Unlocking the Future: Elisa AI Co-creation with Mindtitan

Table of Contents

1. Introduction
2. The Start of the Collaboration
3. Preparation: Technological and Organizational
4. Building Technological Infrastructure
5. Educating on Machine Learning
6. Proof of Concepts and Testing
7. Implementing the Chatbot
8. Introducing the Happiness Index
9. Facial Biometrics and Contact Prevention
10. Future Projects and Innovations
11. Key Roles for Successful AI Projects
12. The Business Problem Owner
13. The Data Engineer
14. The Data Scientist
15. The Product Manager as the Translator
16. Challenges in Collaboration
17. The Slowness of Large Corporations
18. The Need for Patience and Adaptation
19. Dealing with Changes in Personnel
20. The Importance of Evolving
21. The Role of Procedures
22. Dealing with Long Payment Cycles
23. The Impact of Slow Processes
24. Conclusion

Building a Successful Collaboration Between a Big Corporation and a Startup

In today's ever-evolving business landscape, collaborations between big corporations and startups have become more common. These partnerships bring together the resources and experience of established companies with the agility and innovation of startup companies. In this article, we will share a story about the successful collaboration between a large corporation, ELISA, and a startup, Mind Titan, and provide insights into how they navigated the challenges and achieved mutual success.

1. Introduction

In the fast-paced world of business, collaborations between big corporations and startups offer unique opportunities for growth and innovation. This article explores the successful collaboration between ELISA, a prominent corporation, and Mind Titan, a startup specializing in machine learning. Over the course of two years, these two entities have worked together to leverage the power of AI technologies and drive positive change within ELISA's operations.

2. The Start of the Collaboration

The collaboration between ELISA and Mind Titan began in the Second half of 2017. At this time, Mind Titan secured ELISA as their first customer, marking a significant milestone for the startup. The partnership started with a period of preparation, during which both companies strategized their approach to incorporating machine learning technologies. It became evident that many companies in the field of machine learning were ill-prepared both technologically and organizationally. Therefore, a robust plan was put in place to ensure the success of the collaboration.

3. Preparation: Technological and Organizational

To lay the foundation for a successful collaboration, ELISA and Mind Titan focused on preparing themselves both technologically and organizationally. They acknowledged the need to build the necessary technological infrastructure to handle the vast amounts of data required for machine learning. Additionally, they invested resources into educating their teams on the fundamentals of machine learning, ensuring a shared understanding of the concepts and potential uses within ELISA's operations.

4. Building Technological Infrastructure

One of the key aspects of the collaboration was the establishment of a robust technological infrastructure. Mind Titan worked closely with ELISA to design and implement systems capable of collecting, storing, and processing the vast amounts of data needed for machine learning. This infrastructure served as the backbone for the subsequent projects and initiatives undertaken by the collaboration.

5. Educating on Machine Learning

As the collaboration progressed, it became apparent that education would play a crucial role in ensuring its success. Mind Titan took on the responsibility of educating both ELISA's team members and themselves on the intricacies of machine learning. This education process involved a combination of workshops, training Sessions, and knowledge sharing to ensure everyone had a solid grasp of the principles and potential applications of machine learning within the specific Context of ELISA's operations.

6. Proof of Concepts and Testing

To validate their ideas and test the feasibility of implementing AI technologies within ELISA, Mind Titan embarked on a series of proof of concepts. These experiments allowed for the testing of various AI-driven ideas and concepts, ultimately leading to the identification of successful approaches for implementing AI within ELISA's operations. These proof of concepts served as the stepping stones for the subsequent projects undertaken by the collaboration.

7. Implementing the Chatbot

One of the early milestones in the collaboration was the implementation of a chatbot. This conversational interface allowed for intelligent and automated communication with ELISA's customers. The chatbot was integrated with various systems and could understand customer queries and address issues promptly. This implementation proved to be a significant success, streamlining customer communication and enhancing the overall customer experience.

8. Introducing the Happiness Index

Mind Titan and ELISA recognized the importance of customer satisfaction and sought to measure and improve it through the introduction of the Happiness Index. This quality of experience measurement system aimed to identify and address any issues that customers might encounter. By tracking customer satisfaction and analyzing data, ELISA could proactively address any concerns and improve the overall quality of their services.

9. Facial Biometrics and Contact Prevention

Expanding on their previous successes, the collaboration delved into the realm of facial biometrics. This innovation allowed ELISA to verify customer identities using facial recognition technology. Additionally, the collaboration explored contact prevention, aiming to identify customers who might face future issues and provide proactive solutions. These initiatives showcased the potential of AI technologies to revolutionize customer service and support.

10. Future Projects and Innovations

Building on their achievements, the collaboration between ELISA and Mind Titan continued to explore new projects and innovations. Some of these projects, such as voice recognition technology and a machine learning-Based NPS prediction model, aimed to further enhance the customer experience. The collaboration's commitment to continuous innovation ensures that ELISA remains at the forefront of technological advancements in their industry.

11. Key Roles for Successful AI Projects

The successful implementation of AI projects requires the involvement of key roles within both the startup and the corporation. These roles include the problem owner from the business side, the data engineer, the data scientist, and the product manager as the translator between the business and technical domains. Each role plays a crucial part in ensuring the success of AI initiatives and bridging the gap between different stakeholders.

12. The Business Problem Owner

The business problem owner is responsible for identifying and articulating the business problem that AI technologies aim to solve. They possess in-depth knowledge of the process or problem at HAND and serve as the primary point of contact for the AI team. The business problem owner also assesses the delivered results and ensures that they Align with the original problem statement and meet the business's needs.

13. The Data Engineer

The data engineer is the technical expert responsible for designing and implementing the technological infrastructure required for AI initiatives. They possess a deep understanding of data storage, processing, and movement. Their role is to simplify these processes for the data scientists and enable seamless data utilization. While finding experienced data engineers may be challenging, the investment in acquiring or training talent in this role is crucial for successful AI projects.

14. The Data Scientist

The data scientist is the individual responsible for building models, cleaning data, and evaluating the performance of AI algorithms. They possess strong analytical skills and expertise in machine learning techniques. It is essential that data scientists have access to a robust technological infrastructure and collaborate closely with the data engineer to ensure effective data utilization. Hiring data scientists from outside the organization can provide a fresh perspective and accelerate the progress of AI projects.

15. The Product Manager as the Translator

The product manager plays a vital role in bridging the gap between the business side and the technical side of AI projects. They possess a strong understanding of both domains and act as the translator, facilitating effective communication between the two parties. The product manager ensures that the AI initiatives align with the business's goals and objectives and helps prioritize tasks and allocate resources appropriately.

16. Challenges in Collaboration

While the collaboration between a big corporation and a startup brings many benefits, it is not without its challenges. One significant challenge is the inherent slowness of large corporations, which contrasts the fast-paced nature of startups. It can be frustrating for startups to navigate the lengthy decision-making processes and extended timelines often associated with large corporations. Understanding and patience are essential for successfully navigating these challenges.

17. The Slowness of Large Corporations

One prominent challenge faced by startups collaborating with large corporations is the time it takes for decisions and processes to move forward. From signing contracts to gaining access to necessary data, every step of the process can be time-consuming. Startups must adapt to the slower pace of large corporations and manage their expectations accordingly. Patience and persistence are key for maintaining a positive working relationship.

18. The Need for Patience and Adaptation

Both startups and large corporations must exercise patience and adaptability in their collaboration. Startups need to understand that large corporations have established processes and structures in place that take time to navigate. On the other hand, corporations should acknowledge that startups are still finding their footing and may require some guidance and support. Patience and open communication are essential for fostering a successful partnership.

19. Dealing with Changes in Personnel

Personnel changes are inevitable in any collaboration, especially in large corporations. The arrival of new team members, including high-level executives, can sometimes disrupt the flow of the collaboration. Startups must be prepared to reintroduce themselves and Continue selling their ideas and expertise to new stakeholders. By remaining adaptable and persistent, startups can navigate these transitions and maintain the Momentum of the collaboration.

20. The Importance of Evolving

The ability to evolve is crucial for both startups and large corporations in a collaborative setting. The business landscape is constantly changing, and the adoption of new technologies and processes is necessary to stay competitive. Startups can offer fresh perspectives and innovative solutions, while corporations can provide the infrastructure and resources needed for growth. Embracing change and continuously evolving is key to capitalizing on the benefits of collaboration.

21. The Role of Procedures

Procedures play a significant role in any collaboration, providing structure and guidance. While startups often have more flexibility in their processes, large corporations rely heavily on established procedures. It is important for startups to understand and accommodate these procedures, even if they seem cumbersome at times. Procedures can help ensure efficiency, adherence to regulatory requirements, and overall success in the collaboration.

22. Dealing with Long Payment Cycles

A potential challenge for startups collaborating with large corporations is the extended payment cycles often associated with larger organizations. Startup companies, with their limited financial resources, may rely on Timely payments to sustain their operations. Understanding the payment terms and negotiating agreements that align with the startup's needs are essential for maintaining a healthy working relationship.

23. The Impact of Slow Processes

The slow decision-making and lengthy processes within large corporations can have a cascading effect on startups. Delays in executing projects and accessing necessary resources can hinder progress and limit the startup's ability to deliver results. Startups must actively communicate their needs and deadlines, while large corporations must strive to expedite processes without compromising quality or security.

24. Conclusion

Collaborations between big corporations and startups hold immense potential for growth and innovation. The partnership between ELISA and Mind Titan serves as a testament to the success that can be achieved when both parties are committed to open communication, patience, and adaptation. By leveraging the strengths of both sides and overcoming challenges, collaborations can lead to groundbreaking advancements and mutual success in the ever-evolving business landscape.

Highlights:

1. Successful collaboration between a big corporation and a startup.
2. Preparation and education as foundations for successful AI projects.
3. Implementation of a chatbot and the Happiness Index to enhance customer experience.
4. Leveraging facial biometrics for customer identification and contact prevention.
5. Focusing on future projects and innovations.
6. The importance of key roles in AI projects: problem owner, data engineer, data scientist, and product manager as the translator.
7. Challenges in collaboration: slowness of large corporations and need for patience and adaptation.
8. The role of procedures and dealing with long payment cycles.
9. The impact of slow processes on startups and the need for communication.

FAQ:

Q: How did the collaboration between ELISA and Mind Titan begin? A: The collaboration started in the second half of 2017 when Mind Titan secured ELISA as their first customer.

Q: What was the focus during the preparation phase of the collaboration? A: The focus was on building the necessary technological infrastructure and educating both teams on the fundamentals of machine learning.

Q: What successful projects were implemented during the collaboration? A: Projects such as the implementation of a chatbot, the introduction of the Happiness Index, and the use of facial biometrics for customer identification and contact prevention were implemented successfully.

Q: What are the key roles for successful AI projects? A: The key roles include the problem owner from the business side, the data engineer, the data scientist, and the product manager as the translator between the business and technical domains.

Q: What are the challenges in the collaboration between a big corporation and a startup? A: Some challenges include the slowness of large corporations, the need for patience and adaptation, and dealing with changes in personnel.

Q: How can startups deal with long payment cycles in collaborations with large corporations? A: Startups should understand the payment terms and negotiate agreements that align with their financial needs to sustain their operations.

Q: What is the impact of slow processes on startups collaborating with large corporations? A: Slow processes can hinder progress and limit the startup's ability to deliver results. Effective communication and active management of deadlines are crucial in such situations.