Six Elements for Effective AI Adoption

According to the 2020 Mckinsey study, only 16 percent of companies have taken deep learning beyond the piloting stage. Why do some firms succeed while others fail? Issues such as improper AI use cases, data accessibility, maintainability, scalability, governance, and skills make AI adoption harder.

Here are the six elements you need for effective AI adoption.

1. AI Vision, Strategy, and Business Value.

• Have a clearly defined AI vision and strategy with senior management commitment.
• Ensure your AI strategy aligns with your corporate business strategy.
• AI initiatives should be part of the C-suite agenda. The C-suite must understand the value of network effects, the importance of transforming the core for value creation and value capture through AI-infused platform models.

Without purpose, vision, strategy, C-suite commitment, trust, collaboration, transparency, business outcome mindset, you cannot succeed in scaling AI. -Khwaja Shaik, IBM Thought Leader

• Identify and prioritize AI opportunities that generate business outcomes with the highest value. Create an interdisciplinary team of business and technology to identify AI use cases. Selecting business goals collaboratively increases the AI success rate.
• Establish a data-driven culture to drive business innovation.

2. AI-ready Data Acquisition.

• A typical AI Algorithm requires diverse volumes of data. Access to quality data remains a challenge for many organizations. Acquiring quality data effectively and securely from testing to production pipelines matters.
• You need 10-15X times more data than the data required for a typical analytical workload for AI. Finding the right data matters. Build a use-case specific data catalog and feature engineering capabilities. A feature store platform provides significant value.
• Silos lead to failure. Integrate data silos through automated data discovery tools and data virtualization techniques. Ensure real-time access to both transactional and operational systems.
• Leverage a comprehensive data fabric platform to support your AI use cases.
• Think about the data you ingest as it may have huge privacy implications.

If you don't have accessible good data across all your environments then your AI initiatives are bound to fail. -Khwaja Shaik, IBM Thought Leader

• There are several things to consider for your data- lineage, cleanliness, usefulness, labeling, and data integration.
• The more diverse data you have, the lesser the bias you introduce in your business outcomes.

3. AI Algorithms and AI Models drive business value.

• There are so many algorithms to choose-Linguistic, Graph, Agent or Rule-based, Heuristic, Supervised learning, Unsupervised learning, Reinforcement learning, Deep learning, Artificial Neural Networks, etc. The sweet spot lies in choosing the right algorithm that fits your high-value use case.

Drive business value by exploring wide range of use Cases from rules-based algorithms to data-driven black box algorithms. -Khwaja Shaik, IBM Thought Leader

• Map your AI Use cases based on your strategic priorities-Cost optimization, revenue growth, and customer experience.
• Not every AI Model requires huge volumes of data. Your business needs should drive the AI Model selection.

Use data and AI Algorithms strategically with new operating models. Identify the most valuable AI use cases that generates the most business value. -Khwaja Shaik, IBM Thought Leader

• Pair your data scientist with the business domain expert to choose the right AI to use case and AI algorithm combination. Domain expert and data scientist are like two sides of the same coin for your AI initiative.

4. Testing, Testing, and Testing.

• Testing is crucial to eliminate bias, reduce false positives and false negatives.
• Enforce data accessibility, data sharing, data engineering, and MLOps practices. Leverage cloud data lakes for your MVPs.
• It is crucial to infuse human-centered design practices in your testing processes. This will enable you to test for bias and increase the accuracy of your AI models.

Infuse Domain understanding, DataOps, ModelOps, DevSecOps, RACI framework, and holistic governance practices to ensure reliability, reproducability, interpretability/explain-ability, agility, and AI at scale.

• Complete, accurate, and timely data has no value if it cannot be retrieved quickly and easily for your production workloads.
• Data in the POC and data in production are not the same. Ensure robust data architecture and security compliance to scale AI to production.

POC of your use case is not enough. Ensure your Model testing has access to diverse volume of data, parameter configurations, validation criteria, that meets the real-time production volumes. -Khwaja Shaik, IBM Thought Leader

5. AI is a team sport, and you need a hybrid COE structure.

• AI is less of a bolt-on technology and more of business process-infused general-purpose technology to maximize business value. Engage an interdisciplinary AI team-Busines, legal, security, data scientists, AI Architects, and developers.
• Revamp your enterprise architecture to focus on design thinking and information architecture.

Data engineering, Model engineering, and DevSecOps are like a three leg stool for your AI engineering framework. -Khwaja Shaik, IBM Thought Leader

• Apply DevSecOps practices to the ML lifecycle to get the benefits of repeatability, traceability, and scalability.

Don't do it alone. Leverage AI advisory services from the market as AI projects are typically custom. -Khwaja Shaik, IBM Thought Leader

6. Data Governance is fundamental to AI governance.

• Data Governance is needed for data classification, data collection methods, and data archival.
• Develop AI design patterns based on the hosted AI services available from your cloud providers. Provide automated data preparation, cleaning, one-click deployment models.

AI services are a commodity today. What you need is access to high quality data, process reengineering, and business outcome mindset to scale AI. -Khwaja Shaik, IBM Thought Leader

• Invest in self-service tools such as AutoML and augmented workflows. Expand beyond RPA use cases. Make data retrieval and access easy for both humans and machines.

There is a big difference between getting POC test data and making data accessible in production. -Khwaja Shaik, IBM Thought Leader

• Data science is a new technique, and there are no AI regulations today. Trust, bias and privacy are major issues for AI demanding more explainability and governance. Ensure effective collaboration between risk managers and data scientists.
• Instill effective AI governance by raising awareness of AI risks, embedding an ethics framework, monitoring AI applications continuously, and driving new AI applications as market demands.

An effective AI governance must support autonomous capabilities, and end to end AI lifecycle management services including risk, trust, transparency, ethics, fairness, interpretability, accountability, safety, privacy, and compliance. -Khwaja Shaik, IBM Thought Leader

• Getting started with AI is not enough. You need effective collaboration and sound governance to find the right AI use cases, stakeholder alignment, responsible AI, and model management to scale AI.

AI Application life cycle is radically different from traditional application life cycle as data, models, and code dynamically change all the time. -Khwaja Shaik, IBM Thought Leader

Question

What strategic actions are you taking to accelerate AI adoption? Have you redefined your Enterprise Architecture? Where are you in aligning your data strategy with your AI strategy? Have you operationalized your AI?

Please share your thoughts in the comments section below.

For professional insights into complex issues, join the conversation by tweeting Khwaja at @Khwaja_Shaik or connecting with him on LinkedIn.

ABOUT KHWAJA SHAIK

Khwaja Shaik is the award-winning global IT Executive with 25+ years of business technology leadership with IBM, Bank of America, PwC, and GE. He has a worldwide reputation and a proven track record in driving digital transformation and the newest innovations.

As IBM’s Thought Leader, Khwaja’s role is to help clients stay ahead of the digital disruption curve by leveraging Design Thinking, Cloud, IoT, Blockchain, Artificial Intelligence, Cybersecurity, and Quantum Computing. Khwaja is among the most exceptional IBMers appointed with the rare distinction of IBM Academy of Technology member. Top 100 technical leaders providing the direction of IBM with innovation that matters.

As a strong proponent of talent development, Khwaja serves as IBM’s Design Thinking Coach for IBM’s Developer Jumpstart Program, IBM’s BlueHack Mentor driving innovation, and IBM’s Blockchain Mentor to spur the blockchain ecosystem.

Khwaja also serves as McKinsey Global Institute’s Executive Panel Member, MIT Sloan CIO Forum Member, Gartner’s Research Circle Member, MarketsANDMarkets Advisor, and HBR’s Advisory Council Member driving global thought leadership.

As a global influencer, Khwaja frequently blogs on exponential technologies at IBM, LinkedIn, and Twitter. With his passion for interfaith and nurturing global talent in STEM, he serves on the Advisory Boards of Interfaith Center of Northeast Florida and Museum of Science & History, and the University of North Florida’s Computing Advisory Board.

Recipient of outstanding service awards from the University of North Florida, Bank of America, IBM, and Indo US Chamber of Commerce of Northeast Florida. He is frequently interviewed for industry insights or cited in the news, Thought Leadership POVs, and blogs on disruptive technologies.

Khwaja holds an MBA and Engineering degree. He is a frequent speaker on exponential technologies at various forums, including the CIO IT & Security Forum, MHI Supply Chain Conference, IIT Hyderabad, and Indo US Chamber of Commerce of Northeast Florida.

More details on Khwaja’s career and thought leadership activities could be found via Linkedin, Khwajashaik.com or follow him on Twitter @Khwaja_Shaik

"The postings on this site are my own and don't necessarily represent IBM's positions, strategies, or opinions."