Breaking Barriers -
Solving the AI Adoption Challenges in Oracle CX World

1. Main Problems Affecting AI Adoption in Oracle Cloud & its Solutions

Despite Oracle’s leading cost-effective AI capabilities, adoption remains low across many enterprises. I’ve seen firsthand the barriers organizations face when implementing AI-driven solutions. I want to explore the key challenges hindering AI adoption & provide actionable solutions to accelerate its adoption here.

#Problem1 - Lack of Awareness & Clear Use Cases

Organizations often fail to see the tangible benefits of AI in Oracle CX Cloud due to a lack of understanding & guidance from their consulting partners. Without clear use cases tailored to their business needs, CX leaders hesitate to invest in AI-driven transformation.

Solution: The internet is flooded with AI-related content, but having the right partner is key to crafting a successful AI strategy. At SoftClouds, we prioritize innovation & have empowered numerous customers across industries & Oracle products with our AI accelerators. Contact us to explore how we can help drive your AI journey forward.

#Problem2 - Hallucination & Trust Issues

AI models sometimes generate incorrect or misleading information, a phenomenon known as AI hallucination. This can lead to poor customer interactions, incorrect insights & a lack of trust in AI-driven decisions. Additionally, AI robustness issues arise when models fail to adapt to dynamic business conditions or handle edge cases effectively.

Solution: We have many innovative approaches to improve Hallucination. I am documenting a few below:

#1 - Prompt Engineering - Various Prompt engineering techniques may help in better responses. Below are a few examples:

• Provide clear & specific instructions
• Using constraints & format guidelines
• Asking for Step-by-Step reasonings
• Encouraging AI to acknowledge uncertainty
• Parameters tuning
• Leveraging Few-shot learning
• Embed AI implementation with verification process

All the above have capacity to transform the answers accuracy. For example, if we review opensource Llama model documentation released by Meta (Ref: 2302.13971 ), it provides below statistics when we use few-shots learning:

Above statistics example suggests how these small techniques may bring a huge change in model accuracy.

#2 - Choose Right Model - Choosing the right model or AI service is the first key decision. We have a growing array of powerful LLM providers for model selection, each with distinct strengths: Llama from Meta provides open-source flexibility & rapid iteration, Cohere delivers enterprise-grade solutions tailored for business needs, GPT LLMs from OpenAI offer cutting-edge general-purpose capabilities, Claude emphasizes safety & ethical considerations, Mistral provides competitive performance & efficiency & now, we see emerging contenders like Deepseek, Grok, etc.

There are continuous rapid updates from multiple providers. I would suggest looking at internet to see latest evolving models ( List of large language models - Wikipedia). Based on business use-cases, we must also opt for small or specific language models rather than LLMs.

#3 - Model Fine-Tuning - Model fine-tuning is one of the most used approaches now-a-days. It’s a form of transfer learning where a pre-trained model is adapted to perform a specific task by training it on a new dataset.

#4 - RAG (Retrieval- Augmented Generation) Implementation - This is another most sought option to incorporate domain & customer specific knowledge in the model. Oracle provides it via Oracle Gen AI Agent offering. Oracle's Generative AI Agent is a fully managed service that combines the power of large language models (LLMs) with retrieval-augmented generation (RAG) to provide contextually relevant answers by querying enterprise knowledge bases. Oracle currently provides below 3 knowledgebases to choose from:

• Object Storage
• Open Search
• Oracle23ai

#5 - Incorporate Human-In-The-Loop Concept
All the above approaches have many technical parameters & methods. There are many other methods to make response more grounded with verifiable sources of information. I believe we have reached at a juncture where hallucination can really be minimized for most of the use cases along with upcoming sophisticated AI models.

#Problem3 - High Cost & Uncertain ROI

AI implementation involves additional licensing costs, infrastructure investments & the need for skilled resources. Many businesses struggle to justify these expenses, especially when the return on investment (ROI) is not immediately clear.

Solution: Most of Oracle’s embedded features are free of cost to customers. OCI also provides many cost-effective options that can be used based on the need. As per Microsoft’s IDC report, Gen AI is delivering substantial returns, estimated at 3.7 times the investment per dollar sent (Link Ref: Generative AI delivering substantial ROI to businesses integrating the technology across operations: Microsoft-sponsored IDC report – Middle East & Africa News Center).

#Problem4 - Data Privacy Issues

Recently, I came across a very interesting post on Linkedin (Ref: LinkedIn Post by Ravi Surana) – “ChatGPT knows me better than my wife does - maybe even better than I do!”

I also enquired ChatGPT with the below same prompt as mentioned by Ravi: "Based on all our conversations so far, tell me all you know about me. Don’t be shy. Tell me all the good, bad & ugly you noticed about me that you think I probably haven’t noticed."

I was surprised with the answer. Just go ahead & try yourself!

Above exercise also raises questions about data privacy & it’s no surprise that many companies & departments ban GPTs for their employee’s use. Additionally, Enterprise AI implementation faces significant challenges due to data privacy concerns. Businesses must navigate stringent regulations like GDPR & CCPA while ensuring customer data is securely processed & stored.

Solution: Oracle offers enterprise-grade AI with dedicated AI clusters, providing customers with isolated environments for sensitive data. Oracle AI is architected for security & tested in critical sectors, including government, finance & nuclear energy.

Oracle allows for greater control over data access & usage policies. Private subnets & encryption are employed to protect data in transit.

#Problem5 - Resistance to Change & Cultural Barriers

AI adoption requires a shift in mindset. Sales, marketing & customer service teams may resist AI-driven processes due to fears of job displacement or a lack of trust in machine-generated insights.

Solution: AI is a helper or enabler rather than a competitor. It will help in increasing revenue; customer satisfaction & help company further expand. This will provide more job opportunities eventually.

2. AI Offerings from Oracle

I will touch upon various AI capabilities offered by Oracle. You may review AI | Oracle for more details. At a high level - Oracle provides the below three options:

#1 - AI Embedded Features - Oracle is providing multiple features embedded across Oracle applications for its seamless use. Oracle provides all these features free of cost for all its SaaS Cloud applications. Other products (like Siebel) also support embedded AI features but might require related OCI AI services subscriptions.

Since customers are already paying for subscriptions, it makes no sense to overlook the valuable embedded features that come included with relevant subscriptions. This will be a quick win & huge value add for all Oracle customers.

#2 - Oracle AI Agents - Oracle has launched more than 100+ AI Agents across all Fusion Applications.

In the agentic era, Oracle AI Agents are designed to automate complex CX workflows, leveraging generative AI & machine learning for intelligent decision-making. Redwood UI architecture is complementing conversational AI agentic behavior.

#3 - Comprehensive OCI AI Options - Oracle Cloud Infrastructure (OCI) AI services provide a comprehensive suite of enterprise ready AI capabilities, catering to various business needs. Here's a breakdown of key areas:

Core AI Services:

• OCI Generative AI
• OCI Generative AI Agent (RAG)
• OCI Digital Assistant
• OCI Language
• OCI Speech
• OCI Vision
• OCI Document Understanding:
• OCI Machine Learning
• OCI Infrastructure for hosting custom models
• Oracle23ai AI features

3. Promising Future of AI

The AI landscape is undergoing a profound transformation, with industry leaders increasingly sensing that the race towards Artificial General Intelligence (AGI) is rapidly intensifying. This isn't just a gradual evolution; it is a breathtaking acceleration. In the past year alone, we have witnessed multi-fold improvements across critical AI parameters, signaling a significant leap forward.

• IQ Level/Perplexity Score: We are seeing substantial advancements in the quality & complexity of AI outputs, indicating a move towards more nuanced & at times better than human-like understanding.
• Number of Parameters Processed: The sheer scale of AI models continues to expand exponentially, with the ability to process trillions of parameters becoming increasingly common. For simplicity’s sake, assume these parameters as brain power (loosely compared with ~86B human brain neurons, though human brain neurons are more advanced) for LLMs. This increased capacity allows for the capture of more intricate patterns & a deeper understanding of complex data.
• Processing Chips: The development of specialized AI chips is keeping pace with the growing demands of these large models, enabling faster & more efficient processing.

The emergence of innovations like Deepseek is further fueling this rapid progress, bringing forth novel thought processes & challenging established norms. Their contributions are pushing the boundaries of what's possible, accelerating the pace of innovation & bringing us closer to AGI.

My Thoughts

AI adoption in Oracle Cloud is not just a technology challenge – it is a combination of strategy, education & execution. By addressing integration complexities, improving data quality & fostering a culture of AI-driven innovation, businesses can unlock AI’s full potential to drive superior customer experiences. As solution consultants of SoftClouds, our role is to guide organizations like yours through this transformation, ensuring they extract meaningful value from AI-powered solutions.

At SoftClouds, our mission is to guide organizations through this journey, helping them navigate challenges, seize opportunities & extract real value from AI-powered solutions.

We would love to hear your thoughts! Feel free to share your feedback, insights, or questions – let us start a conversation. Reach out to us at info-at-softclouds-dot-com & let us explore how AI can drive meaningful transformation for your business.