Dean DeBiase is a best-selling author and Forbes Contributor reporting on how global leaders and CEOs are rebooting everything from growth, innovation, and technology to talent, culture, competitiveness, and governance across industries and societies.
C-Suite Leaders Question AI’s Bottom Line Impacts Going Into Holidays
By Dean DeBiase
December 10th, 2024
The frenzied pace of AI hype has C-suite leaders’ heads spinning. If you lost track of how many announcements came out AWS’s re:Invent 2024 conference, you’re not alone. Not to be out done, Microsoft aligned OpenAI then announced “12 Days of OpenAI”, with CEO Sam Altman posting on X: “Each weekday, we will have a livestream with a launch or demo—some big ones and some stocking stuffers … we’ve got some great stuff to share, hope you enjoy! Merry Christmas.”
Merry indeed. Over the past two years, I’ve been advising, speaking, podcasting and reporting on the AI arms race that’s turning best laid plans upside-down. There are hundreds of automation technologies that are being evaluated in C-suites and boardrooms across the globe.
From intelligent chatbots to predictive algorithms, businesses have been working on transforming their competitiveness and operations—but alas, they have barely made an impact on the bottom line.
There are dozens of reports and studies talking about the immense potential of AI, what it will look like in the years to come, with several suggesting it will contribute $15 trillion to the global economy by 2030. That’s a lot. Smart leaders are looking at it strategically, as a new frontier to not just survive—but to thrive and create entirely new business ventures and even new sectors.
Exciting times. But for all the innovation enthusiasm, my question remains: How can organizations truly harness AI to boost growth and financial ROI outcomes?
What Gets Measured Gets Done
There are dozens of vendors taking on this challenge, leveraging cutting-edge AI and machine learning technologies to help drive innovation across industry sectors. One tech leader that is investing heavily in building proprietary AI and amplifies business outcomes through tailored AI-driven solutions is Tech Mahindra. With strategic partnerships, including collaborations with NVIDIA, the company is on a mission to accelerate AI adoption in emerging domains like Generative AI and Quantum Computing.
I recently met Lakshmanan Chidambaram (CTL), President and Head of Americas Leadership Council, Tech Mahindra and Americas Head, Mahindra Group (M&M.NS), to understand how they are actually helping businesses unlock the financial power of AI. Many leaders like Chidambaram agree that AI’s potential extends far beyond cost-cutting, and is closer to a few of my favorite things—creating efficiencies, entirely new growth categories, and more profitable business models.
AI has undeniably generated significant excitement, with industry leaders touting its transformative potential. As AI models become faster, smarter and more reliable like SLMs, organizations are racing to capitalize on these huge investments.
But again, how can the return on investment (ROI) match these sky-high expectations? I asked Chidambaram, “The answer lies in AI’s ability to evolve beyond isolated functions, like chatbots or customer service automation, and become a comprehensive tool integrated into core business processes. Take the financial services industry, for example; with a robust, enterprise-wide AI strategy, it is possible to attain up to 40% higher ROI than those with fragmented efforts. Successful ROI from AI requires strategic alignment across all business functions.”
Moving From Experimentation To Creation
According to estimates, the global manufacturing industry was valued at $3.2 billion in 2023 and is expected to see the largest financial impact due to AI, with the sector expected to reap a gain of $20.8 billion by 2028. AI has the potential to drastically alter the sector’s economic impact. The same report highlights that by integrating AI to predict equipment failures and automate routine quality control checks, enterprises have reported a 40% reduction in downtime and an almost 15% increase in overall productivity. This directly translates into cost savings of over $100 million annually.
The key to achieving real ROI from AI investments lies in its integration with automation at scale. From multi-national OEMs like GM to mid-cap providers like PBC Linear, manufacturers are embracing AI-driven automation to revolutionize their assembly line operations. By leveraging automation and cobotics with AI, manufacturers are beginning to streamline their operations, improve product quality, and minimize costly errors.
Commenting on ways these companies can measure AI ROI, Chidambaram said,“It is time to move from experimentation to value creation. To ensure that AI investments pay off, businesses need concrete methods to measure success. One approach, derived from an AI verification tool used across multiple industries, focuses on four key metrics: operational efficiency, cost savings, revenue generation, and customer satisfaction.”
Auditing AI Validation, Assurance And Governance
Tech vendors are scrambling to develop tools that help companies ensure robust validation and governance across the end-to-end lifecycle of their AI projects. CTL’s company recently launched TechM VerifAI, an automated framework for validating AI systems in real-time, for compliance with industry standards and regulations.
It is a 360-degree validation framework across the GenAI lifecycle, with customizable metrics, microservices-based architecture, that can integrate into existing technology stacks to improve AI value realization for enterprises. This is important because it starts with validating data quality in the discovery and pre-development stages—followed by testing AI models, frameworks, and hyper-parameters in the actual development stage to ensure security and accuracy. You might want to re-read that, because companies have skipped those early steps only to reboot and restart AI projects later.
Expanding these emerging issues, Chidambaram agrees, “Most businesses have not moved from pilots and experiments to enterprise-level adoption of AI, due to the absence of a robust validation and assurance framework. We address this need with a comprehensive framework for assessing, auditing, and certifying AI solutions so enterprises can responsibly leverage AI for growth, success, and scale at speed by automating their validation and verification processes.”
Strategic Path To Maximizing AI Investments
AI must be implemented enterprise-wide rather than in isolated functions. Integrating AI into both customer-facing operations and back-end processes ensures businesses can extract value at every touchpoint.Further,AI should not be treated as a siloed project or technology experiment. Instead, AI initiatives must be aligned with broader business objectives, whether it’s improving customer retention, reducing costs, or expanding into new markets.
The most significant ROI from AI comes from tailoring solutions to specific industry needs. For example, in retail, AI can optimize pricing and promotions based on real-time demand forecasting, while in agriculture, AI-driven drones can optimize crop yield by monitoring field health. AI systems must be continually trained, refined, and adapted as business conditions evolve. This ensures that businesses remain agile and can capitalize on new opportunities as they emerge.
From AI Aspirations To Business Results
The promise of AI is undeniable, but its true power lies in its ability to help the C-Suite deliver financial value, not just technological advancements. In today’s fast-paced market, where businesses are battling inflationary pressures, labor shortages, and intense competition, AI provides a pathway to sustained growth and profitability. However, the key to unlocking this value is in strategically integrating AI across the organization, continuously measuring its impact, and aligning it with core business objectives. With the right approach, AI becomes an enabling market-maker—and more importantly, a game-changer for the bottom line.
