4 Strategies for a Robust — and Profitable — AI Future

Generative AI has reached a Big Bang moment — it is everywhere at once.

Many companies see technology as a driver of business transformation, delivering unprecedented productivity and growth. IBM Institute for Business Value survey of 3,000 global leaders, half said they already use generative AI in their products and services. The same study found that a majority of CEOs also say competitive advantage lies in who has the most advanced generative AI.

As we accelerate the serious adoption of generative AI in enterprises, it is time for executives to ensure that their path to generative AI is safe and responsible while preparing their employees for the upcoming changes.

Business leaders need to 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 right foundations are in place and at the core of any AI-powered business.

Here are some key areas to consider when shaping generative strategies:

1. Good Gexceeding

Sound governance principles ensure the safety and integrity of AI tools and systems. They establish frameworks, rules, and standards that guide AI research, development, and application to ensure safety and fairness.

Because AI is a product of highly engineered code and machine learning created by humans, it is susceptible to human biases and errors. Generative AI efforts can include data hallucinations, compliance violations, and unfair or biased output.

Corporate leaders should draw on diverse expertise from different sectors such as academia, industry and government who can contribute insights including research, implementation experiences and regulatory guidance to ensure the implementation of sound governance practices to support AI adoption.

Collaborating with partners who have built solutions on proven AI technology can also help integrate it into the business. By combining these resources and expertise, leaders can leverage a trusted ecosystem of partners to help them address the complex challenges of AI management and ensure that resource constraints don’t limit management efforts.

2. A pplatform approach

The growth of cloud-native workloads and related applications is leading to a significant increase in the volume of data that enterprises must manage. Generative AI will further increase dependence on cloud resources while increasing the demand for compute power.

Everyday AI workloads and AI training base models will drive compute demands more than ever. To scale AI, executives need to coordinate efforts that optimize their data and compute resources across multiple cloud and on-premises environments.

A key element of many of these technology decisions is the hybrid cloud – combining public and private clouds with on-premises infrastructure to create a single, flexible IT infrastructure.

Platform and Hybrid Cloud Organizations AND AI at the core, are best positioned for success. Start thinking about providing access, governance, security, and control of AI-dependent data and services across your hybrid cloud environment.

Partners can provide resources and support to effectively implement and support your cloud and AI solutions. This can include offering technical training and ongoing support while ensuring your cloud and AI solutions adhere to compliance and security standards. This is especially important for sensitive data or regulated industries.

3. Risk management

Corporate leaders should not be wondering whether it is worth researching AI, but how to do it responsibly.

Generative AI pilots can fail due to data quality issues and poor risk controls, so CEOs should identify and mitigate these risks to protect their reputation and business. AI projects will soon generate even more mission-critical tasks while requiring access to the most trusted data. Organizations must demand responsible use of this data and the large language models on which their AI is trained.

Partnerships can bring a variety of apps, offerings, and solutions built with trustworthy AI, as well as different perspectives, to these conversations. By combining resources and knowledge to drive innovation, organizations can accelerate progress in identifying where things can go wrong and how to deal with the situation if they do.

4. Retraining

This is key because many enterprises need greater skills and experience to implement AI and accelerate its adoption at scale.

Collaborations with consulting organizations, system integrators, AI startups, and industry experts provide access to the knowledge and solutions necessary to design, implement, and optimize AI solutions.

This can also help accelerate the learning process while encouraging employees to use educational institutions, online learning platforms, and training providers to access a variety of training resources, including courses and workshops tailored to AI skill development.