AI Anomaly Detection: Transforming Industry Operations
2024-12-09
In this interview series, we explore the world of AI Automation and MLOps, addressing various topics such as common mistakes companies make when starting with AI, how to be a good or bad customer for an AI supplier, and the types of systems that are often underestimated as an AI supplier. In this latest interview, Pablo Tapia, the CTO and founder of Tupl, alongside Rafa Ballesteros, Tupl's Head of Business and Technology for North America, discuss key steps to take when partnering with the right AI supplier.
When a company seeks to hire a third party for any job, a thorough study of the budget, pros and cons, ROI, and more is essential. Now, imagine the effort and trust required to hire a third party as your AI software developer. This decision demands even greater caution and confidence. Despite being the first step toward future improvements, companies often "misbehave" when hiring an AI supplier. You may ask for a quick proof of concept, haggle over price, or develop the project once it has been created, believing this approach is unfair and not optimal. In reality, this leads to less commitment from the AI provider in developing the project.
When customers approach the final stages of purchasing or deploying AI solutions, one common objection that often arises is the demand for a proof of concept (POC). While this request is reasonable and demonstrates a customer's desire for validation, it is important to understand the implications and costs associated with POCs.
POCs require significant resources and investment. Companies cannot afford to allocate their limited resources to numerous free POCs. The most committed customers are those willing to invest in these POCs, indicating they have a clear business case and budget allocation for the initiative. Early experiences have shown that investing in numerous free POCs without a committed budget often leads to wasted efforts and unfulfilled potential.
To address this issue, it is essential to emphasize the importance of a proof of value rather than just a proof of concept. This approach ensures that both parties are invested in the success of the project from the outset. A proof of value demonstrates not only the technical feasibility of the solution but also its potential impact on the business. This collaborative approach aligns expectations and ensures that the investment in the POC leads to meaningful outcomes.
Another common objection customers raise when deciding on AI solutions revolves around pricing. Potential clients often focus too much on the cost rather than the value provided by the solutions. It is crucial to convey that pricing is always a small fraction of the significant value delivered by advanced AI solutions.
When customers concentrate solely on price without considering the business outcomes and impact, they miss the larger picture. AI solutions should be evaluated based on their potential return on investment (ROI), which includes improved efficiency, reduced costs, and enhanced decision-making capabilities. By shifting the focus from price to value, customers can make more informed decisions that align with their long-term goals.
Additionally, concerns about pricing models frequently arise. The software industry has largely transitioned to a Software as a Service (SaaS) model, which ensures continuous research, development, and product improvement. This model benefits both the provider and the customer by providing predictability and ongoing value enhancement. SaaS prevents the stagnation that can occur with one-offs and supports continuous growth and improvement.
Investing in SaaS ensures that customers receive the best possible product at any given time, avoiding the pitfalls of outdated solutions. It also provides the flexibility to scale and adapt as the business evolves. By embracing the SaaS model, customers can leverage the full potential of AI solutions while maintaining a predictable and manageable cost structure.
While it might seem advantageous for companies to develop internal teams to handle automation, several inefficiencies often arise. Building internal capabilities is valuable, and continuous learning is essential. However, it is crucial for organizations to recognize their core purpose and the limitations of their internal teams.
Customers are not software developers and typically do not foster the expertise required for high-quality software development. They lack the necessary processes, experience, and market perspective that specialized AI software companies possess. Internal teams should focus on their domain expertise, leveraging tools and platforms to build strategies, develop machine learning models, create pipelines, and establish metrics.
Developing software frameworks and maintaining them requires significant time and resources, often resulting in suboptimal outcomes. Internal teams usually cannot sustain the necessary key resources and expertise over time, leading to inefficiencies and potential failures in their automation projects. These inefficiencies can hinder the progress of automation initiatives and divert attention from the organization's core objectives.
By partnering with specialized AI suppliers, organizations can leverage cutting-edge solutions tailored to their needs, ensuring that their focus remains on their core competencies. AI suppliers bring a wealth of experience, expertise, and industry knowledge that internal teams may lack. This collaboration leads to better results, streamlined processes, and ultimately, a more significant business impact.
Having explored the common objections customers face when deciding on AI solutions, including the challenges related to proof of concept, pricing, and the inefficiencies of internal teams, it becomes clear that addressing these issues is crucial for successful AI integration. However, beyond overcoming these obstacles, there are additional, often-overlooked benefits that TuplOS provides, which can significantly enhance the customer experience and operational efficiency.
A benefit of TuplOS that many customers often overlook is the platform's openness and flexibility, which empowers customers to build and customize their own applications. This capability provides two significant advantages that can transform how organizations operate and innovate.
Firstly, TuplOS offers customers unprecedented control over their AI solutions. Rather than being entirely dependent on a third-party company, customers can modify and adapt the platform to meet their specific needs. Whether incorporating a new data source or adjusting the strategy of an existing algorithm, TuplOS allows for these changes seamlessly. Initially, Tupl's team may assist in creating the first applications due to the customer's lack of familiarity with the system. Over time, however, customers can gradually take over these tasks, starting with creating key performance indicators (KPIs) and features, and eventually developing their own models and applications.
Secondly, the openness of TuplOS unlocks a hidden gem of learning potential for customer engineers. The platform's no-code features enable even junior engineers or technicians to quickly grasp the basics and start creating valuable solutions. This continuous learning and skill development foster an environment of innovation and improvement within the organization. Engineers can experiment, learn, and grow, leading to a cycle of continuous enhancement and value creation.
Navigating the complexities of partnering with an AI supplier involves addressing several critical missteps, from understanding the true value of a proof of concept to recognizing the importance of a flexible and sustainable pricing model. Additionally, while internal teams play a valuable role, leveraging the expertise and advanced solutions of specialized AI suppliers like Tupl can lead to far greater efficiency and success.
Beyond these considerations, the often-overlooked benefits of TuplOS offer a transformative opportunity for customers. The platform’s openness and flexibility not only provide control and customization but also empower engineers to continuously learn and innovate. This unique combination fosters a culture of ongoing improvement, ultimately driving significant business impact.
By focusing on these critical aspects and leveraging the hidden strengths of TuplOS, organizations can maximize the value and effectiveness of their AI initiatives, ensuring long-term growth and success in an increasingly competitive landscape.
