TechWave: A Gartner Podcast for IT Leaders

The challenges of a modern, data-driven enterprise demand modern tools capable of dealing with the volume and, more importantly, the diverse uses of personal data. In addition, the pace at which modern privacy regulations are proposed and adopted has continued to accelerate. This has fueled adoption of privacy technology by organizations looking to standardize a global privacy approach for handling personal data. Privacy-driven trust can serve as a key differentiator when customers are looking for a reason to pick one brand over another in a homogeneous market.

Strategic Planning Assumption

By the end of 2024, three-quarters of the world’s population will have its personal data covered by modern privacy regulations.

Executives seeking a positive balance between the organization’s overall success and corporate reputation should recognize that a mature privacy program is the entire organization’s responsibility. Privacy and data protection officers may take the lead, but CxOs have their respective responsibilities as well. In this podcast, we explore these issues and more.

Host Frances Karamouzis is joined by our expert analyst, Bart Willemsen. Willemsen focuses on privacy-related challenges in an international context, as well as on ethics, digital society, and the intersection with modern technology, including AI.

For 2024 and likely the next decade, business value creation will not happen without the successful blending of data and analytics (D&A) and software engineering at the core. Technology can be a failure point when not handled correctly, but it is often not the biggest roadblock to progress. Digital business acceleration will depend equally, if not more, on how you organize the required roles, skills and culture to drive this transformation.

AI investment continues unabated, often with hundreds of proposed initiatives. Almost all initiatives demand data, which requires that cost, risk and time values are assigned to proposed use cases. In this podcast, we explore business and IT leaders’ quest to understand and assess AI-ready data.

Digital Vanguard CxOs are 1.5 to 2 times more likely to achieve their value targets from digital. In this podcast featuring expert analyst Jaime Capella, we explore the characteristics of a Digital Vanguard CxO and, more importantly, what makes them more successful at digital initiative outcomes.

The 2024 Gartner CIO and Technology Executive Survey found that 81% of respondents believe that building, developing or customizing digital technologies for the business area should be the responsibility of IT departments (led by CIOs). Only 15% of CIOs believe that this responsibility should be shared equally with business areas, according to the survey.1

The ultimate responsibility for building, developing or customizing this software, whether within the CIO organization or in lines of business, falls to software engineering leaders. These leaders are in a key position to enable their organization to become builders of software, because they are at the intersection of business and technical domains, between strategy and implementation. But, to do so, they must build a world-class software engineering organization.

In this podcast, we explore the role of software engineers and developers as AI and generative AI are infused into their future. Gartner expert analysts discuss a few of the many layers of this complex topic in the following areas:

• Reality versus hype — The quest of business and IT leaders separating out what is reality versus hype in order to be pragmatic. More specifically, software engineering leaders must ensure they stay grounded on what is reality so they can prioritize and start piloting and experimenting.
• Value and measurement — All leaders need to be able to track and measure things so they can determine what they want to put in production, scale and make part of their long-term approach. Above and beyond that, they need to be able to quantify the value.
• Skills and talent — This involves a deeper understanding of the value of software engineering expertise, performance and productivity.
• Trends and the future — As always, it is important for leaders to stay abreast of what is coming and how to prepare for it.

To address these important focus areas, we discuss several important concepts in the podcast. A few are highlighted below.

Reframe the ROI Conversation

The current ROI conversation is focused on cost reduction. Gartner experts are focused on guiding leaders to value generation. It is important to stop thinking of AI as cost-reduction mechanisms or a tool that could help reduce headcount. Instead, it’s important to focus on AI and GenAI as force multipliers that enhance developer experience to such an extent that they enable activities that deliver real business value.

Amplification Fallacy

There is an idea that generative AI will “amplify” people’s skills. However, if you carefully think about the concept — “amplifying” something just makes it louder, it doesn’t make it better or higher quality. As such, it is important to identify and investigate the differentiated impact across the software development life cycle and specific developer skills.

Some initial findings show that GenAI provides a bit more of a productivity boost for junior developers. However, there is also countervailing data that less experienced developers overtrust the outputs of GenAI and are thus more error prone and more likely to introduce security vulnerabilities.

For more senior developers, the starting point is that they have the expertise to know what good looks like, as they already have deep knowledge of a problem space, of architectural standards, of best practices and experiential knowledge. Hence, if they are open to using new tools, experimentation and tinkering, they are the ones who can quickly iterate and figure out the best ways to prompt and interact with GenAI coding assistants.

Augmentation Versus Agency

One of the most critical and foundational concepts for the success of AI is trust — engendering trust for both the creators and consumers of the solutions. Software engineers are among the creators of the solutions. The spectrum of increasing trust begins with a low trust level where augmentation rules the day. As trust increases, more tasks are offloaded but not entire roles. Imagine an AI assistant in a craftsman’s workshop. As we arrive at a level of trust where we can offload roles, think of the full apprentice or journeyman. With increasing reliability comes increasing trust, and with increasing trust we transition from “tool-based extension” (augmentation) to “social extension” (we recognize AI as having agency).

Two of the many predictions Gartner analysts have published on this topic and we explore in the podcast are:

• By 2028, 75% of enterprise software engineers will use AI coding assistants, up from less than 10% in early 2023.
• By 2028, systematic adoption of AI coding assistants in 2023 will result in at least 36% compounded developer productivity growth.

Evidence

1 2024 Gartner CIO and Technology Executive Survey. This survey was conducted online from 2 May through 27 June 2023 to help CIOs determine how to distribute digital leadership across the enterprise and to identify technology adoption and functional performance trends. Ninety-seven percent of respondents led an information technology function. In total, 2,457 CIOs and technology executives participated, with representation from all geographies, revenue bands and industry sectors (public and private).

Disclaimer: The results of this survey do not represent global findings or the market as a whole, but reflect the sentiments of the respondents and companies surveyed.

Our host Frances Karamouzis is joined by Arun Batchu and Phillip Walsh, who are both expert analysts in Gartner’s software engineering leaders team. Batchu is a vice president, and he helps software engineering leaders build their software design, development and people strategies. Walsh is a senior principal analyst who helps software engineering leaders develop and implement strategies to build a world-class software engineering organization.

This TechWave podcast is based on Gartner’s research, The Impact of the “U.S. Executive Order on AI.”

U.S. President Joe Biden has issued an

Executive Order on the Safe, Secure and Trustworthy Development and Use of Artificial Intelligence

(the “EO”), which also underscores AI’s promise of innovation and competitive advantage. It specifically calls for Americans’ privacy and civil liberties’ protection, equity and civil rights advancement, and consumers’ and workers’ support, reinforcing the U.S. Blueprint for an AI Bill of Rights.

The EO considers this “the most significant actions ever taken by any government to advance the field of AI safety.” The U.S. is sending a clear signal that AI and GenAI is far more than a disruptive technology; it has far-reaching consequences to impact every aspect of daily life, national and global economies, military matters, and the future of the planet. As such, unlike many other areas of technology or disruptive forces where government organizations have a low reaction time — this is different, and executives will be tested accordingly.

One way that our expert analyst, Lydia Clougherty Jones, euphemistically summarizes the message of the EO during the podcast is: “Step Up or Step Aside.” The overall message is that if you are in executive leadership, you have responsibilities with regard to AI. You must proactively take measures to be compliant and prevent harm. As such, “Even if you are not ready for AI, you need to be AI ready.”

Executives should adjust leadership priorities, reconcile AI investment with redistributed risk and prepare today for a more regulated tomorrow:

• The Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (the “EO”) sends a strong message to the private and public sectors: AI is not a new technology; it’s a leadership responsibility.
• The EO’s mandates and directives redistribute the risks of loss associated with responsible AI failures, which will affect current and future AI investments, and the projected corresponding ROI.
• The EO has major impacts on U.S. federal agencies and those who work with them. It will undoubtedly have a significant impact on the global AI vendor and solutions market, including to address the new standards for the development, functionality, use and output of AI.

The EO will have a far and wide impact on AI strategy and the ROI on AI investment. It will alter and redistribute the risk of loss from AI harms considered too costly for AI benefit or value. It will also change corporate and individual behaviors arising from new regulatory frameworks, alongside the industry self-regulation we are already beginning to see take effect. Together, this creates an opportunity for new market solutions, but the oversight of vendors by the government and the commercial sector will be different. In the podcast, several examples are discussed.

Note: Gartner does not provide legal advice or services, and its research should not be construed or used as such. While this podcast involves a discussion on a topic that has many legal issues, Gartner does not provide or apply any legal rules or terms to its clients’ or prospects’ specific business.

Our host Frances Karamouzis is joined by our expert analyst Lydia Clougherty Jones, a senior research director in the Data and Analytics group. She covers data and analytics strategy, D&A value, and derisking, among many other topics. Importantly, Jones practiced law for two decades — with a focus on emerging technologies and business transformation — before joining Gartner. Nevertheless, it is crucial to understand that Gartner does not provide legal advice or services, and its research should not be construed or used as such. While this podcast involves a discussion on a topic that has many legal issues, Gartner does not provide or apply any legal rules or terms to its clients’ or prospects’ specific business.

Generative AI has revealed applications’ potential to operate intelligently, which has created the expectation for intelligent applications. IT leaders must understand the foundational changes affecting applications and decide their strategy to ensure continued alignment to target business outcomes.

What are Intelligent Applications?

Intelligent applications include intelligence — which we define as learned adaptation to respond appropriately and autonomously — as a capability. This intelligence can be utilized in many use cases to better augment or automate work.

As a foundational capability, intelligence comprises a number of AI-based services — especially machine learning, semantic enginesvector stores and connected data. Consequently, intelligent applications deliver experiences that dynamically adapt to user context and intent. Sometimes, user experiences are no longer necessary because applications interoperate with other applications autonomously.

Intelligent applications can synthesize their interfaces between other applications (self-integrating applications) — as well as users — in ways that are appropriate to the prevailing circumstances, and they can do so proactively (see Figure 2). For example, an intelligent application can pull functionality (i.e., ordering software from a catalog) into a conversational interface based on user intent and context, or adapt it to external APIs for data exchange.

Why Is This Trend Important?

The way applications work is changing dramatically. Intelligence — in the form of a suite of AI features and functionalities — is becoming a foundational capability. This is expanding the roles that applications can play across a broad range of employee- and customer-facing business activities, and between applications themselves: increasing their level of agency.

Intelligent applications transform the experiences of customers and employees, further impacting product owners, architects, developers and governing roles. As applications play a fundamental and pervasive role throughout our working and social lives, these transformations will have far-reaching consequences (e.g., in terms of the types of jobs available to future generations).

AI is surpassing the limits reached and imposed by traditional programming that uses explicit rules, relationships and instructions. AI learns rules implicitly. Combined with access to connected data, AI can model context and intent to operate autonomously. This can improve work through augmentation, or eliminate it through automation.

As AI continues to advance, it’s causing us to reappraise its capabilities and applications. The progress and speed of such advances — especially in the wake of generative AI applications such as ChatGPT — are providing insight into the nature of intelligence itself. AI can now mimic human behavior so successfully that it can not only help or even replace people at work, but it can also, in some circumstances, fool people into believing it’s human. As such, the scope of AI’s application to work and automation is shifting from routine and mundane tasks, such as invoice processing, to nonroutine and creative tasks, such as copywriting.

Why is this Trending?

Business disruption due to talent/skill shortages is one of the biggest external threats to business after economic threats, according to the 2023 Gartner’s Board of Directors Survey. Workforce (e.g., retention and hiring) is the second biggest priority for 2023 and 2024. The top priority is digital technology initiatives, with AI/machine learning considered the top breakthrough technology.

Intelligent applications have entered the mainstream. Over 50% of respondents to the Gartner AI Use-Case ROI Survey reported that they have a form of intelligent application in their enterprise application portfolios. Yet, a lack of effective automation/tools is the biggest barrier to worker productivity, according to one-third of respondents to the 2023 Gartner Workforce Optimization Survey.

Key to AI’s advance is content — facts modeled for human comprehension. Content includes text, image, video and audio formats. AI can now identify and extract facts from content and remodel these as data for processing. It can use this data as the source from which to synthesize new content — the generative in generative AI. Most enterprise data is in the form of content, such as documents, and central to all activities that involve people.

Content also makes up the interfaces through which users interact with applications, and code is itself content. As such, intelligence extends to adapting applications’ form and function through re-composition, re-engineering their parts to optimize performance, extend reach and expand purpose.

What are the Business Implications?

Intelligence as a capability can apply to all applications. The impact and implications are therefore pervasive across all use cases touched by applications (operational-, employee- and customer-centric use cases). Examples include:

• Optimization and automation of business processes, such as inventory management. For example, generative AI working with AI-based automated stockout measures can deliver natural language insights to managers — ensuring the right level of inventory to match demand. This improves customer satisfaction and related financial metrics.
  • Example: GA Telesis leveraged an AI-based application using Google’s Vertex Generative AI Platform, with its sales processes to synthesize purchase orders for aircraft replacement parts automatically.1 This significantly cut GA Telesis’ response times to sales inquiries, thus optimizing and preserving the customer experience.
• Assistance throughout the digital workplace to help with many tasks, including drafting documents, automating process workflows, answering questions and generating business insights. For example, digital workplace application suite vendors and their intelligent assistants, such as Microsoft Copilot and Google Duet AI.
  • Example: Bank of England created a cognitive search application solution using Squirro to enhance its document search and internal knowledge management capabilities.2 This application used machine learning term extraction workflows, coupled with dashboards, to provide a more unified knowledge search system and streamline data management.
    
    
• Customer relationship management with chat-based interfaces facilitating agent-based and self-service support. For example, generative AI can produce an automated summary of a customer service agent’s audio interaction with a customer. The code writes one summary for the customer, indicating what advice was given. The code writes a second summary that summarizes the client’s issue and adds to the customer service knowledge base.
  • Example: CallRail partnered with AssemblyAI to provide capabilities, such as automatic transcript highlights, and redaction of personally identifiable information.3 This not only provided customer service agents with essential insights much more quickly, but also improved CallRail’s call transcription accuracy by 23%.
    
    

The opportunities created by intelligent applications should be focused on expected outcomes, such as:

• Simplification and personalization of experiences for both employees and customers.
• Optimization of processes combined with a reduction in human error.
• Simplification of applications, and reduction of their number, to deliver business processes.

The C-suite of many enterprises is increasingly asked by their CEO and boards to provide strategic guidance for GenAI as well as about the appropriate investments their organizations should make in this technology. Most enterprises are struggling with how to identify, vet, prioritize and guide funding decision models for generative AI. Gartner high-level guidance is to segment GenAI investments to look at several factors including value alignment to business goals, benefits, costs and risks.

A deeper dive into one of these variables — namely cost — requires enterprises to stratify GenAI initiatives across a spectrum of categories. A full description and graphic depiction of these categories is the focus of the on-demand webinar Generative AI Realities: Proactive Approaches for Quantifiable Business Results.

In the webinar and in this podcast, Gartner experts explore the following five categories of cost:

• Category 1 — Targeted purchase ($10,000 to $50,000)
• Category 2 — Embedded ($50,000 to $250,000)
• Category 3 — Horizontal or vertical off-the-shelf solutions ($250,000 to $1 million)
• Category 4 — Situational build ($1 million to $5 million)
• Category 5 — Market maker ($5 million to $100 million)

In the podcast, Gartner experts discuss these cost categories along with risk and many of the other variables that must be analyzed to proactively plan GenAI investments.

Gartner has also published several predictions related to enterprise challenges and the perils of GenAI investments:

• By 2025, growth in 90% of customized enterprise deployments of GenAI will slow as costs exceed value, resulting in pressure on vendors to introduce innovations and pricing models.
• By 2028, more than 50% of enterprises that have built their own large language models from scratch will abandon their efforts due to costs, complexity and technical debt in their deployments.

Our host Frances Karamouzis is joined by senior director analyst Nate Suda, who covers tech, finance, value and risk in Gartner’s CIO group. He focuses on digital value creation, digital strategy and digital execution.

Enterprise architect (EA) leaders are professionals who firstly operate at the enterprise level and act as internal management consultants. The primary goal is to facilitate executives’ execution of defining business and IT strategy and goals, developing business and operating models and measurements to deliver the objectives and key results. Gartner has found that the majority of enterprise architects report into the CIO or CTO and, as such, spend a great deal of time on the IT strategy and portfolio. This leads to EAs providing guidance and governance through reference architectures, models, principles and guidelines.

Technology innovation (TI) leaders are focused on identifying, informing and keeping track of disruptive innovations and successfully bringing them to the organization. While EAs may be expected to assess trends and identify innovation opportunities, Gartner’s finding is that TI leaders are more often aligned to a CTO and primarily tasked with taking advantage of technology innovation.

Gartner research has identified four different CTO personas — meaning four different types of CTO roles:

• The CTO as the digital business leader, who is the main force to accelerate digital transformation and innovation.
• The CTO as the digital business enabler, who is more focused on optimizing business operations.
• The CTO as the IT innovator, who is the visionary and main change agent.
• The CTO as the COO of IT, who is the CIO’s right hand and focused on optimizing the IT operations.

While all personas exist, nowadays the digital business leader, digital business enabler and IT innovator are most common.

EA and TI leaders must:

• Continuously scan and respond to disruptions by evaluating a variety of trends, beyond just technology, to inform their impact on innovation.
• Guide their technology strategies by understanding the broad technology trends that will affect the near-term planning horizon.
• Scout emerging technologies to understand the discrete technologies that are on the horizon to anticipate their impact on the organization.
• Seek diverse viewpoints and experiences to achieve higher rates of successful business innovation; when people from diverse backgrounds and perspectives come together, their unique thoughts pave a path for innovation.

In this podcast, Gartner experts explore the value propositions, challenges and research publications for these two critical roles.

The world is experiencing a very high level of disruption as a result of geopolitical and social changes. The old patterns of operating are increasingly ineffective due to their lack of speed, agility and proactivity. In this rapidly changing business environment, sourcing, procurement and vendor management (SPVM) leaders have a unique opportunity to determine their transformation and define a new vision for technology acquisitions and vendor relationships. SPVM leaders must balance speed, flexibility and vendor relationship building with managing and mitigating risk. 

SPVM leaders must prepare for the following trends:

• Increasing proliferation of unproven vendors and technologies: The pace of innovation is accelerating with an ever-increasing number of new vendors, products, services and solutions staking their claim to market share.
• Rising number of business technologists: Technology acquisitions are transforming every business unit, function and department within companies.
• Transitioning toward the product-centric IT organization: IT will continue to contribute significantly to the attainment of key business objectives with the focus on aligning products to capabilities or customer journeys. SPVM leaders will be an integral part of the repositioning of IT. 
• Continuing geopolitical and social uncertainty: The world is changing, the status quo is being challenged and the emergence of a new geopolitical architecture affects all facets of business, including technology.

Challenges for SPVM Leaders

SPVM leaders must react to the following challenges:

• Without market intelligence capabilities, SPVM leaders lack the necessary data to drive informed decisions. “Buyers’ remorse” is a major issue for enterprises as technology buyers very often regret the purchase they made. 
• The current-state sourcing, procurement and vendor management operating model does not support “autonomous” tech buying and cannot support “business-led” buying effectively. 
• In this product-centric era, contracting is stuck in time, and antiquated contracting vehicles make it hard to manage vendors and expectations effectively. 
• Geopolitical and social volatility have an increased impact on supply chain, risk and talent.

Recommended Actions

To be successful, SPVM leaders must:

• Move toward a dynamic sourcing approach while developing market intelligence capabilities.
• Build a flexible SPVM operating model that formalizes business-led technology buying.
• Create product-centric contract proposals.
• Develop a vendor and market risk management program to address constant regulatory changes, environmental, social and governance (ESG) considerations, and major geopolitical shifts.

The role of IT sourcing, procurement and vendor management is changing. SPVM leaders need to decide if they will elevate their role and become internal commercial advisors, or will remain the leaders of a tactical, low-value group that will continue to be an afterthought.