How to Accelerate your Generative AI Pilot with Azure AI

Generative AI can help organizations tap into the value of their in-house data with a layer of task intelligence. At a fundamental level, it can consume raw data such as CRM records or internal documents to automate low level cognitive tasks.

Think of generating an email reply based on an ongoing conversation, in a few seconds. Or building a new presentation referencing company reports with a simple prompt.  

Such intelligence, when applied at scale, can raise the collective efficiency of a business process. Organizations can save precious manhours on repetitive tasks along with improving the quality of outcomes. We have observed three common themes in which generative AI can add value.

Knowledge work refers to tasks that involve thinking, analyzing and problem-solving. It’s the kind of work where professionals use their expertise, creativity and judgment.

Professionals can utilize generative AI tools to channel their expertise towards more complex tasks, leaving simpler things to be handled by the system.

Consider the following knowledge work tasks:

1. Build a marketing strategy: Highly complex and requires critical thinking
2. Write a white paper: Employs language skills and creative thought process 
3. Schedule a meeting: Can be done without deep thinking  

While the first two tasks are best suited to professionals, they needn’t do everything on their own.

They can prompt a generative system to compare competitors for the strategy. And come up with the right words for the white paper as they brainstorm ideas. This accelerates their pace while stimulating creativity along the way.

The third task can be fully automated based on a fixed procedure.

The result? The organization achieves greater productivity and performance as each professional achieves more.

Great customer experience depends on the quality of interactions a customer has with your business. But as customer volumes grow and expectations rise, this becomes harder to achieve.

Generative AI systems, with their human-like text and voice generation capabilities, can help organizations live up to their customer experience promise. A generative AI chatbot can better understand the nuances of a customer question and provide rich answers, rather than just referring to FAQs.

Going further, this capability can be delivered via voice, email, chat and video – delighting customers on their chosen channel of communication. The AI system applied to digital experiences, such as an e-commerce website, can interpret customer behaviour to facilitate personalized recommendations. The same personalization can also be applied to communication, service support and buying preferences.

Many customer service support software are already using generative AI to assist customer service agents with real-time call inference and summarization. This can help agents resolve concerns faster and more often.

Most organizations have internal processes that are considered “too complex to automate.” However, not automating these processes can put companies at a competitive disadvantage.

With generative AI, researchers have found ways to automate parts of a complex process while ensuring organizational safety.

For instance, consider the process of reviewing legal documents. It has largely been a labour-intensive and time-consuming task where legal professionals meticulously pore over pages of text, searching for critical clauses, discrepancies and potential risks.

An AI system trained on the methodologies of a law firm can learn to swiftly extract relevant information from lengthy documents, identifying key terms and provisions. It can spot ambiguous language, hidden caveats and grasp legal terms at a much faster speed.

In theory, by automating reviews, law firms can scale their operations and make their services more affordable for their clients. The same automation can be replicated to an organization’s specific process needs.

The promises of generative AI are well-understood, but the question is, how can your organization make them real? Like any other technology, enterprise adoption of generative AI isn’t a simple decision. It requires several infrastructure, safety and human considerations before it becomes viable.

Here, we discuss the key facets of generative AI transformation from the lens of IT decision-makers.

Introducing a new generative AI tool in your workspace may present the following challenges:

Understanding the risks

A generative AI tool promises innovation, but it also introduces risks such as ethical concerns, biases and unintended consequences. These risks affect adoption – companies want to ensure they invest in AI systems that are risk averse and have proven safety frameworks. It’s critical to ensure transparency and accountability in AI models before adopting a new system.

Data complexity

Most generative AI systems thrive on data but understanding and managing it remain challenging. Decision-makers need to invest in robust data governance, quality assurance and data literacy programs. A clear understanding of data lineage, privacy and security is vital for successful gen AI adoption.

Scaling from pilots to production

Launching generative AI pilots is relatively straightforward; scaling them to create meaningful value is where the real challenge lies. Tech leaders should focus on rewiring their organizations for distributed digital and AI innovation. This involves building cross-functional teams, upskilling employees and aligning incentives. Additionally, selecting the right technology stack, including large language models (LLMs) and cloud providers is critical for success.

There are two broad paths to generative AI adoption: build your own solution with greater control or selectively infuse AI capabilities into your current systems.

Building an in-house generative AI solution allows organizations to tailor it precisely to their unique needs. Whether it’s generating personalized content, automating creative tasks or enhancing product recommendations, a custom solution can align seamlessly with the organization’s domain expertise.

Organizations retain full control over their data when creating an in-house solution. This control is crucial for industries with strict privacy regulations (such as healthcare or finance). Custom solutions allow data to stay within organizational boundaries.

However, developing a custom solution demands specialized skills. Organizations must invest in hiring or upskilling data scientists, machine learning engineers and domain experts. Additionally, maintaining and evolving the solution requires ongoing resources.

The second alternative is to bring third-party AI capabilities into your current systems without reinventing the wheel. Training AI models from scratch requires specialized expertise and time. Pre-built solutions can help bridge this gap. They allow organizations to leverage state-of-the-art algorithms without extensive development cycles.

AI modules, such as natural language processing (NLP), computer vision or recommendation engines, offer immediate value. By integrating them into existing systems, organizations can automate repetitive tasks, streamline workflows and reduce manual effort.

These solutions, however, must be scrutinized for their claims. It’s inevitable that the solution adheres to industry standards (such as PIPEDA). Vendors’ security practices and data handling must be verified and AI modules must be checked to see if they align with your organization’s policies and regulatory requirements.

As you kickstart your generative AI journey, you need AI talent, a robust compute platform and innovative generative AI adoption models. These three core components would be deeply instrumental in paving the way for generative AI success, whether you build your own solution, integrate an API or create a middle ground.

Microsoft offers a roster of services and products to meet these overarching needs. Their trusted cloud platform with easy-to-deploy AI services can help accelerate your generative AI adoption. Here are three top reasons why organizations may want to try Microsoft for generative AI innovation.

Microsoft’s suite of AI services provides a quick start advantage for organizations, allowing them to leapfrog the initial development stages. Here’s how:

• Azure AI as a Service: Leverage a set of AI capabilities, including vision, language, speech, decision and integration with OpenAI. These pre-built services are easier to get started with across a variety of use cases such as chatbots, image analysis or text insights. Azure AI provides ready-made tools that accelerate development.
• Text and Search: Use Azure AI search for content generation, summarization, code generation and semantic search powered by retrieval augmented generation (RAG).
• AI Studio: Access a collaborative environment where data scientists, developers and business analysts can work together. By streamlining the development process, AI Studio enables organizations to focus on solving real-world problems rather than grappling with technical complexities.

Scalability is essential for organizations aiming to deploy AI solutions across their entire ecosystem. Microsoft’s Azure AI infrastructure serves as the bedrock for scaling generative AI solutions.

With the global expansion of Azure OpenAI Service, businesses gain access to OpenAI’s models, including GPT-4 and GPT-3.5-Turbo, across multiple regions. This availability empowers organizations worldwide with reliable generative AI capabilities.

Additionally, Microsoft has also introduced AI-optimized compute offerings as part of the Azure platform. 4K GPU clusters and H100 virtual machines are designed to handle AI workloads with greater performance capabilities at scale.

Microsoft generative AI services are guided by six principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency and accountability. These principles offer a pathway to build AI solutions that remove harmful elements such as toxicity, bias and inaccuracy from generative AI.

The company has introduced tools like its Human-AI Experience Workbook and AI Fairness Checklist to operationalize the core principles and support responsible AI development. By incorporating these tools, organizations can overcome some of the known challenges of generative AI systems and eliminate adoption pitfalls to include responsible design throughout their AI investments.

CDW is an award-winning Microsoft partner with a long history of facilitating business transformation. With generative AI, we’re continuing the legacy with the same trust for our customers.

We provide a comprehensive IT strategy with Azure as its foundation, offering systems integration and managed services. As an Azure Expert MSP, we have a pool of technical experts and world-class architects to ensure high-quality implementations.

We also offer guided tours of Azure OpenAI services, helping organizations understand and apply generative AI technologies effectively.

As you tackle AI innovation, CDW can accompany you in the journey to provide stability, reliability and a nimble development cycle for application modernization and overall IT strategy enhancement.