Preparing for AI: The Complete Guide

Prepare your organization for the AI revolution

The Current State of Generative AI

Generative AI has emerged as the hottest trend in both commercial and public sector enterprises as AI has the potential to revolutionize how businesses and government agencies operate. Salesforce’s latest State of IT report found that 86% of IT leaders expect generative AI to play a prominent role at their organizations soon. The company’s March survey of 500 IT decision-makers revealed that most (57%) believe generative AI is a “game changer.”

  • 67% of IT leaders surveyed said they have prioritized generative AI for their business within the next 18 months.
  • 33% said it was a top priority.
Business Challenges of Generative AI

Benefits of Generative AI

From content creation and design synthesis to problem-solving and decision-making, the transformative potential of generative AI is reshaping the way businesses operate. Generative AI models, like those designed for natural language processing or image generation, can produce high-quality, diverse outputs from textual content to digital images. This capability opens vast opportunities for innovation in areas such as marketing, product design, customer service and content creation. In the public sector, it enables more efficient processing of information, enhances public engagement through tailored communication and can assist in complex problem-solving tasks that would otherwise require extensive human labor.

For both the public and private sectors, there are two benefits that remain paramount.

Reduce Costs

Additionally, and of great importance to the c-suite, generative AI has the ability to drive efficiency and cost-effectiveness. By automating creative processes and data analysis, AI systems reduce the time and resources traditionally required to produce new content or insights. For instance, businesses can automate the generation of marketing materials, product descriptions and even code, freeing up human resources for more strategic tasks. In the public sector, generative AI can be used for efficient policy modeling, data-driven decision-making and automating routine administrative tasks, all leading to better resource allocation and cost savings. This efficiency not only accelerates project timelines but also allows for scaling operations in a way that would be impractical with human-only teams.
Cloud Data

Tailored Outputs

The adaptability and learning capability of generative AI make it a valuable asset for both commercial and public enterprises. These AI models can be trained on specific datasets to generate outputs that are tailored to the unique needs and goals of an organization. For businesses, this means the ability to create highly customized and targeted content or products, enhancing customer engagement and satisfaction. In the public sector, generative AI can adapt to various challenges, from predictive modeling for urban planning to personalized communication strategies for public awareness campaigns.
Business Challenges of AI

Business Challenges of Generative AI

Commercial and public sector enterprises often find themselves unprepared to develop use cases and optimize technology to leverage the capabilities of generative AI due to several key challenges.
Organizational Change Management: Governance Implementation

Lack of Trained Experts

To begin, there’s a significant gap in technical expertise and understanding. Generative AI is a complex and rapidly evolving field, requiring specialized knowledge in machine learning, data science and AI ethics. Many organizations lack the in-house expertise needed to develop, deploy and manage generative AI systems effectively. This expertise gap makes it challenging for them to understand the potential applications of generative AI fully, let alone integrate them into their existing processes and systems.
Business Agility: Deep Expertise

No Data Modernization

Data poses another critical challenge for many businesses. Generative AI requires large volumes of high-quality, diverse data for training and operation. Many enterprises struggle with data collection, management and processing, hindering their ability to feed appropriate and sufficient data into AI models. Issues like data privacy, ethical considerations in data sourcing and the need for domain-specific data further complicate this scenario. Without the right data, it’s difficult to train effective generative AI models or develop relevant use cases.
Your Data Checklist

Preparing for AI: Your Data Checklist

Integrating generative AI into an enterprise’s tech stack is a complex process that involves several key components including data infrastructure, governance and automation. As your organization prepares for AI, there are key elements to address:
Cloud Data

Invest in Robust Data Infrastructure

The backbone of any generative AI system is its data infrastructure. This encompasses the storage, processing and management of large datasets that AI models use for training and operation. Enterprises must ensure their data infrastructure is robust, scalable and capable of handling the high volume and velocity of data typical in AI applications. This includes investing in high-performance computing resources, cloud storage solutions and data warehousing technologies that can support the intensive workload of generative AI models.
Cloud Data

Ensure Data Quality and Management

Alongside infrastructure, the quality of data is paramount. Generative AI models are only as good as the data they’re trained on. Enterprises need to establish processes for continuous data cleaning, validation and augmentation to ensure the data feeding into AI models is accurate, diverse and representative of real-world scenarios. This involves implementing data management tools and practices that can handle complex data types, including unstructured data like images, text and audio.
Cloud Data

Establish Ethical Data Acquisition

In the era of increasing data privacy concerns, how and where data is sourced is a critical aspect of setting up generative AI. Enterprises need to adhere to ethical guidelines and legal standards for data collection, ensuring that the data used is obtained with consent and is compliant with regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). This requires a framework for ethical data acquisition and regular audits to ensure compliance.
Cloud Data

Adhere to AI Governance and Compliance

Effective governance is crucial in managing the risks associated with generative AI. Enterprises must develop a governance framework that includes policies and procedures for data usage, AI model training and output monitoring. This framework should align with industry standards and regulatory requirements, ensuring that the AI systems are transparent, accountable and fair.
Cloud Data

Implement Advanced Security and Privacy Measures

As generative AI systems often deal with sensitive data, ensuring the security and privacy of this data is non-negotiable. This involves implementing advanced cybersecurity measures like encryption, access control and network security. Additionally, AI-specific security concerns, such as model stealing and data poisoning, must be addressed through techniques like federated learning and differential privacy.
Cloud Data

Consider Scalability and Integration

The generative AI solution must be scalable and easily integrable with existing tech stacks. This involves adopting microservices architecture, containerization technologies like Docker and orchestration tools like Kubernetes. These technologies allow AI applications to scale efficiently and integrate seamlessly with other enterprise systems, facilitating a more agile and responsive IT environment.
Cloud Data

Optimize Model Training and Development

The development of generative AI models requires a robust set of tools and platforms for training and testing. This includes the use of machine learning frameworks like TensorFlow or PyTorch and the deployment of automated machine learning (AutoML) solutions to optimize model development. Continuous integration and delivery (CI/CD) pipelines should be established for the iterative development and deployment of AI models.
Organizational Change Management Frameworks

Enhance Automation and Workflows

Integrating generative AI into business workflows demands a high level of automation. Automation tools are needed to streamline the process of data ingestion, model training and deployment. Additionally, AI-driven automation can be used to optimize business processes, enabling more efficient decision-making and operational efficiency.
Organizational Change Management Frameworks

Implement Ongoing Monitoring and Maintenance

Ongoing monitoring and maintenance are critical for the long-term success of generative AI applications. This includes the continuous monitoring of AI models for performance, accuracy and bias, as well as regular updates and retraining of models with new data. Enterprises need to deploy monitoring tools that can provide real-time insights into AI operations and facilitate proactive maintenance.
Preparing Your People for Generative AI

Preparing for AI: Your People

AI capability requires skilled data expertise from humans. The successful implementation of generative AI requires a team with the right skills and expertise. This includes data scientists, AI/ML engineers, data engineers and domain experts. Enterprises should invest in training and development programs to build these capabilities internally or partner with external experts who can train and build in tandem.

Cloud Data

Understanding of AI and Machine Learning Principles

Proficiency in AI and machine learning principles is critical. This includes understanding various algorithms, neural networks and data processing techniques. Experienced professionals are more likely to be aware of the latest advancements in the field and understand the nuances of different AI models, leading to more efficient and effective solutions.
Cloud Data

Data Handling Skills

Managing the vast amounts of data required for training generative AI models is a complex task that requires specialized skills. Experienced data scientists and engineers are better equipped to ensure data quality, handle data preprocessing, and manage large datasets, which are essential for the success of generative AI.
Cloud Data

System Integration Knowledge

Integrating AI into existing enterprise systems can be challenging and requires a deep understanding of both the AI technology and the existing IT infrastructure. Experienced professionals can more effectively navigate these challenges, ensuring seamless integration and minimal disruption to existing processes.
Cloud Data

Understanding Ethical and Legal Implications

As AI technology evolves, so do the ethical considerations surrounding its use. Seasoned professionals are more likely to be familiar with the ethical and legal implications of AI, helping to navigate these complex issues effectively.
Cloud Data

Adaptability and Problem-Solving Skills

AI development often involves unforeseen challenges and the need for creative problem-solving. Experienced professionals are typically better equipped to adapt to new challenges and find innovative solutions.
Cloud Experts

Effective Resource Management and Team Leadership

Developing AI requires significant resources, including time, personnel and computing power. Experienced leaders can more effectively manage these resources, ensuring projects are completed efficiently and within budget.
Preparing for AI with Consulting Services

Preparing for AI with Consulting Services

Navigating the complexities of Generative AI requires a strategic approach. In a rapidly evolving landscape, third-party consulting services can serve as vital partners for companies preparing to embark on this transformative journey. Consulting services can help organizations usher into the future of AI with tailored expertise, meticulous training, risk mitigation and adept Change Management.
Business Agility Maturity

Expert Technologists

The specialized knowledge held by third-party consultants proves invaluable. Consultants bring expertise to efficiently curate and optimize datasets that require a nuanced understanding of diverse data types, model architectures and industry-specific applications. Their in-depth knowledge ensures that models are finely tuned for specific industry applications, mitigating the risk of suboptimal performance. Third-party consultants possess the requisite skills to tailor solutions to your unique business needs, ensuring that the integration of Generative AI is not only seamless but also aligned with strategic business goals.
Risk Management Process

Mitigate Risk

Consultants play a pivotal role in Risk Management, offering insights into potential challenges and implementing strategies to safeguard against unforeseen issues. Beyond technical proficiency, consultants offer an objective perspective, identifying unique challenges and tailoring solutions that align with your business objectives. This collaborative approach not only accelerates the integration of Generative AI but also minimizes risks and maximizes the return on investment.
Business Agility Product Management

Change Management

When preparing your organization for AI, the importance of Change Management cannot be overstated. Third-party consultants guide organizations through the necessary cultural and operational shifts that enable a seamless integration of Generative AI into existing workflows. Consultants facilitate a smooth transition by guiding organizational adjustments, ensuring that teams are equipped to leverage the transformative power of Generative AI.
xScion Data Services for AI

Build a Robust Foundation for Generative AI Success with xScion

As strategic allies in business, xScion’s mission is to help organizations Turn Change Into Value®. Our experts can help your organization prepare for the world of Generative AI by enhancing your organization’s technical capabilities, mitigating risk and providing a holistic approach to change across the entire enterprise. We enable your teams to focus on core business objectives while leveraging dedicated skills from our experts to extract the maximum value from your data. By entrusting your Generative AI preparation process to our seasoned technologists, your organization can tap into a reservoir of expertise designed to help facilitate a successful and future-ready adoption of Generative AI.

Organizational Change Management: Vision and Strategy Development

Deep Expertise

Our subject matter experts bring  deep expertise in Data Infrastructure and Data Governance, and keep up with emerging trends to ensure our customers’ AI strategy is efficient, cost-effective, compliant and secure.
Business Agility Maturity

Train to Sustain™ Learning

Our knowledge transfer and Dojo training lets your team learn, test and create AI tools while receiving hands-on training from our experts throughout the project ensuring lasting success.
Organizational Change Management Frameworks

Organizational Change Management

Our team helps align AI initiatives with your strategic business goals, ensuring your organization as a whole is prepared for and receptive to the benefits that AI can bring.

Ready to Turn Change Into Value®?

Our team of experts are ready to help.