Quick summary: Businesses can no longer avoid the pressure to operationalize generative AI in ways that produce bottom-line results. Three foundational elements—a robust cloud architecture, Agile transformation, and a product management approach—can help ensure a successful implementation of GenAI-powered solutions and set them up for success in delivering ROI.
As we roll into 2024 and look ahead at the trends that will advance digital transformation this year, we can probably all agree that generative AI will continue to dominate the conversation.
It’s been just over a year since ChatGPT burst onto the scene to disrupt disciplines ranging from event planning to code writing. Over the course of last year, businesses took the opportunity to “kick the tires” and take this new technology out for a spin in rigidly controlled internal use cases. In 2024, organizations will be under pressure to step out of the experimental sandbox and begin operationalizing GenAI to deliver bottom-line results—while judiciously avoiding the technology’s well-documented risks.
Fortunately, we’ve been down this road before. Generative AI is the latest in a long line of business/technical disruptors that gave us mobile technology, online search, and even the internet itself. Today businesses can apply the lessons learned from previous technology revolutions in managing this dynamic development—once they have the proper foundation in place.
In this article, we’ll explore three fundamental prerequisites—cloud maturity, Agile transformation, and product management approaches—that organizations must master for the effective operationalization of GenAI, paving the way for a new era of innovation and efficiency in the digital workplace.
Fortunately, we've been here before: Generative AI is the latest in a long line of business/technical disruptors that gave us mobile technology, online search, and even the internet itself.
Cloud maturity: the cornerstone for unleashing generative AI
The global cloud AI market is expected to grow at a compound annual growth rate of 40 percent between 2023 and 2030. Because generative AI solutions require massive calls on compute, storage, and networking, a mature and robust cloud computing infrastructure plays a pivotal role as the catalyst for innovation and efficiency.
Scalability and dynamic resource management
Generative AI models—especially large ones like GPT-3.5, GPT-4, and GPT-4 Turbo—require extensive computational resources. A robust cloud architecture provides the scalability organizations need to allocate resources dynamically based on the needs of various GenAI workloads. The business can ensure efficient utilization of resources while also avoiding performance-draining bottlenecks during high-demand periods.
Training data storage
GenAI models require large datasets for training; fortunately, when working with large language models (LLMs) and foundational models, most of the training is done for us. With a mature cloud infrastructure, organizations have a scalable, secure environment for storing enormous amounts of data in a way that ensures accessibility and reliability. The ability to manage and organize training datasets effectively facilitates training and deployment of generative AI models.
Parallel processing and distributed computing
Training complex generative AI models involves performing various computations simultaneously. A robust cloud architecture supports parallel processing and distributed computing, enabling faster model training via interconnected processing units. This capability is crucial for minimizing model training times and improving overall efficiency.
On-demand resources and cost efficiency
Cloud platforms offer on-demand provisioning of resources, allowing organizations to scale infrastructures based on their immediate requirements. Different GenAI solutions may have varying workloads at different times, and a mature cloud architecture allows organizations to optimize costs by adjusting resources in real time.
Integration with AI services and tools
Cloud providers offer a wide range of services and tools that can complement GenAI solutions, including pre-built machine learning models, data processing tools, and deployment solutions. One innovative service that is gaining traction is retrieval augmented generation (RAG), which involves customizing LLMs with the company’s proprietary databases and knowledge bases, enabling outputs that are deeply aligned with the business’ unique operational context. Leveraging these pre-built, integrated services can streamline the development and deployment of generative AI applications, reducing the time and effort required for implementation.
Agile transformation: a critical gear in the GenAI engine
The iterative and collaborative nature of Agile practices aligns seamlessly with the dynamic development cycles inherent in generative AI projects. This synergy ensures that organizations can adapt quickly to changing requirements, iterate on models efficiently, and accelerate deployment of GenAI solutions.
Iterative development and rapid prototyping
Agile methodologies’ emphasis on iterative development and rapid prototyping align well with the exploratory nature of generative AI projects. By segmenting the development process into short iterations, teams can quickly test and refine different aspects of GenAI models. This iterative approach facilitates continuous improvement and helps teams adapt to evolving requirements and insights.
Adaptability to changing requirements
As stakeholders deepen their understanding of GenAI’s capabilities and potential use cases, requirements can change. Agile methodologies enable teams to adapt to fluid requirements by fostering close collaboration between developers, data scientists, and end users. This flexibility is crucial for accommodating adjustments in model objectives, data sources, or performance criteria throughout the development lifecycle.
Collaboration across disciplines
Successful GenAI solutions require collaboration among various groups, including data scientists, machine learning engineers, domain experts, and end users. Given their emphasis on cross-functional teamwork and regular communication, agile methodologies help ensure that the diverse expertise needed for generative AI projects is woven into the development process.
Frequent feedback loops
Agile promotes the establishment of frequent feedback loops, allowing stakeholders to regularly review and provide feedback on the GenAI solution’s progress. Continuous feedback is crucial for refining GenAI model outputs to improve accuracy and align the solution with user expectations. Rapid feedback loops help identify issues early in the development process, reducing the risk of delivering a solution that doesn’t meet user needs or business objectives.
Agile methodologies prioritize risk mitigation through incremental development and continuous testing. For generative AI, where uncertainties may arise because of model complexity or questionable data quality, this approach is key. Regularly assessing and addressing risks ensures potential issues can be resolved early in the development cycle.
Product management: maximizing GenAI’s potential
Adopting a product management approach in developing generative AI solutions offers a strategic advantage rooted in versatility and efficiency. Some GenAI use cases require the highest-performing foundational models, while others can get the job done with simpler, lower-cost options. Viewing GenAI initiatives through a product management lens facilitates the matching of solutions to specific needs to optimize the balance of performance and cost.
A product management approach also imparts the adaptability that is crucial for navigating the varied landscape of generative AI applications. Businesses position themselves not only to keep pace with the dynamic GenAI landscape, but also to stay ahead of the curve by strategically aligning their applications with the optimal foundational models for diverse use cases.
Generative AI solutions benefit from product management’s user-centric design philosophy. Product managers can work closely with stakeholders to identify and prioritize features that align with user requirements, ensuring that the GenAI solution provides tangible value and addresses real-world problems.
Market research and competitive analysis
Product management requires conducting thorough market research and competitive analysis to position a product effectively. In the context of generative AI, understanding the market landscape is crucial for differentiation and success. Product managers can analyze how similar solutions are being used, identify gaps in the market, and position their GenAI-powered product strategically for maximum impact.
Product lifecycle management
Treating a generative AI solution as a product requires comprehensive lifecycle management. Product managers can oversee not only the development phase, but also the deployment, maintenance, and eventual upgrade or retirement of the solution. They can thus ensure that the generative AI model remains relevant, meets evolving user needs, and is supported throughout its lifecycle, preventing obsolescence and maximizing its long-term value.
Business strategy alignment
Product managers play a crucial role in aligning their GenAI solution with overall business strategy, continuously assessing how the product contributes to organizational goals, revenue generation, and market positioning. This strategic alignment helps them prioritize development efforts, allocate resources efficiently, and ensure that the generative AI solution remains aligned with broader business objectives and priorities.
Monetization and ROI optimization
For organizations looking to derive value from generative AI solutions, product managers can explore monetization strategies and optimize ROI. This involves defining pricing models, identifying revenue streams, and evaluating the economic impact of the generative AI product. By taking a product management approach, organizations can ensure that their investment in generative AI translates into tangible business value and sustainable financial returns.
Sample use case: a new lease on life for virtual assistants
To cite one use case as an example, businesses are leveraging GenAI to breathe new life into virtual agents (VAs), also known as “chatbots.” These self-service customer tools are certainly nothing new, and their popularity among businesses and consumers has waxed and waned over the years. The large language models (LLMs) that give GenAI solutions their conversational capabilities have opened up new possibilities for successfully serving customers without the need for intervention by human agents.
Tapping into these heightened capabilities requires each of the foundational elements we explored above:
- A mature cloud infrastructure can handle the massive amount of data required for the VA to respond to customer inquiries accurately while also ensuring the speed necessary for a positive experience.
- Businesses that have undergone a successful Agile transformation will have the flexibility needed to continuously adapt the VA to changing customer needs, additional sources of data, and evolving technologies.
- Treating the VA as a product enables the team to remain focused on end users and to ensure continuous alignment with business strategies.
Maximizing the possibilities of GenAI—present and future
As digital transformation advances in 2024, generative AI remains in the spotlight as businesses transition from experimental exploration to operationalization for bottom-line impact. Three pivotal prerequisites—cloud maturity, Agile transformation, and product management approaches—serve as the foundational pillars for navigating the dynamic landscape of generative AI. These strategic elements collectively pave the way for organizations not only to harness the transformative power of GenAI, but also to foster a new era of innovation and efficiency in the digital workplace.
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Lionel Bodin is the Senior Director of Digital Transformation at Logic20/20. He manages highly complex, multi-faceted digital programs related to CRM systems, cloud and on-prem implementations, big data, and more.