9 Essentials to Compete in the AI Race

While we are still in the early days of the latest applied artificial intelligence technology cycle, the recent pace of AI development has been supersonic. With vast opportunities for disruption across many markets, the field is wide open.

This wave of innovation is more complex than the cloud and mobile cycles, plus the stakes for future success are higher—AI has challenged tech companies’ operating models, and will continue to do so. How can you ensure that you stay ahead of the curve?

Tech executives must approach AI strategically, focusing on affordability and applicability within business models. Embracing AI's potential requires R&D and collaboration—both internally and externally. Preparing your workforce for data literacy is vital, and building a strong tech foundation is non-negotiable. Tech leaders who navigate this path wisely will reap AI’s many rewards.

Do you have the combination of innovative vision and operational discipline necessary to unlock AI’s full potential at organizations that need it?  We would love to hear from you.

Not sure what it takes? Here are our thoughts:

1. Leadership Consensus: Adopting AI goes beyond technology. It involves reshaping operations, customer engagement, and go-to-market strategies. Organizations and senior leadership teams need to be fully aligned and supportive of a plan for enterprise-wide AI adoption with respect to risks, strategies, and budgets.

2. Creating a Clear Path for Adoption: If you are a senior leader within your company, you can enable transformation and help guide AI adoption by connecting the need for innovation to your culture, employees, and clients. Tell the story of AI and innovation through a lens of applicability.

3. Org Design: AI solutions that are embedded into existing applications or that are purposefully deployed to automate, enhance productivity, or completely transform business processes will lead to a corresponding requirement to rethink organizational structures. Proactively consider what role AI will play in your business as you design future-fit roles and organizational structures.

4. Citizen Scientists: Embedding AI solutions will provoke rethinking traditional role definitions too. While some roles may be eliminated, many more will be enhanced. Some will be created to seize AI’s full potential (e.g., prompt engineers). Developing a data-literate workforce is crucial.

5. Quick Wins: Experimentation is key. Creating an environment for quick AI wins allows for learning and adaptation. To unlock AI's full potential, substantial R&D is required for greater efficiency and quality.

6. Building Strong Foundations: To successfully implement AI, organizations need the right data infrastructure to embed AI/ML models into tech stacks. Recognize the convergence of computing at the storage, edge, network and infrastructure layers and invest wisely.

7. Affordability Matters: While AI can work wonders, companies must assess the cost-effectiveness of each applied solution. The return on investment should align with strategic financial goals.

8. Data Veracity and Ethics: AI integration depends on data quality, governance, and compliance. Clearly define “rules of the road” that guide data usage, AI-generated results evaluations and actions, and how AI usage will be communicated to customers. There are already many privacy and risk considerations that must be weighed as you align AI with your business strategy.

9. Don’t Go It Alone: Big tech companies are able to build massive AI technologies because they can afford the cost of computing. If you have gaps in your infrastructure, industry collaboration is key for developers to make rapid advancements in AI. The classic “build, buy, partner” framework applies here—leveraging open source LLMs can accelerate overall AI development for your company.

Until next time, we hope you enjoy the articles below.