'AI holds significant potential, but the technology itself isn't useful unless it's applied to the right use case in business,' says Sanjib Sahoo, EVP and chief digital officer at Ingram Micro.
As AI makes it way deeper into business operations, its potential to transform business operations remains vast. However, Ingram Micro's Sanjib Sahoo said the key to success lies in aligning AI with clear business goals and applying it to solve real-world challenges.
While the future of AI in business is full of opportunities, companies must approach adoption with a strategic, thoughtful mindset to ensure long-term success.
"Proactive AI adoption starts with the right mindset: people, mindset and innovation," Sahoo, EVP and chief digital officer of the Irvine, Calif.-based IT distributor, told CRN. "It's crucial to focus on customer needs and business goals first. Then, use AI as a tool to achieve those objectives."
Two years after launching its transformative Xvantage platform, Ingram is taking the next step in its plan to offer a highly personalized experience to solution providers with Ingram Micro Ultra. The offering aims to transform its interactions with solution providers by integrating cutting-edge AI and a tailored rewards system.
"With Ultra, we are transitioning from a transaction-based model to one of interaction," said Sahoo. "The program is designed to reward partners not just for their transactions but for their engagement and loyalty. By leveraging Ingram Micro's 40 years of data and solutioning capabilities, we can offer a more comprehensive and customized experience."
At Ingram Micro's One conference in Washington, D.C. last week, the distributor also announced Xvantage Enable and Xvantage Assist, new tracks that will further educate, train and inform channel partners about the opportunities and challenges surrounding AI. The two tracks will also help accelerate the adoption of AI among channel partners and their customers by enabling them to better navigate the evolving technology landscape with greater confidence. "Assist is about capabilities. Enable is about go-to-market strategies," Sahoo said. "Together, they create a powerful ecosystem for our partners."
Check out what else Sahoo said about AI, business maturity and how building a strong AI foundation can help organizations with their AI strategy.
How do you define the role of AI in business today?
AI holds significant potential, but the technology itself isn't useful unless it's applied to the right use case in business. The real power of AI lies in how you match its capabilities to business needs. It's not about having an AI strategy in isolation, it's about integrating AI into your existing business strategy. That's when you start driving real value. There's a lot of talk about what AI can do, but we need to focus on what it is actually doing for businesses today.
Can you elaborate on the difference between having an AI strategy and integrating AI into a business strategy.
Having an AI strategy is more about technical exploration, like setting up a center of excellence, hiring data scientists and testing large language models or machine learning algorithms. But a business strategy is about solving real business problems. For example, if you're looking to improve customer experience or operational efficiency, you use AI to address those specific issues. The business strategy stays the same, you simply leverage the technology to solve problems. That's the key distinction.
What do you see as the biggest mistake companies make when trying to adopt AI?
The biggest mistake is trying to go too big too quickly and focusing too much on the technology itself. AI adoption should start small. Think big, but start with one or two use cases. The critical thing is to ensure that your operations team and data teams are aligned as AI and operations need to work hand in hand. We talk about 'AI ops,' where operational teams are closely integrated with data and technology teams to solve real business challenges.
Why is business maturity important before diving into AI adoption?
Business maturity is crucial because it sets the foundation for AI adoption. If your data is messy or you have outdated systems that haven't been rationalized, you'll spend more time cleaning data than applying AI. The maturity of your business also impacts your alignment across teams, your ability to attract the right talent and your overall readiness to scale. If you're not ready, you risk investing time and resources into something that won't pay off.
What signs indicate a company is ready to integrate AI into its operations?
A key sign is when a company starts focusing on business outcomes and uses data to drive those outcomes. For example, if you're using AI to improve customer service, look at the key performance indicators (KPIs). Are your customer satisfaction scores improving? Is the number of support tickets decreasing? If you're seeing positive results, it's a sign that you're using AI effectively. But remember, AI will never be perfect so the goal should be to continuously show incremental improvement.
How can businesses identify the right use cases for AI?
It's all about matchmaking. First, identify the most complex business problems you need to solve. Then, evaluate the capabilities of different AI technologies to determine which ones can address those problems. Start with a small proof of concept, measure the results and refine your approach. The goal is to identify where AI will deliver the most value and build from there.
In your keynote, you mentioned the importance of the three S's: speed, scale and service. How can businesses integrate these into their AI strategy?
AI can drive speed, scale and service across different areas of a business. Speed could mean improving the speed of decision-making or processing transactions, while scale could relate to expanding into new markets or customer segments. Service is all about improving the customer experience. The key is to map AI's capabilities to these specific use cases and start with a few targeted areas where AI can have the most immediate impact.
With legacy systems and siloed data, how can businesses build a strong data foundation to support AI initiatives?
Building a strong data foundation starts with data rationalization. You need to assess your systems, clean your data and break down silos to ensure that data is accessible and usable. We built a proprietary data mesh that aggregates data from different sources. Once you have a solid data foundation, start with smaller, manageable data stores focused on specific business areas, like sales or operations, and tackle use cases from there. Don't try to fix everything at once.
How can business leaders encourage proactive AI adoption within their companies?
Proactive AI adoption starts with the right mindset: people, mindset and innovation. It's crucial to focus on customer needs and business goals first. Then, use AI as a tool to achieve those objectives. Leaders need to create a culture that encourages experimentation, where failure is seen as a learning opportunity. It's also important to set clear success metrics. AI is not about creating fancy algorithms, it's about achieving measurable business results.
Looking ahead, what do you see as the biggest opportunity for AI in business over the next three to five years?
The biggest opportunity is creating more integrated, seamless experiences across value chains. AI will allow businesses to focus on their core value propositions while integrating with other ecosystems, ultimately optimizing cost structures and improving efficiencies. AI will drive innovation not just through algorithms, but by helping businesses rethink how they operate and deliver value to customers.