How to become an AI+ enterprise

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 illustrates the AI+ use case funnel that an AI+ enterprise adopts to systematically and rigorously transform use cases into broad-reaching AI enterprise solutions that deliver high ROI, aligned across delivery, operations, security and governance.  

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 and modernized applications being acted upon by AI digital workers
• Data pipelines process incoming data to help ensure that the data sources used by AI are current and valid

AI pipelines pull in data, retrain and augment as needed based on metrics such as drift and accuracy

An AI+ enterprise knows how to continually enhance applications, data and AI models throughout their lifecycle, helping to ensure that only trusted and approved AI functions go live.

Operations

Incidents occur, even in an AI-first world. However, an AI+ enterprise uses AI not only to delight customers but also to solve IT problems. With the right tools, an AI+ enterprise can significantly increase employee productivity. Examples include:

• Detecting and correcting when an application is being constrained and automating capacity increases
• Providing visibility and insight across the enterprise to enable higher levels of automation and achieve predictive maintenance
• Reducing security threats by closing security gaps before they arise. For example, by using compliance control scanning of terraform templates to fail provisioning if controls are not met.

An AI+ enterprise also recognizes that alongside the necessary tools, fostering a culture that embraces AI and trains talent is crucial. This culture encourages experimentation and expertise growth. It requires people trained to harness, evaluate, and accelerate AI, rather than fearing it.

Secure and govern AI on the hybrid cloud platform

To deploy AI, particularly gen AI, at scale in production, organizations must establish a secure and governed environment. The scale and impact of next-generation AI emphasize the importance of governance and risk controls. An AI+ enterprise mitigates potential harm by implementing robust measures to secure, monitor and explain AI models, as well as monitoring governance, risk and compliance controls across the hybrid cloud environment.

Pairing existing cloud governance with new AI governance controls is essential, requiring continual focus to comply with emerging regulatory changes, such as NIST AI Risk Management Framework, the European Union’s Artificial Intelligence act, ISO/IEC 42001 AI Management, and ISO/IEC 23894 AI Risk Management.

Get started today

IBM wants to work with you to become an AI+ enterprise, providing impactful use cases, strategies, architectures and hands-on experiences to:

• Understand your current trajectory
• Shape your AI+ enterprise strategy with points of view and models
• Co-create target state solutions and architectures
• Specify governance and risk posture
• Customize a business value case
• Co-develop key near-term engineering sprints to prove the value of your AI+ enterprise strategy

Future articles will delve deeper into each AI+ domain, showcasing IBM’s perspective through architectures, demos and strategies.

Source: ibm.com

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How CDOs create lasting culture change through employee empowerment

Do you remember when you first began to think about data? Long before we learn about the concept, from the moment we are born, we are absorbing, consuming, sorting and organizing data.

Nowadays, not all enterprises use data in a strategic way. Only recently have business leaders had access to much of the important data affecting their enterprises, and the tools to understand it at a granular level.

The growing importance of the CDO

The Chief Data Officer (CDO) role is relatively new to the c-suite. Its evolution into a board-level position is telling, as businesses worldwide come to understand just how much they can benefit from being a data-first organization. Put simply, the goal of the CDO is to help the organization make decisions based on data. But their reach doesn’t stop at the boardroom. Their strategies affect the entire company, making the CDO a powerful agent of change.

As a CDO weaves data decisions into daily workflows, their success is measured along three dimensions:

◉ Growth of the top line

◉ Expansion of the bottom line

◉ Reduction of risk

As CDO at IBM, an essential part is being an evangelist for data literacy and data democratization in service of those dimensions above. Building a data-first culture involves talking a lot about it. So, in a recent episode of the podcast Sunny Side Up, I spoke with Asher Mathew and explained how the most effective way to see success is by transforming your company’s culture to a data-first philosophy.

You can listen to that conversation right here.

So, how does an organization do this in a holistic and lasting way? By building a data-first culture.

Building a data-first culture

Every organization has mountains of data that, when properly analyzed, provide a risk-averse growth strategy unique to the needs of their business or industry. An effective CDO must pay attention to that data, understand how to apply it to advance the company’s business goals, and be prepared to articulate their case at the board level.

Changing an organization can be daunting, which is why it’s crucial to develop a data strategy that aligns with your business strategy. For example, when I joined IBM, I knew their business strategy focused on selling Hybrid Cloud and AI. We thoroughly understood what that journey meant for a consumer, but not so much in terms of an enterprise. In time we recognized we needed to become an AI enterprise, to be in a position to help organizations do the same and infuse AI into their business processes. Therefore, our data strategy became about transforming IBM into an AI enterprise and then showcasing our story to our clients. Of course, you can’t do that successfully without a data-first culture.

To usher in this change, we created a variety of specialized business units to work toward infusing AI throughout the company.

◉ The data standardization and governance unit ensures that all the data we use is fit for purpose.

◉ The adoption unit is fully empowered to work directly with various departments and implement an AI transformation. We recognized that AI needed to be incorporated into our company-wide workflows so the participation of this unit is critical to ensuring that the company’s data and AI platform has full adoption across the company.

◉ The data officer council is made up of members from organizations throughout the company who help validate the direction set by the Chief Data Office and remain responsive to business needs.

Many of IBMs clients look like us: they are complex organizations filled with potential that’s difficult to unlock. Since it’s a challenge to change such complex ecosystems, it is important that IBM be a definitive showcase for the kinds of transformation we can offer to our clients and partners.

The successes of a data-first culture

Let me share some examples of our success. In 2016, we laid down our data foundation to deliver trusted, enterprise-wide datasets and standards. Once we deployed our AI solution along those vectors, we saw a boost in operational efficiency: an over 70% improvement to the average business process cycle time, which is the time a process takes to complete from start to finish.

We had a massive effort around risk mitigation from 2016-2018 to respond to the introduction of GDPR in the EU. More than that, however, it was an opportunity to innovate and develop new services and governance frameworks to facilitate better compliance efforts at scale.

With the creation of our AI Accelerator Team in 2018, we started to focus on revenue growth. The AI Accelerator team takes our internal data and AI transformation and showcases it to our clients to help them drive similar changes within their organizations.

Data literacy is at the core of each unit’s success. This should be the primary goal of any CDO change agent. Data is crucial to everybody’s lives, not just to people in organizations like ours. The public at large needs to understand what can be done with data. The cultural change element of the CDO role is concerned with influencing how data is used from within and being the example, that others can follow. If the CDO does not focus on this, how can they expect anyone else to care?

Above all, we want to empower our people to be data-driven; move at the speed of their insights, to observe the data, act on it and not ask for permission.

You must be prepared to inspire and support your teams to thrive, and more realistically, to fail fast, but learn through that failure. The CDO is in a position to understand all elements of the change, how to navigate challenges, how to learn through failure and how these components transform the culture of the enterprise. Effective communication is critical at the C-suite level, and the data you’re using becomes an integral part of the business strategy.

If you found these ideas intriguing, I invite you to join me in a deeper discussion about exploring data and technology with leading CDOs, CAOs, and CTOs at the upcoming IBM Chief Data Technology Summit Series.

Source: ibm.com

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We must check for racial bias in our machine learning models

As a data scientist for IBM Consulting, I’ve been fortunate enough to work on several projects to fulfill the various needs of IBM clients. Over my time at IBM, I have seen technology applied to various use cases that I would have never originally considered possible, which is why I was thrilled to steward the implementation of artificial intelligence to address one of the most insidious societal issues we face today, racial injustice.

As the Black Lives Matter movement started to permeate throughout the country and world in 2020, I immediately wondered if my ability to solve problems for clients could be applied to major societal issues. It was with this idea that I decided to look for opportunities to join the fight for racial equality and found an internal IBM community working on projects that were to be released through the Call for Code for Racial Justice.

Adding my two cents to TakeTwo

There were numerous projects that were being incubated within IBM but I found myself drawn to one in particular that was looking both an implicit and explicit bias. That project was TakeTwo and was to become one of the seven projects that was released as an external open source project just over a year ago. The TakeTwo project uses natural language understanding to help detect and eliminate racial bias — both overt and subtle — in written content. Using TakeTwo to detect phrases and words that can be seen as racially biased can assist content creators in proactively mitigating potential bias as they write. It enables a content creator to check content that they have created before publishing it, currently through an online text editor. Think of this like a Grammarly for spotting potentially racist language. TakeTwo is designed to leverage directories of inclusive terms compiled by trusted sources like the Inclusive Naming Initiative.

Not only did TakeTwo allow me to apply my expertise to improve the project, but it also afforded me the opportunity to look inward at some of the implicit racial biases that I may have held but have been formerly unaware of. Working on TakeTwo was great way to work on a mission that matters for the world, while also providing a chance for self-reflection.

See the solution action: 

The Data Challenge

While working on TakeTwo it became abundantly clear that although the solution aims at detecting bias by fielding and evaluating massive amounts of data, it’s important to recognize that the data itself can hold implicit bias in itself. By leveraging Artificial Intelligence and open source technologies like Python, FastAPI, JavaScript, and CouchDB, the TakeTwo solution can continue to evaluate the data it ingests, and better detect when bias exists within it. For example, one word or phrase that may be acceptable to use in the United States may not be acceptable in Japan – so we need to be cognizant of this to the best of our ability and have our solution function accordingly. As someone who is passionate about data science, I know from firsthand that our model is only as good as the data we feed it. On that note, one thing I’ve learnt from working on this project is that we need better data sets that can help us train the machine learning (ML) models that underpin these systems. Kaggle datasets has been a great starting point for us, but if we want to expand the project to take on racism wherever it exists, we’ll nee more diverse data.

On a related note, the skills needed on projects like these go way beyond just data science. Particularly for this project, it was important to leverage linguistics experts who can help define some of the cultural nuances that exist in language that a system like TakeTwo either needs to codify or ignore. Only by working cross-discipline can we get to a workable solution.

Be part of the future

The value that ML and AI bring to enhancing solutions like TakeTwo is inspiring. From hiring employees, to getting approved for a loan at the bank, ML and AI is permeating into the way we interact with one another and can help ensure we remove as much racial bias as possible for business decision-making. As technologists, we have a distinct responsibility to produce models that are honest, unbiased, and perform at the highest level possible so that we can trust their output.

You can check out how we at IBM are building better AI here. TakeTwo continues to make strides in developing and strengthening its efficacy, and you can contribute to making this open source project better. Check out TakeTwo and all of the other Call for Code for Racial Justice Projects today!

Source: ibm.com

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