IDC’s Quick Take

The recent IT outage caused by silent updates pushed out by CrowdStrike to its Falcon agent exposes an issue that is at the heart of how the IT industry operates. It highlights the contrasting trust and attestation mechanisms taken by operating system vendors like Microsoft, Apple, and Red Hat in allowing its ecosystem of independent software vendors (ISVs) direct access to certain parts of the operating system stack and especially software that can potentially severely negatively impact the system kernel.

While this issue impacted Windows devices– network and human centric – managed by CrowdStrike, none of the iOS, MacOS, or even Linux devices were affected. That is very telling and should compel vendors like Microsoft and Apple to take a long hard look at what “openness” means in the wake of regulations like EU’s Digital Markets Act (DMA). It should also compel the largely Windows-dependent customer base to redefine their long-term cyber recovery strategy. It should include making a shift to more modern operating system environments.

Event Highlights

On July 19, 2024, at 04:09 UTC, a sensor configuration update was released by CrowdStrike for Windows systems as part of the Falcon platform’s protection mechanisms. This update contained a logic error that led to a “blue screen of death” (BSOD), affecting certain systems. A remediation was implemented by 05:27 UTC on the same day.

According to CrowdStrike, the impact of this event was specific to customers using Falcon sensor for Windows version 7.11 or higher. It needs to be pointed out that to make their endpoint protection products effective, vendors like CrowdStrike require access to the system files. Any configuration issues with these files can lead to unpredictable behavior at best and leave the system in an unrecoverable state at worst.

The resulting outage caused disruptions to airlines, businesses and emergency services and could be the largest IT outage in history. In time, we will know whether the scale and impact of the outage will reach the level of the “NotPetya” cyberattack in 2017. At the time of writing, two days later, airlines – the biggest group of affected enterprises – were still reeling from the outage.

It is important to note that this incident was not caused by a cyberattack but rather routine update to configuration files, often referred to as “Channel Files.” In the context of the Falcon sensor, Channel Files are integral to the behavioral protection mechanisms that safeguard systems against cyber threats. These files are dynamically updated multiple times daily. The Falcon sensor’s architecture, designed to incorporate these updates seamlessly, has been a foundational component.

In Windows environments, Channel Files are typically located within the directory path C:\Windows\System32\drivers\CrowdStrike\, identifiable by their “C-” prefix. Each file is uniquely numbered, serving as an identifier that aids in the management and deployment of updates. For instance, Channel File 291, denoted by the filename “C-00000291-“, plays a crucial role in how Falcon assesses the execution of named pipes—a standard method for interprocess communication within Windows systems.

The significance of Channel File 291 came to the forefront during an update aimed at neutralizing the threat posed by malicious named pipes associated with prevalent Command and Control (C2) frameworks. The update introduced a logic error, leading to a system crash.

IDC’s Point of View

For historical context, this is not the first time something like this has happened. For example, in 2010, McAfee had an issue with a “DAT” file. The issue with McAfee’s DAT file version 5958 caused a reboot loop and loss of network access on Windows XP SP3 systems due to a false positive that misidentifies the Windows binary “svchost.exe” as the virus “W32/Wecorl.a”. In 2017, Webroot released an update that misidentified Windows system files as malware and Facebook as a phishing site. This update quarantined essential files, leading to instability in numerous computers. In 2021, a mass internet outage was caused by a bad software update by Fastly, there have been many others.

This situation – which is not unique to CrowdStrike – exposes four key issues that are fundamental to the IT industry and its complex ecosystem of ISVs.

First, it exposes the fact that by giving its ecosystem ISVs direct access to the system kernel, the operating system vendor is essentially removing itself from the trust value chain. Thus, the trust value chain now only includes the ISV and its customers.
Second, the process of silent updates in which the customers implicitly rely on the QA process employed by the ISV leaves them inadequately prepared for drastic and timely intervention in the case of mass outages that leave the system in an unrecoverable state.
Third, this situation is a wake-up call for the industry on what a system of checks and balances means and what kind of accountability operating system vendors, ISVs and customers must play to avoid this kind of a situation from repeating itself.
And finally, fourth, this situation indirectly exposes the fragile human-centric Windows stack that unlike modern network-centric Unix and Linux operating systems cannot robustly manage exception errors instead defaulting to a manually recoverable state.

The first point exposes contrasting approaches taken by leading operating system vendors. On one side there are vendors like Apple that take a very prescriptive and closed approach to endpoint protection making it almost impossible for any ecosystem ISV/provider like CrowdStrike to push out configuration changes that can potentially catastrophically impact on the operating system (e.g., iOS or macOS) kernels. Apple has been a fierce advocate of a “walled garden” approach implementing stringent attestation mechanisms to ensure that no one – and we mean no one – gets to modify the system kernel without express approval from Apple. This has made Apple run afoul of the European Commission, and its hawkish regulatory approach to open up operating systems under the premise of fair competition. On the other hand, Microsoft takes – or more importantly was forced to take – a more open approach enabling at least a dozen ISVs in offering modern endpoint protection software. Here too, regulation forced their hand. For example, according to Microsoft it cannot legally wall off its operating system in the same way Apple does because of an understanding it reached with the European Commission following a 2009 complaint to give makers of security software the same level of access to Windows that Microsoft gets.

And then there is the ecosystem of Linux vendors that are staunch proponents of validating software from third parties to ensure enterprise customers from inadvertently creating kernel level “panics” en masse.

The second and third points above speak to the process of “silent patching” or “silent upgrades” where the customers often do not have the luxury of QA’ing updates – and especially endpoint updates – before they are rolled out. For one, they are too frequent. For another, as history shows, they are often harmless. Though, all it takes is one outage to question if this implicit trust needs to be revisited.

The final point speaks to a fundamental difference between modern operating systems that are Unix- or Linux-based and are designed from the ground up to be network centric. This is in contrast with Windows variants – especially those used in embedded environments like terminals used by Banks and Airlines – that are modified human-centric operating systems. While the IT industry has slowly migrated their infrastructure stacks to Unix or Linux environments, for convenience, the embedded side often remains on Windows (running on “bare metal”). Unfortunately, that leaves them exposed to cyber security threats and hence the reliance on external providers. When there is a catastrophic failure of any kind, the only recourse is to send the IT guy with a USB drive to fix all the affected units. System rollback and recovery – available in many virtualized environments – is unfortunately not an option.

For those unfamiliar with CrowdStrike, CrowdStrike is positioned as a leader in the 2024 IDC MarketScape for worldwide modern endpoint security for enterprises. With its cloud-native platform and lightweight sensor agent, and as an early-to-the-market provider of EDR capabilities, CrowdStrike quickly gained prominence in the modern endpoint security market, expanded its cloud-native platform into adjacent security categories that also benefit from large and diverse data sets, integrated threat intelligence, and centralized analysis and, in the process, increased its wallet share among security-spending decision makers.

In this instance, CrowdStrike’s strength aggravated the issue. The cloud-based analytics backend architecture has a lightweight agent on the endpoint that allows the solution to scale really well according to CISOs with whom IDC speaks. In this instance, the platform also allowed the solution scale the logic error.

To be clear, we are discussing more than a cybersecurity issue. CrowdStrike and its competitors are simply relying on endpoint configuration management and more broadly IT service management framework that has been developed by Microsoft for managing Windows devices. The problem with this framework is that it is a one-way street. Once the system goes into a recovery mode there is no automatic way to recover it. Network recovery is possible but only if it has been set up by the enterprise.

For any affected entity, the effort to correct the logic error issue in the CrowdStrike platform was the easy part. Identifying effected systems is even easier than that as blue screens of death are easy to see – you can perhaps still see them in many public places or lights out environments where the institutions lacked the wherewithal for fast manual intervention. Recovering the hyperconnected and hardened systems into a known good state or safe mode for remedial action is the hard part. And ensuring it does not happen again is perhaps the hardest part.

This incident – and the scale at the entire IT industry globally is reeling from it – should be the basis for CIOs to ask their organizations to:

Update Cyber Recovery Procedures: Revise their recovery strategies to include automated or remote recovery in situations where the system requires manual intervention.
Shift to a better operating environment. Push for more reliable and verifiable solutions from OSE vendors and endpoint security providers. These must be built on network-centric operating systems.
Short-Term Focus Shift: Prioritize basic IT Service Management issues over emerging technologies like AI and Generative AI. The depth and breadth of the impact from a basic unsupervised configuration change shows that sometimes the small things matter the most.

The insights provided in this blog are sourced from IDC’s Endpoint Security and Future of Trust research. If you are interested in learning more about cybersecurity best practices businesses should embrace you can listen to the recent IDC Webinar, Cybersecurity Norms and Trends: How Does Your Business Stack Up via the link below.

IDC analysts Ashish Nadkarni and Matthew Eastwood contributed to this article.

In today’s crowded marketing landscape, the 4-O Marketing Matrix offers a critical upgrade from the traditional 4 Ps model (Product, Price, Place, Promotion). While the 4 Ps remain foundational, they fall short in addressing the complexities of modern customer engagement. The 4-O Matrix—encompassing Online, Offline, Onsite, and Offsite strategies—provides a comprehensive framework that aligns with shifting consumer behaviors and preferences.

The 4-O Marketing Matrix encourages marketers to diversify their promotional tactics across Online, Offline, Onsite, and Offsite channels. This approach not only broadens the scope of engagement opportunities but also aligns more closely with the multifaceted journeys of today’s consumers. By integrating this matrix with the traditional 4 Ps, organizations can enhance their promotional outreach, drive higher conversion rates, and build greater trust with their prospects and clients.

In an era where single-format promotional efforts (typically email marketing) dominate the landscape, the necessity for a more expansive and empathetic marketing model is clear. The 4-O Marketing Matrix represents a pivotal evolution in marketing strategy, urging marketers to leverage a variety of tools and technologies to engage with audiences in a more meaningful and impactful way.

Adopting the 4-O Marketing Matrix is not just about expanding the promotional mix; it’s about embracing a more holistic and customer-centric approach to marketing that resonates with the complexities of the modern market.

Thinking Outside the (In)box

Relying too heavily on email has limited the reach and effectiveness of many organization’s efforts and simultaneously overlooks consumers’ changing preferences. Once the cornerstone of digital outreach, email has become a comfort zone, leading to inbox overload and diminishing returns as consumers grow disinterested in repetitive messages. This focus fails to recognize the importance of personalization and relevance in terms of both content, channel, and context.

Consumers today crave authentic connections and personalized experiences. Email campaigns, often impersonal and detached due to both their content and the nature of the medium, fail to meet these expectations.  By concentrating too much on email, businesses miss out on opportunities offered by social media, messaging apps, and other digital platforms that facilitate meaningful connections.

The limited diversity in contemporary marketing outreach is a consequence of the industry’s own oversights. Martech vendors, influenced by the preferences of marketing users, especially non-marketing executives, have emphasized readily quantifiable and commonly referenced funnel metrics, like Marketing Qualified Leads (MQLs). This focus on simple metrics obstructs a holistic understanding of the “Real” customer journey and pushes marketers towards myopic email-centric campaigns. 

Modern tools that track lead scores based on limited activities miss critical insights from the ‘real’ customer journey, including interactions on competitors’ sites, analyst assessments, peer reviews, and user communities. This focus on short-term, funnel-based MQL targets inspired the creation of the 4-O Marketing Matrix, which encourages organizations to adopt a more holistic approach and engage prospects throughout their entire lifecycle.

Exploring the 4-O Marketing Matrix

The 4-O Marketing Matrix is a revolutionary model that encourages marketers to broaden their promotional approaches beyond traditional methods.

This model is divided into four components: Online, Offline, Onsite, and Offsite. Each of these elements plays a crucial role in creating a comprehensive marketing strategy that addresses the diverse needs and preferences of today’s consumers.

Online and Offline: Bridging Digital and Physical Worlds

The Online component focuses on digital interactions that occur through various electronic devices, offering marketers a vast playground for digital campaigns, social media engagement, and more. In contrast, Offline marketing involves physical, in-person experiences that can create lasting impressions through human touch and personal interaction. Balancing these two aspects allows marketers to cover the entire spectrum of consumer engagement, from the convenience of digital to the authenticity of face-to-face encounters.

Onsite and Offsite: Reaching customers where they are

Onsite marketing refers to promotional activities conducted on a brand’s platforms, such as its website, physical store, or other “owned” environments. These efforts are directly under the brand’s control and provide a space to fully express the brand’s message. Offsite marketing, however, takes place on external platforms, reaching consumers where they spend their time outside of the brand’s direct influence. This could include social media, third-party websites, or even physical locations not owned by the brand.

By leveraging both Onsite and Offsite marketing, brands can ensure they are not only drawing consumers into their own controlled environments but also engaging with them in spaces where they already exist and feel comfortable. This dual approach maximizes visibility and interaction opportunities, making it easier to connect with a wider audience.

The Power of the 4-O Marketing Matrix

By integrating Online, Offline, Onsite, and Offsite elements, the 4-O Marketing Matrix helps marketers move beyond traditional tactics to create dynamic, impactful campaigns. This holistic approach not only enhances brand visibility but also builds deeper connections and trust with consumers. The 4-O Matrix is more than a theoretical model—it’s a practical guide for developing customer-centric strategies that adapt to the evolving marketing landscape.

Embracing the 4-O Marketing Matrix allows marketers to craft strategies that are not only comprehensive but also deeply resonant with the modern consumer’s lifestyle. It’s about enriching the dialogue, understanding their journey, and being present in ways that are both meaningful and impactful.

Navigating the Future of Marketing with the 4-O Framework

The 4-O Marketing Matrix stands out as a novel and crucial framework for organizations seeking to deepen market engagement and build meaningful connections. This model emphasizes a balanced approach across Online, Offline, Onsite, and Offsite dimensions, offering a comprehensive blueprint for moving beyond traditional promotional tactics.

The 4-O Marketing Matrix guides marketers to transcend conventional strategies and adopt a more nuanced understanding of customer engagement. It enables organizations to navigate modern marketing complexities with agility, optimizing every touchpoint for maximum impact. By integrating this matrix, businesses can engage customers more successfully through innovative Offline events, impactful Online campaigns, or effective Onsite and Offsite strategies.

This approach fosters a holistic and adaptable engagement strategy, unlocking new opportunities for growth, customer loyalty, and market leadership. Embracing the 4-O Marketing Matrix ensures that organizations not only keep up with the evolving market but also lead towards a more connected and customer-centric future.

When facing critical business decisions about targeting the right audience, consider integrating IDC Strategic Market Insights (SMI) into your process. Learn more here.

AI has come a long way, turning from a futuristic concept into a driving force behind some of today’s most exciting and impactful innovations, but that benefit comes at a cost.  AI requires performance-intensive computing achieved with high core count CPUs, coprocessors such as GPUs, and high-speed networking, which can require up to 10 times the amount of power for AI infrastructure compared to general-purpose computing. While organizations need to minimize the environmental impact of AI, the true sustainability promise of AI is how to make all industries more sustainable. 

Energy and Carbon Estimates 

IDC estimates that AI datacenter energy consumption was 23.0 Terawatt hours (TWh) in 2022, growing at a CAGR of 44.7% and reaching 146.2 TWh by 2027.  To put that into perspective, the forecasted total for 2027 exceeds the estimated 2021 country usage of Sweden, Argentina, or the United Arab Emirates¹. In recent years, the datacenter industry has made significant strides in sustainability. Despite being expected to account for 18.0% of the carbon emissions in 2027, AI is expected to account for 14.6% of all datacenter carbon emissions. Those sustainability efforts are apparent, but unfortunately, carbon emissions are still expected to grow by 2027. 

Completing the Journey to Net Zero

Regardless of industry, most organizations see the business value of environmental goals, with many having set net-zero targets. In IDC’s Datacenter Operations and Sustainability Survey, datacenter operators indicated that Improving Sustainability was their second-highest priority. While sustainability goals can be a tapestry of many initiatives, three principles stand out: energy sourcing, efficiency, and circularity. 

Energy Sourcing

Data centers significantly lower their carbon footprints by leveraging renewable energy sources such as solar, wind, and hydroelectric power, whether onsite generation via microgrids or funded via power purchase agreements, long-term contracts between an electricity generator and a buyer to purchase renewable energy at predetermined prices. Many sustainable data centers also invest in energy storage solutions to effectively balance supply and demand. Technologies such as advanced battery systems and thermal storage help ensure a consistent energy flow, even when renewable sources are intermittent or the generation grid mix is unfavorable. 

In addition to traditional carbon-free renewables, the industry is starting to see investment and implementation via hydrogen and nuclear power. 

Efficiency

Simply put, efficiency is maximizing datacenter performance and minimizing resource usage, including energy, space, and hardware. Furthermore, sustainable datacenters often implement energy-efficient infrastructure, such as advanced cooling systems, workload consolidation and optimized server configurations, to minimize energy consumption. By prioritizing these green energy initiatives, sustainable datacenters also pave the way for more resilient and cost-effective data management solutions. 

Efficiency is not limited to energy.  Datacenter water efficiency focuses on minimizing water usage in cooling systems and other operational processes to reduce environmental impact. Techniques such as liquid cooling, evaporative cooling, and water reclamation systems help data centers achieve this goal by optimizing water consumption and recycling. By implementing these strategies, datacenters can significantly lower their water footprint while maintaining optimal performance and cooling efficiency. 

Circularity

AI is driving the need for IT asset refreshes. Datacenter capacity planning that includes the circularity, or resale, value of IT assets can open up investment capacity for GenAI budgets and new equipment. IDC forecasts the market for refurbished IT equipment and attached services to reach nearly $15 billion in 2028. 

Responsible processing of the used datacenter assets, whether they get recertified for redeployment and resale, harvested for parts, or recycled, represents an opportunity to not only create investment capacity but also be part of meeting corporate sustainability targets. IDC research shows that while organizations increasingly value a broad set of sustainability factors when it comes to IT procurement, the sense of shared sustainability aspirations with suppliers and partners comes in as a top 2 requirement. 

Another example of circularity is waste heat reuse. Liquid cooling, a staple in AI datacenters, involves capturing the heat generated by servers and repurposing it for other uses, such as heating nearby buildings or industrial processes. In addition to its energy efficiency, liquid cooling enhances energy efficiency and reduces the data center’s environmental footprint.  

AI For Sustainability 

While AI will undeniably consume significant amounts of energy, making every effort to implement sustainable AI practices is crucial. This energy use should be viewed as an investment in a more sustainable world, as AI has the potential to drive substantial improvements across various industries. Despite the projected increase in energy consumption, IDC forecasts that datacenters in total, not just AI, will account for only 2.5% of global energy use by 2027, highlighting its relatively modest footprint. The true value of AI lies in its ability to enhance sustainability in sectors such as agriculture, manufacturing, and transportation by optimizing resource use, reducing waste, and improving efficiency. Thus, embracing AI responsibly can lead to a net positive impact on global sustainability efforts, outweighing its energy demands. 

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

In the dynamic world of technology, startups and growing tech vendors are constantly seeking innovative ways to stay ahead of the curve. The rise of generative AI (GenAI) offers a transformative opportunity, but leveraging its full potential requires more than just adoption—it necessitates a strategic approach called GenAI Engineering. This blog post delves into why GenAI Engineering matters for tech vendors and startups and how it can be a cornerstone of your growth strategy. 

The GenAI Boom: A Catalyst for Innovation 

Since the launch of ChatGPT in November 2022, the potential of GenAI has become evident across industries. From GitHub CoPilot to DALL-E and Google Bard, GenAI applications have showcased incredible capabilities in automating tasks, enhancing creativity, and improving decision-making processes. This surge in GenAI adoption is particularly relevant for tech startups and vendors who are uniquely positioned to harness these advancements for rapid innovation and market differentiation. 

The Pitfalls of Consumer-Focused GenAI 

While consumer-focused GenAI services have ignited interest, they often fall short in addressing the specific needs of enterprises, especially those in the tech sector. Startups and tech vendors require GenAI solutions that align with business objectives like scalability, accuracy, privacy, and cost-efficiency. For instance, concerns about data security, intellectual property, and the accuracy of GenAI outputs are paramount for these organizations. 

What Is GenAI Engineering? 

GenAI Engineering integrates concepts and decision-making between three overlapping and interdependent domains:

Data Domain: High-quality data is the bedrock of successful GenAI projects. Startups must focus on data sourcing, quality, and privacy. Questions like where the data is sourced, its appropriateness for the intended outcomes, and its security are crucial. 

AI Models Domain: Selecting and customizing the right GenAI models is essential. Startups need to consider the types of models that best suit their needs, how to fine-tune these models, and ensure their outputs are reliable and high-quality. 

Outcomes Domain: GenAI implementations must be outcome-driven. This involves choosing the right implementation approach, determining the degree of autonomy for AI components, and selecting appropriate infrastructure platforms. 

Three Reasons Why GenAI Engineering is Critical for Startups and Tech Vendors 

GenAI Engineering is the disciplined approach to implementing GenAI technologies in a way that aligns with business goals and maximizes value. For startups and growing tech vendors, this means: 

Strategic Implementation: GenAI Engineering bridges the gap between strategy and execution, ensuring that GenAI projects are aligned with business outcomes, resources, and constraints. 

Scalability and Flexibility: By systematically applying clear business and technology principles, startups can scale their GenAI implementations efficiently, adapting to changing market demands and opportunities. 

Innovation and Competitive Edge: GenAI Engineering empowers startups to innovate rapidly, offering customized solutions that differentiate them from competitors and appeal to their target markets. 

Governing Factors in GenAI Engineering  

For tech startups, the following factors are critical: 

Value: Focus on outcomes that improve productivity, enhance product offerings, and drive growth. Startups need to evaluate the potential ROI of GenAI projects. 
Resources: Assess available resources, including data, skills, tools, and infrastructure. Startups often operate with limited resources, making strategic resource allocation vital. 
Constraints: Navigate industry regulations, internal policies, and risk management. Understanding these constraints helps in developing responsible and compliant GenAI solutions. 

Collaboration: The Heart of GenAI Engineering 

Effective GenAI Engineering ideally involves collaboration across various roles, such as CISOs, CDOs, data engineers, data scientists, developers, and non-technical domain experts. However, startups often lack the resources to have all these roles in-house. Here are practical steps for startups to initiate GenAI engineering: 

Leverage Partnerships: Collaborate with universities, research institutions, and other tech startups. These partnerships can provide access to expertise, resources, and infrastructure that may be beyond the reach of a startup. 
Utilize Open Source Tools: Take advantage of open-source GenAI tools and platforms. Communities like Hugging Face and GitHub host a multitude of projects that can accelerate your development efforts without significant upfront costs. 
Engage with GenAI Platforms: Use AI platforms provided by major cloud providers like AWS, Google Cloud, and Azure. These platforms offer ready-to-use models, development tools, and infrastructure support that can help startups implement GenAI solutions quickly and cost-effectively. 
Hire Freelancers and Consultants: Bring in external experts on a project basis. Freelancers and consultants can provide the specialized skills needed for specific tasks without the long-term financial commitment of full-time hires. 
Build a Cross-Functional Core Team: Assemble a small, cross-functional team with diverse skills. Even with limited resources, having a core team that includes data engineers, developers, and business analysts can drive GenAI projects forward. 
Invest in Training: Upskill existing employees through training programs focused on GenAI technologies. Online courses, workshops, and certifications can equip your team with the knowledge needed to implement GenAI solutions effectively. 

Establishing a GenAI Center of Excellence (CoE) 

For many startups, creating a GenAI Center of Excellence (CoE) can be a strategic move. A GenAI CoE can: 

Centralize Expertise: Bring together experts from various domains to drive GenAI initiatives. 

Promote Best Practices: Share success stories, establish standards, and ensure consistent application of GenAI Engineering principles. 

Drive Innovation: Act as a hub for exploring new GenAI opportunities and developing cutting-edge solutions. 

Practical Steps  

Start with Data: Ensure you have a solid foundation of high-quality data. Implement robust data governance practices to maintain data integrity and privacy. 

Choose the Right Models: Evaluate different GenAI models and select those that best align with your business goals. Consider fine-tuning and customizing models to meet specific needs. 

Focus on Outcomes: Define clear business outcomes for your GenAI projects. Ensure that every implementation is aligned with these outcomes to maximize value. 

Invest in Skills: Build a team with the necessary skills and expertise. Invest in training and development to keep your team updated on the latest GenAI advancements. 

Foster Collaboration: Encourage collaboration across different roles and teams. Establish clear communication channels and collaborative tools to facilitate teamwork. 

Key GenAI Use Cases for Startups and Growing Tech Vendors

Understanding the potential use cases for GenAI can help startups identify where to focus their efforts: 

Task Productivity: Simple tasks like summarizing reports, generating job descriptions, or creating code snippets. GenAI can automate these tasks, freeing up valuable time for employees to focus on more strategic activities. For instance, integrating GenAI capabilities into everyday tools like email clients or project management software can significantly boost productivity. 

Business Function or Process Improvement: Enhancing specific business functions such as marketing, sales, customer service, or procurement. By integrating GenAI with corporate data, startups can streamline processes and improve efficiency. For example, a GenAI-powered chatbot can provide 24/7 customer support, handling common queries and escalating more complex issues to human agents. 

Industry-Specific Product/Service Innovation: Developing innovative products or services tailored to specific industries. This often requires custom-built or heavily customized GenAI models. For instance, a health tech startup might develop a GenAI model trained on medical data to assist doctors in diagnosing diseases or recommending treatments. 

Conclusion 

For tech startups and growing vendors, GenAI Engineering is not just a strategic advantage—it is a necessity. By adopting a disciplined approach to GenAI implementation, these organizations can unlock new levels of innovation, scalability, and competitive edge. As the GenAI landscape continues to evolve, those who invest in GenAI Engineering today will be the leaders of tomorrow. 

Ready to take your startup to the next level with GenAI? IDC’s leading-edge expertise and solutions created with growing tech vendors in mind help you navigate these challenges and implement effective safeguards. Contact us today to learn how IDC can help you create successful GenAI strategies and thrive in the Era of AI Everywhere. 

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

The telecommunications sector stands at the forefront of technological innovation, especially in the realms of Artificial Intelligence (AI) and Application Programming Interfaces (APIs). The World Economic Forum highlights the critical role of telecommunications in managing AI risks and ensuring the security of vital infrastructure, provided data protection and ethical considerations are prioritized.

At a time in which AI and network APIs are poised to reshape the telecommunications landscape, numerous pivotal questions emerge.

Watch on-demand webinar: Revenue Enablers for the Future Telco: APIs, AI, and Emerging Tech.

To address this curiosity and foster a deeper understanding, we’ve compiled a list of the most pressing questions currently dominating the industry discourse. Let’s embark to uncover the answers that will shape the future of telecommunications.

Q: What is Generative AI (GenAI) and how can it be used in telecommunications?

GenAI involves algorithms that enable computers to create new content from existing data. It’s being evaluated for use in enhancing customer engagement, network optimization, and creating new services.

Q: What are the investment trends in GenAI among telco companies?

More than a third of the companies surveyed are doing initial testing of GenAI models and focused proofs of concept, and almost a fifth are investing significantly in GenAI.  There’s a notable interest in developing GenAI use cases, especially in network optimization and customer service enhancements.

Q: How are telcos leveraging network APIs for monetization and service improvement?

Telcos are using network APIs to deliver new applications, improve customer experiences, and partner with developers for B2B2X applications. The adoption of 5G and network APIs is seen as a significant opportunity for monetization.

Q: What are the challenges and strategies for telco companies in adopting AI and APIs?

Challenges include skills gaps, data privacy concerns, and ensuring API interoperability. Strategies involve partnering with hyperscalers, focusing on customer-centric values like transparency and empowerment, and investing in cloud-native technologies and advanced orchestration solutions. AI requires data as an input and so breaking down data silos to create readily available single source of truth becomes critical to any AI strategy.

Q: What is the future outlook for AI and API adoption in the telco sector?

AI and API adoption is expected to drive network and operational efficiency, enhance customer experiences, and open new revenue streams through innovative services and partnerships. Collaboration with technology partners and a focus on ecosystem-driven approaches are key to leveraging these technologies effectively.

Empowering Your Strategy with IDC Tools

Planning: Understand the Total Addressable Market (TAM) and Serviceable Available Market (SAM) for informed business decisions with IDC’s custom data and market models.

Marketing: Develop a comprehensive messaging strategy that aligns with your campaigns and sales activities. IDC’s marketing messaging workshops can guide your thematic planning.

Sales Enablement: Equip your sales team with the skills to showcase GenAI features effectively and address objections through IDC’s sales mastery classes and GenAI sales playbook.

For a deeper dive into how AI and APIs are revolutionizing the telecommunications industry, we invite you to watch our on-demand webinar. Click here to watch our on-demand webinar “Revenue Enablers for the Future of Telco: APIs, AI and Emerging Tech” to unlock valuable insights and answers to your most pressing questions.

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The path to enabling omnichannel business has required continual evolution that included adding net new applications and systems, evolving through digital transformation, AI and mobile everywhere, to the current era of Generative AI (GenAI) and intelligent edge computing.

Now the industry is on a journey to becoming future-forward, autonomous, and resilient (FAR), leveraging AI and GenAI to take retail reinvention even farther. Let’s explore how retailers can continue to improve on omnichannel by focusing on the customer, with the most efficient product flows and engaging employee experiences in mind. The path from Omnichannel to FAR is continuous and ongoing as illustrated in the following graphic.

GenAI and Intelligent Edge Computing: Pioneering FAR

The current era of AI-everywhere underpins FAR, and requires an exploration of GenAI’s potential, coupled with investment in intelligent edge computing. Software investments are increasingly directed towards GenAI platforms capable of creating personalized content, designing new products, and enhancing customer service with sophisticated chatbots/virtual assistants.

Hardware investments now focus on high-performance computing systems to support the demanding requirements of GenAI algorithms. Distributed edge platforms, AI PC’s and AI-chips will improve compute response, throughput and efficiency. This will make AI at the edge very possible in stores. Services are evolving to include ethical AI consulting, ensuring that the use of GenAI aligns with privacy and fairness standards.

The supply chain is experiencing a revolution with AI-driven predictive analytics, autonomous systems, and real-time tracking. RFID mandates from a relatively short, but very important list of retailers will drive traceability and improved inventory control in non-food products. The frontline workforce will benefit as the focus on serving customers well is prioritized as routine tasks are automated.

Advice for Continuing to Navigate the Journey to FAR

The reinvention of retail in the AI era is a testament to the industry’s resilience and adaptability. It promises a future where retail experiences are not only more engaging and convenient but also where back-office efficiencies, the supply chain and frontline workforce play a crucial role in delivering value through a blend of technology and human touch.

In an era marked by rapid technological advancements and shifting consumer expectations, retailers face the imperative to evolve. Retailers need to embrace becoming future forward, autonomous and resilient. FAR transformation is not just an ambition but a necessity for thriving in the competitive landscape.

Following are recommendations for retailers aiming to navigate the journey to FAR successfully:

Become Future-Forward by Embracing Technological Innovation

• Invest in AI, Machine Learning, and GenAI – Leverage AI, machine learning (ML), and GenAI to enhance every aspect of your business, from personalized customer experiences to efficient supply chain management. GenAI can revolutionize customer experiences, human resource and finance operations, IT estate management, product design and content creation. Predictive analytics can forecast trends and optimize inventory planning and sourcing. Importantly, workflow and work processes are being revolutionized with the help of GenAI, with near-term ROI possible.

• Adopt Intelligent Edge Computing – Implement intelligent edge computing to process data closer to where it is generated, reducing latency and improving customer experiences. This technology supports real-time decision-making in areas like inventory management, loss prevention, and personalized in-store promotions.

Explore Traceability Applications – Utilize traceability applications to enhance supply chain transparency and security. This can build trust with consumers by providing verifiable information about product origins, manufacturing processes, and sustainability practices.

Become Autonomous Through Decentralization and Automation

• Empower the Workforce – Empower your frontline employees with the tools and authority to make decisions in real-time, enhancing customer service and operational efficiency. This can be supported by AI-driven insights and mobile technologies. Empower the back-office and mid-office with tools that speed decision processes in planning, human resources, finance, and operations. Robotic process automation (RPA) can eliminate unnecessary steps in business processes.

Implement Autonomous Systems – Deploy autonomous systems, such as automated warehouse and distribution capabilities, and autonomous delivery vehicles and drones, to streamline operations and reduce reliance on manual processes. This not only improves efficiency but also allows your workforce to focus on higher-value tasks. 

Become Resilient

• Diversify Supply Chains – Build resilience by diversifying your supply chain, reducing dependency on single sources, and exploring local or regional suppliers. This can mitigate risks related to geopolitical tensions, natural disasters, and global pandemics.

• Develop a Robust Digital Infrastructure – Ensure your digital infrastructure is robust, scalable, and secure. This includes investing in cloud computing, cybersecurity measures, and disaster recovery plans to safeguard against data breaches and ensure business continuity.

Foster Strong Relationships with Customers and Partners – Build strong relationships with your customers and business partners. Engage with customers through personalized experiences and responsive customer service. Collaborate with partners to innovate and co-create value.

The retail industry’s journey highlights a broader trend towards a FAR transformation. Retailers are leveraging AI to create immersive, efficient, and tailored shopping experiences while ensuring their supply chain and frontline workforce are equipped to thrive in this new landscape. Retail organizational DNA needs to adapt to continuously learning and adapting to consumer needs by leveraging a technological foundation that is inherently smarter and nimbler.

Conclusion

The journey from the dawn of omnichannel to the FAR era reflects a broader trend towards increasingly sophisticated and adaptable, data-driven, automated, and personalized retail experiences. As technology continues to advance, the challenge for retailers will be to balance investment in innovation with the need to deliver tangible value to consumers.

The evolution of retail investments tells the story of an industry in constant flux, striving to meet the ever-changing demands of consumers in an increasingly digital world. As technology continues to advance, the possibilities for retail are endless. The integration of AI into retail operations has transitioned from a competitive advantage to a necessity, with significant impacts to technology investment, business processes, and the workforce.

Becoming future-forward, autonomous, and resilient requires a holistic approach that encompasses technological innovation, cultural transformation, and strategic partnerships. By embracing these principles, retailers can navigate the challenges of the digital era, meet evolving consumer expectations, and secure a competitive edge in the marketplace.

The journey towards this transformation may be complex, but the rewards—sustained growth, operational efficiency, and enhanced customer loyalty—are well worth the effort.

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

The retail industry has been on a transformative journey since the dawn of omnichannel in 2009, evolving through digital transformation, AI and mobile everywhere, to the current era of Generative AI and intelligent edge computing.

This evolution has not only reshaped retail software, hardware, and services investments but also significantly impacted the supply chain and frontline workforce. The industry is on a journey to becoming future-forward, autonomous, and resilient (FAR), leveraging AI and GenAI to take retail reinvention even farther. Let’s explore this comprehensive transformation and its implications for the future of retail. Retailers can continue to improve on omnichannel by focusing on the customer, with the most efficient product flows and engaging employee experiences in mind. The path from Omnichannel to FAR is continuous and ongoing as illustrated in the following graphic.

The birth of Omnichannel

I am often credited with coining the term “omnichannel”, which was discussed internally at IDC in late 2008 but published in publicly available editorials in 2009 (RIS News and Chain Store Age). Omnichannel encapsulates a vision for a shopping experience that transcends traditional channel boundaries. This concept emerged from the recognition that consumers were no longer shopping in silos but were instead leveraging multiple channels simultaneously to make informed purchasing decisions.

At its core, omnichannel retailing is about creating a cohesive customer experience across all available shopping channels, including in-store, online, mobile, and social media. This approach is designed to meet the customer where they are, providing flexibility and convenience at every touchpoint.

Technology serves as the backbone of this new retail reality, enabling consumers to navigate through different channels seamlessly. Retailers leveraging cloud, AI, and mobile technologies are better positioned to offer these integrated experiences, thereby not only meeting but exceeding customer expectations.

The implications of this shift are profound. Omnichannel shoppers tend to spend 15-30% more than those who shop via a single channel. Moreover, their loyalty extends beyond mere transactions, influencing others in their network and contributing to a positive brand perception. Retailers that recognize and adapt to this behavior stand to gain significantly in terms of customer loyalty and spending. Omnichannel shoppers are the majority with 77.4% reporting that they actively shop in stores and online (IDC Retail Insights Consumer Sentiment Survey, June 2024). Add shopping within social media apps to the mix and omnichannel influence is even greater.

The Dawn of Omnichannel: Laying the Foundation

In 2009, the retail industry began to embrace the omnichannel approach, aiming to provide a seamless shopping experience across online and offline channels. Initial investments focused on software solutions for integrating these channels, such as eCommerce platforms and Customer Relationship Management (CRM) systems. Hardware investments aimed at enhancing the in-store experience with upgraded Point of Sale (POS) systems and in-store Wi-Fi. The supply chain saw the beginning of digital tracking systems, while the frontline workforce had to adapt to new technologies, requiring training and adjustments in their roles.

Digital Transformation: Expanding Capabilities

As the decade progressed, the focus shifted towards digital transformation, necessitating a broader range of investments. Retailers poured resources into developing mobile apps and optimizing websites for mobile shopping, recognizing the growing trend of smartphone usage. Cloud computing services became essential, offering the scalability needed to handle increasing online traffic and data storage. Hardware investments expanded to include ruggedized- and consumer- mobile devices and tablets for sales assistants and interactive kiosks to enrich the in-store experience.  The supply chain benefited from investments in digital planning and logistics platforms, improving efficiency and visibility. The frontline workforce faced the challenge of integrating digital tools into their daily operations, necessitating ongoing training, and unleashing a desire to be connected to information to improve customer service. The era also saw a rise in cybersecurity investments, protecting the vast amounts of consumer data being collected. Regulations started emerging that require that retailers give Consumers choice in what and when data is collected with the ability to request that the data is not shared and/or deleted.

AI and Mobile Everywhere: Enhancing Operations and Improving Profitability

The late 2010s marked the maturation of the “AI and Mobile Everywhere” era, and the 2020 pandemic accelerated investment in touch-free technologies and flexible last-mile and omnichannel order orchestration capabilities. Retailers started integrating AI across various operations, from personalized recommendations, product assortments, pricing, and promotions, to inventory management. Data management and governance was prioritized as retailers sought to centralize one version of the truth for customer data (in CDP’s), product data (in MDM’s and / or PIM’s), and inventory data (in a central repository (ERP, WMS, or Merch Planning) supporting merchandising, supply chain, and commerce applications).   Hardware investments included AI-enabled cameras and sensors for inventory tracking and customer movement analysis within stores. Services expanded to include AI training for employees and partnerships with AI technology providers to develop custom solutions. This era emphasized the importance of data analytics, with significant investments in tools to analyze consumer behavior and preferences

The supply chain saw the introduction of AI for predictive analytics and autonomous vehicles and drones for delivery. Investments in AI-powered software solutions surged, alongside the adoption of integrated mobile technologies for enhanced customer engagement. The frontline workforce began to depend on AI and mobile tools for better customer service and workforce management self-service, improving experiences for the customer and workforce.

Generative AI and Intelligent Edge Computing

The exploration of Generative AI’s potential, coupled with advancements in intelligent edge computing, represents the latest phase in retail’s evolution. Software investments are increasingly directed towards Generative AI platforms capable of creating personalized content, designing new products, and enhancing customer service with sophisticated chatbots/virtual assistants. Hardware investments now focus on high-performance computing systems to support the demanding requirements of Generative AI algorithms. Distributed edge platforms, AI PC’s and AI-chips will improve compute response, throughput and efficiency, making AI at the edge very possible in stores. Services are evolving to include ethical AI consulting, ensuring that the use of Generative AI aligns with privacy and fairness standards. The supply chain is experiencing a revolution with AI-driven predictive analytics, autonomous systems, and real-time tracking. RFID mandates from a relatively short, but very important list of retailers will drive traceability and improved inventory control in non-food products. The frontline workforce will benefit as the focus on serving customers well is prioritized as routine tasks are automated.

As we look to the future, it’s clear that the retail industry’s technology investment landscape will continue to evolve. The integration of technology into retail operations has moved from a competitive advantage to a necessity. Retailers must stay abreast of technological advancements, such as Generative AI, to remain relevant in a rapidly changing market

Retailers will test, pilot and implement capabilities that improve business performance and protect the future of the business. Forever pragmatic, AI and Gen AI will be applied where the economics of investment make sense. Becoming future-forward, autonomous, and resilient requires a holistic approach that encompasses technological innovation, cultural transformation, and strategic partnerships. By embracing these principles, retailers can navigate the challenges of the digital era, meet evolving consumer expectations, and secure a competitive edge in the marketplace. The journey towards this transformation may be complex, but the rewards—sustained growth, operational efficiency, and enhanced customer loyalty—are well worth the effort.

Part 2 will continue with advice for the technology buyer as they seek to transform with FAR in mind.

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