Generative AI Deployment Strategies

Generative AI is arguably one of the most loved technologies today. [1] What started as a mere buzzword has become a basic necessity for any organization looking to increase efficiency, scale productivity, and generally improve their technology game.

The question is no longer if organizations will adopt generative AI, but rather how and when they will do it. There is however one problem that most organizations might have to deal with along the way. Implementing enterprise-wide generative AI solutions takes more than a huge investment and willpower – it requires a carefully planned approach that ensures data privacy, minimizes bias, and upholds ethical standards.

This article will explore generative AI deployment strategies in their entirety, including some of the most effective strategies, key considerations, and future trends.

Introduction to generative AI deployment strategies

A recent Salesforce State of IT Report revealed that 86% of IT stakeholders expect generative AI to play a huge role in their organizations. [2] As is the characteristic with most early-stage, high-stake markets, organizations are pouring a lot of resources into the development of cutting-edge generative AI tools.

However, as they grapple with the vast array of deployment choices to use, they must also appreciate that each option comes with a unique set of tradeoffs.

Essentially, while a particular option may favor the organization in one way or another, say for example, saving costs, it may mean less control over enterprise data, which may not work for organizations dealing with sensitive data.

Read more: Generative AI Strategy Is a Must-Have: How to Build It

GenAI deployment options can range anywhere from pre-packaged and wholly customizable to a mixture of both. Some of the main technologies employed in GenAI deployment include:

Out-of-the-box generative AI tools

Out-of-the-box generative AI tools and solutions come as pre-packaged solutions that don’t require further customization. In most cases, this includes both the application and the GenAI model that powers it. A good example of this is OpenAI’s ChatGPT.

Custom-built app on an out-of-the-box deployment model

This type of application is a step up from the out-of-the-box approach. The application sits on top of an out-of-the-box GenAI model but undergoes further customization to increase its reliability and effectiveness in specific use cases. A good example of this is Jasper[3].

The content creation and marketing generative AI tool utilizes OpenAI’s GPT models like GPT-3 and later versions like GPT-4 by integrating its proprietary features and AI tools to enhance the base models’ proficiency.

Custom-built app on a fine-tuned model

As the name suggests, this type of application sits on top of an in-house developed model fine-tuned for specific use cases. Some of the biggest GenAI platforms like OpenAI and Hugging Face allow developers to utilize various open-source GenAI models to create customized models adept at each use case.

Read more: AI Development: In-house vs. Outsourcing

Custom-built app on a model built in-house

This is the top choice for organizations looking to secure their private data. Custom-built applications like these typically sit on models that have been developed in-house from the ground up and trained on a mix of private and publicly available data. This makes them especially popular for organizations that handle sensitive data, such as financial and healthcare organizations.

Key considerations for effective deployment strategies

As organizations consider integrating generative AI tools into their workflows, they must understand the factors that ensure successful implementation.

Here are a few tips to get you started:

Focus on data governance and security

Like with most digital technologies, the first place to start when developing and deploying generative AI is laying a foundation for trust in its design, its function, and how its outputs are used. While focusing on effective data governance alone is good enough, the current AI landscape demands that organizations secure their models to prevent misuse and exfiltration of personalized information. [4]

In most cases, this often means operating on a private platform where you have complete control over what data the model has access to and the people authorized to use it. This approach should also be accompanied by strict guardrails and governance dictated by responsible AI practices. [5]

Implementing generative AI-specific usage and governance policies could help manage potential risks associated with the generative AI capabilities embedded in customer relationship management (CRM), enterprise resource planning (ERP), and other enterprise applications.

That said, securing the GenAI system isn’t just limited to the system itself. You should also look outwards and employ strict governance policies regarding access to the network infrastructure and organizational security policies. You could also consider third-party risk management and ongoing monitoring to ensure your governance policies remain intact.

Read more: Building an AI-Ready Infrastructure: Key Considerations and Strategies

Create an effective gen AI strategy that prioritizes a quick ROI

Generative AI, regardless of the industry it is applied in, has numerous usage scenarios. These impressive capabilities may be too overwhelming to manage if you apply different strategies to each use case. Therefore, instead of applying a sole governance approach in your deployment strategy, you should consider applying strategies that can scale and deliver ROI across the organization.

For instance, GenAI has impressive deep retrieval capabilities where it helps organizations derive actionable insights from unstructured data. When used in a single area, this capability would most likely deliver modest value. Conversely, when rolled out on every function and line of business, the ROI could be spectacular.

For greater ROI, you could consider focusing on core business processes. You may find that the greatest impact lies in specific areas that, if properly leveraged, could enhance business operations across the board.

Commit to specific high-value use scenarios

The ideal generative AI strategy should be based on important business processes, and organizational readiness. [6] When done right, a use case of moderate value could have greater ROI than a greater value, one-off use case in the long run.

Since GenAI models are pre-trained, it should be easy to get your use cases up and running within 90 days of deployment. However, to increase the likelihood of success, you may need a core GenAI team comprised of technological specialists, analysts, and business leaders. The reasoning behind this is pretty simple – your business leaders know what the organization needs. As such, they are better able to identify specific needs and collaborate with IT specialists and analysts to identify the relevant data needed to customize GenAI models for greater efficiency and effectiveness.

Set up an AI factory for fast, repeatable, and verifiable results

Once your use cases are up and running, your core team can identify any issues, allowing you to close gaps in technology, data, and skills. At this point, it is also easy to identify successes that you can further replicate with an AI factory.

A GenAI factory comprises a series of pods; each focused on a specific domain or line of business. Each pod also combines technology and business expertise for greater value delivery.
An ideal generative AI factory’s pod should contain data scientists, data engineers, business analysts, and two GenAI-specific roles – a model mechanic to oversee and customize the model’s interworkings and a prompt engineer to refine the model’s output.

Transform your workforce to grow ROI

Now that your GenAI factory is up and running, you should shift your focus to integrating more enterprise data into the model and using it to transform processes. Essentially, your goal here is to ensure your entire workforce benefits from the model by making it an integral part of major processes and transforming how your workforce utilizes it to make their work faster and easier.

To achieve this, you need to ensure an organization-wide user experience to make it easier for the workforce to utilize the model both directly and as an embedded capability within enterprise applications.

Continuously monitor the model’s cost, performance, and alignment with business objectives

Effective GenAI monitoring efforts should start with a responsible AI framework that covers strategy (for the board and CEO), controls (for chief compliance and risk officers and other individuals responsible for the implementation of controls responding to identified risks), core practices (for the AI factory including business analysts and data scientists), and responsible practices (for chief information security officers).

The approach should also include comparisons with historical datasets for easier output validation, periodic audits by data scientists and domain specialists, and collaboration with other players with the right objective perspective to ensure the organization’s objectives are being achieved.

Future trends in generative AI and deployment strategies

Moving forward, expect to see generative AI trends focused on three main pools – rapidly evolving technological advances, fast digital transformations, and increased emphasis on the societal and global impact of AI.

Some of the most notable trends to look forward to include:

Growth in multimodality

As consumers familiarize themselves with GenAI capabilities, they are demanding more advanced models that can accept inputs in different formats and generate outputs in multiple modalities. OpenAI’s GPT-5, Google’s Gemini, and some other models have adhered to this demand. In time, multimodal GenAI models will likely seize a unique selling point and become a general consumer expectation for generative AI tools.

Wider adaptation of AI-as-a-Service

AI as a service has already gained widespread popularity in AI and ML business use cases. However, it is only just taking off for GenAI. As the widespread adaptation of GenAI intensifies, businesses with limited resources to build and deploy their own models will likely turn to managed service firms and consultants that specialize in GenAI.

Eventually, AI modeling as a service (AIMaaS) will significantly grow in market share as more companies turn towards working with lightweight, customizable, open-source models to reach new audiences.

Significant workforce disruption and reformation

GenAI is poised to radically change the workplace and workforce. The workforce is likely to experience the first changes as they leverage GenAI capabilities to support and automate mundane and routine tasks. Eventually, this trend will see enterprise-wide adoption, leading to significant disruptions. Some professions will gain more appeal while others may become irrelevant altogether.

Final thoughts

Generative AI has unmatched potential to revolutionize the workplace. Besides the cosmetic content generation capabilities, GenAI can also help in analytics and automation. However, to realize these benefits, organizations must first formulate effective generative AI deployment strategies that not only support organizational objectives but also align with ethical, compliance, and responsible AI practices.

References

[1] Scienccedirect.com, The sudden disruptive rise of generative artificial intelligence? An evaluation of their impact on higher education and the global workplace
https://www.sciencedirect.com/science/article/pii/S2199853124000726, Accessed on August 16, 2024
[2] Salesforce.com, Trends in IT, https://www.salesforce.com/news/stories/trends-in-IT/, Accessed on August 16, 2024, Bigid.com, What is AI Governance,
[3]Jasper. ai, Jasper Free Trial, https://www.jasper.ai/free-trial?_from=ads&fp_sid=1-g-CjwKCAjw8fu1BhBsEiwAwDrsjINYNtX6Ztzz9qP_e-lNGgOs05uKicuIiS96NqllnQ2aZfbuvtjEwxoCZKcQAvD_BwE&gad_source=1, Accessed on August 16, 2024
[4] https://bigid.com/blog/what-is-ai-governance/#:~:text=AI%20governance%20guidelines%20can%20foster,developing%20and%20deploying%20these%20technologies. , Accessed on August
[5]Google. ai, Responsible AI Practices, https://ai.google/responsibility/responsible-ai-practices/,Accessed on August 16, 2024
[6] Analytics8.com, Five Pillars of Effective Generative AI Strategy, https://www.analytics8.com/blog/five-pillars-of-an-effecitve-generative-ai-strategy/ ,Accessed on August 16, 2024