TechWave: A Gartner Podcast for IT Leaders

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.

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.

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.

Hyperautomation and intelligent automation investments (inclusive of artificial intelligence) have continued unabated for over five years. The growth has been brisk. While adoption and spending were consistently growing, the trigger of generative AI awareness (spurred by the release of ChatGPT in November 2022) has fueled the growth even more. ChatGPT hit 100 million users in one week and has over 1 billion monthly views on its website. 

However, only a small percentage of organizations can showcase value or benefits that are identifiable, traceable and quotable on an “earnings call.” This is because few organizations have determined the appropriate or best ways to measure these initiatives, which span myriad technologies such as AI, low code, robotic process automation (RPA) and integration platform as a service (iPaaS). Therefore, Gartner launched a research study called “Gartner’s Hyperautomation 100.” The focus is on capturing case studies that feature organizations that have delivered over $100 million in value or triple digit benefits (over 100%) and consistently quantitatively measure those benefits across many different initiatives.

In this podcast, we unveil the first of several enterprises that Gartner interviewed. We captured their multiyear journey to not only delivering over $100 million in value but also to measuring it, quantifying it and publicly sharing these achievements because they are traceable and part of an ongoing consistent process of vetting and approving the funding of these initiatives. 

This podcast features Ericsson, the world leader in the rapidly changing environment of communications technology. It develops, delivers and manages hardware, software and services to enable the full value of connectivity. The official name of the organization is Telefonaktiebolaget LM Ericsson (parent), but it is commonly referred to as Ericsson. Founded in 1876, Ericsson worldwide revenue in 2022 exceeded $26 billion and the company had over 104,000 employees. 

In 2016, Ericsson started from scratch with no staff and no capabilities in its Enterprise Automation and AI team. At the very beginning, it followed a mantra: “Think big, start small, scale quick.” The terminology it used internally was to set a course for “Radical Transformation Driven by Exponential Technologies.”

During the first two years (2016-2017), six initiatives were funded and delivered. Fast forward to mid 2023, when the aggregated number of initiatives has surpassed 300. All the initiatives are funded by the business units as there is no predefined budget. As such, the Enterprise Automation and AI team at Ericsson must prove its value and build confidence with the business unit leaders to get funding for the next project. One of the many ingredients for this success was to measure and quantify the value for each initiative. Internal stakeholder demand (i.e., the amount that business units fund) has increased 22 times since the 2016 launch of the team. It has essentially doubled every year since 2016.

Generative AI refers to artificial intelligence techniques that learn a representation of artifacts from data and use it to generate brand-new, completely original artifacts at scale that preserve a likeness to original data.

Generative AI enables computers to generate brand-new, completely original variations of content (including images, video, music, speech and text). It can improve or alter existing content, and it can create new data elements and novel models of real-world objects, such as buildings, parts, drugs and materials.

In this podcast, our guest Daryl Plummer takes us through a number of ways to consider the impact of generative AI. We discuss the topic through the lens of a buyer or consumer of the technology, a business leader and a technology service provider.

Gartner’s high-level messages regarding generative AI include:

• AI is no longer just a technology or tool. We should no longer treat AI as just a technology. It is no longer just a business tool. AI is now in a place where it is reshaping our society and impacting what it means to be human.
• Don’t say “No,” but ask “How?” We believe advances in AI and the impacts on society cannot be stopped. We must embrace AI and take a “proactionary” approach, which means that we are in favor of advancements that are executed responsibly, through appropriate experimentation.
• Where there is disruption, there is hope. While we believe that AI should not be hyped, it should never be underestimated. We are at the beginning of many more breakthroughs. And while we acknowledge the fear that surrounds AI, we emphasize the hope. Nothing should be seen as impossible — not even artificial general intelligence.

Recommendations

Executive leaders responsible for innovation and managing disruption should:

• Understand that AI is advancing rapidly, with an enormous impact on society and business. 
• Address fears of potential job losses incurred from AI technology advances by emphasizing the goal of AI is to augment human capability, not replace it.
• Collaborate across teams by mixing engineering skills with social sciences. 
• Balance opportunity with risk. Assess and mitigate risk of using new types of AI.
• While further innovating AI, work on their cutting-edge use cases and governance simultaneously.

Frances Karamouzis, distinguished VP analyst, hosted our expert Daryl Plummer, who is also a distinguished VP analyst. Plummer’s research focuses on the strategic issues of cloud computing and digital disruption, and the unfolding of the future through predictions, trends and evolving digital business cycles.

Gartner’s 2022 Drivers of Secure Behavior Survey reveals that 69% of employees bypassed their organization’s cybersecurity guidance in the last 12 months. Further, 74% said that they would be willing to bypass cybersecurity guidance in the future too, if it helped them or their team achieve a business objective (for example, meet an urgent deadline and/or revenue target).1 This willful disregard of security guidance stems from friction that slows down employees and makes it more inconvenient for them to do their work. Moreover, over 90% of survey respondents who admitted behaving unsecurely indicated that they knew their actions would increase cybersecurity risk levels for the organization and, unfortunately, they did them anyway

This cybersecurity-induced friction (hereafter referred to as “friction”), or the “unnecessary” effort exerted by employees to do their work due to the presence of cybersecurity measures, not only drains employee productivity but also pushes them to adopt unsecure practices.

It might be convenient and self-serving for cybersecurity teams to think about friction as the “small price” everyone needs to pay to safeguard the organization. But employees often don’t share this view and are willing to circumvent cybersecurity controls if these controls hamper them from doing their work. To drive secure behaviors, CISOs need to move away from thinking about friction as a natural and even desirable consequence of cybersecurity measures (a “necessary evil”) and focus instead on identifying and reducing friction that employees experience.

In this podcast, we explore Gartner research related to cybersecurity leadership, operational models and shifts in approaches, and delivery of value. Examples include:

Evolving role of a CISO from a technical focus to executive leader (whose primary focus is helping business leaders make informed cyber-risk decisions).

New cybersecurity teams, functions and processes to address the evolving business environment, such as cybersecurity creating more linkages with the business and working toward shared responsibility.

Shifts in cybersecurity policy design and enforcement. There is a shift toward liberalizing the cybersecurity policy toward co-creation with the business as well as making policies less prescriptive and more flexible. This will enable users to have more autonomy for improved execution for security controls.

Evidence

1 2022 Gartner Drivers of Secure Behavior Survey. This survey was conducted via an online platform from May through June 2022 among 1,310 employees across functions, levels, industries and geographies. The survey examined the extent to which employees behave securely in their day-to-day work, root causes of unsecure behavior, and the types of support and training they received from their organizations to drive desirable secure behaviors. We used descriptive statistics and regression analysis to determine the key factors that drive or impede employees’ secure behaviors and their development of cyber judgment.

Gartner forecasts that enterprise IT spending will exceed $4.6 trillion in 2023. That’s an increase of 5.2% in constant currency. Of that, business and IT services represents about $1.4 trillion, which is the second largest area of technology spending (second only to telecommunications). Gartner’s quarterly update continues to forecast 8.5% growth for the year in constant currency terms (see Forecast: IT Services, Worldwide, 2021-2027, 1Q23 Update).

One simple way of talking about the IT services market is to understand some of the players that compete for enterprise spending of business and IT services. Examples include firms such as Accenture, IBM, Capgemini, EPAM, TCS, HCL, Cognizant and Wipro, as well as the more management, strategy or process-driven firms such as McKinsey & Co., EY, PwC and KPMG. Interestingly, the largest firms in the world collectively only account for less than an estimated 25% of the market. In the last 25 years, none of the firms have had more than single-digit share. This results in thousands of midtier and smaller companies — a very large long tail of companies.

In this podcast, Gartner VP analyst Sandra Notardonato shares her insights into the market dynamics of the overall sector. The primary reason that this sector is of interest to enterprises is that business and IT services are incurred by over 90% of all enterprises regardless of industry or geography. The strategy, design, deployment, system integration and ongoing management are critical enablers to any cost, growth or innovation-driven business goals.

Artificial intelligence (AI) research and deployment company OpenAI recently announced the official launch of ChatGPT, a new model for conversational AI. According to OpenAI, the dialogue provided by this platform makes it possible for ChatGPT to “answer follow-up questions, admit its mistakes, challenge incorrect premises and reject inappropriate requests.”

Since its launch, social media has been abuzz with discussions around the possibilities — and dangers — of this new innovation, ranging from its ability to debug code to its potential to write essays for college students. In this podcast, we sit down for a conversation with Bern Elliot, VP Analyst at Gartner, to discuss the broader implications of this innovation and the steps that organizations should take regarding the use of these tools. 

Gartner has published a number of research pieces regarding ChatGPT (see recommended reading below). Here are a few of the common questions that set the stage for our interactive dialogue on the podcast.

Q. What is ChatGPT and how does it work? Chat Generative Pretrained Transformer, or ChatGPT, is a chatbot and generative language tool launched by OpenAI in November 2022.1 The ChatGPT models compute the most probable set of letters or words when given an initial starting phrase, or “prompt.” ChatGPT is built on top of OpenAI’s GPT-3 family of large language models and enables interaction with a model via a conversational user interface. ChatGPT was trained using 300 billion words taken from books, online texts, Wikipedia articles and code libraries, then fine-tuned with human feedback.

On 16 January 2023, Microsoft announced the introduction of Azure OpenAI Service, which includes ChatGPT along with language models and added enterprise services..2 It is important for enterprise planners to distinguish between the OpenAI ChatGPT and the Azure OpenAI Service. The Azure version promises significant enterprise operational features, but is still emerging at the time of writing.

Q. What role will ChatGPT play in the enterprise? ChatGPT, and foundation models like it, will be used as a tool alongside many other hyperautomation and AI innovations. It will form part of architected solutions that automate/augment humans or machines, and autonomously execute business and IT processes. As generative AI takes its place alongside existing approaches to work, ChatGPT or other competitors will be used to replace, recalibrate and redefine some activities and tasks that form part of many job roles.

Q. How much does ChatGPT cost? The current research preview version of ChatGPT, which is the only version users could access up to the end of January 2023, is free of charge. However, there is no guarantee that this free service will persist, and it could be withdrawn at any time. OpenAI recently announced the launch of a pilot subscription plan for ChatGPT Plus for $20 a month.3

ChatGPT will also come to the Microsoft Azure OpenAI Service soon, but the pricing for that is currently being rolled out.4 It is possible that significant elements will be bundled with different Microsoft 365 software subscriptions.

Q. Should I provide ChatGPT-powered experiences directly to my customers? No — this is too high a risk at present for most use cases, except in rare cases, possibly related to gaming or entertainment, where the correctness or impartiality of the content may have less scrutiny.

Gartner expects the ChatGPT service to change rapidly over 2023 and to be complemented by other offerings. It is important for enterprise planners to distinguish between the OpenAI ChatGPT and the Azure OpenAI Service. 

Gartner also expects several competitors will enter this market alongside ChatGPT. In particular, Gartner expects organizations like Baidu, IBM and Google to come to market early in 2023, along with a crop of smaller players. For example, on 6 February 2023, Google announced the introduction of its own offering, Bard.

Footnotes:

1 Introducing ChatGPT, OpenAI.

2 Azure OpenAI Service, Microsoft.

3 Introducing ChatGPT Plus, OpenAI.

4 General Availability of Azure OpenAI Service Expands Access to Large, Advanced AI Models With Added Enterprise Benefits, Microsoft.

Host Frances Karamouzis is joined by our expert analyst, Bern Elliot. Elliot is a Gartner vice president and distinguished analyst. His research focus is artificial intelligence generally, with an added focus on natural language processing (NLP), machine translation, and customer engagement and service.

Organizations are in the midst of transformational change that is reshaping our world and expanding our reach into worlds we create and those we have yet to explore. This degree of change will force shifts in how people, and businesses, relate to each other. Old challenges remain, new challenges unfold and endless opportunities abound.

Executive leaders should promote the use of technological, political, economical, social/cultural, trust/ethics, regulatory/legal and environmental (TPESTRE) — which Gartner refers to as a “Tapestry” — as a planning tool for uncertainty. Gartner’s Tapestry should be used as a starting point for an ongoing trendspotting initiative (see The Gartner Trendspotting Framework: Driving Operations, Innovation and Strategy).

To succeed in a disruptive future, enterprises must continually scan and respond to disruptions. These disruptions threaten corporate positions in the marketplace and jeopardize the digital transformation wins that companies have worked so hard to achieve (see Inventing the Future With Continuous Foresight).

Executive leaders must evaluate a variety of trends, beyond just technology, and their impact on strategic planning. Gartner’s Tapestry research is a 360-degree perspective of potentially game-changing technological, political, economical, social/cultural, trust/ethics, regulatory/legal and environmental trends that will assist leaders in their strategic planning efforts.

In this podcast, our expert analyst Marty Resnick joins us to explore how you can utilize Tapestry for strategic planning.

Host Frances Karamouzis is joined by our expert analyst Marty Resnick. He is one of the leaders of Gartner’s Futures Lab, our home for unconventional, speculative and futuristic research. The mission of the Gartner Futures Lab is to prepare leaders for uncertainty by exploring new ways of imagining the future. By starting with the question “What if …,” we help you determine your uncharted next mission-critical priorities.