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In a digital era where innovation is the currency of success, businesses’ embrace of generative AI solutions is the market’s new gold rush.
From custom GPTs to Copilots, to fully custom large language model (LLM) solutions, these AI-driven technologies are no longer reserved for the tech elite, but instead are rapidly becoming essential tools for every knowledge worker.
However, the path from ideation to implementation, rollout, and scaling of generative AI technologies is not so straightforward. Because the technology is so new and the field is moving so fast, there is no standard path, set of best practices, or playbook for successfully developing a generative AI solution.
As such, it can be difficult to know where to start or what is even a stable platform to build on. Taken together, these challenges and uncertainties make it so that building generative AI tools for use in the business setting can feel like a modern digital odyssey — a journey that requires a roadmap.
Fortunately, the Fusion Team at Proactive Technology Management has developed just such a roadmap, one that we are eager to share.
In this post, we’ll discuss each stage of the LLM generative AI adoption journey in detail, including Exploration, Piloting and Implementation, Evaluation and Scaling, and Productization and Innovation.
Image Source: Austin Henley’s Blog
As you will discover by reading further, generative AI solutions often begin as ideas for how a particular business process can be accelerated with AI. The need to validate the feasibility of the use case leads to the development of personal productivity LLM tools, which are often hugely successful, but limited in impact by their personal nature, and also potentially at risk of leaking company secrets if using consumer-grade tools.
Faced with these challenges, businesses find that scaling these proof of concept solutions up safely for team- or company-wide use may best be attempted alongside a trusted technology partner to help keep development on track.
To this end, we’ll discuss how the Fusion Development team at Proactive Technology Management can effectively accelerate your organization’s generative AI adoption journey, ensuring you get the most out of these cutting-edge generative AI technologies at every step.
The genesis of any transformative AI solution begins with exploration. In the next section, we will discover generative AI’s potential to transform your workflows, using tools like private ChatGPTs and Azure AI Studio to craft proof of concepts that are as innovative as they are effective at addressing the unique challenges of your business.
The initial phase of any journey into generative AI begins with exploration. This is where we lay the foundation for successful implementations, venturing into the realm of what’s possible.
Here, imagination meets practicality as we ideate, discuss business cases, and match use cases with the value they can bring to your organization.
The potential applications for generative AI LLM technologies in the business setting are vast. Let’s examine several scenarios where generative AI can drive value for SMBs:
Each of these use cases has the potential to revolutionize how tasks are performed, freeing up your knowledge workers to focus on higher-level strategic initiatives.
When designing LLM systems for business use, it is best not to just talk about potential; to make a successful business case, you will need to prove the value the fully developed solution will provide. By employing technologies such as ChatGPT for Teams, utilizing platforms like the OpenAI Playground, and leveraging the Azure AI Studio, it is possible to build Proof of Concepts (PoCs) that not only demonstrate the capabilities of models like GPT-3.5 Turbo, GPT-4, and LLaMA 2 but also highlight the tangible benefits they can offer to your business.
Notably, Each of these platforms has an enterprise-grade commitment to data privacy and security, so it is safe to experiment with company data while proving out concepts using these platforms, as we have discussed previously here.
For more information about how to work with these platforms to elevate personal GPTs and copilots into enterprise-grade productivity tools fit for rollout company-wide, see our previous post, “Transform Custom GPTs and Copilots into Auditable, Enterprise-grade AI Applications. In this article, we describe how Custom GPTs and Copilots are invaluable in boosting personal productivity and proving out generative AI capabilities, and we further detail how their full potential is realized when they are transformed into tools that fit seamlessly within the enterprise infrastructure, addressing the key challenges of governance, security, integration, and scalability.
Once we have a platform to experiment on and a use case in mind, the journey continues with initial prompt engineering — where we create the first interaction points between your knowledge workers and AI. This stage is characterized by trial and error as we experiment with different phrasings and instructions to the LLM to coax it to produce exactly the type of content we need.
Here, we also begin to select information from your knowledge base that might be relevant to inject into the language model for retrieval-augmented generation tasks, allowing our custom tool to ground itself in specific data that is proprietary to your business.
Taken together, this stage is characterized by starting to shape the AI to understand and respond within the context of your business needs. The focus is on learning how best to communicate with and guide the AI to meet your specific business challenges.
Here, at this nascent stage, we are also setting the foundation for deeper integration. While access to APIs and external systems may be limited, this is the time for laying the groundwork, ensuring that the subsequent phases of piloting, implementation, and scaling are built upon a solid use case with prompts capable of producing useful results.
The exploration phase is all about understanding the ‘art of the possible’ with generative AI and beginning to shape it to serve your business’s unique needs. It’s about looking at the tools and technologies available and starting to imagine how they can be applied to create value for your organization. Continue the journey with us as we delve into the Piloting and Implementation phase, where ideas start to manifest into transformative solutions.
The transition from exploration to action is marked by the Piloting and Implementation phase. It is here that we start to see the fruits of our labor as we capitalize on our proof of concept’s success. The piloting phase allows us to take your teams’ collaboration to the next level, deploying Copilots, GPTs, and LLM-driven custom web appplications within departments to refine prompts, process input, and orchestrate the AI. With an emphasis on data governance and compliance, we ensure that your AI journey is secure and responsible.
The deployment of Copilots and GPTs within specific teams is our first step towards integration. It’s a collaborative effort, where we work hand-in-hand with your departments to ensure the AI complements your workforce, enhancing productivity and innovation safely and securely.
As usage of an AI tool increases, edge cases and exceptions that were not encountered while proving out the tool will begin to crop up. Crafting prompts becomes an art form in this stage. Working with an expert technology partner, businesses find themselves meticulously engineering the natural language prompts that drive success with generative AI solutions, guiding the AI away from generating irrelevant or inaccurate content—what we call ‘hallucinations’—and towards producing high-quality, precise outputs. This prompt fine-tuning is pivotal as it ensures the AI’s outputs are not only accurate but also align perfectly with your business’s tone, style, and information requirements.
Through piloting, we not only improve the quality of the responses while ensuring alignment with the team, but also prove the use case’s value to the business. This is the proving ground where the AI must demonstrate its worth, showing that it can handle the scale and complexity of enterprise tasks. It is about evidencing the AI’s ability to streamline workflows, enhance decision-making, and ultimately contribute to your bottom line. By fully instrumenting LLM solutions, business gain access to the analytics needed for data-driven decision making regarding the value of LLM solutions, ensuring a laser focus on strong ROI.
With the great power of AI comes great responsibility to use these solutions ethically, and hence it is important that businesses establish robust data guardrails before a tool sees company-wide use. This involves setting up systems for logging, monitoring, and alerting to ensure compliance and maintain the integrity of your data. It’s a safety net that guarantees the AI operates within the parameters of your business ethics and legal requirements, providing a comprehensive solution for compliance alongside acceptable use policies.
What demonstrating value and need for compliance share in common is a strong need for data and analytics on the performance of piloted LLM solutions. To meet this objective, we deploy database systems that meticulously store inputs, outputs, and evaluations. This data repository becomes the bedrock for continuous AI improvement. By analyzing these data, we can fine-tune the AI, demonstrate its value, and ensure that it evolves and adapts to serve your business better over time.
As the AI pilot progresses, we see a confluence of technology and human expertise. This phase is about ensuring that as we implement these advanced tools, they are precise, safe, and effective—aligned not just with your business goals but also with your operational ethos. Join us in the next section as we delve into the Evaluation and Scaling phase, where we ensure that the AI systems are not just performing well but are also ready to be scaled across the organization to unlock their full potential.
The Evaluation and Scaling phase is where we measure, benchmark, and position AI tools for broader deployment. It is a critical juncture where we assess the performance and scalability of our AI solutions, ensuring they are primed for company-wide adoption.
At this stage, we dive even deeper into the analytics to understand how our AI tools are being used across the pilot teams. By monitoring the interactions, response quality, response times, and user satisfaction, we can make informed decisions on the tool’s effectiveness and impact. This deep analysis informs the next steps of integration and scaling, ensuring that the AI tools are not only useful but also used optimally.
Benchmarking performance against industry standards and internal KPIs helps us ensure that our AI solutions are competitive and effective. We continuously fine-tune the AI to improve its accuracy, speed, and usability. This ongoing optimization process ensures that the AI tools are not just a one-time investment but a long-term asset that grows and improves with your business.
A key component of scaling is integration. Our AI tools can be equipped with plugins and APIs that allow them to retrieve knowledge and take actions within your existing digital ecosystem. This seamless integration ensures that the AI can operate in real-time, pulling in data, providing insights, and even executing tasks as needed, thereby becoming an integral part of your operational workflow.
Once we’ve fine-tuned our AI tools and confirmed their efficacy, we deploy them at scale. This means making them available across your organization, for both real-time interactions and batch processing. We ensure that these deployments are secure, reliable, and capable of handling the increased demand that comes with company-wide usage.
An essential aspect of scaling is cost management. We evaluate alternative AI models and deployment strategies to ensure that you get the most value for your investment. This cost optimization exercise might involve switching to different AI models that offer similar outputs at a lower cost or restructuring the way the AI tools are deployed to maximize efficiency.
Finally, we prepare for the company-wide rollout of our AI solutions. This involves training sessions for employees, creating detailed documentation, and setting up support structures to assist users as they adapt to the new tools. By doing so, we ensure that the transition to using AI is as smooth and efficient as possible, leading to widespread adoption and maximization of the AI’s potential.
As we conclude the Evaluation and Scaling phase, we’re not just deploying tools—we’re embedding capabilities that transform how your business operates. Stay with us as we move to the final stage, Productization and Innovation, where we solidify AI’s role in driving your business forward.
The journey from generative AI adoption to enterprise-wide implementation culminates in the Productization and Innovation phase. This is the stage where AI tools transition from being functional to foundational, where they are not only embedded in workflows but also begin to spur new avenues for growth and innovation.
When developing generative AI solutions for business, it is important to recall that AI need not be a static entity; it can be a system that learns. Fine-tuning these systems becomes an ongoing process, as we continuously enrich Retrieval-Augmented Generation (RAG) with responses validated through real-world use. This cycle of learning and improving ensures that AI tools remain at the cutting edge, providing increasing value over time.
Indeed, post-release refinement is a key part of the product lifecycle. User experience insights and feedback become integral to the iterative development process, allowing us to refine the AI system further. This feedback loop is essential to maintain alignment with user needs and expectations, ensuring sustained adoption and satisfaction.
As the AI matures, we hone in on industry-specific and function-specific use cases. Deep prompt engineering and hyperparameter optimization are deployed to tailor the AI’s output to the nuances of particular sectors or job roles. This results in highly specialized tools that can understand and respond to the context of specific professional environments with remarkable precision.
The quality of AI-generated content is paramount. We implement automated systems for evaluating the relevance, accuracy, and usefulness of content generated by AI. These systems work continuously, ensuring that the outputs meet the high standards expected by users and stakeholders.
While internal use cases provide a solid foundation for AI tools, exploring external-facing applications can open up new revenue streams and business models. We work with you to refine these models and validate these use cases, ensuring they are viable, safe, and add value both to the company and its customers.
The final step in productization is the automation of deployment and monitoring. This ensures that AI solutions are always available, performing optimally, and providing the insights and automation that businesses rely on. By automating these processes, we can scale AI tools rapidly and reliably, ready to meet the demands of a dynamic market.
Innovation does not end with deployment. It is a continuous journey, with each cycle of feedback and refinement leading to new capabilities and opportunities. As AI tools become more integrated into the fabric of business operations, they not only streamline current processes but also pave the way for entirely new business models and strategies.
With the Productization and Innovation phase, we fully see AI’s role as a catalyst for ongoing transformation. The AI solutions that started as pilots are now driving forces for strategic innovation and growth. As we embrace this new era, the journey of generative AI adoption becomes a perpetual cycle of discovery, enhancement, and evolution, ensuring that your business remains at the forefront of the digital revolution.
As we conclude our exploration of the transformative journey towards adopting custom Copilots and GPTs for modern knowledge workers, we can see that starting with custom GPTs and Copilots for personal productivity is an excellent entry point into the realm of generative AI. These tools offer a glimpse into the future of work, where AI-driven assistants are not just a luxury but a necessity for staying competitive. As prototypes, they serve as a foundation for enterprise tools ready for wider adoption, showcasing the potential of AI to revolutionize business operations.
However, the journey doesn’t end with prototyping. Transitioning these tools from personal productivity aids to enterprise-grade solutions presents challenges, including governance, monitoring, integration, and ensuring affordable scalability. These hurdles underscore the need for a strategic approach to AI adoption—one that goes beyond the capabilities of the tools themselves.
This is where Proactive Technology Management steps in, bridging the gap for enterprise adoption of generative AI. Our expertise in developing custom solutions built on LangChain and the Open AI Assistants API addresses the complexities of deploying AI at scale. We don’t just create AI agents that work; we imbue them with enterprise-grade analytics, monitoring, and integration capabilities—elements that are crucial for successful implementation but often beyond the reach of businesses venturing into AI alone.
Our value proposition is clear: not only can we develop effective AI agents faster and more efficiently than you could on your own, but we also equip them with the necessary infrastructure for secure, compliant, and scalable enterprise use. Our solutions are designed to evolve with your business, ensuring that your investment in AI continues to deliver value long into the future.
It’s important to recognize that the generative AI transformation odyssey doesn’t follow a straight path, marching through exploration to piloting and implementation, then on to evaluation and scaling, and finally to productization and innovation.
Instead of being so linear in their progression, businesses often jump back and forth between these stages, pivoting to address the most pressing needs and opportunities that arise. As an Agile development partner, Proactive can help judge when to shift stages, and make the most of every opportunity that arises. This adaptive approach ensures that generative AI technologies are not just implemented, but woven into the fabric of the organization’s strategy.
Indeed, in embarking on this journey with Proactive, you’re not just adopting new technology; you’re embracing a strategic partnership that will catapult your business into a new era of efficiency and innovation. The path to transforming your operations with AI may be complex, but with Proactive by your side, it’s a journey that promises unparalleled rewards.
Contact Proactive Technology Management today to discuss how we can start your journey with generative AI, transforming the digital landscape of your business and unlocking the full potential of your knowledge workers, propelling them towards a reality where AI is not just an assistant but a core driver of your business success.
To learn more about how the fusion development team at Proactive is transforming modern work with next-generation business intelligence, hyperautomation, and generative AI, visit our Fusion Development landing page.
