Scaling Generative AI: Strategies for Success

Successfully scaling generative AI (GenAI) is not merely a technological challenge but a multifaceted endeavor encompassing organizational culture, talent management, and strategic decision-making.

Technology is undoubtedly a primary driver of successful scaling. Your business needs a robust and adaptable infrastructure to support the growing computational demands of Gen AI models.

But hardware and software alone won’t get you there. You must also establish streamlined data management processes that ensure the integrity, security, and accessibility of the vast amounts of data required to train and fine-tune GenAI models.

Equally important is cultivating a supportive organizational culture that embraces generative AI’s potential. You need to empower your teams to explore the boundaries of what is possible with these powerful technologies. Hiring and retaining people with the necessary skill sets is another success factor.

This white paper explores the critical success strategies you must consider when scaling your organization’s generative AI capabilities.

Chart a Course for AI Use

When navigating the shifting seas of generative AI, it’s best to start by charting a course. It seems like new advancements in GenAI happen every day as leaders in the field race compete with each other. Meanwhile, smaller companies find innovative new uses for the technology.

It’s a safe bet that your employees are already using GenAI in some form, whether it’s to generate code, create blog content, or take meeting notes. The initial response of many businesses—particularly those handling sensitive data—has been to block all access to AI tools in the name of risk mitigation. This isn’t the wisest course of action for two reasons: first, it limits innovation, and second, it puts your company and employees behind the learning curve as your competitors surge ahead.

Instead, form a risk mitigation team focused on creating a balanced plan for AI adoption that aligns with your business objectives and risk tolerance. Begin by evaluating how GenAI could transform your industry and business operations. As part of this exercise, identify specific use cases where AI could drive efficiency, innovation, or competitive advantage.

Once you know how GenAI meshes with your business strategy, figuring out policies and guidelines is much easier. It may make sense to provide open access to low-risk groups, like Marketing, but be more restrictive for groups that handle your most sensitive data, like accounting, sales, or HR. For the latter group, you could put safety measures in place, such as a warning that pops up before employees enter sensitive data into an ML model or use a public GenAI tool like ChatGPT.

Explore AI Opportunities

GenAI has turned many a business model on its head. Businesses leveraging the technology have realized significant gains in productivity and cut operating costs. After studying the available tools and products, you and your team should assemble a list of the areas within your organization that might benefit from generative AI. You should also note the areas that won’t be enhanced or might even be negatively impacted.

For example, a McKinsey report shows that GenAI has a high impact on marketing and sales functions across industries but a low impact on corporate IT and HR teams.

You’re likely to identify several areas and business functions within your organization that can benefit from GenAI—too many to deploy all at once. The next step, therefore, is prioritizing. Your technical leads (CIO, CTO, etc.) will need to assess feasibility and resources to determine where to focus GenAI implementations.

Part of this process is estimating the true costs and ROI of these AI initiatives. For this, tech leads may need to team up with Finance. Calculating the costs is tricky because there are many factors.

  • Multiple Model Costs: You may need to pay fees to use multiple AI models from different companies.
  • Model Interactions: You might need to combine outputs from multiple AI models for some queries. Each model could have its own usage fee.
  • Ongoing Usage Fees: There are recurring charges for using AI services.
  • Human Oversight Costs: You’ll need people to review and quality-check the AI outputs. This adds labor costs on an ongoing basis.

It may be worth engaging an AI consultant to make sure you account for all associated costs.

Review the Engineering Impact

Looking again at the McKinsey report, GenAI has the highest productivity impact on software engineering in the technology industry. Banking, pharma and medical devices, telecom, and others are also seeing significant productivity gains for the software development function.

GenAI has proven adept at accelerating software development. According to McKinsey, more software engineers leverage AI to develop, refactor, and document code 20 to 50 percent faster. GenAI also helps automate testing and allows QA teams to find those pesky edge cases more efficiently. It can even help pay down technical debt faster.

Scaling generative AI impacts IT operations, as well. GenAI frees up IT resources to handle more complex tasks by automating routine tasks. For example, GenAI can easily handle tasks such as:

  • Resetting passwords
  • Responding to status requests
  • Performing basic diagnostics
  • Accelerating issue triage and resolution through
  • Analyzing, identifying, and prioritizing events
  • Generating SOPs, incident postmortems, performance reports, and other documentation.

Engineering and IT are perhaps the most turbulent business functions impacted by AI, with new developments happening almost every day. You almost need a daily update to stay current (we like The Rundown).

Decide Where to Invest

Scaling generative AI capabilities doesn’t necessarily mean spending buckets of money. As with any software decision, you should first look for existing tools that meet your business needs and only invest in developing a proprietary GenAI solution if it delivers a significant competitive advantage.

There are essentially three levels of investment when it comes to GenAI. The least expensive involves connecting to open-source AI models through a chat interface or an API, with little or no customization. It’s a relatively fast, easy, and inexpensive way to leverage GenAI for certain business functions.

If you’re looking for greater customization, more proprietary capabilities, or higher security and compliance, you can integrate existing AI models with your internal data and systems.

Most often, businesses do this in one of two ways: by hosting the model on your business’s infrastructure (either on-premises or in the cloud) or by aggregating your data and deploying a copy of an AI model on a third-party cloud infrastructure.

Both approaches provide access to powerful AI models while addressing data privacy and security concerns. The choice between the two will depend on your organization’s specific workload requirements, data sensitivity, and existing infrastructure.

Finally, you can build your own AI model. This option is the most expensive and complex, requiring enormous volumes of data, deep AI expertise, and massive computing power. The cost is not small; you’ll likely spend several million dollars or even hundreds of millions to build and train the model. Few businesses have deep enough pockets or the technical expertise to undertake such a complex, massive project.

Upgrade Your Technology

Your organization will likely use several GenAI tools with different features and capabilities. To maximize your investment, these tools need to interact with each other and your existing systems or applications. This may require you to upgrade your technology architecture.

Technology upgrades are particularly important if you want to scale GenAI beyond simply integrating with open-source models. If your goal is to integrate generative AI models into your internal systems and applications, you may need to modernize or strengthen your technology architecture.

Recent improvements in software tools have made it much easier to connect powerful AI language models with other computer programs and data sources. This allows the AI models to access and use information from different places when responding to questions or requests.

As your team evaluates current and emerging AI integration capabilities, they will need to define standard practices. This includes setting rules for how the AI models interact with other systems, like specifying data formats and identifying which user or AI model is making requests. These consistent integration patterns will be important for successfully using AI across the organization.

5 Key Architecture Elements

Five key elements must be incorporated into your technology architecture to integrate Gen AI effectively.

  1. Context Management and Caching. AI models must be able to immediately retrieve relevant data so they can understand the context and generate quality output. They can then cache answers to frequently asked questions to enable faster responses.
  2. Policy Management. You must take measures to ensure AI models have appropriate access to your data assets to prevent the AI from generating outputs that contain sensitive data.
  3. A Model Hub. This centralized repository is where pre-trained AI models are hosted, shared, and made available for discovery and use by authorized individuals in your organization.
  4. A Prompt Library. This collection stores pre-written, optimized prompts that can be used as starting points or templates for generating content with GenAI systems.
  5. An MLOps Platform. A machine learning operations (MLOps) monitors the performance of the GenAI models your business deploys.

As your team works to improve the company’s technology infrastructure, they face a rapidly expanding market of generative AI tools and services. You may have to comb through a complex landscape of AI tools and services to find the right mix of GenAI tools for your business.

Leverage Your Data

Generating value from GenAI models relies on having a solid data architecture that connects the models to your internal data sources. Leveraging your data in this way provides the models with meaningful context and allows them to be fine-tuned, resulting in more relevant and valuable outputs.

To accomplish this, your technical leads will need to work closely together on the following key areas.

Organizing Data for AI Models

Your tech leads must categorize and organize the company’s data so GenAI models can use it. This involves developing a comprehensive data architecture that covers both structured and unstructured data sources.

Technology leaders must establish standards and guidelines to optimize data for use with AI models. This may involve augmenting training data with mock records to improve diversity, converting media files into standardized formats, adding metadata for traceability, and ensuring that all data is current.

Bolstering the Infrastructure

Existing IT infrastructure or cloud services must be able to support the storage and handling of the vast volumes of data required for GenAI applications. You may need to invest in additional storage and computing resources.

Building Data Pipelines

A critical step is developing data pipelines that connect generative AI models to relevant internal data sources that give the data context. This “contextual understanding” is needed for the models to generate meaningful and accurate outputs.

Establish a GenAI Team

If yours is one of the many businesses moving toward a product and platform operating model, it’s vital to integrate GenAI capabilities into this model. It allows you to build on your existing infrastructure and rapidly scale the adoption of GenAI across the organization.

The first step is setting up a dedicated GenAI platform team. This team’s focus is developing and maintaining a platform service where product and application teams can access approved AI models on demand.

The GenAI team also defines standards for integrating these AI models with your internal systems, applications, and tools. They develop standardized approaches to manage risks.

Staffing the Team

To be effective, the GenAI team needs to be staffed with people who have the right skills and expertise. This may include:

  • A senior technical leader to manage the team
  • Software engineers to integrate AI models into existing systems
  • Data engineers to build pipelines connecting models to data sources
  • Data scientists to select models and engineer prompts
  • MLOps engineers to deploy and monitor multiple models
  • ML engineers to fine-tune models with new data
  • Risk experts to manage security issues like data leakage, access controls, output accuracy, and bias

Depending on your specific use cases, you may not need all of these roles, or you may need to add others. For customer-facing applications like chatbots, for example, you may need to include product managers and UX/UI designers.

Your AI team should start with just the priority use cases. As they become more experienced and learn what works best, they can address less urgent use cases. Prioritization should be a collaborative effort between your tech leads and the heads of the various teams that want to leverage GenAI tools.

Match the Training to the User

Generative AI can significantly increase employee productivity and enhance their abilities. However, not everyone will experience the same gains—even if they’re in the same role.

One study showed that GenAI helped highly skilled software engineers write code 50 to 80 percent faster. Junior developers using the same tool were 7 to 10 percent slower. The reason is that AI-generated code needs to be reviewed, validated, and improved, which junior developers struggle with.

On the other hand, for less technical roles like customer service, GenAI significantly helps lower-skilled workers, increasing productivity and reducing employee churn.

GenAI will impact nearly every role in your company, which means almost every employee will need training. To prevent wasted training effort, focus on increasing people’s skills based on what they need for their role and proficiency level.

Reassess Risk Mitigation

Generative AI presents new ethical questions and risks that need to be addressed. These include:

  • AI “hallucinations”: AI bases its answers on probability, which sometimes leads to the delivery of incorrect responses because they have the highest likelihood of being right. This can lead to wildly inaccurate responses.
  • Data breaches: If permissions and controls are not set up correctly, AI might access and expose personal private information, proprietary company data, and other sensitive material you might not want the world to see. 
  • Inherent bias: Human bias in the large data sets the AI models are trained on can result in GenAI returning biased results.
  • IP theft: Many artists and others have claimed that GenAI tools have used their intellectual property without permission. This has led to the development of anti-AI tools like Nightshade and Glaze.

You will need to become knowledgeable about ethics, humanitarian issues, and compliance requirements. Not only will this keep your business on the right side of the law, but it will also prevent damage to the company’s reputation.

Addressing these new risks requires significantly reviewing cybersecurity practices and updating the software development process. Risks need to be evaluated and mitigated before model development even begins. This will reduce issues and avoid slowing down the process.

Proven ways to mitigate hallucinations include limiting the AI’s creativity in forming responses, providing more relevant data context, using libraries that restrict what can be generated, using separate AI models to check outputs, and adding disclaimers. Early use cases should focus on areas where errors have a low impact to allow learning from setbacks.

To protect data privacy, it will be critical to tag sensitive data, set up access controls for different data domains, add extra protection when data is used externally, and include privacy safeguards. For example, some organizations restrict access by role.

To mitigate intellectual property risks, insist that GenAI model providers maintain transparency about the data sources, licensing, and ownership rights of the data sets used.

Embrace the Future

Generative AI is here to stay and will only grow—and grow rapidly—in the future. Businesses that fail to leverage GenAI at scale will quickly fall behind their competitors.

If you’re struggling to find a footing in the shifting sands of GenAI, a custom AI software development company like Taazaa can help. We have the expertise to help you leverage GenAI so you can lead the competition rather than running to catch up

Learn more about our AI development capabilities, or contact us today.

Taazaa Inc

Taazaa Inc.

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