How to become an AI+ company

We have all witnessed the transformative power of generative artificial intelligence (AI), which can help companies solve pressing business challenges while reshaping all aspects of human society and commerce. By 2024, companies will face significant disruption that will require them to redefine labor productivity to prevent unrealized profits, protect their software supply chains from attacks, and embed sustainability into their operations to remain competitive.

AI is at a turning point that will exponentially advance the prosperity and growth of organizations. Generative AI (Gen AI) introduces disruptive innovation into all aspects of business. We are responsible for the development of new products and services from the front office to the back office through continuous technological modernization.

Many organizations have implemented AI, but new approaches are needed to maintain competitive advantage and drive business growth. That means simultaneously evolving your AI strategy, demonstrating its value, strengthening your risk profile, and adopting new engineering capabilities. This requires a holistic corporate transformation. We call this transformation the transformation to an AI+ enterprise.

Let’s become an AI+ company

AI+ companies innovate with AI as their primary focus, understand that AI is fundamental to their entire business, and that AI impacts all aspects of their business, including product innovation, business operations, technology operations, as well as people and culture. I recognize it.

AI+ companies integrate AI as a leading feature across their business. They understand that if one area of ​​the business adopts AI while other areas lag or resist (due to valid concerns), problems such as Shadow AI will be exacerbated, making it difficult to implement the overall strategy.

Benefits of becoming an AI+ company

The vast business opportunities enabled by AI, which Gartner predicts will bring $3 to $4 trillion in economic benefits to the global economy across industries, make companies aware of the investments needed to use AI effectively and see dramatic return on investment (ROI). I’m asking for it. Before investing in AI use cases.

By becoming an AI+ company, customers can realize ROI to improve not only their AI use cases, but also the associated business and technical capabilities needed to deliver AI use cases to large-scale production.

Data-mature organizations that embed AI+ innovation models into their corporate structure and culture can generate up to 2.6x higher ROI.

IBM developed the AI+ Enterprise Transformation to provide clients with the business and technology strategy, architecture, roadmap, and hands-on experience to become AI+ enterprises.

AI+ enterprise innovation

Through IBM’s depth in AI and hybrid cloud, we’ve found that companies that become AI+ companies can realize business outcomes faster. What’s exciting is that many of the clients we work with are already excelling in AI and have discovered activities that accelerate the growth of their businesses by adopting AI+ Enterprise Transformation to run AI in large-scale production.

Figure 3 summarizes AI+ enterprise transformation, highlighting several domains across the organization that AI+ enterprises must address to bring AI into production at scale.

• Key use cases that improve business performance
• Responsible AI technologies that enable these use cases
• A well-designed data foundation to drive AI initiatives
• Transform applications to deliver AI experiences and modernize applications to handle AI requests.
• Hybrid cloud platform with integrations to run AI, data, and applications on demand
• Build pipelines for continuous updates, improvements, and fixes of apps, data, and AI by securing deployments with scans and guardrails
• By using AI to predict (and recover from) errors before they occur through day 2 operations, you create a culture where employees embrace the value of AI rather than fearing replacement.
• Security, governance, risk and compliance mechanisms are essential not only for managing AI, but also for managing the IT assets that run AI and provide evidence of compliance.

Start with use cases

The most important step for AI+ companies is identifying innovative use cases. After experimenting with different options, companies choose high-value use cases that demonstrate faster ROI. You can then deliver this to production across your IT environment, laying the foundation for additional use cases and driving continuous innovation.

Figure 4 illustrates the AI+ use case funnel that AI+ enterprises adopt to systematically and rigorously transform use cases into broad AI enterprise solutions that deliver high ROI, aligned across delivery, operations, security, and governance.

Utilizing the right AI technology

After identifying use cases, the next step for AI+ enterprises is to select appropriate AI technologies and architectures. Often these decisions are made too quickly. Ensuring compliance requires a careful approach.

Consider the following:

• Do we need a public-based model?
• Should I build it myself? If so, where does it run?
• Should I use a Search Augmented Generative (RAG) model by combining my data with a public-based model?
• Are you using Gen AI right away? How can you master Prompt Engineering? When do you need to make quick or fine adjustments?
• Which approach requires an on-premise GPU?
• Where do you use Gen AI, predictive AI, and AI orchestration? For example, when automating password change requests, do you need a 175 billion parameter disclosure-based model, a small-scale fine-tuned model, or AI orchestration for API calls?

As you pinpoint AI technologies, your decisions will impact other areas of AI+ enterprise transformation. Read on to get more insight.

Provides a strong data foundation

AI fundamentally relies on data. AI+ companies ensure that data used in AI is trustworthy, transparent, and has clear lineage and efficacy. Otherwise the risk becomes too great. We have all seen examples of companies providing AI based on weak data foundations, resulting in undesirable outcomes. These outcomes typically fall into one of three categories, none of which are undesirable.

• Not useful: The customer is still unimpressed with your results. Examples include outdated data, hallucinations, etc.
• Perplexing: AI produces aggressive output based on data used. Examples include hate, abuse, name-calling, and prejudice.
• Finance/Crime: Violations of existing and new data and AI regulations. Examples include copyright law, the European Union’s Artificial Intelligence Act, the Digital Operational Resilience Act (DORA), and data sovereignty law.

The AI+ company helps designers confidently source, prepare, transform, secure, and deliver data where it needs to be for AI.

Application innovation and modernization

Innovating with new AI-based applications is essential to delivering great experiences. It is also important to modernize existing applications that interact with AI. When an AI-based human resources assistant offers to perform tasks for employees, it is important to ensure that the application being invoked can handle the increased traffic. These tasks often involve API calls to legacy applications running on architectures unsuited to handling the sudden demands of AI assistants. This often results in slow response times and a frustrating experience.

AI+ companies excel at delivering innovative AI applications to their customers and modernizing existing applications to meet the new requirements presented by AI.

Hybrid cloud platform

Once we understand AI, data, and applications, the discussion naturally moves to “Where do we run these solutions?” In our experience, the answer depends on a variety of factors that can change over time and require a flexible platform.

Adopting a hybrid cloud platform based on open technologies allows AI+ enterprises to make informed decisions without limiting their business.

As Figure 5 shows, hybrid cloud architecture improves the overall business in a number of ways.

• Flexibility to train and tune large models
• Flexibility to train and tune smaller models
• Where you can perform inference: on-premises, in a private cloud, or on edge devices.
• Applications using the RAG architecture experience reduced latency when running close to the model.
• Data sovereignty laws limit data relocation, so having the ability to move AI and applications to data is essential.
• Create an AI+ fabric that provides interconnectivity across IT and business environments

We continuously build and improve apps, data, and AI.

Once AI, data, and applications run on a well-designed hybrid cloud platform, AI+ companies build pipelines and toolchains to continuously improve and deliver with full automation. for example:

• Platform Pipelines uses Terraform and Ansible to provision and update your infrastructure and the software that runs on it.
• The application pipeline integrates and delivers code updates for both innovative applications that deliver AI experiences and modernized applications run by AI digital workers.
• Data pipelines process incoming data to ensure that the data sources used by AI are up-to-date and valid.
• The AI ​​pipeline pulls, retrains, and enriches data as needed based on metrics like drift and accuracy.

AI+ companies know how to continuously improve applications, data, and AI models throughout their lifecycle to help ensure that only trusted, approved AI capabilities are enabled.

operate

Even in a world where AI comes first, accidents happen. But AI+ companies use AI not only to satisfy customers but also to solve IT problems. With the right tools, AI+ companies can significantly increase employee productivity. Examples include:

• Detect and correct when applications are throttling and capacity increases.
• Deliver visibility and insight across your enterprise to enable higher levels of automation and achieve predictive maintenance.
• Reduce security threats by closing security gaps before they occur. For example, using compliance control detection in a Terraform template, provisioning will fail if the control is not met.

AI+ companies also recognize the importance of fostering a culture that embraces AI and trains talent along with the necessary tools. This culture encourages experimentation and the growth of expertise. This requires people trained to leverage, evaluate, and accelerate AI, rather than fearing it.

Protect and manage AI on a hybrid cloud platform

To deploy AI, especially Gen AI, at production scale, organizations must create a secure and managed environment. The scale and impact of next-generation AI highlights the importance of governance and risk control. AI+ companies implement robust measures to secure, monitor, and account for AI models and monitor governance, risk, and compliance controls across hybrid cloud environments to mitigate potential harm.

It is important to combine existing cloud governance with new AI governance controls to comply with new regulatory changes such as the NIST AI Risk Management Framework, the European Union’s Artificial Intelligence Act, ISO/IEC 42001 AI Management, and ISO/IEC 23894 AI. It requires constant focus. Crisis management.

Get started now

IBM wants to partner with you to provide impactful use cases, strategy, architecture and practical experience to become an AI+ company.

• Understand your current trajectory
• Establish AI+ enterprise strategy through perspectives and models
• Co-create target state solutions and architectures
• Governance and risk status assignment
• Customize your business value case
• Co-develop major short-term engineering sprints to demonstrate the value of your AI+ enterprise strategy.

Future articles will delve deeper into each AI+ domain, showing IBM’s perspective through architecture, demos, and strategy.

Visit IBM Hybrid Cloud Architecture Center