Hari Sonnenahalli is a thought leader and seasoned enterprise architect at NTT Data Business Solutions (NDBS).
The speeded-up rate of digital evolution has changed business practices, accelerating automation, customer experience and workflow efficiency. With businesses moving the boundary of innovation forward, artificial intelligence (AI) is the next logical step, enabling companies to achieve new levels of productivity, intellect and competitive advantage.
AI is not just an upgrade to operations; it is a fundamental business driver of change. The companies that adopt AI strategically will define the future, and those that wait will be left behind.
From Crisis To AI-Driven Innovation
The digital acceleration was not a proactive measure but a response to circumstances when the Covid-19 pandemic erupted. Before the crisis, companies were satisfied with brick-and-mortar business models, having predictable revenue streams and stable supply chains. Digital transformation was viewed as discretionary rather than obligatory.
However, with global disruptions forcing businesses to reshape strategies, businesses had to innovate rapidly to be viable. One of our customers abandoned on-premises projects suddenly and turned to an e-commerce-first approach, transitioning from sales-driven forecasting to a data-driven method that allowed them to make data-driven decisions.
Other examples include Alibaba, which, motivated by ChatGPT’s launch, developed its own AI models. Newcastle Industrial Benefits (NIB), an insurance firm, used AI-powered digital assistants, achieving $22 million in cost reductions and expanding its customer base without hiring additional support staff.
AI is not an option—it’s a necessity to be competitive as a business.
AI As The Engine Of Digital Transformation
As businesses looked over their historical data, the importance of transforming raw data into intelligent insights came to light. Businesses realized that AI-driven intelligence could provide a strategic advantage, setting the stage for future breakthroughs.
For this to be harnessed, companies invested in cloud computing, hyperscalers, and AI-driven analytics. Kroger expanded its online presence using automation. Walmart invested in curbside delivery and robots to simplify logistics. Amazon cemented its digital dominance driven by AI, integrating AI across supply chain optimization, recommendation engines and cloud services.
The AI revolution had begun, and AI had become its powerhouse. No longer just a process efficiency enhancer, AI has changed the way companies automate processes, connect with customers and disrupt markets.
Addressing The Most Pressing AI Challenges
There’s no doubt that AI is disruptive, but how do businesses implement it? Most business leaders feel overwhelmed by the quantity of information on AI’s potential.
Here are the questions I think that matter the most:
- How is AI scaled up?
- How can we identify which processes to prioritize with AI?
- How is historical data leveraged successfully?
- How do we train vendors and partners to deploy AI capabilities?
- How do we overcome organizational resistance to AI?
A structured, bottom-up approach is the optimal plan to deal with these questions.
Overcoming Resistance: The Human Factor In AI Adoption
Resistance to AI is inevitable. Every technological revolution has been met with skepticism before it proves its worth. The AI revolution is not an exception.
Organizations need to overcome three key challenges to ensure smooth adoption:
1. Clear Communication By Leaders: Leaders should deliver a unifying and optimistic message about the function of AI and how it can supplement (not serve as a substitute for) humans.
2. Comprehensive Training And Onboarding: Employees must be empowered, not intimidated. They should be trained through systematic training courses so that they can work with AI.
3. Phased Implementation Strategy: AI adoption should be gradual so that teams have time and resources to get used to it.
Once fears have been alleviated, companies will see increased inclusiveness and engagement in AI adoption. Developing a loop of feedback between decision-makers and frontline teams ensures concerns are heard, validated and acted on, paving the way to a successful AI transition.
Training Vendors And Outside Partners In AI
Adopting AI is not just an in-house change—vendors and partners also must be aligned with the AI vision. But not all partners will be able to adapt soon. Companies should solidify their AI business plan before revealing it to vendors. They can then hold workshops and training sessions to educate vendors on AI-driven transformations.
They should also be mindful of the potential of information overload and consider a phased implementation of AI, allowing incremental adoption. A good rule of thumb is to reach roughly a 90% consensus before employing a full integration of AI processes. An efficiently coordinated AI ecosystem will foster efficiency, cooperation and innovation across the entire supply chain.
Transitioning To Data-Driven Decision Making
Business forecasting has evolved from being sales-driven to being data-driven. Businesses need to assess data readiness before adopting AI:
• Data Structuring And Quality: AI models require clean, well-structured and well-organized data to be precise.
• Predictive Analytics And Modeling: Companies should utilize AI-driven analytics to make inferences, anticipate patterns, and enhance decision-making.
• Data Acquisition Strategy: If in-house data is insufficient, firms must find, acquire and integrate external data sets.
Positioning The AI And Scaling It Up
AI implementation follows a Crawl, Walk, Run strategy, allowing gradual adoption while identifying and addressing challenges. Prioritizing high-volume and peripheral processes minimizes risks and bottlenecks. Key focus areas include:
- Repetitive and time-consuming tasks
- High-volume data analysis
- Decision-making assistance
- Internal process optimization
Scaling AI For Long-Term Success
Data expansion plays an imperative role in scaling AI. If the data volume and quality increase, then the training of the data is efficient. To train in quality data, cloud computing and edge technologies can be procured. It is important to build an in-house team of data engineers who can help implement machine learning operations for continuous monitoring, training and learning.
AI converts data into a predictive asset out of an operational byproduct, allowing intelligent decision-making.
AI As The Future Of Business
AI is not just a complement to digital change—it is the future frontier. Businesses that embrace AI strategically will gain a competitive advantage.
The AI revolution is already here, and the question is merely whether your business will be a leader or racing to catch up.
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