At present, integrating AI and Machine Learning into business processes is the ultimate requirement of digital leaders. This approach improves the enterprise’s value proposition to customers and modernizes day-to-day operations. Moreover, AI and Machine Learning adoption touch virtually all phases of the enterprise, thereby driving commercial and operational gains.
However, the successful creation and integration of AI/ML models into processes or applications require massive expertise in domains such as data processing and model building. Unfortunately, not every tech leader has the resources to employ internal teams of AI and ML professionals and organize these initiatives. As an alternative, leaders can outsource AI and ML projects to offshore consultants and speed up transformation.
The Relevance of AI/ML Outsourcing
One of the most common questions faced by business leaders who are planning to launch AI ML solutions is whether to develop it with an in-house team or offshore dedicated professionals. Opting for in-house development of AI/Ml solutions results in inadequate risk and resource management. By ignoring key risk considerations, enterprise leaders fail to deliver lucrative AI projects.
- There is a scarcity of skilled AI and Machine Learning experts. According to the International AI Talent Report 2024, the number of AI specialists worldwide was only 21,000 (15%) at the end of 2023. There is a rising demand for AI experts and data scientists, but the supply of talent market does not meet it.
- Machine Learning and deep learning models need to be inputted with large amounts of quality-rich data to achieve better performance. Data cleansing and discovery can be time-intensive processes. Even if tech leaders have access to clean, significant data relevant to the model, handling Big Data is tedious and requires experience.
- Hiring, onboarding, and training an in-house team of AI and ML experts is a costly endeavor and the process can take months
That’s why, outsourcing emerges as a strategic option for digital leaders who need fast and cost-effective AI ML project delivery.
5 Steps Followed by Offshore AI ML Partners for Enterprise-wide Adoption
1. Identify Specific Objectives and Requirements
After onboarding dedicated AI ML consultants, leaders need to pinpoint their anticipated goals and requirements of projects. They must determine why they want to leverage Artificial Intelligence and decide the areas that need to be augmented with this technology.
This helps consultants to evaluate the enterprise’s current infrastructure, operational processes, and business objectives and determine project feasibility. Additionally, the consultants engage in conversations with different team members to gain an understanding of their specific necessities and explore potential use cases of AI and Machine Learning. This could involve implementing AI for, automating routine tasks or time-consuming processes and enhancing data processing/decision-making accuracy through Machine Learning.
By aligning AI and ML-powered deployments with overarching business goals, consultants can lay a strong base for successful transformation.
2. Build Data Management Policies
Data is the quintessential resource for creating AI and ML models, and the effective preparation of data is vital for model development.
Understanding this, consultants from an offshore AI and ML services provider begin by categorizing the necessary data, its origin, and the strategy for storing, managing, and processing it. By designing an access control policy in line with data privacy guidelines, the consultants can regulate secure information accessibility. The consultants also explore methods for evaluating and generating value from massive datasets. By setting up a well-structured data ecosystem, the consultants facilitate efficient data storage, management, and processing. The consultants recommend using a Cloud-based data storage solution to ensure safety and alignment with operational requirements.
3. Promote Intelligence Among Workforce
Establishing an AI-powered enterprise necessitates promoting a persistent AI culture among teams and employees. It’s crucial to begin by imparting AI/ML understanding and knowledge to employees, providing them with the ability to leverage AI tools for their tasks, and fostering a technology-driven mindset.
To achieve this culture and mindset, consultants provide training programs and workshops. These initiatives prompt team members to acknowledge the importance of AI in the business ecosystem and equip them for the journey of enterprise-wide AI transformation.
4. Formulate AI Strategy
This stage helps in deciding how the enterprise will apply Machine Learning and AI. The consultants understand the enterprise’s challenges and opportunities, data and analytics skills, and techniques. By using this information, consultants can determine which AI initiatives are ideal for an organization. The stages involved in AI strategy formulation are:
- Using strategy analysis and process mining tools to assess an enterprise’s data infrastructure
- Generating a portfolio of potential AI initiatives to better understand the possible ROI benefits for the enterprise
- Conducting AI/ML value projection to ensure that an enterprise doesn’t invest more than what the project is expected to return
- Utilizing off-the-shelf AI tools and Machine Learning models for launching test projects and determining the viability of projects
- Categorizing problems with scalability and creating custom and workable solutions
5. Integrate AI Solutions in Operations
Once consultants build accurate and efficient AI/ML solutions that have been systematically tested and modified for business purposes, they can integrate these solutions into active operations by adapting teams and processes to support the implementation.
During this stage, it’s important to enable effective collaboration among stakeholders to plan and implement AI solutions tactically. Additionally, consultants provide comprehensive training for the workforce and support teams involved to ensure a smooth transition.
6. Monitor and Evaluate the Performance
After the incorporation of AI or Machine Learning in operations, the consultants perform continual monitoring and evaluation to ensure productivity and practicality. By establishing KPIs that align with business objectives, consultants facilitate robust assessment and the continuous tracking of areas for enhancement.
Additionally, consultants retain the data and model’s relevance by constantly upgrading them. Consultants can also compare the AI solution’s performance data with the target and baseline information, and search for patterns, trends, and dissimilarities. This helps in continuous improvement. Continuous enhancement of AI/ML solutions ensures that the processes perform optimally in all scenarios, enabling tech leaders to adapt to changes and uphold a competitive edge.
Overcoming Transformation Obstacles With AI ML Consultants
Data Bias and Quality
AI and Machine Learning initiatives drive successful outcomes only when the data used to train the models are efficient. Biased, limited, or poor-quality data can adversely impact AI performance and result in unfair or deceptive outcomes. When tech leaders hire consultants from an offshore AI and Machine Learning services provider, they can have quality-rich, representative data to build and train their models. These experts can review datasets for potential biases and apply measures to alleviate them.
To mitigate inherent data bias, consultants can implement programs to expand the assortment of their datasets and the diversity of their workforce. More diversity and inclusion among employees means people of many viewpoints and different experiences are feeding systems the data points to learn from.
Resistance to Change
Some team members and departments may be hesitant to embrace AI and Machine Learning. New technologies often mean innovative ways of execution that contribute to faster transformation. By opting for outsourcing, tech leaders can educate teams and stakeholders about AI and how it will support them. Give team members opportunities to get hands-on experience with Machine Learning models. Leaders should also enable communication and feedback channels available to employees so that they can raise their concerns and get clarity from offshore AI/ML experts. With greater inclusion and involvement, resistance to change tends to fade.
ROI Quantification
Precisely evaluating Return on Investment (ROI) in AI and ML development is difficult, since it requires tracking costs, KPIs, and long-term impact on business consistently. AI projects often involve numerous aspects, such as data acquisition, model creation, and infrastructure maintenance. Assessing the expenses associated with these aspects and aligning them with the anticipated benefits can be tricky. Furthermore, traditional metrics may not effectively capture the complete value produced by AI initiatives.
To quantify the ROI and value discovery, consultants build and deploy an affordable Minimum Viable Product (MVP). Upon successful validation, the MVP can be iteratively scaled to attain greater business value. This method ensures tactical investment in AI ML initiatives and tech leaders can identify tangible business benefits before deciding to spend large-scale resources.
Closing Thoughts
With the AI and Machine Learning landscape evolving continuously, the lack of in-house AI expertise is one of the major roadblocks business leaders face in their journey to constructive transformation. To navigate this challenge and enhance AI-driven business outcomes, partnering with offshore service providers is the ideal option. The dedicated consultants formulate a well-defined strategy by identifying business requirements and setting up a change management plan by collaborating with stakeholders.