We have all been witnessing the transformative power of generative artificial intelligence (AI), with the promise to reshape all aspects of human society and commerce while companies simultaneously grapple with acute business imperatives. In 2024, companies confront significant disruption, requiring them to redefine labor productivity to prevent unrealized revenue, safeguard the software supply chain from attacks, and embed sustainability into operations to maintain competitiveness.
AI is at a turning point, driving exponential advancements in an organization’s prosperity and growth. Generative AI (gen AI) introduces transformative innovation to all aspects of a business; from the front to the back office, through ongoing technology modernization, and into new product and service development.
While many organizations have implemented AI, the need to keep a competitive edge and foster business growth demands new approaches: simultaneously evolving AI strategies, showcasing their value, enhancing risk postures and adopting new engineering capabilities. This requires a holistic enterprise transformation. We refer to this transformation as becoming an AI+ enterprise.
Become an AI+ enterprise
An AI+ enterprise innovates with AI as the primary focus, understands that AI is fundamental to the entire business, and recognizes that AI impacts all aspects of the business: product innovation, business operations, technical operations, as well as people and culture.
Figure 1: Transforming into an AI+ enterprise is at the core of what our team at IBM does
An AI+ enterprise integrates AI as a first-class function across the business. They understand that if one area of the business adopts AI while others lag or resist it (due to valid concerns), this exacerbates issues like Shadow AI, making it challenging to implement a holistic strategy.
Benefits of being an AI+ enterprise
The vast business opportunity with AI, forecasted by Gartner to bring USD 3 to 4 trillion in economic benefits to the global economy across industries, prompts companies to recognize the investment required to use AI effectively, and are demanding a dramatic return on investment (ROI) before investing in an AI use case. By becoming an AI+ enterprise, clients can realize the ROI not only for the AI use case but also for improving the related business and technical capabilities required to deliver AI use cases into production at scale.
Figure 2: ROI potential by transforming into an AI+ enterprise
Organizations with high data maturity that embed an AI+ transformation model into the enterprise fabric and culture can generate up to 2.6 times higher ROI. IBM has developed AI+ Enterprise Transformation to equip clients with the business and technical strategy, architectures, roadmaps and hands-on experience to become an AI+ enterprise.
AI+ Enterprise Transformation
With IBM’s depth in AI and hybrid cloud, we have discovered that companies becoming an AI+ enterprise leads to faster realization of business results. What’s exciting is that many clients we work with are already excelling in AI, and by adopting AI+ Enterprise Transformation, they uncover activities that accelerate their business growth through running AI in production at scale.
Figure 3: AI+ enterprise domain overview
Figure 3 summarizes AI+ Enterprise Transformation, highlighting the multiple domains across the organization that an AI+ enterprise needs to address to bring AI to production at scale:
Key use cases that enhance business performance
Responsible AI technology to implement these use cases
A well-designed data foundation to fuel AI initiatives
Application innovation to deliver AI experiences, and application modernization to handle AI requests
Hybrid cloud platform, including integrations, to run AI, data and applications as required
Building pipelines for continuous updates, enhancements and fixes of apps, data and AI, with deployment protection through scanning and guardrails
Day-2 operations using AI to predict (and repair) failures before they happen, fostering a culture where employees embrace AI’s value instead of fearing replacement.
Security, governance, risk and compliance mechanisms are essential not only for governing AI but also for managing the IT estate running AI, providing evidence for regulatory compliance.
Start with the use cases
The most important step for an AI+ enterprise is identifying transformative use cases. After experimenting with various options, the enterprise selects high-value use cases that show faster ROI. It then delivers them into production across the IT landscape, laying the groundwork for additional use cases and fostering ongoing innovation.
Figure 4: AI+ use case funnel to deliver AI solutions to production at scale
Harness the right AI technology
After identifying use cases, the next step for an AI+ enterprise is choosing the appropriate AI technology and architecture. Often, this decision is made too quickly. It should be approached thoughtfully to help ensure suitability. Consider the following:
Do you need a public foundation model?
Should you build your own?
If so, where will it run?
Should you use a retrieval augmented generation (RAG) model by pairing your data with a public foundation model?
Do you use gen AI out of the box?
How can you master prompt engineering?
When should you prompt-tune or fine-tune?
Which approach requires on-premises GPUs?
Where do you harness gen AI vs. predictive AI vs. AI orchestration?
For instance, when automating password change requests, do you need a 175 billion parameter public foundation model, a fine-tuned smaller model, or AI orchestration to call APIs? As you pinpoint your AI technology, your decision impacts the other domains of AI+ Enterprise Transformation. For more insights, keep reading.
Deliver a strong data foundation
AI relies fundamentally on data. An AI+ enterprise ensures that the data used for AI is trustworthy, transparent, and has clear lineage and efficacy. Otherwise, the risks become too significant. We have all seen examples of companies delivering AI built on weak data foundations, leading to undesirable outcomes. These outcomes typically fall into one of three categories, none of which are desirable:
Not useful: Customers remain unimpressed with your results. For example, stale data, hallucinations and more.
Embarrassing: Offensive output emerges based on the data used in AI. For example, hate, abuse, profanity and bias.
Financial/criminal: Violations of existing and emerging data and AI regulations. For example, copyright laws, the European Union’s Artificial Intelligence act, Digital Operational Resilience Act (DORA), data sovereignty laws and more.
An AI+ enterprise empowers architects to confidently source, prepare, transform, protect and deliver data to the required locations for AI.
Innovate and modernize applications
Innovating with new AI-based applications to deliver outstanding experiences is essential. It’s also crucial to modernize existing applications that interact with AI. if an AI-powered human resources assistant offers to perform actions for employees, it is vital to ensure that the application being called can handle increased traffic. Frequently, these actions involve calling APIs to legacy applications running on architectures unfit for handling the sudden demands of the AI assistant. This often leads to a disappointing experience due to slow response times. An AI+ enterprise excels in delivering innovative AI applications to its customers and modernizing existing applications to meet the new demands AI presents.
Hybrid cloud platform
Once AI, data and applications are understood, the discussion naturally shifts to “Where do we run this solution?” In our experience, the answer depends on many factors, which can change over time, requiring a flexible platform. Adopting an open technologies-based hybrid cloud platform enables an AI+ enterprise to make informed decisions without limiting its business.
Figure 5: AI+ enterprise hybrid cloud architecture
As shown in figure 5, a hybrid cloud architecture enhances the entire business in various ways:
Flexibility in where to train and tune large models
Flexibility in where to train and tune smaller models
Where to perform inferencing on-premises, in private clouds or even on edge devices
Applications using RAG architectures experience less latency when running close to the models
Data sovereignty laws limit data relocation, so having the ability to move AI and applications to the data is essential
Creating an AI+ fabric that provides interconnectivity across the IT and business landscape
Continuously build and enhance apps, data and AI
When AI, data and applications run across a well-designed hybrid cloud platform, an AI+ enterprise builds pipelines and toolchains to continuously enhance and deliver with full automation. For example:
Platform pipelines provision and update infrastructure and the software running on them using Terraform and Ansible
Application pipelines integrate and deliver code updates for both innovative applications delivering AI experiences…
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