As generative AI (GenAI) continues to disrupt various industries, its impact on cybersecurity has become a central topic of discussion. With the power to transform threat detection, response strategies, and overall security posture, GenAI introduces both significant opportunities and complex challenges.

This blog delves into five critical questions about how GenAI is reshaping cybersecurity analytics, offering insights for organizations looking to navigate this evolving landscape.

1. What Makes GenAI Different from Traditional AI in Cybersecurity?

Artificial Intelligence has long been a cornerstone of cybersecurity, driving significant advancements in threat detection, endpoint protection, and automated responses. Traditional AI systems excel at analyzing historical data, identifying patterns, and detecting anomalies based on past behaviors. These capabilities have been instrumental in helping organizations fend off cyber threats by anticipating actions that mimic known attack signatures.

However, GenAI represents a new frontier in AI capabilities, fundamentally altering how cybersecurity operates. Unlike traditional AI, which is reactive, GenAI can generate new data, content, and even potential attack vectors in real-time. This shift means that GenAI can be leveraged to create highly personalized phishing attacks, automate the generation of sophisticated malware, and simulate complex cyber-attacks with unprecedented precision.

For instance, traditional AI might analyze email traffic to identify potential phishing attempts based on known patterns. GenAI, on the other hand, could create entirely new and convincing phishing emails tailored to individual targets, making it much harder for employees to discern legitimate communications from malicious ones. This ability to generate novel content on the fly dramatically increases the challenge for cybersecurity teams, who must now defend against threats that have never been seen before.

In essence, GenAI is not just an evolution of existing AI technologies but a transformative force that introduces new dynamics into the cybersecurity landscape. It offers defenders powerful tools for proactive security measures but also empowers attackers with enhanced capabilities, necessitating a rethink of traditional defense strategies.

2. Does GenAI Favor the Attacker or the Defender?

In the ongoing battle between attackers and defenders in cybersecurity, GenAI appears to have initially tipped the scales in favor of the attacker. The inherent asymmetry in cybersecurity—where attackers need to succeed only once, while defenders must protect against every possible threat—becomes even more pronounced with the introduction of GenAI.

Attackers can exploit GenAI to conduct volume attacks, generating countless variations of malware, phishing campaigns, and other cyber threats with minimal effort. This capability allows them to overwhelm traditional defenses, which may not be equipped to handle the sheer volume and diversity of attacks that GenAI can produce. For example, GenAI can create hundreds of phishing email variations, each slightly different, making it difficult for automated filters to catch them all.

Moreover, GenAI enhances the precision of these attacks. By ingesting large datasets, GenAI can tailor attacks to specific individuals or organizations, increasing the likelihood of success. For instance, a GenAI-powered attack might analyze social media profiles, public records, and previous interactions to craft a highly personalized phishing email that is nearly indistinguishable from legitimate communication.

Another area where GenAI gives attackers an edge is in the realm of endless simulation. Attackers can use GenAI to simulate defenses, testing various attack strategies in a controlled environment before launching them in the real world. This capability allows them to refine their tactics, identify potential weak points in a target’s defenses, and optimize their attacks for maximum impact.

However, defenders are not without recourse. To counter these sophisticated threats, defenders must harness GenAI’s capabilities for their own advantage. This involves leveraging GenAI for advanced threat detection, dynamic risk assessment, and automated response strategies. For instance, defenders can use GenAI to analyze network traffic in real-time, identifying and mitigating threats as they emerge.

The challenge for defenders is to stay ahead of the attackers in this rapidly evolving landscape. This requires a proactive approach, continuous adaptation, and a deep understanding of how GenAI can be both a tool and a weapon in the cybersecurity arsenal.

3. What Are the Biggest Challenges of Implementing GenAI in Cybersecurity?

While the potential of GenAI in cybersecurity is immense, its implementation comes with a host of challenges that organizations must navigate carefully. One of the primary concerns is data privacy and security. GenAI systems require vast amounts of data to function effectively, and this data often includes sensitive information such as personally identifiable information (PII), proprietary corporate data, and intellectual property.

The collection, storage, and processing of this data introduce significant risks. If not managed properly, there is a danger that this data could be misused, exposed, or even compromised by malicious actors. For instance, a breach in a GenAI system could potentially expose not just the data it was trained on but also the AI models themselves, leading to a cascade of security issues.

To mitigate these risks, organizations must implement robust data governance policies. This includes ensuring that data is anonymized where possible, implementing strict access controls, and regularly auditing data usage to ensure compliance with regulatory requirements. Additionally, organizations must be transparent with stakeholders about how their data is being used by GenAI systems, addressing any concerns about privacy and security.

Another significant challenge is the accuracy and reliability of GenAI outputs. Unlike traditional AI systems, which are designed to recognize patterns in existing data, GenAI creates new content based on its training data. This means that if the training data is incomplete, biased, or otherwise flawed, the outputs of the GenAI system can be inaccurate or misleading.

For example, a GenAI model trained on biased data might produce skewed threat assessments, leading to false positives or, worse, false negatives that leave critical threats undetected. The phenomenon of “hallucinations,” where GenAI fills in gaps with incorrect information, further complicates matters. In a cybersecurity context, this could result in flawed defense strategies or misguided responses to perceived threats.

To address these challenges, organizations must implement continuous monitoring and validation of GenAI outputs. This involves regularly testing the AI models against known benchmarks, auditing the decision-making processes of the AI, and ensuring that human experts are involved in reviewing and validating critical outputs. The concept of “human in the loop” is particularly important here, as it allows organizations to combine the speed and efficiency of GenAI with the judgment and experience of seasoned cybersecurity professionals.

Finally, the cost associated with implementing and maintaining GenAI solutions can be a significant barrier. GenAI systems require substantial computational resources, including powerful hardware, vast amounts of storage, and advanced software tools. Additionally, the ongoing costs of supporting these systems—such as updates, model retraining, and data management—can strain IT budgets.

Organizations must carefully weigh the costs and benefits of GenAI adoption, considering not only the immediate expenses but also the long-term implications for their cybersecurity strategy. In some cases, the investment in GenAI may be justified by the potential for enhanced security and operational efficiencies, while in other cases, it may be more prudent to focus on optimizing existing AI systems.

4. Will the Adoption of GenAI-Powered Cybersecurity Products and Procedures Happen Rapidly?

The adoption of GenAI in cybersecurity is likely to follow a pattern of slow initial uptake followed by rapid acceleration. Early adopters, particularly large enterprises and tech-savvy organizations, are already integrating GenAI into their security frameworks. These early implementations focus on tasks such as automated threat detection, natural language processing for security logs, and predictive analytics.

However, several factors will influence the pace of broader adoption. Trust in GenAI’s outputs is paramount—organizations need to be confident that the AI’s decisions are accurate, reliable, and free from bias. This trust must be built through transparency, rigorous testing, and clear communication about how GenAI systems work and how decisions are made.

The cost of implementation is another critical factor. As mentioned earlier, GenAI systems require significant investment in hardware, software, and ongoing support. For many organizations, particularly small and medium-sized enterprises (SMEs), these costs may be prohibitive. However, as the technology matures and economies of scale take effect, the costs are expected to decrease, making GenAI more accessible to a wider range of organizations.

The ability to integrate GenAI with existing security infrastructures will also play a significant role in adoption. Many organizations have already invested heavily in traditional AI and cybersecurity tools, and they may be hesitant to replace or overhaul these systems. However, as GenAI demonstrates its value—whether through enhanced threat detection, faster response times, or improved operational efficiencies—organizations are likely to increasingly embrace it as a complement to their existing security measures.

Regulatory compliance is another area that will impact the adoption of GenAI. As governments and industry bodies begin to regulate AI use, particularly in sensitive areas like cybersecurity, organizations will need to ensure that their GenAI implementations comply with these regulations. This could include requirements around data privacy, transparency, and accountability, as well as specific guidelines for how AI systems should be tested and validated.

Cyber insurers will also play a crucial role in determining how quickly GenAI is adopted. If insurers begin to offer lower premiums or more comprehensive coverage to organizations that use GenAI-powered cybersecurity tools, this could incentivize broader adoption. Conversely, if insurers view GenAI as introducing new risks, they may increase premiums or impose stricter conditions on coverage, potentially slowing down adoption.

As more GenAI-powered products become available, we can expect a surge in adoption as businesses seek to capitalize on the efficiencies and enhanced capabilities that GenAI offers. However, this will require a careful balance between innovation and risk management to ensure that GenAI is deployed safely and effectively.

5. What Role Will Trust Play in the Future of GenAI in Cybersecurity?

Trust will be the cornerstone of any successful GenAI implementation in cybersecurity. Organizations must not only trust that the data being used by GenAI systems is handled securely but also that the AI-generated insights and outputs are reliable, accurate, and free from bias. In an environment where false positives can lead to wasted resources and false negatives can result in catastrophic breaches, establishing this trust is paramount.

Data Privacy and Security
One of the primary concerns with GenAI is the handling and treatment of data. Cybersecurity professionals need to ensure that sensitive information, including personally identifiable information (PII) and proprietary corporate data, is protected when used by AI systems. In this context, cybersecurity vendors must be transparent about how data is processed, stored, and managed. If a third party manages a GenAI system, what happens to the data it handles? Is it stored securely? How is it used in future iterations of the AI model? These are critical questions that organizations must address to maintain trust in the system.

In addition, the potential for exposing sensitive data through poorly governed AI systems could lead to severe regulatory consequences, reputational damage, and financial losses. The role of robust data governance and strict adherence to compliance standards will become increasingly important as organizations integrate GenAI into their cybersecurity workflows.

Accuracy and Reliability of AI Outputs
Trust also hinges on the accuracy of GenAI’s outputs. GenAI models are probabilistic, meaning they generate outputs based on likelihoods, not certainties. This can introduce errors, especially when the underlying data is incomplete or biased. In cybersecurity, where precision is critical, the risk of AI-generated “hallucinations”—outputs that are not grounded in factual data—can have serious implications. These hallucinations could lead to misidentification of threats, incorrect incident responses, or overlooked vulnerabilities.

To mitigate these risks, organizations must implement processes that ensure the continuous auditing, testing, and validation of GenAI outputs. This is where the concept of “human in the loop” becomes essential. While GenAI can rapidly process and analyze vast datasets, it is vital that human experts remain involved in reviewing and validating its findings. Cybersecurity professionals bring context, judgment, and experience that AI models lack, making their oversight crucial to ensuring that GenAI’s decisions are sound.

Transparency and Governance
Another critical component of trust is transparency. Organizations using GenAI must have visibility into how the AI systems make decisions. Gone are the days of “black box” AI, where models operate in a vacuum without explanation. Today, cybersecurity professionals expect to understand the logic behind AI outputs, especially when these outputs are used to inform critical security decisions.

GenAI vendors must prioritize transparency, offering clear insights into how their models function, how data is used, and how conclusions are reached. This level of visibility allows organizations to audit AI-driven decisions, identify potential flaws in the system, and continuously refine their models to improve performance.

Building Long-Term Trust
Trust in GenAI is not a one-time achievement but an ongoing process. As AI systems evolve and learn, so too must the frameworks for monitoring and governing their use. Organizations that invest in strong governance models, foster a culture of transparency, and integrate human oversight into their AI processes will be better positioned to harness the full potential of GenAI while minimizing its risks.

Conclusion: Navigating the GenAI Frontier in Cybersecurity

Generative AI represents a transformative force in cybersecurity, offering unprecedented capabilities to enhance threat detection, response strategies, and overall security posture. However, with these advancements come significant challenges that must be carefully managed. Organizations must weigh the benefits of GenAI against the risks it introduces, particularly when it comes to data security, trust, and the evolving dynamics between attackers and defenders.

By addressing the five critical questions outlined in this blog, businesses can better prepare for the future of GenAI in cybersecurity. They must recognize that while GenAI offers immense potential, its success depends on robust data governance, transparency, continuous oversight, and the integration of human expertise. The future of cybersecurity will be shaped by how well organizations can balance innovation with the responsibility of safeguarding their digital environments.

The key takeaway is clear: GenAI is a powerful tool, but it must be implemented with care. As we move into an era where AI-driven solutions become increasingly central to cybersecurity strategies, businesses that prioritize trust, transparency, and collaboration between human and machine will be the ones that thrive in this new frontier.

This blog is based on the latest IDC Research on cybersecurity and GenAI. We recommend the following resources for more information on the latest trends in cybersecurity:

Five Big Cybersecurity Questions About GenAI in Cybersecurity Analytics Answered: Volume One

Five More Big Questions About GenAI in Cybersecurity Answered: Volume 2

Cybersecurity Norms and Trends: How Does Your Business Stack Up?

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IFA has traditionally been a home entertainment and appliances show targeting the European market, but this year there were a lot of global PC announcements as vendors were eager to get the word out on AI PCs, particularly after Computex in Taiwan a few months ago. We were excited to be on the ground in Berlin this month to catch the action.

Chip vendors led the charge, with Intel providing more details for Lunar Lake, now officially named Core Ultra 200V, with the “V” designating a premium against Meteor Lake. One of the most important details provided was around battery life, which looks more promising now than what was discussed at Computex. If real-world tests hold true when shipments commence later this year, Intel will be on better footing, especially given its competitive advantage with developers. Qualcomm didn’t stand by idly by though; it also released its lower-tier Snapdragon X Plus in both 8 and 10-core variants, allowing OEMs to target the US$700 range that Lunar Lake isn’t addressing yet.

With new chips comes new design wins with PC OEMs, who showcased an array of models, as well as a number of attention-grabbing concepts. My teammates Tom Mainelli and Linn Huang will be diving deeper into some of these developments in a dedicated report, but there were some high-level highlights, including some other devices at the end for good measure:

Lenovo introduced its Intel-exclusive Aura Edition products within the ThinkPad and Yoga lines, featuring Smart Modes that adapt device settings based on usage, Smart Share that leverages Intel’s Unison phone sharing, and Smart Care that provides a nice feed into Lenovo’s services arm. Lenovo separately talked up its AI PC Fast Start, which is not about boot speeds like the name might suggest, but instead, a lifecycle service to help organizations deploy AI. What might have garnered the most attention though – and such differentiation is important in such a crowded week of launches – was Lenovo’s voice-driven Auto Twist AI PC, which was a concept only but nonetheless elicited oohs-and-ahs.

Acer talked up battery life on its new Swift notebooks powered by Intel, AMD, and Qualcomm, while also diving into gaming, including a 600 Hz monitor, a detachable controller in its Project DualPlay concept, as well as its Nitro Blaze 7 handheld gaming PC. We are concerned that the Windows-based handheld market doesn’t have room for so many vendors yet, but we also think that existing designs have plenty of room for improvement, so Acer’s entry in this segment is a welcome one in that regard.

ASUS rolled out updates to its consumer product lines as well as a new P-series in its commercial ExpertBook lineup, focusing on SOHOs and creators and leveraging ASUS’ consumer-leaning strengths. Its newly acquired NUC team also showed off its ultrasmall desktop lineup, including a Lunar Lake-based one with a Copilot+ button and fingerprint readers.

Honor rolled out its Magic Notebook Art 14 featuring a detachable webcam that comes with a built-in storage bay on the side. Pogo pin-based webcams are not new in the industry (Lenovo has a range of Magic Bay accessories for its ThinkBooks), but this nonetheless is in character for Honor, which isn’t afraid to use its engineering skills to differentiate its hardware. Indeed, Honor also showcased its impressively thin yet durable Magic V3 foldable phone, which is now ready for overseas markets rather than being China-only. In the process, Honor unveiled more progress on Yoyo, its AI agent in China, which can take over a user’s screen to automate application tasks like a human would.

Phones were not a major focus at IFA, but Google had its Pixel lineup on-site, and HMD presented samples of its Barbie phone off-site. TCL and Transsion’s Tecno brands had booths on the show floor, with TCL advertising its Microsoft-powered AI features on its phones and Tecno interestingly veering into PCs with an ultrasmall form factor gaming desktop loaded with an RTX 4060. Smaller Chinese phone brands like uleFone, Cubot, Oukitel, and Doogee also maintained booth presences, similar to their participation at shows like MWC.

Chinese wearables vendors were present, with earwear being a significant focus given IFA’s consumer electronics legacy. Hisense showcased its TVs, leveraging its official marketing partnership with the recent hit game Black Myth: Wukong. Appliances like smart vacuums and electric scooters were prominent, especially from Chinese vendors. But AR/VR headsets were limited, and there was only one AI pin that we noticed: the Plaud NotePin notetaker, which was featured at a small media-only event the night before the show floor opened.

The momentum around AI PCs has continued to build, which is a good thing for the sake of generating awareness. And software like Lenovo’s Creator Zone and Intel’s AI Playground that make a range of multimodal models easy for users to access is a good thing. Unfortunately, there is still a lack of big use cases otherwise, which means that much of the upcoming 4Q24 device launches will be more of a supply-side push of the latest processors. The good thing though is that battery life comparisons are now crystallizing, and this may very well be the more important thing given the industry’s ongoing struggles to compete with the perception of MacBooks lasting all day.

Discover how IDC’s AI Use Case Discovery Tool can elevate your AI strategy—learn more here.

Climate Week

From September 22-29, New York City hosts Climate Week, an annual event that brings together global leaders, policymakers, businesses, and civil society to tackle the pressing challenges of climate change. The week features a series of conferences, workshops, and exhibitions to promote sustainable practices, address climate justice, encourage international cooperation, and raise public awareness. Each year, we are reminded that the past 12 months have marked yet another “Top 10” high for global temperatures, emphasizing the urgent need for action—though the pace of change remains frustratingly slow.

The Data Center Dilemma

The data center industry has come under increasing scrutiny for its environmental impacts and rapid growth, driven partly by AI technologies’ energy demands. For example, in its sustainability report released in May, Microsoft said its emissions grew by 29% since 2020 due to the construction of more data centers designed and optimized to support AI workloads.  Likewise, Google reported increased electricity demand driven by artificial intelligence, and its growing fleet of data centers has caused the company’s greenhouse gas emissions to grow by 48% above its 2019 baseline, creating a challenge for the tech giant in meeting its carbon neutrality goals by 2030.

These companies should be applauded for their transparency and courage to tell the facts as they are.  It demonstrates leadership, promotes learning and improvement, encourages industry standards, and, most importantly, highlights the problem. This ever-increasing facility growth and electricity consumption poses a significant struggle as data centers strive to balance the need for increased computational power with the imperative to minimize their environmental footprint. IDC calculates worldwide data center energy consumption for 2023 to be 352TWh and projects a 19.5% CAGR, growing to 857TWh by 2028.

While harder to quantify due to of a lack of standards and available information, scope 3 emissions from constructing and outfitting data centers with IT equipment also contributes to industry emission growth.  

Why Aren’t We Seeing More Progress?                               

While AI garners most market attention, when surveyed, datacenter operators indicated that improved environmental sustainability was their second-highest initiative, ahead of AI, but behind business and financial management.

So, if the data center operators know and prioritize the problem, why isn’t more progress being made?

Like most complex problems, there are a multitude of factors that contribute to it. 

Demand – The first is the demand for digital services.   As enterprises pursue digital transformation and invest in artificial intelligence to create unique value, the demand for data centers and the associated electricity consumption is rising substantially.  Organizations are unwilling to sacrifice operational improvements and gains they expect from these efforts to meet sustainability goals.  
Global political cooperation and policy. It will take a combination of political agreements, like the Paris Agreement, and stricter local regulations on emissions to drive the change in behavior and investment in renewable energy infrastructure.
Transition to Renewable Energy. As governments and businesses set ambitious targets to reduce carbon emissions, the demand for renewable energy is outpacing supply, and globally, electric demand is outpacing new generation supply.  Many electricity grids were built for centralized, fossil-fuel-based power generation and are not yet fully optimized to handle the decentralized and intermittent nature of renewable sources like wind and solar.

Taking Immediate Steps on Energy Efficiency

The phrase “Think globally, act locally” has been part of our collective consciousness since the 1970s, resonating across various societal challenges due to its timeless relevance. Today, this principle particularly applies to datacenter operations. While most datacenter operators may not have the influence to shape global policies or invest directly in large-scale renewable energy initiatives, they can drive change by focusing on energy efficiency at a local level to reduce demand.

While some companies or individual executives prioritize sustainability metrics, viewing environmental responsibility as a critical driver of their decisions, others see these efforts as secondary or “soft” benefits. For this latter group, decision-making is anchored in hard financial metrics, focusing on return on investment (ROI). Energy efficiency initiatives appeal to both types and are aligned with top datacenter priorities.

Electricity costs account for the most significant portion of data center facility operating expenses—ranging from 45% to 60%—depending on location and data center type.  Simultaneously, growth in industrialization and electrification have led to higher electricity demand, outpacing generation, which is expected to cause electricity prices to increase. The combination of rising electricity prices and increased electricity consumption is poised to make data centers significantly more expensive to operate.

To assist organizations in understanding the potential impact, IDC published The Financial Impact of Increased Consumption and Rising Electricity Rates in Data Center Facilities.  The research includes scenario planning for a 1 MW data center in the United States, Germany, and Japan.   IDC projects that a typical 1 Megawatt data center consumed 6.6 Gigawatt hours (GWh) of electricity in 2023 and will grow to between 13 to 16 GWh by 2028 through capacity expansion, increased utilization, and higher density deployments. Simultaneously, IDC expects energy costs to continue to grow above historical levels.  The percentage growth in electricity spend will exceed a CAGR of 15% in all cases, with most scenarios showing growth of over 20%. When measured in absolute spending increase, it is expected to near or exceed $1,000,000 annually.

While the model assumes energy efficiency improvements, an organization can expect to save between $500K and $1,900K with energy efficiency improvements that are 10% greater than the industry average.

Conclusion

Prioritizing energy efficiency in data centers is no longer just a matter of environmental responsibility—it’s essential for sound datacenter financial management. As the demand for digital services, AI, and cloud computing continues to soar, the pressure on organizations to minimize their carbon footprint while managing rising electricity costs will only intensify. By taking action now—investing in more energy-efficient technologies and optimizing operations- organizations can reduce their environmental impact and significantly cut operating expenses, their top two priorities. The financial and environmental stakes are clear, and the time to act is now. Energy-efficient data centers are essential for the future of business and the planet.

The digital economy is frequently regarded as a beacon of innovation and growth, both influencing and being influenced by ICT spending. Today, there is increasingly more impact on the economy from the digital world. However, the digital economy itself is a complex ecosystem, influenced by countless macroeconomic factors that shape its development and trajectory. There are global economic trends in inflation and overall technology, “background” developments in raw material supply chains that influence technology hardware procurement, and ongoing, fast-paced advancements in emerging technology. Understanding these influences is crucial for uncovering the full picture of the digital economy’s potential and challenges.  

Understanding the economic impact of new technologies and quantifying it is not immediate. IDC’s Worldwide Digital Economy Strategies Program, in collaboration with IDC’s Data & Analytics team, developed a Digital Economic Impact model over the years and recently applied that to the key technology of the moment: artificial intelligence (AI). We chose AI, as it is not only on everyone’s mind, but is also a paradigm shift that’s reshaping industries, economies, and societies at an unprecedented pace. As we explore the macroeconomic factors influencing the digital economy it becomes clear that AI is both a product of these factors and a key driver of change within this dynamic landscape.  

According to our model we found that Business AI (consumer excluded) will contribute $19.9 trillion to the global economy and account for 3.5% of GDP by 2030. You can read the report or press-release for full details, but how did we calculate this? Our economic impact analysis leveraged data from our Spending Guide and other sources to help understand both the immediate impacts of AI spending and the interaction with broader economic forces at play which we explain below. 

Understanding How AI Impacts the Economy: Economic Impact Models 

As aforementioned, AI will account for 3.5% of GDP by 2030. To estimate the overall impact of a technology product or service, IDC developed an economic impact methodology that combines IDC’s knowledge of the market and internal data with a standard analytical framework that leverages the most updated countries input-output (I/O) tables.  

In brief, the economic impact of AI can be sub-categorized into direct, indirect, and induced effects. 

Direct Effects

Direct effects refer to the income generated by providers of artificial intelligence solutions or services from their direct sales to customers. In other words, it is the revenue of solutions/services providers when directly selling their products to end users. Essentially it is the revenue of an AI vendor when selling their solutions or services.

As a concrete example let’s take the case of a company that develops and sells AI-driven customer service chatbots. When this company successfully sells its chatbot solutions to online retailers, that revenue generated from these sales represents the direct economic impact of AI.

Indirect Effects

Indirect effects involve the economic impact related to the AI supply chain and the advantages gained by entities that adopt AI, such as enhancements in productivity and revenue growth. This category also includes the influence that organizations or technology providers exert on a regional or national level through their AI-related operations. Indirect effects are further divided into “backward” and “forward” categories. Backward indirect effects refer to the economic effects on supply chains and industries that provide inputs to AI-driven sectors, in other words, revenues generated in local industries impacted by AI. Forward indirect effects refer to effects on AI adopters that benefit from the adoption of AI technology in terms of productivity, revenue growth, and other business parameters.

More concretely, backwards indirect effects include all inputs supplied to AI solutions from the backend: including PCs, chips, computing, colocation datacenter operators, energy providers, internet providers, and more.

On the forward effects side, this includes concretely any increase in revenue coming from different factors such as the introduction of enhanced products or services, improvements in production and sales processes, or gains in customer acquisition that result from the implementation of AI.

Induced Effects

Induced effects stem from increased household income due to AI-related activities, leading to higher consumer spending and broader economic benefits. These are secondary effects, referring to economic stimulus coming from increased household income, including existing and new employees linked to the AI value chain across direct and indirect effects layers. People will spend part of their new wages in the economy, thus generating additional economic impact.

For example, let’s take a manufacturing company with an ambitious AI strategy that has installed a dedicated AI team, hired specialists, etc. This company may pay higher salaries to this AI team due to the increased demand and profitability of AI products. As these engineers receive higher incomes, they have more disposable income to spend on goods and services within their community, perhaps buying a car or dining out more frequently. These purchases inject additional money into the local economy, benefiting various sectors such as the automotive industry, restaurants, and construction businesses. It serves as a “ripple effect” of increased consumer spending stemming from AI-related economic activities.

Things To Watch Out For

These numbers, however, do not mean the journey from investment to monetization and economic impact is straightforward. In the case of AI, many companies are starting to question which use cases truly add value, and we are also seeing that regulation and questions about the ethical use of AI are increasingly important topics. From our Global Future Enterprise Resiliency & Spending Survey, tech decision makers (IT and LoB) reported an overage of 37 GenAI PoCs in the last 12 months, with only 5 making it into production, on average. Out of these 5, they reported a 68% success rate. That means a lot of PoCs failed, a testament to the long road ahead for AI’s real impact. While it is true that AI doesn’t necessarily guarantee immediate returns, AI’s economic impact will play out over time as the market matures. It is also crucial to keep this long-term perspective in sight while making executive decisions on implementation and deployment.

Going Forward

The interplay between AI and the broader macroeconomic factors is reshaping the digital economy in profound ways. Direct, indirect, and induced effects of AI all underscore AI’s role as a transformative force within the global economy. The model we applied here can also be applied to other kinds of transformative technologies.

Contributing Authors:

Carla La Croce – Research Manager, Data and Analytics, Europe
Lapo Fioretti – Senior Research Analyst, Emerging Technologies and Macroeconomics
Andrea Siviero – Senior Research Director, MacroTech, Digital Business, and Future of Work

Discover how IDC’s AI Use Case Discovery Tool can elevate your AI strategy—learn more here.

Digital business is just standard business in 2024.

Companies striving to participate in the digital economy are looking to invest in technology that fits the needs of their company size, employee personas and functions, industry verticals, and current level of technology maturity. Vendors hoping to sell and implement digital technologies need to consider all these factors when meeting with a potential customer. Segmenting and grouping customers by all these factors can help technology vendors provide the best messaging and service.

At IDC, we do research that considers the impacts of all these factors, and that gives us insight behind the tech buying curtain into the needs and wants of buyers at all stages of their digital journeys.

The Small and Medium Business Market

In the recent IDC Webcast, “Behind the Tech Buying Curtain: What Vendors Need to Know,” Katie Evans, IDC Senior Research Director for Worldwide Small and Medium Business Markets, said there are three key topics keeping SMB tech buyers up at night: AI/automation, macroeconomic woes, and heightened security concerns largely fueled by AI, remote work, and the moving to the Cloud.

The forward-looking investment priorities for SMBs are all automation and AI related—process automation, connectivity automation, AI (non-GenAI), and GenAI, according to IDC’s Worldwide Small and Medium Business Survey. Rising energy prices highlight SMB’s top macroeconomic woes in that survey, while implementing technology securely is the largest technology challenge.

Macro Factors Impacting Investment

IDC’s Digital Economy Strategies research, led by Research Analyst Elisabeth Clemmons, focuses in part on the macro landscape impacting businesses, even those focused in specific countries or localities. Impactful events and trends include skills shortages, inflation, supply-chain constraints, energy crises, tensions between countries and war, and elections around the world. These events impact technology buying patterns, causing companies to be more cautious with their investments.

AI is the technology most likely to persevere and be prioritized in the face of these macro impacts, with AI spending, the AI provider supply chain, and the economic stimulus among AI adopters is projected by IDC’s Macroeconomic Center of Excellence to be 3.5% of global GDP by 2030.

The heightened impact of and investment in AI is contributing to two more macro trends impacting businesses: digital regulation and potential raw materials shortages. Data and AI is a top regulatory target with governments taking diverse approaches, while the sheer amount of data in the world is expected to more than triple by 2028—necessitating over 50 times the current annual production levels of neodymium and other critical raw materials.

C-Suite Leaders Increase Focus on AI

Despite any concerns and challenges that AI my pose, CEOs are scaling AI initiatives, dedicating budgets to AI, and ensuring AI projects receive greater visibility, according to Nupur Singh-Adley, Research Manager leading IDC’s C-Suite Tech Agenda research.

As C-suite leaders focus on building digital businesses buoyed by AI technology, they are also mindful of managing risk, including heightened cybersecurity threats and regulations. This is leading to prioritized spending on security, risk, and compliance technologies and greater scrutiny of cybersecurity and risk management at the board level.

Along with considering risk, CEOs are also prioritizing responsible AI while building digital businesses that are focused on trust, critical evaluation of tech vendors, and sustainability.

Digital Capability Perception vs. Reality

While companies of all sizes are facing challenges both internally and externally and they digitize, it’s important for technology vendors to consider that many are also overestimating their digital capabilities. In IDC’s Digital Business and AI Transformation Strategies research, we conduct an annual Digital Business Scorecard that measures the digital capabilities of businesses in four areas: Digital Business Models, Data, Operational Processes, and Organization.

Overall, when asked to assess themselves, 41% of those surveyed in IDC’s Digital Executive Sentiment Survey believed they were a “mostly digital business.” However, when applying our Digital Business Scorecard methodology, which correlates investments and strategies to business outcomes, only 11% of that same survey sample were categorized as “Digital Business Leaders.”

Digital Business Leaders are taking holistic digital strategies and have implemented world-class technology. They view data as a top AI priority while also expanding its use in core operational processes. They have a digital technology architecture that is in lockstep with IT strategy and are focused on improving recruiting and employee engagement.

These are the standards that many of those companies that think they are digital aren’t quite achieving—often finding themselves bogged down by inferior technology, not focusing on capitalizing on the value of their data, not looking to automate and standardize processes, and not taking the view of technology as a competitive advantage.

The Important Role of Technology Vendors in the Digital Economy

Technology vendors are key to digital business and AI transformation strategies—and many companies need vendor expertise and guidance to help them become digital business leaders. Vendor messaging to these companies can be challenging—but it helps to know how company size, macroeconomic impacts, C-suite leadership, and digital capability and perception impacts buying conversations.

It’s helpful for vendors to segment their customers, and specialize their strategy and messaging to those individual segments. That’s beneficial for all parties and will help companies succeed in the digital economy.

Learn what matters most to your customers with IDC’s AI Use Case Discovery Tool—find out more.

When something breaks in your home, your office, or in a venue you frequent what is your expectation? Is it that you will just deal with having a broken product, offline printer, or down elevator? Most of us would expect a service technician will show up to the rescue to return the given product or asset to operations so we can get back to productivity.

In IDC’s recent Product Innovation and Aftermarket Services Survey, service leaders noted a priority to improve service (quality and speed) to customers. But too often, aftermarket service organizations have focused on just ensuring a warm body arrives on a customer site within the service level agreement (SLA) with little importance put on actually achieving resolution or enhancing the customer experience.

As customers explore options for the services they receive, aftermarket service providers will need to get better at delivering more than just the minimum to enable the field service team to become experts on engaging a customer in a special and personalized way. Field service and the aftermarket are too often driven by meeting a SLA. This minimum requirement of meeting a service window of 4-8 hours after a failure has been reported, or processing a warranty claim within 30 days, or ensuring an asset is available 80% of the time has long been the norm. Meeting minimal requirements is quite profitable for the service organization, but can be short-sighted as competitors enter the market and begin to offer service, support, and enhanced experiences of the same or better quality.

To address this pending disruption of competitive factions and heightened customer expectations, field service organizations will need to prioritize value and not just meeting an SLA. This will raise the cost to serve in the short term but in turn result in having the right to request more share of customer wallet as value delivered improves for the customer or operator. This shift to value and enhanced/personalized experiences will ultimately require better quality data, contextualized customer insights, and freed up time to focus on delivering value. Artificial intelligence (AI) provides an opportunity to close the gap between data and insights on the front line. IDC defines AI as the ability of computers to learn without being programmed, applied to large sets of data for business advantage. But how should field service organizations reconcile the hype around AI to usher in the era of intelligence at the point of service? Field service organizations should prioritize the following as they explore the potential of AI in the coming weeks, months, and years:

Understand the pulse of your employees and customers. Voice of customer and voice of employee activities often are established for the primary benefit of the organization (i.e., increase sales/margins, increase retention rates). In this new era of AI, field service organizations will need to listen to the needs and concerns of customers and employees. As AI becomes more pervasive across industries, field service organizations must tackle the elephant in the room around AI – privacy, and job displacement. Too much of the discussion around AI in the B2B world has been the fear that it will replace jobs or result in IP theft. This view of potential negatives neglects to amplify all of the potential positive outcomes of what AI can offer, Educating customers and service employees about the value of AI and how these technological capabilities can improve the service experience, customer outcomes, and employee productivity is crucial to adoption and comfort. Without understanding customer and service employees’ fears about AI, organizations will struggle to maximize the opportunities that will come with this innovative technological advancement.  
Shift the KPI that measure success in the field. The promise of AI in field service revolves around improved operational efficiency, predictive/prescriptive service outcomes, and improved productivity of the team. However, there is a bit of a gap between the current metrics that are being measured and what should be measured in the AI era. If AI is to improve the speed of service, technicians should be measured on the value they are providing to the customer and not on how many more jobs they can complete. The improved speed of issue resolution as a result of AI providing better answers to the reason for failure should allow the humans on the service team to focus on the customer. This shift in what role a field service technician can play in customer outcomes is profound, no longer is the technician solely in place to turn a wrench but to prioritize customer engagement. Therefore, the KPI that matter aren’t work orders closed in a given day but experiential and value based. These new metrics may be more difficult to measure but will tell a better story of customer impact, future revenue opportunities, and lifetime value.
Highlight the positive and address the (potential) negatives. Right now, there are too many field service technicians that can efficiently get on site in front of a customer or asset but fail to resolve the issue on a first visit. Issue resolution is becoming more and more complex as assets are smarter, supply chain networks struggle with resiliency, and the field force ages out. The ability to have the right part, right skills, right insights, at the right time is becoming a fairy tale for too many service organizations. On the front line is the field service engineer who has to advise a customer or operator in need that service cannot be completed resulting in assets, products, and equipment remaining down. Service leaders must communicate to the field service team both the in office planning/dispatching teams and the engineers in the field the ability for AI to drive insights and efficiency while reducing non-value added task work. The skepticism of technology from service teams has preceded the AI era, but the AI conversation brings with it the fear of machines taking over to the detriment of the humans. However, fear comes from a lack of communication, visibility, and buy-in around strategy and execution. AI can enable service workers to be the expert in a time of customer need and also free up their time from rote administrative tasks. AI must become an opportunity for the service team and not a murky monster.

Artificial intelligence will have a large impact on the field service organization and the customer experience. Service leaders need to understand the opportunity, embrace the challenge, and educate customers and employees to ensure the AI era is a net positive driving growth of the organization.

For more information on CX and AI, read our other blogs:

Expand Prospect Outreach and Engagement With the 4-O Marketing Matrix
Empowering Sales Management With AI
GenAI Marks a Shift to Intelligent Experience Orchestration

Learn what matters most to your customers with IDC’s AI Use Case Discovery Tool—find out more.