Home    Career    Contact Us

White Papers

Designing AI Transformation Strategy for the Organization

   -   Abhinav Ranjeet, Sr. Manager, In2IT Technologies Pvt Ltd

We have seen that every business is looking forward to being an intelligent business in 2019. However, the journey towards AI transformation is not merely a sprint, but a marathon. Investment in AI is growing, and gaining momentum from organizations across all industries. C-suite is constantly thinking about ways to adopt AI into their corporate strategy. As per Gartner, AI adoption can occur at different levels in an organization, depending on how closely it is being incorporated into their corporate strategy.

The AI transformational journey can be categorized into five levels, from Awareness to building as part of the business DNA.

AI Maturity Model

• Back-Office Application AI Enablement
• Mini Ai Projects for Internal Buy-In
• Enterprise Data Lake
• 360-Degree Collaboration for AI Capabilities
• Organizational Change Management for AI Adoption

A holistic evaluation around the prevailing IT landscape is quintessential before diving into AI transformation. Your AI journey will be quicker and more rewarding when built upon the resilient pillars of a well-architectured IT landscape.

A well-structured AI transformation approach needs to focus on five predominant areas:

Evaluating the existing back-office applications

Most of the back-office applications used across various industries are legacy in nature, customized to an extent where periodic upgrades are very arduous. Using digital transformation mechanisms, organizations are attempting to make their back-office applications cloud hosted, and replacing them with state of the art SAAS. The modern cloud suite of SAAS applications enables organizations to transform their businesses with the latest intelligent technologies such as AI and machine learning.

Switiching to these modern SAAS applications, enables organizations to make a quantum leap in accomplishing AI enablement. Switching costs are generally lower, as ERP vendors are ready to absorb their existing licensing of legacy application in their SAAS subscriptions. There are a few organizations experimenting with bolt-in AI solutions that can be easily be placed over their back-office applications.

Designing enterprise data lake

Enterprises that successfully derive business value from their data, have a strategic advantage over their competitors. Business leaders are looking for solutions that will assist them in data-driven decision making. Deploying a well-designed Enterprise data lake will be a major milestone for organizational AI transformation.

Designing enterprise data lake

A data lake is a unified scalable repository that allows organizations to store all of its structured and unstructured data. Enterprises can easily store their raw data without much structuring data and run different types of analytics; ranging from vanilla dashboards and visualizations to big data processing, real-time analytics, machine learning and deep learning to guide enhanced decisions.

Executing mini AI projects for internal buy-in

Successful implementation of measurable pilot AI projects is imperative for internal stakeholder buy-in. The first few projects should be effective enough to gain an acquaintance with AI. These projects should have clearly defined and measurable objectives that create business value with tangible results within 6-8 months.

Building up impetus leads to an extension of successful AI projects. This process is a repeatable and reliable model that can be used in any organization.

Creating a 360-degree ecosystem of AI capabilities

In the AI era, an imperative for corporations is the formation of a centralized AI team that has deep domain knowledge and can build AI solutions around that. Outsourced partners with deep technical AI expertise can help enterprises gain initial momentum faster.

Corporations can develop customized curriculum training programmes for its employee’s dependant on their role. AI training could be conducted in parallel and along with AI pilot project implementation depending on the kind of in-house projects and users to be trained. Hiring a few AI facilitators to deliver some in-person content can also help motivate employees to learn AI techniques.

Organizational change management for AI adoption

Impact areas of analytics and AI span across almost all functions and managerial roles, such as marketing, advertising, HR, finance, and enterprise information management. Although standard change management methodologies provide guidance to manage organizational change management during the process of AI adoption, it is still critical to consider the “personality” of an organisation and its work culture.

Organizational change management for AI adoption

A multi-level organization change management approach is imperative for successful AI adoption across the organization.

AI is a supporting mechanism that will assist employees in surfacing their true value within the organization, and in-turn, improving the overall operational efficiency of the organization. Business leaders make decisions based on real time data and state-of-the art Machine learning models.

Investing in AI transformation programmes will strengthen enterprises in staying ahead of its competitors and leverage AI capabilities to significantly advance in the IT industry. In the long run, AI will enable corporations to build unique competitive advantages in new, revolutionary ways.