Editor’s note: The following is a guest post from Kate Woolley, general manager of IBM’s Partner Ecosystem.
Generative AI has hit a Big Bang moment — it's everywhere all at once.
Many companies see the technology as a driver of business transformation, delivering unprecedented productivity and growth. An IBM Institute for Business Value survey of 3,000 global leaders found half already use generative AI in their products and services. The same survey found the majority of CEOs also say competitive advantage depends on who has the most advanced generative AI.
As we accelerate serious enterprise generative AI adoption, now is the time for the C-suite to ensure their journey to generative AI is safe and responsible while preparing their employees for the changes ahead.
Business leaders must consider trusted vendors and partners with proven capabilities across industries and geographies to help scale. With the right network of software and technology vendors, consultants, suppliers and resellers, you can help ensure the proper groundwork is in place and at the core of any AI-enhanced business.
Here are four critical areas to address while shaping generative AI strategies:
1. Good governance
Sound governance policies ensure AI tools and systems are safe and ethical. They establish the frameworks, rules and standards directing AI research, development and application to ensure safety and fairness.
Since AI is a product of highly engineered code and machine learning created by people, it is susceptible to human biases and errors. Generative AI efforts can include data hallucinations, compliance violations and unfair or biased content output.
Corporate leaders should pull from the diverse expertise of sectors such as academia, industry and government, which can contribute insights ranging from research to implementation experience to regulatory guidance, to ensure robust governance practices are in place to support AI implementation.
Working with partners that have built solutions on proven AI technology can also help integrate it into a business’s operations. By pooling these resources and expertise, leaders can enlist a trusted ecosystem of partners to help them address complex challenges of AI governance and ensure resource limitations do not constrain governance efforts.
2. A platform approach
The surge in cloud-native workloads and related applications is leading to a significant increase in the volume of data enterprises must manage. Generative AI will further increase reliance on cloud resources while driving higher demands for computing power.
Everyday AI-informed workloads and training AI foundation models will push computing demands further than ever. To scale AI, the C-suite should coordinate efforts that optimize their data and compute resources across multiple cloud and on-premise environments.
The linchpin for many of these technology decisions is hybrid cloud – the combination of public and private clouds with on-premises infrastructure to create a single, flexible IT infrastructure.
Organizations with a platform approach, with hybrid cloud and AI at the center, are best positioned for success. Start thinking about providing access, management, security and control of AI-dependent data and services across your hybrid cloud footprint.
Partners can provide resources and support to effectively deploy and support your cloud and AI solutions. This could involve offering technical training and ongoing assistance while ensuring that your cloud and AI solutions adhere to compliance and security standards. That’s especially important when dealing with sensitive data or regulated industries.
3. Risk management
Corporate leaders shouldn’t question whether to explore AI, but how to do so responsibly.
Generative AI pilots can fail due to problems with data quality and inadequate risk controls, so CXOs should identify and mitigate these risks to protect the company's reputation and operations. AI projects will soon drive even more mission-critical workloads while needing access to the most trusted data. Organizations must demand responsible use of that data and the large language models upon which their AI is trained.
Partnering can bring a multitude of apps, offerings and solutions built with trustworthy AI, as well as diverse perspectives into these conversations. By pooling resources and knowledge to drive innovation, organizations can accelerate progress in identifying where things can go wrong and how to handle the situation if they do.
4. Reskilling
This is crucial as many enterprises need more skills and experience to deploy AI and accelerate adoption at scale.
Partnering with consulting organizations, systems integrators, AI startups and industry experts provides access to knowledge and solutions essential for designing, implementing and optimizing AI solutions.
This can also help accelerate the learning process while simultaneously encouraging employees to engage with educational institutions, online learning platforms and training providers for various training resources, including courses and workshops tailored to AI skills development.
