Top 7 Predictions from Experts at Cubix for Generative AI

22 Jul, 2024

7 min read

Top 7 Predictions for Generative AI by Cubix

Generative AI is at the heart of the global technology revolution, transforming almost all major industries. From content creation to software development to research, there’s no stopping AI. From multi-modal AI to domain-specific models, it’s clear that AI, as a technology, has gone way beyond its “buzz phase.” It is poised to be an integral part of our lives and realize an end-to-end intelligent automation revolution.

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 this groundbreaking technology’s true potential. Industry pundits say it’s just a matter of time. In this blog, Cubix’s industry experts have made predictions regarding 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 elevate human potential and efficiency. Instead of handling tedious tasks on your own, you can delegate GenAI to handle all such hassles for you, enabling you to get things done faster and direct the extra effort and time toward tasks that require higher creativity, reasoning, and emotional intelligence, where humans excel in comparison to AI.

Zohaib Ali, a senior project manager at Cubix, has helped numerous healthcare professionals achieve their healthtech transformation ambitions. Here’s what he 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 only take inspiration from AI-generated designs. They may 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 are also of the view that in the near future, users will have their very own personalized AI assistants that will have 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.

Here’s what Shoaib Abdul Ghaffar, a senior AI engineer at Cubix, had to say about the prospects of GenAI:

“The better Generative AI gets, the further scientific progress will accelerate in tackling humanity’s greatest challenges, from disease to environmental threats.”

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 rather well-known at this point. 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 be able to 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:

Retail assistants answering shopper questions and managing inventory
Agricultural robots assessing crop health and optimizing growth
Warehouse robots efficiently locating items amidst clutter
Inspection drones monitoring infrastructure quality and hazards

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 its 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 join their heads and come up with measures to improve processing efficiency and optimize energy consumption.

Here are two key factors making AI energy consumption a pressing priority:

1. Massive Carbon Footprint: GenAI’s carbon footprints are already massive, and considering its adoption growth trajectory, demand will only increase. Making this technology environmentally sustainable will become an enormous challenge for technology enterprises.

2. Realizing Sustainable Innovation: In order to meet the emission reduction targets while staying competitive, tech leaders must find a way to balance GenAI innovation with environmental diligence across machine learning pipelines.

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:

Optimized model architecture and algorithms
Energy-efficient hardware provisioning
Renewable energy procurement
Carbon offset purchases
Workflow orchestration optimizations
Monitoring and reporting frameworks

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, Farhan Zia, Head of Marketing at Cubix, is of the view that the future points towards consolidating multiple modalities within singular GenAI platforms for various use cases, including marketing automation, and we’re already seeing companies like OpenAI, Google, and Anthropic accelerate toward this goal.

For instance, leading AI models like ChatGPT4o, Claude 3.5, and Gemini 1.5 now accept both text and image inputs. However, the quality of their responses is still under making. To make multi-modal GenAI more mainstream, more robust integration of modalities at the architecture level is required, which is, as we see, 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, there are two pivotal factors that will drive this shift:

1. Surging Market Demand: As more sectors recognize GenAI’s potential, companies desire tailored solutions that fit their specific use cases rather than one-size-fits-all tools.

2. Increased Availability of Domain-Specific Models: Soon, companies will have access to several open-source, domain-specific models to build upon instead of starting everything from the ground up.

The advantages of domain-specific GenAI models include:

Higher accuracy for niche applications.
Lower computation resource requirements.
Reduced hallucination risks.
This paves the way for wide adoption beyond pioneering tech firms towards 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. There are two core reasons behind the steep growth trajectory predicted by Gartner:

1. Synthetic Data: Generative AI models like GPT-4o, Claude 3.5 Sonnet, and Jasper AI can instantly generate vast volumes of synthetic text, audio, image, video, and graph data on their own.

2. Data Privacy: Companies can’t leverage real user data to ensure compliance with changing data privacy regulations like GDPR and CCPA, so they have to opt for synthetic data generation.

Enterprises can leverage synthetic data for the following use cases:

Simulate hypothetical market environments to stress test strategies
Rapid prototypes of new software, products, and business workflows
Model extreme events to build robustness
Anonymize real data to expand training data
Transfer learning to new use cases with limited real samples

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 quite unrealistic. However, it has the potential to streamline and catalyze your workflows and daily life tasks, driving efficiency across all forefronts. 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, to say the least, 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.

Cubix’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?

Well, 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.