11 Steps to Boost AI Adoption in Your Organization

Published On February 21, 2018
In Global, Technology, Data Analytics, Blog Archives

The pace at which organizations consume and adopt AI and analytics is determined as much by business issues as by technology.

We believe that if an organization takes the following 11 steps across 4 functional pillars of business, people, process, and data, it can significantly boost the adoption and consumption of Artificial Intelligence across business processes.

Here are our recommendations:

BUSINESS

1. Determine Clear and Quantifiable Goals
It is undoubtedly pertinent to set clear and quantifiable business goals in all AI initiatives as it answers the fundamental question of what do you want from AI. You need to structure every engagement aligned to measurable business benefits that are to be delivered within a defined timeframe.

2. Define the Relevant Business Context
Equally important is to establish a deeper understanding of business and the consumer context to articulate how the business solution would be adopted and consumed in your organization.

3. Monitor and Measure Usage
Closely observing the health of the AI environment in your organization is the key to its successful adoption. Ensure that once the solution is rolled out, your organization knows exactly how and what to monitor and measure. Figuring the usage patterns and error occurrences early within the system can help you streamline its usage and improve its adoption significantly.

4. Strike the Right Balance
It is critical not to upset the existing applecart while being excited to introduce a new solution. An unchecked rapid rise of new data sources and new ideas can easily overwhelm the existing processes and solutions. To ensure successful adoption and consumption, you need to balance the AI solutions with the existing business processes and analytics standards and systems. It is important to create a roadmap as to how the new AI solutions shall be assimilated with the legacy processes to determine newer approaches to decision-making.

PROCESS

5. Collaborate. Collaborate. Collaborate.
We can’t stress enough about the fundamental need to ensure seamless collaboration among the stakeholders and service or solution providers for successful adoption of AI in your organization. You need to focus on co-learning and combining the best insights and capabilities across the spectrum to build on each other’s strengths to increase the adoption and consumption.

6. Prepare for Simplicity and Scale
Intuitive design and modular approach to your AI systems will promote ease-of-use and scalability, in turn boosting the adoption and consumption of AI within your organization. The simple trick is to ensure that you are obsessed with promoting ease-of-use and self-service across your AI solutions. As users interact more with the AI solutions, they shall increasingly explore AI approaches, data, relationships, and interdependencies to create contextual business narratives of their own.

7. Be Adaptive
And just as you are encouraging intuitive design and modularity of the AI solutions in your organization, focus on ensuring its adaptability. Successful AI solutions are nimble to ever-changing business environments. Leverage the relationships to understand how the business community at large and its analytics needs are evolving to design the AI solutions that can easily be updated in terms of new requirements, data sources, metadata, and emerging techniques.

DATA

8. Don’t Compromise with Data Quality
Lack of good quality data is one of the biggest roadblocks to successful AI adoption. It is imperative that data has the right lineage and permissible purpose to serve the customers. Hence, it is quite critical for you to pay rapt attention to data quality as much as to the analytic solutions.

9. Automate Data Preparation Process
It is difficult to unlock broader value from AI without a ready-to-use data platform. Manual and inefficient data preparation steps and processes derail many a great adoption and agile consumption strategies. Hence, automate your data preparation process to create a trusted source for insights, planning, and decision-making.

PEOPLE

10. Engage Users Continuously
Developing a well-defined solution is just the beginning. Consumption is its litmus test. Most change management and adoption initiatives fail because of a lack of consistent and continued user outreach, and interaction with business on the whole. It is important to continuously promote training and awareness in addition to regular meetings with business users.

11. Expand User Base
It is given that to achieve higher adoption rates and maximize value and ROI, you need to encourage much wider usage. Simply, more participation translates to more benefits for your organization. Determine the user persona and focus on addressing as many distinct needs of each broad type of users. Each new user, in turn, will offer their own source of information and insights that will significantly help to increase AI consumption.

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Siddharth Pant
Siddharth Pant
About the Author

Siddharth is the Head of Analytics at SG Analytics. He has 18 years’ experience in managing analytics practices across diverse industries. He has worked closely with Fortune 500 companies in setting up analytics, data management, business intelligence, and technology related practices and engagements. Siddharth holds an MBA from IIM Ahmedabad.

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