Key Strategies for AI Investment Success
AI tools have become a cornerstone of many businesses’ operations, and investing in the industry is a no-brainer for many business leaders. But as new players emerge, questions of where and how to invest continue to be in flux. With its innovative and cost-effective approach to developing large language models and its rapid market penetration, Chinese company DeepSeek has taken many by surprise and shaken up the AI marketplace. Disruptors like DeepSeek are challenging the dominance of AI giants such as OpenAI and Google.
For tech-enabled businesses, this presents both an opportunity and a challenge: how to invest in AI solutions that drive growth while ensuring stability. Members of the AI Think Tank—a group of leading experts in AI and technology—offer their insights into how businesses can make informed AI investments that align with long-term goals while remaining adaptable to future innovations.
9 Critical Factors for Strategic AI Investments
1. Define AI Investment Objectives with a Clear, Strategic Vision
Rodney Mason, Head of Marketing and Brand Partnerships at LTK, emphasizes that companies should prioritize specific AI solutions that align with their long-term business objectives. “Ultimately, a well-executed AI strategy can enhance operational efficiency while also empowering the organization to drive innovation and remain competitive,” he explains. Mason advises businesses to assess AI solutions based on scalability, adaptability and ethical considerations while maintaining a phased implementation strategy to mitigate risk.
2. Experiment and Evaluate: The 90/10 Rule
Greg Clement, Founder and CEO of Realeflow, encourages a proactive approach to AI investment. “We follow the 90/10 rule with technology and believe that 10% of innovations are going to deliver over 90% of the results,” he says. Clement suggests using the P3 framework—people, product and process—to evaluate AI investments, ensuring that any technology implemented enhances team efficiency, improves products and streamlines operations.
“Companies should establish a dual-track strategy—one focused on sustaining core operations with established AI solutions, and another dedicated to innovation.”
3. Balance Stability with Innovation Through a Dual-Track Strategy
Manasi Sharma, Principal Engineering Manager at Microsoft, highlights the importance of balancing stability with exploration. “Companies should establish a dual-track strategy—one focused on sustaining core operations with established AI solutions, and another dedicated to innovation,” Sharma notes. She also underscores the role of ecosystem partnerships in AI development, advocating for collaboration with startups and research institutions to drive co-innovation while maintaining reliability.
4. Optimize AI Costs Through Vendor Diversification
Jim Liddle, Chief Innovation Officer at Nasuni, stresses the importance of cost-effective AI deployment. “Not every use case requires best-in-class performance, and the premium pricing of established AI providers may not always justify the marginal improvements in capability,” he explains. Liddle recommends that businesses diversify their AI vendor relationships to reduce dependency risks and maintain negotiating power while leveraging open-source AI for innovation.
“China-based DeepSeek claims to have invested less than $6 million to train their model—a stark contrast to the billion-dollar budgets of U.S. AI giants.”
5. Leverage Open-Source AI for Cost-Effective Innovation
Gordon Pelosse, Executive Vice President at AI CERTs, points to the democratization of AI as a game-changer. “China-based DeepSeek claims to have invested less than $6 million to train their model—a stark contrast to the billion-dollar budgets of U.S. AI giants,” he notes. Pelosse suggests that businesses consider open-source platforms as viable alternatives to costly proprietary models while being mindful of potential security and differentiation challenges.
6. Establish AI Ethics, Compliance and Trustworthiness
Suri Nuthalapati, Data and AI Practice Lead at Cloudera, underscores the importance of ethical AI frameworks and adaptive governance. “Companies must invest in ethical AI frameworks to build trust, ensure compliance and stay ahead of evolving regulations,” he states. He also recommends that businesses establish innovation labs to test emerging AI technologies before full-scale implementation.
“The best AI fits into your existing systems without causing chaos.”
7. Prioritize AI Scalability and Integration
Nikhil Jathar, Chief Technology Officer at AvanSaber Technologies, considers DeepSeek “a game-changer that’s rewriting the rules of the AI world.” Regardless of the particular AI product, Jathar highlights the need for AI solutions that seamlessly integrate with existing workflows. “The best AI fits into your existing systems without causing chaos,” he asserts. Jathar advises companies to focus on scalability, cost-effectiveness and customer support when choosing AI vendors.
8. Adopt a Portfolio-Based AI Investment Approach
With the surge in AI adoption—McKinsey shares that 65% of organizations across industries report regularly using generative AI—choosing which tools to invest in can be a challenge. Balaji Dhamodharan, Global Software Analytics Leader at AMD, recommends a diversified approach to AI investments. “Key considerations include cost-effectiveness versus precision, agility in responding to AI advancements and strategic partnerships to mitigate risks,” he explains. He advises businesses to monitor regulatory changes and ensure compliance as AI governance evolves.
9. Align AI with Business Operations and Change Management
Justin Newell, CEO of INFORM North America, stresses the importance of aligning AI investments with operational goals. “The primary focus should be on improving customer experience, increasing employee satisfaction and optimizing efficiency while implementing AI in an ethical way,” he states. Newell also highlights the importance of selecting the right AI partners for long-term success.
Takeaways for Leaders Investing in AI
- Define clear AI investment goals to ensure alignment with long-term business objectives.
- Adopt a dual-track strategy that balances stability with innovation.
- Diversify AI vendors to optimize costs and reduce dependency risks.
- Leverage open-source AI where appropriate, but assess potential challenges.
- Prioritize AI ethics, compliance and security to build trust and avoid regulatory pitfalls.
- Focus on seamless AI integration and scalability to better future-proof investments.
Charting a Course: Factors to Consider in AI Investment Decisions
The rise of AI disruptors like DeepSeek signals a major shift in the AI landscape, forcing businesses to rethink how they invest in artificial intelligence. By defining clear investment strategies, balancing stability with innovation, leveraging cost-effective AI solutions and maintaining ethical governance, companies can tap into AI’s potential while mitigating risks. As AI technology continues to develop as a driving force, organizations that adopt a thoughtful and flexible approach will be better positioned to remain competitive.
