decision-making capabilities, robots will be able to operate semi-autonomously across dynamic real-world environments, from factories to construction sites. Some sample use cases may include:
Additionally, data continually captured from embedded sensors provides rich feedback to refine GenAI modeling, enabling robots to learn complex new skills faster. The fusion of GenAI with advanced RPA promises to reshape workflows across many industries.
4. Energy Optimization Gartner suggests that by 2028, nearly a third of companies globally will optimize GenAI specifically for energy efficiency, driven by rising sustainability concerns. Currently, though, most implementations remain highly energy intensive.
Most GenAI models, like GPT, require exceptional computational power to facilitate their intensive model training processes. Considering how mainstream such models have gotten, they must ensure maximum availability and swift responses.
To achieve this, GPT utilizes a plethora of energy and resources for its training and development.
According to Alex de Vries, a Dutch Data Scientist who interviewed with The New Yorker, a single Google search with AI integrated into it consumes ten times more energy (3 KWh) than the traditional Google search.
This is counterintuitive for sustainability, and technology leaders must come up with measures to improve processing efficiency and optimize energy consumption.
Here are two key factors making AI energy consumption a pressing priority:
Leading enterprises globally, including Nokia, Google, Meta, Amazon, Microsoft, and IBM, have already started taking concrete measures to realize Green AI transformation. Here are some of the strategies business leaders are implementing:
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While individual measures each contribute incremental gains, collectively, they can minimize GenAI’s environmental impact without hindering functionality.
5. Multi-Modal Generative AI Currently, most GenAI models specialize in a single modality, such as text, image, audio, video, or code generation. However, Avena predicts that the future points toward consolidating multiple modalities within singular GenAI platforms for various use cases, including marketing automation. Companies like OpenAI, Google, and Anthropic are already accelerating toward this goal.
For instance, leading AI models like ChatGPT-4, Claude 3.5, and Gemini 1.5 now accept both text and image inputs. However, the quality of their responses is still under development. To make multi-modal GenAI more mainstream, more robust integration of modalities at the architecture level is required, which is the future of GenAI.
Mr. Zia believes that the groundwork for such multi-modal capabilities already exists today across different GenAI models. Unifying them will enable remarkably human-like generative AI to transform productivity software, metaverse experiences, accessibility tools, and more over the coming years.
6. Domain-Specific Generative AI Models Gartner claims that by 2027, over 50% of GenAI models will be specialized for specific functions or industries rather than used for general purposes. For instance, you may have a personalized AI assistant that suggests outfits for different occasions based on your current wardrobe.
Now, two pivotal factors will drive this shift:
The advantages of domain-specific GenAI models include:
This paves the way for wide adoption beyond pioneering tech firms toward traditional enterprises across most industries. Rather than reinventing the wheel, businesses should first evaluate tapping into readily available domain-specific models that could be fine-tuned to meet internal needs. This provides a faster and lower risk pathway compared to developing fully custom solutions.
7. Synthetic Data Generation By 2026, over 75% of companies are expected to harness GenAI to create synthetic data to enhance training datasets. Two core reasons behind the steep growth trajectory predicted by Gartner are:
Enterprises can leverage synthetic data for the following use cases:
Synthetic data unlocks innovation possibilities beyond relying solely on visible, real-world information. Business leaders should educate themselves on synthetic data capabilities to recognize promising use cases.
Ready to Lead the Change in the Age of Generative AI? Claiming that GenAI will make the world a better place would be unrealistic. However, it has the potential to streamline and catalyze your workflows and daily tasks, driving efficiency across all fronts. With AI handling repetitive tasks and processes, humans will have more time to dedicate to tasks requiring emotional intelligence, creativity, problem-solving, decision-making, and relationship-building.
While the prospects are exciting, we must also consider AI’s massive carbon footprint and develop methods to optimize it for better energy efficiency for a tech-forward and sustainable future.
Avena’s approach to AI-first digital transformation is unique and impact-driven. It helps businesses drive actionable intelligent automation across their processes, augment their digital products, and enable significant financial gains while ensuring environmental sustainability.
Contact our seasoned AI development experts to learn more about our GenAI initiatives and how we can leverage this technology to modernize your business.
Frequently Asked Questions
Will generative AI replace programmers? GenAI won’t fully replace programmers. It can guide you through the basic syntax, explain codes, highlight errors, and suggest changes, but the overall development process requires the creative logic-building capabilities of programmers.
What is the future scope of generative AI? The scope of GenAI is virtually endless with use cases across numerous industries and sectors, including automating information tasks, optimizing systems, and amplifying human creativity.
Will generative AI replace designers? Instead of replacing them, GenAI will collaborate and rapidly iterate creative processes for designers, who can then curate the best creations and add their own artistic touch if required. By outsourcing repetitive jobs, designers can focus on high-value personalization.
What is the main purpose of generative AI? The core purpose of GenAI is to embed responsive intelligence throughout information systems, augmenting human capabilities and ease of use rather than replacing people entirely. When responsively implemented, GenAI can positively influence and improve lives.