For enterprise architects and strategic portfolio managers, artificial intelligence presents both opportunities and risks—adding layers of complexity to an already sprawling IT landscape.
In many ways, as AI tools begin to explode onto the market, these professionals are in a tricky position. Top management, faced with a flood of competing ideas for AI investment, are demanding they “do something.” But the best path forward is often far from clear. Not only do they need to evaluate a wide range of novel AI products, often from upstart vendors they’ve never worked with or have never even heard of. They also must decide which uses of AI will deliver the most value and be ready to answer tough questions about the potential implications—while at the same time putting the AI-enabled future of the enterprise in motion. Above all, they must manage all of this new complexity without losing sight of their core business objectives, while ensuring the adoption of AI accelerates their goals as an enterprise, rather than bogging down their IT environment.
The stakes of this are high. Enterprises that get the AI revolution “right” will be able to unlock efficiencies by introducing smarter working practices, automating tasks, and launching new products and services that can integrate and access data from anywhere. This promise, and the fear of being left behind, is poised to unleash massive disruption. According to McKinsey, the uptake of generative AI software—one of the first classes of AI to gain mass appeal but certainly not the last—is now happening three times faster than the spread of SaaS after its launch in 2000. McKinsey projects enterprise gen AI spending to rise from $15 billion in 2023 to up to $250 billion by 2027.
Enterprise generative AI spending will rise from $15 billion in 2023 to up to $250 billion by 2027.
At the same time, this new era presents significant challenges. AI tools are only as good as the data they ingest and are trained on; for many businesses, especially large ones, that data is siloed across multiple systems and varies widely in quality. Businesses are already grappling with the demands AI will place on their IT infrastructure and how they’ll stay compliant with new AI regulations across multiple jurisdictions. AI systems use significant amounts of energy, which underscores the importance of sustainable IT practices, and some of them pose complicated ethical questions. A successful AI strategy, therefore, must seek to achieve sustainable AI and ethical AI in parallel. Despite the oft-repeated fear of “robots coming for your job,” IT departments also face growing shortages of talent with the technical skills needed to deliver AI-embedded products and services seamlessly for their customers.
Soon, it will be unthinkable not to have intelligence integrated into every product and service. It’ll just be an expected, obvious thing.
This paper is designed to help IT leaders—and especially these architects and portfolio managers—begin to navigate these many complexities. By exploring what AI means for enterprise IT, and for the disciplines of enterprise architecture (EA) and strategic portfolio management (SPM), we hope we’ll reassure you that the rise of AI need not feel overwhelming—so long as you take the time to carefully plan out an enterprise-wide AI strategy. We’ll examine the questions you should ask when building this strategy, what your decisions about IT will mean for your goals as a business, and how AI, itself, could eventually aid this entire process by supercharging the abilities of enterprise architects and strategic portfolio managers to “master the challenge.” Above all, we’ll show you why your enterprise needs an EA & SPM tool in place, today—like Alfabet—to prepare you for the change that’s coming, and make sure, in this era of both fear and excitement, that your business is positioned to thrive.
Building an enterprise AI strategy
For all the hype surrounding AI, the challenges it poses to today’s IT planning professionals are not entirely new. As long as software has existed, companies have faced tough choices—not only about what systems and applications to invest in, but how the underlying architecture must adapt to make sure they “play well” with the existing IT landscape. EA & SPM are already widely used for this purpose. EA offers a single source of truth—to simplify your IT landscape and understand the implications of IT change. By grouping the elements of your IT into functional portfolios, SPM helps you connect the dots between this change and your goals as a business.
EA & SPM, together, offer companies a range of benefits. By providing real-time transparency into the IT landscape, they help reduce IT complexity, reign in technical debt, and better align business and IT in transformation projects, from strategy to execution. Not only does this allow businesses to innovate and respond to new customer demands more quickly; it also builds resilience to market disruptions, and can help scale back costs significantly. One global telecommunications firm used Alfabet to consolidate applications and cut software costs in half. By streamlining its IT architecture, an Alfabet customer in the financial sector was able to increase productivity and reduce the amount of time spent administering data by up to 80%.
Why is an enterprise AI strategy important?
EA & SPM products like Alfabet are ideal for developing enterprise AI strategies, as well. In evaluating the flood of competing ideas for AI investment, portfolio managers need a way to define a broad AI vision, derive specific AI initiatives and projects, and understand the demands AI will place on the organization—all while juggling input from multiple stakeholders, and making sure their budgets aren’t exhausted.
Doing this successfully requires navigating some critical decisions related to your AI goals as an enterprise and your quest to translate AI into business value.
The first question to ask is simple: What do I want to achieve? Businesses must first decide which use cases to address and, in turn, which AI models to adopt to go about them. Building and pre-training your own models is resource intensive and requires extensive testing. For most companies, achieving a strong ROI through this route is unlikely. This leads many to the next set of questions: How can I choose the right pre-built solution, and adapt it to my situation? Is there one that’s ideal for my use case? What are the costs of inferencing? How can I fine-tune the solution to adjust it to my needs, and how can I set up testing routines to ensure output quality? Is fine tuning it with my own data needed or is parameter efficiency tuning enough? What about hosting and deployment?
None of this easy: Every decision involves the potential of added costs and infrastructure changes. Even if you “only” integrate ready-to-go third party AI solutions or applications, you need to be aware of vendor dependability, risks and possible integration challenges. In either scenario, you need to ensure the reliability of the AI-produced data and be able to integrate that data into other systems safely. You also need to guarantee IP integrity, and make sure you’re not falling afoul of ever-shifting regulations.
A holistic view of IT
Once you have a grasp of what AI-related questions to ask—and what decisions must be made—a next step is to make sure you have a comprehensive understanding of your existing architecture. EA & SPM cannot help you decide where you’re going if you don’t have an accurate, complete and real-time view of your IT at present.
Temporarily, it may feel like you’re backtracking: You may find yourself asking basic questions like, “What is in our application portfolio?” For large enterprises, though, with IT scattered across multiple locations, achieving this exhaustive view isn’t as simple as it seems. This is what EA & SPM tools were built for: Not only capturing all projects, technologies, processes and applications. But identifying interdependencies—so when you do pursue AI-driven IT change, you’ll know exactly how your new project will impact everything else.
What IT change means for business
The next step in developing an enterprise AI strategy involves identifying the dimensions of business value that are most important—and are the best candidates for AI-driven enhancements. Is management most concerned with optimizing costs, expanding into new markets, or creating products and services that will lure new customers and make your brand stand out? An EA/SPM tool can help you break down this high-level strategy into executable goals and visualize their impact on current and future IT and business capabilities. From there, it can help you analyze planned IT changes, understand their wider impact, and create roadmaps for a well-timed implementation. With the right EA/SPM tool, you’ll have a detailed understanding of what needs to change and why—and exactly how to make that change happen.
AI is transforming enterprise architecture and strategic portfolio management, too
For today’s enterprise architects, AI isn’t just a new class of software that EA & SPM can help make sense of. It’s also a tool that could soon have a profound impact on the EA & SPM discipline, itself. This is especially true for businesses with established EA & SPM practices. The greater a company’s EA & SPM maturity, the better positioned it typically is to embrace new AI-driven approaches that will make their daily work more efficient, drive architecture changes that are driven by data, and ultimately launch new AI-enabled activities that push the boundaries of what the discipline makes possible.
As in many industries, AI is already changing the working practices of enterprise architects and portfolio managers. According to Gartner, this will only accelerate in the years ahead:
By 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously.
Increasingly, AI tools will support day-to-day work in the following areas:
- Minimizing repetitive tasks—by automating incident checks and test apps for data quality, offering real-time in-context support, and translating human prompts into SQL and other coding languages.
- Architecture modeling—by analyzing existing architecture, and generating model diagrams to give each stakeholder their own, unique perspective.
- Data sourcing, discovery and creation—by automating APIs and data connections, harmonizing data from different sources and formats, and filtering out data of poor quality.
- Analyzing complex data sets—for threat and opportunity analysis, scenario planning, business/IT alignment, and process optimization.
Architecting for the future
The architecture changes required by new AI tools and applications demand careful consideration: An architecture fit for the era of AI must accommodate an ever-evolving array of bots, decision and recommendation algorithms, self- and machine-learning systems, and potentially foundation models or large language models (LLMs) like those powering gen AI today. Increasingly, EA & SPM will need to accommodate new demands related to the following domains:
- Business—including mapping AI strategy, objectives and organizational structure, and defining AI KPIs and other measurements.
- Models—by defining model rules, guardrails, learning methods, APIs and analytics.
- Data—through managing and modeling AI data, processes, rules, quality assurance and automated systems of data control and governance.
- Infrastructure—by addressing requirements for AI hosting and integration, the need to scale over time, and the new risks that come with AI-embedded systems.
EA & SPM, supercharged
Perhaps the most exciting way AI is transforming enterprise architecture is through the new sets of use cases it makes possible. In the near future, AI tools will not only free enterprise architects and portfolio managers of routine tasks, giving them more time to focus on higher value, strategic work. But AI will also help them simulate future IT scenarios, optimize resource allocation, and generate analysis that supports IT investment decisions, effectively supercharging IT planning.
AI-assisted Enterprise Architecture (EA) activities are evolving rapidly, with several capabilities expected to become technically feasible in the near future. These include enhanced IT reporting, which will improve the management and evolution of the EA function, as well as asset and data discovery, enabling more effective sourcing and management of the digital portfolio to guide solution design and delivery. Additionally, AI will play a key role in optimizing business and IT processes, facilitating digital business strategy and supporting technological innovation.
Looking further ahead, more technically advanced and futuristic use cases are on the horizon. These include enterprise architecture scenario planning, which will help organizations generate responses to future market uncertainties, and business and operating model design, where AI has the potential to act as a strategic partner, shaping business strategies.
SPM will soon be getting a makeover, too. AI is already fostering greater collaboration within companies by enabling business users to access and generate data independently from IT. By taking snapshots of complex EA views, AI tools can generate insights that help these non-technical users make data-driven decisions quickly, without IT’s constant intervention. Soon, it will be possible to use AI-driven solutions to generate and analyze enterprise-wide IT investment strategy and scenarios—and make faster, more informed decisions related to planning for innovations that include AI, itself. In other words, you’ll be using AI tools to unpack how AI can best benefit your business—and done right, you’ll be far better off for it.
The right tool for the road ahead
If all of this feels daunting, we get it. The truth is, when it comes to AI, we still don’t know exactly what’s coming, or how fast. But we do know that change is afoot—and that the pace of it will only accelerate as AI makes product development cycles ever faster, and process optimization is increasingly automated. In this environment, the ability to manage change is more vital than ever. Without a dedicated system to maintain ownership, and oversight, of this rapidly evolving IT landscape, enterprises will find it ever harder to stay on top of their existing architecture, let alone make the right investments that will benefit their bottom line for years to come.
Being prepared begins with having the right set of tools in place. Whether your enterprise already has an EA & SPM platform on hand, or is new to the discipline, it’s time to start thinking how you’ll adapt it to the AI era—to be ready to react as quickly as possible when management decides it’s time to ride the wave of AI disruption.
Wherever you are on your EA & SPM journey, and your strategy for AI-adoption, there has never been a better time to adopt a tool like Alfabet, the industry-leading EA & SPM platform from Software AG. Alfabet is unique in enabling alignment of business strategy and IT execution across the various portfolios of IT investments, business and IT change, digital products and services and IT assets. Unlike many tools on the market, it builds these SPM capabilities on top of a strong EA foundation, which helps enterprises more boldly pursue business opportunities, define their position in the market, and build greater resilience to operational risks. This strong EA/SPM synergy is one reason leading analysts consistently rank Alfabet as a market leader.
Would you like to learn more about preparing your enterprise architecture for AI—and how Alfabet can help you get there? Check out our webinar on AI and other key trends that are reshaping EA and SPM. Or get in touch to request a free trial and experience Alfabet in action.
