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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: 

  1. Strategic Implementation: GenAI Engineering bridges the gap between strategy and execution, ensuring that GenAI projects are aligned with business outcomes, resources, and constraints. 
  1. 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. 
  1. 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. 

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On June 6th, 2024, we held an award dinner at a prestigious location in central Copenhagen and announced the CIO of the Year in Denmark.

The job of a Chief Information Officer (CIO) is often a challenging one. In some organizations, the job is mostly centered around helpdesk, running Microsoft Office packages, and being a steward of antiquated systems. Sarcastic observers even renamed the role as “Careers Is Over” to reflect the legacy aspect of the job. However, a CIO also has an expanded role as organizations transform digitally with a much wider potential influence and career upside. This was well illustrated when examining the five shortlisted CIOs.

In each of the organizations of the five shortlisted CIOs, we interviewed the CEO, the CFO, and the candidate CIO themself in separate, in-depth interviews and these interviews yielded multiple interesting lessons learned.

Lesson 1: The modern, high-impact CIO reports to the CEO and is part of the executive leadership team.

Gone are the days of IT being a cost center and reporting to the CFO. All five organizations were transforming traditional businesses into digital businesses and viewed the CIO as a key enabler of overall strategic change process. These modern CIOs facilitated change by setting up ‘digital boards’ to help prioritize digital initiatives across the entire business and to ensure buy-in from non-IT stakeholders.

We also saw many examples of the CIO enabling change through educational activities involving other executive leaders, to provide them with a better understanding of what technology can do for core business activities.

Lesson 2: The successful CIO often has a dual profile that balances technical IT foundation with business acumen.

The traditional CIO often had a technical background and aimed for traditional IT goals such as system availability and reliability, issue resolution time, total IT cost, etc. but were unable to effectively contribute to digitalization of business processes.

Newer, more business savvy, CIOs have since appeared with business backgrounds, but were often not able to properly understand and control major IT initiatives due to the lack of technical understanding. Many of today’s successful CIOs have a dual background with a strong technical foundation with a business overlay (e.g. shorter business degree) or vice versa.

Lesson 3: Successful CIOs balance pragmatism with boldness

The organizations we spoke to clearly aimed to purchase standard software where possible, cloud / Software-as-a-Service solutions where possible, and to adopt out-of-the-box processes where possible. We found no desire to reinvent the wheel. However, in many cases, the organizations took a bold approach where it made business sense.

They insourced systems and application development of differentiating nature, where many iterative changes were expected. Other critical areas, such as cyber security and data management, were also managed with strong in-house expertise.

Finally, new solution areas using emerging technologies, such as artificial intelligence and machine learning, were developed in-house and used actively to brand the organization as innovative.

Lesson 4: Instead of asking for money, new CIOs save their way to resources and credibility.

New CIOs are often met with high IT ambitions coupled with flat IT budgets at best – a difficult situation indeed. Instead of asking for new budgets, the new CIOs typically identified substantial IT savings and spent their first six to 12 months carving out savings, streamlining and consolidating contracts and employees. They then reinvested the resulting savings into new digital initiatives without having to ask the executive committee for an additional budget. In other words, they provided new services and capabilities within the existing IT budget and gained respect and reputation this way.

As more organizations use more and more technology the role of the CIO has expanded with it. CIOs have to combine both technical knowledge with business acumen to help drive their organization’s digital and IT ambitions.

 

IDC’s CIO of the Year 2024 Award, in Denmark

The five shortlisted organizations of the award represented diverse sectors, including insurance (TopDanmark), public sector (Danish Courts Agency), manufacturing (Finland-based consumer brand conglomerate Fiskars), membership organization (Danish Industry), and professional services (Ramboll).

Martin Wood, the CIO of Danish Courts Agency was awarded the title of CIO of the Year in Denmark. Martin heads up an IT function in a public sector agency and has managed to deliver a string of highly visible digital initiatives that are turning cumbersome legal processes digital, automated, and accessible. All projects were delivered via in-house resources (as opposed to the traditional public sector RFPs), within budget and within the allocated time. A worthy winner indeed.

If you want to know more about the CIO of the Year award, please visit the CIO event site (site in Danish).

We have an eBook which is designed to provide CIOs and digital business leaders with a comprehensive understanding of the critical shifts, strategic imperatives, and emerging opportunities that will shape the digital landscape over the next five years, download here.

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Bo Lykkegaard - Associate VP for Software Research Europe - IDC

Analyst Profile
Bo Lykkegaard is associate vice president for the enterprise-software-related expertise centers in Europe. His team focuses on the $172 billion European software market, specifically on business applications, customer experience, business analytics, and artificial intelligence. Specific research areas include market analysis, competitive analysis, end-user case studies and surveys, thought leadership, and custom market models.

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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Large businesses residing in the EU are due to publish their first ESG/sustainability reporting under the new EU CSRD legislation in 2025 and non-compliance forms a major risk. IDC’s MarketScape helps organizations select the right technology partners for the CSRD reporting journey.

EU CSRD regulation is imminent, and the risks of not being prepared for this new EU directive are significant for any organization operating within EU markets. With a majority of affected companies not sufficiently prepared, more and more companies are seeking support from technology vendors.

We recently published a MarketScape that offers a holistic assessment of the technology vendors in this relatively new space. Services and technology providers have rapidly started to build capabilities to support customers with the data and technology challenges associated with CSRD reporting and wider corporate ESG/sustainability efforts. With tech vendors expecting these offerings to form a significant future growth opportunity, the race for market share has only just started. The 2024 IDC MarketScape European ESG Technology Services for CSRD Compliance is meant to guide organizations in the selection of technology service providers offering CSRD reporting services in order to identify the most suitable solutions for their organization for today as well as tomorrow’s requirements.

ESG Reporting Set On An Equal Footing with Financial Reporting

At a time where sustainability is no longer a choice but a necessity, corporate reporting on environmental, social, and governance (ESG) factors is taking a significant leap forward with the European Union (EU) Corporate Sustainability Reporting Directive (CSRD), which is placing sustainability reporting on par with financial reporting.

The CSRD, which came into force in January 2023, mandates a phased implementation. The first set of large, listed organizations will have to report according to the 12 European Sustainability Reporting Standards (ESRS) as early as 2025 for their financial year 2024. This new directive is a much stricter regulatory framework than the previous Non-financial Reporting Directive (NFRD), which was not strictly enforced and applied to a very limited number of companies.

The ESRS key performance indicators (KPIs) are now clearly defined and are much more comprehensive, amounting to over 1,000 data points for certain industries. They will be strictly enforced, as they now require external auditing (assurance). Moreover, the ESRS not only cover data points from within the organization but also include upstream and downstream metrics, such as Scope 3 emissions which are harder to obtain in a regular and reliable way as they lie outside the direct control of an organization.

Technology Partner Help To Achieve CSRD Compliance   

This new directive sets high standards for data quality, the processes and workflows related to gathering data, as well as data governance. It requires an operating model with workflows, assigned responsibilities, and accountability. Through the harmonization of ESG reporting metrics under the CSRD, a much greater level of comparability and transparency will be achieved. This will have a major impact on companies’ risk profiles and thus gains significant attention at the board and C-levels.

The required ESG KPIs creates a data challenge for organizations, as typically ESG data is not readily available or held in formats that make it hard to collect and process on a regular basis. Organizations subject to CSRD regulations are seeking help from technology service providers to get ready for CSRD reporting and ensure compliance. Those providers, in turn, are positioning themselves to support customers with their CSRD reporting efforts and developing technology service offerings.

Navigating Partner Selection for CSRD Compliance

CSRD compliance is complex and difficult to handle by companies using exclusively their internal resources. Often there is a legacy ESG reporting practice in place due to voluntary initiatives such as reporting according GRI, TCFD, SASB and/or other frameworks. The majority of businesses, however, are not in the position to fully reuse the data, processes and workflows for CSRD compliance. Several important aspects should be considered when selecting a partner for CSRD compliance:

Consider a Full Spectrum of Services and Tech Partners

CSRD partner ecosystem differs in terms of focus areas and core capabilities for CSRD reporting services, namely:

  • Advisory-led vendors, typically part of audit companies that have expanded into the IT domain, have comprehensive management consulting capabilities.
  • Systems integration (SI)-led vendors have broad capabilities across technology consulting, SI, and outsourcing services with comprehensive expertise in data architectures and integration/modernization of new or legacy business systems.
  • Software Specialists: Specialist tech vendors include players that have ESG services as their core business and, in the current case, offer specialist software solutions (ESG reporting software) enhanced with consulting services.
  • Multi-disciplinary: These vendors have a broad set of capabilities across professional services, IT consulting, SI, technology assets, and ongoing IT and business process operations.

The IDC MarketScape ‘ESG Technology Services for CSRD Compliance‘ evaluates 11 technology service providers across these areas.

Make Project and Change Management Capabilities a High Priority

Forming a new discipline, CSRD reporting is relatively volatile from the process and content perspectives on both sides – vendors as well as their clients. It is a learning journey with stringent rules and extensive requirements regarding transparent communication between the provider and its customer. Apart from business advisory and technology implementation, services and technology vendors are dealing with shifting the mindset, supporting stakeholders to make the case for CSRD reporting, adding burden to existing tasks, reskilling and training own and customers’ employees. Many organizations embarked on the CSRD compliance journey relatively late, creating pressure on internal stakeholders and external partners. Project and change management capabilities will be of crucial importance in order to lead the CSRD project to success.     

Aim for Process, Workflow, and Technology Repeatability and Scalability

CSRD reporting is currently mostly perceived as a business cost item that organizations need to invest time and resources into to achieve compliance. Focus should be on the fact that the organization is also collecting valuable ESG data and insights that can be leveraged for further business transformation: a sustainable digital transformation that is able to drive future business growth. To be able to gain actionable analytics and forecasting capabilities, ESG data processes and workflow need to be digitized for repeatability and scalability. Implementation of ESG data management platforms combined with robust ESG data governance and integration with existing systems will ensure that organizations will be able to capitalize on their investments by leveraging ESG business insights for, for example, generating efficiencies and cost savings across business and/or creating new business models based on sustainability value propositions

Zuzana Kovacova - Senior Research Manager - IDC

Analyst Profile
Zuzana Kovacova has over 15 years of research and market intelligence experience in technology markets, advising technology providers and users on strategies and operational practices. She is a senior research manager for IDC EMEA Sustainability Strategies and Technologies, helping technology providers measure and maximize the impact of their own sustainability initiatives and technology solutions.

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.

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

Overcoming GenAI Pilotitis and Acute POC Syndrome

Welcome to the wild world of AI adoption, where companies are caught in a whirlwind of buzzwords, shiny new toys, and the constant fear of missing out. Today, we’re looking at a peculiar plague sweeping across Western Europe’s businesses: pilotitis and its close cousin, acute proof of concept (POC) syndrome.

Picture this: eager companies, bright-eyed and full of hope, diving headfirst into the AI pool. As a recent IDC survey showed, companies are running an average of 40 GenAI POCs annually. Forty! That’s a lot — and given their limited experience and expertise, it’s impressive. But is it really getting them anywhere?

Out of FOMO, these companies sometimes act like kids in a toy store, grabbing every shiny AI gadget they see. “Ooh, look at this LLM! Check out that ML algorithm!” But as any parent can tell you: Too many toys may make you miss out on the real fun.

AI Adoption Problems

The diagnosis? Experimentation is great. It’s how we learn and grow. But when you’re running more POCs than there are weeks in a year — and some companies really do, with 7% reaching up to 99 POCs annually — you might have a case of pilotitis. Symptoms include:

  • An insatiable appetite for new AI projects
  • An inability to follow through on successful pilots
  • A severe allergy to scaling anything beyond the POC phase
  • Chronic “shiny object” syndrome
  • KPI amnesia, or forgetting to define or measure success metrics for AI initiatives

The Consequences of Unstoppable Pilots

The prognosis? Well, not great. Just one-third of companies report highly successful GenAI POCs. The rest achieve mediocre results, with nearly half achieving success rates of 50–70%. It’s like getting a C+ in school — not failing, but not exactly something that makes mom proud.

And there’s more: Some companies aren’t even evaluating their POCs’ success. It’s like they’re running around in circles, not knowing if they’re making progress or just getting dizzy.

So what’s the cure for the pilotitis epidemic? First, we need to identify the underlying causes:

  1. The “AI is Hot and New” Factor: Companies are so smitten with the idea of AI that they forget to ask, “But does it actually solve our problems?”
  2. Cost Confusion: AI projects can be expensive. Without clear ROI metrics, it’s easy to keep throwing money at pilots without seeing returns.
  3. Skills Shortages: Finding the right talent to implement AI solutions is tough. Competences are in high demand, experts are scarce, and it may take forever to find someone you can afford.
  4. Coordination Chaos: IT and business teams often struggle to work together effectively, leading to a disconnect between tech capabilities and business needs.
  5. Fear of Commitment: Some companies are so afraid of making the wrong choice that they’d rather keep piloting forever than commit to a full-scale implementation.

How Tech Providers Can Help Their Customers

The treatment? AI technology providers and their partners have a unique opportunity to play doctor and help clients overcome pilotitis. After all, healthy clients support long-term business relationships. How can “tech doctors” cure their ailing patients?

  1. Offer Scalable Proof-of-Value Approaches: Help clients quickly demonstrate value from GenAI in specific use cases, then provide a clear path to scale. It’s like a doctor helping to expand a toddler’s diet — we start with grated carrots and end up eating a full-course meal in a Michelin-starred restaurant.
  2. Differentiate Between Experimentation and POC: Establish clear guidelines for each stage. It’s like the difference between medical research and clinical trials — in research, we’re exploring possibilities, but in trials, we’re testing specific hypotheses with measurable outcomes.
  3. Outcome-Based Pricing: Link fees to project success. It’s like being a personal trainer and only getting paid if your clients actually lose weight — suddenly, everyone’s motivated to see results!
  4. Introduce Integrated Cost Management Tools: Help clients track and control expenditures throughout the AI project life cycle. It’s like giving them a financial fitness tracker for their AI projects.
  5. Provide Post-POC Support and Road Mapping: Offer comprehensive guidance for scaling successful POCs. It’s like offering post-op doctor’s recommendations.
  6. Offer End-to-End Change Management Support: Go beyond tech implementation and help with the human side of AI adoption. It’s like being both a personal trainer and a therapist for your client’s AI journey.

 

These approaches will help you remember that pilotitis and POC syndrome are just growing pains. With the right approach and a little help from their “tech doctors,” companies can overcome these challenges and move from endless experimentation to meaningful AI implementation.

To all businesses out there drowning in pilots and POCs — it’s time to start turning those experiments into real-world solutions. And to tech providers: Your mission is to be the best AI doctors you can be. Help your clients understand and manage their symptoms — and watch them grow healthier and stronger.

Ewa Zborowska - Research Director, AI, Europe - IDC

Analyst Profile
Ewa Zborowska is an experienced technology professional with 25 years of expertise in the European IT industry. Since 2003, she has been a member of the IDC team, based in Warsaw, researching IT services markets. In 2018, she joined the European team with a specific emphasis on cloud and AI. Ewa is currently the lead analyst for IDC’s European Artificial Intelligence Innovations and Strategies CIS.

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

In the realm of data platform decision-making, organizations typically consider several dimensions when making their choices. These encompass aspects including functionality, performance, scalability, cost, flexibility, and alignment with specific use cases.

The following are some of the key criteria of data platform decision-making. It’s worth noting that one of the most-hyped databases right now, in support of AI, is vector databases. We’ll explain why.

Data Model and Schema Flexibility

Organizations assess whether the database supports their data model requirements. Some may need the flexibility of schema-less or schema-on-read models. Others may require the rigidity of a relational model. The choice depends on factors like the structure of the data — is it simply rows and columns of numbers? is it a mix of images, videos, and documents? — and the need for agility to adapt to evolving schemas.

With the rise of Hadoop, many organizations began to store more of their data for analysis, now or in the future. Open source Hadoop offered data storage on commodity hardware, while more traditional proprietary data warehouses were almost certainly far more expensive. The trouble is that Hadoop lacked a schema — a structure for the data warehouse — making it harder to extract the data when you need it (though workarounds are now available).

As mentioned above, vector databases are garnering a lot of attention because of the rise of AI. Reasons for this include:

  • Efficient Similarity Search or Nearest Neighbor Search: Vector databases are optimized for nearest neighbor search, a fundamental operation in many AI applications such as recommendation systems, image retrieval, and natural language processing.
  • High-Dimensional Data Handling: AI models, especially in NLP and computer vision, generate high-dimensional embedding vectors. Vector databases can store and index these vectors efficiently, allowing for rapid querying and analysis.
  • Semantic Search: By leveraging embedding vectors that capture semantic information, vector databases enable more intuitive and relevant search results compared to traditional keyword-based searches.
  • Multimodal Search: Vector databases support the integration of various data types (text, images, audio) by converting them into a common vector space, allowing for unified search and analysis across different modalities.
  • Clustering and Classification: Vector databases support operations like clustering and classification directly on the stored vectors, aiding in tasks such as customer segmentation, anomaly detection, and pattern recognition.

Vector databases are pivotal for AI because they provide the necessary infrastructure to store, manage, and query high-dimensional vectors efficiently. This capability is foundational for enabling fast, scalable, and intelligent AI systems across various applications and industries.

Performance and Scalability

Performance considerations include factors like query speed, throughput, latency, and concurrency. Scalability relates to the ability of the database to handle growing volumes of data and increasing user loads without sacrificing performance. Organizations evaluate whether the database can scale horizontally (adding more servers) or vertically (upgrading existing servers).

Consistency and Durability

Consistency refers to the degree to which data remains in a consistent state across distributed systems, especially in the event of failures or concurrent transactions. Durability relates to the ability of the database to ensure that committed transactions persist even in the face of system failures. Organizations weigh the trade-offs between consistency, availability, and partition tolerance based on their application requirements.ACID is key to relational, transactional databases. ACID compliance refers to a set of properties that ensure the reliability and integrity of transactions in a database system. The acronym ACID stands for Atomicity, Consistency, Isolation, and Durability, each representing a fundamental aspect of transaction management.

ACID is spoken of in somewhat hushed tones by NoSQL vendors. When pushed, some will say they offer “ACID-like” compliance. For many modern use cases, ACID-like is good enough. But speak to a database developer dealing with transactional systems — like core banking systems — and they will tell you their regulators and other stakeholders require “pure” ACID compliance. Compliance with ACID standards can help organizations meet regulatory requirements and maintain data governance.

Data Integrity and Security

Organizations prioritize databases that provide robust mechanisms for maintaining data integrity (e.g., through constraints, transactions, and validations) and enforcing security (e.g., encryption, authentication, authorization, and auditing). Compliance with regulatory requirements such as GDPR, HIPAA, or PCI-DSS may also influence database selection.

Ease of Development and Maintenance

This encompasses factors like developer productivity, ease of learning, availability of tools and libraries, and support for programming languages and frameworks. Organizations seek databases that streamline the development process, facilitate debugging and monitoring, and minimize operational overhead.

Total Cost of Ownership (TCO)

TCO considerations include both up-front costs (e.g., licensing fees, hardware costs) and ongoing expenses (e.g., maintenance, support, scaling). Organizations evaluate databases based on their ability to deliver value relative to their costs over the entire life cycle of the system.

Ecosystem and Integration

Organizations assess the database’s ecosystem, including its compatibility with existing infrastructure, integration with other systems (e.g., data warehouses, analytics platforms, the cloud), availability of third-party tools and services, and community support. Integration capabilities influence factors such as data migration, interoperability, and extensibility. There is also the issue here of deployment venue: on premises, in the cloud, hybrid, or multicloud.

 

By evaluating data platforms along these lines, organizations can make informed decisions that align with their business objectives, technical requirements, and constraints. Vector databases are certainly one of the hottest tickets in town in support of AI — but different use cases have different priorities.

Over the last 30 years, the mobile phone industry has been through two major revolutions. The first revolution began when the mobile phone emerged, transforming the way we communicate by introducing mobile communications into our lives. The second revolution emerged in the latter half of these three decades when smartphones disrupted everything else in our lives.

Today, with 3.1 billion smartphones in use globally, these devices play a critical role in the world. The latest technological development that has taken the tech world by storm is the launch of AI-powered smartphones. Although AI is not new, it gained significant attention with the launch of ChatGPT and the capabilities of Generative AI (GenAI). Leveraging Large Language Models (LLMs), a new revolution is coming to the smartphone – intelligence.

Defining AI Smartphones*

According to IDC, Gen AI smartphones are defined as devices featuring a system-on-a-chip (SoC) capable of running on-device Generative (Gen AI) models more quickly and efficiently leveraging a neural processing unit (NPU) with 30 Tera Operations Per Second (TOPS) or more, using the int-8 data type.

The smartphone SoCs being designed and marketed by silicon vendors with next-gen AI smartphones in mind will increase in the future as they continue to push forward the NPU technology. However, to date, here are a few that qualify based on the definition above:

  • Apple A17 Pro
  • MediaTek Dimensity 9300
  • Qualcomm Snapdragon 8 Gen 3
  • Samsung Exynos 2400

Market Opportunity

The latest IDC forecast estimates that Gen AI smartphone shipments will grow 364% year-over-year in 2024, reaching 234.2 million units.

Despite the current macroeconomic environment and the fact that consumers are keeping their devices longer, the potential of Gen AI on a smartphone is expected to drive significant demand over the coming years. This segment is projected to be the fastest-growing segment in the smartphone category during the forecast period, outperforming the non-AI-enabled smartphone segment. Growth will continue into 2025 with an expected increase of 73.1%, followed by moderate double-digit growth for the rest of the forecast period. By 2028, worldwide Gen AI smartphone shipments will reach 912 million units, resulting in a compound annual growth rate (CAGR) of 78.4% for 2023-2028.

Reshaping the Mobile Experience

AI will enable manufacturers to offer unique and intelligent features, experiences and even services to their users. Since the introduction of the first iPhone and Android smartphones in 2007 and 2008, and particularly after the introduction of app marketplaces by Apple and Google, users have become used to interacting with the smartphone by using apps. The more powerful the apps, the better it is. With AI, the fewer apps the phone will need and the more capable can use data contextually to assist the user, the better it will be. This “app-less” world will revolutionize the user experience, requiring the phone to better “know” its users while ensuring personal data remains private and secure.

The interaction with the smartphone will shift from touch to voice, as “intelligent” voice assistants become our true personal digital assistants. These conversational digital assistants, fully integrated with the device, will be game-changers, providing compelling reasons for users to upgrade their smartphones.

Although a full AI experience is still in development, less than 18 months after the introduction of ChatGPT, several vendors announced their AI strategies and devices showcasing some intelligent capabilities. These include:

  • Samsung Galaxy S24 Ultra: Some of the key AI features include a transcription summariser built into the voice recorder, real-time voice translation, and Circle to Search, a tool developed by Google that allows users to draw a circle around anything on screen and search it on Google.
  • Xiaomi 14 Ultra: Features AI-generated subtitles for video calls and an AI Portrait feature that lets users take a selfie and add a different background.
  • Google Pixel 8 Pro: Offers features like summarising recorded conversations, suggesting replies to messages, and creating AI-generated wallpapers. The camera also benefits from AI with Magic Editor (moving or removing objects); Best Take (selecting the best shot), and Video Boost (enhancing video colour and lighting).
  • Apple Intelligence: The new suite of AI features will come to the iPhone, iPad and Mac later this year with the latest OS versions announced at Apple Worldwide Developers Conference. The AI features will include rewriting text and proofreading, generating email replies, and content summarization. Users will be able to generate images from text based on note contents and remove objects from photos. Siri, Apple’s digital assistant will become more conversational.
  • OPPO AI Strategy: Betting big on AI, OPPO aims to incorporate over 100 Gen AI features across its lineup of AI-enabled smartphones in 2024. Unlike other players, OPPO aims to democratize AI by introducing these features to more affordable price points.
  • Honor Magic 6 Pro: The device promises AI-powered user experiences and it is the first Honor’s all-scenario strategy, featuring cross-OS collaboration and AI designed with a human-centric approach.
  • Motorola Razr 50 Ultra: Will run Google Gemini as the main digital assistant, offering AI out of the box. Features include recognizing photos, summarizing text, answering voice queries, and changing message tones before sending. It includes Motorola’s AI tool, Style Sync, which creates a wallpaper based on the colors and patterns of a specific photo.

Use Cases

Gen AI smartphones are expected to disrupt different aspects of our lives. IDC identified several use cases that can drive adoption and have a positive impact:

  • Work Environment: According to an IDC survey, employees view the smartphone as one of the most important tools in the workspace. AI will streamline tasks by summarizing content from meetings and documents, allowing users to focus on discussions. AI will also summarize email threads, suggest replies based on the conversation, and help manage calendars based on requests received via email or messages.
  • Healthcare and Wellbeing: Smartphones have become central to various wearables (through apps) that collect data on vital body signals, such as blood pressure, heart rate, and blood oxygen. AI will monitor this data from all sensors, alerting users to potential risks and suggesting dietary and exercise plans for a more active lifestyle. Acting as a digital personal trainer, AI will provide guidance beyond simple tracking.
  • Education: AI is already an important tool in the classroom, both directly and indirectly. Students increasingly rely on tools such as ChatGPT for content research, note-taking, and transcribing and summarizing lessons. AI on smartphones will make these tasks mobile, with the camera becoming an important source of prompts. Additionally, AI will help students manage their study plans and understand complex subjects more quickly and efficiently.
  • Entertainment: For most people, the smartphone is the device of choice for content creation, taking photos and creating videos for social media. It has also become the primary tool for streaming content and gaming. AI will optimize visual effects, providing a more immersive experience, and act as a “gaming assistant”, enhancing the overall experience and increasing engagement. Also, by generating images and videos from text on the device, users can share content more creatively.
  • Mobility: Users increasingly rely on smartphones for directions and finding information about locations. AI will improve commuting and travel by efficiently analyzing traffic patterns, user travel journeys, and frequent destinations. It will offer improved planning by integrating travel needs with calendar appointments, making commutes more efficient.

The rise of Gen AI smartphones will become the next technological revolution. As the most widely adopted consumer electronics, Gen AI smartphones are set to reshape the mobile experience through unparalleled enhanced intelligence. Smartphones will bring new features and use cases, unlocking the next phase of intelligence for these devices. As AI features and applications grow, consumers will prioritize these advanced capabilities in their next purchase.

*For a more detailed technical definition and explanation about the definition of IDC’s AI smartphone, you can read our blog, The Future of Next-Gen AI Smartphones.

For a look at industry and segment spending forecasts for broader AI and GenAI use cases, see IDC’s ⁠AI and GenAI Spending Guide.

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

Francisco Jeronimo - Vice President, Data & Analytics - Devices - IDC

Analyst Profile
Francisco Jeronimo is VP for Data and Analytics at IDC EMEA. Based in London, he leads the research that covers mobile devices, personal computing devices, emerging technologies and the circular economy trends across EMEA. His team delivers data on personal computers, tablets, smartphones, wearables, PC monitors, PC gaming, enterprise Thin Client devices, smart home, augmented reality and virtual reality, and sales of used devices. He provides in-depth analysis of the strategies and performance of the key industry players.

Cybersecurity threats continue to increase. According to ENISA’s 2023 Threat Landscape report, there were around 2,580 observed incidents in the EU between July 2022 and June 2023. In the previous reporting period, there were less than 800. ENISA reported that 19% of events targeted public administrations, by far the largest industry.

Government chief information security officers (CISOs) are not resting on their laurels. They understand that cybersecurity is important.

IDC’s EMEA Cross-Industry Acceleration Survey, conducted in December 2023, found that 91% of government executives were planning to maintain or increase their level of spending in cybersecurity. In the many one-on-one conversations that IDC Government Insights analysts have had with government CIOs, CISOs, and other IT executives, cybersecurity always stands out as a “ring-fenced” item protected from budget cuts.

But given the rising volume, variety and velocity of threats, ring-fencing budgets is not enough — a change of paradigm is in order.

Paradigm Shift: From Protecting the Perimeter to National Survivability

The most forward-looking governments understand that the old mindset focused on protecting the individual government agency, or even the individual system or digital citizen service app, is insufficient. They understand that governments play a key role in national digital and physical services and infrastructure resilience and survivability.

To deliver on this higher purpose, government CISOs need to look beyond their organizational boundaries, take a whole life-cycle approach to cybersecurity, and provide knowledge to non-security experts to enable them to act responsibly.

Looking beyond organizational boundaries means collaborating across public administrations and the private sector to create a resilient ecosystem. Beyond the NIS2 Directive’s mandatory obligations — such as establishing at least one computer security incident response team (CSIRT) in each EU member country — resilience comes through the collective effort of cybersecurity specialists within ministries of defense, police forces, intelligence agencies, and all other public administrations.

In a recent IDC conversation with a regional government institution’s senior IT leader, it was emphasized how important it is for all levels of government to collaborate in the ecosystem to protect against the spike of attacks that usually occurs in the months prior to and during major events like the Olympics, the World Expo, the FIFA World Cup, or a G7 meeting. Participants should include transportation operators, payment and banking service providers, telcos, utilities, and travel and hospitality companies.

Taking a life-cycle approach to cybersecurity means caring for the hygiene of systems, protecting them and responding to events from development through termination. Hygiene starts with security by design, DevSecOps and application security best practices, and vetting hardware and software supply chain bills of material for security and compliance requirements.

Data hygiene is paramount — not only to comply with regulations that protect sensitive data, but also to increase the resilience and visibility of all data sets critical to government operations. Holistic protection requires enhancing the observability of the broader landscape.

CISOs should demand that their cybersecurity solution providers make available AI/ML solutions and AIOps practices that can increase the productivity of observability and detection.

Incident response must be grounded on governance processes and structures that enable timeliness and coordination. Throughout the life cycle, government CISOs should regularly upgrade their team’s skills, not only traditional cybersecurity skills but also legal skills, AI ethics, bias testing, and prompt engineering.

Ensuring that security and non-security experts have the right knowledge to act responsibly is a major people and organizational transformation effort. Investing in programs to raise the cybersecurity awareness of civil servants, other industries, and the general public is critical.

It is also important for CISOs to articulate the value of cybersecurity to the elected and appointed officials who make budget decisions. CISOs and their teams that are able to articulate the value of cybersecurity in terms of business risks will raise their profile internally and be recognized as strategic decision makers.

Technology developments will help CISOs accelerate the paradigm shift. In particular, the automation and orchestration of processes related to security and privacy will help address the skills gap and accelerate the detection of malicious behaviors, threat response, and remediation actions.

European government CIOs and CISOs that combine tool investments with a holistic approach to cybersecurity will boost the resilience of their organizations, of the communities they serve, and increase citizens’ trust in government. Those that focus on siloed system protection and legacy operating models and competencies will not be able to respond to threats and will be relegated to the role of gatekeepers who eventually lose influence and budget.

To learn more, explore the latest IDC research

Massimiliano Claps - Research Director - IDC

Analyst Profile
Massimiliano (Max) Claps is the research director for the Worldwide National Government Platforms and Technologies research in IDC's Government Insights practice. In this role, Max provides research and advisory services to technology suppliers and national civilian government senior leaders in the US and globally. Specific areas of research include improving government digital experiences, data and data sharing, AI and automation, cloud-enabled system modernization, the future of government work, and data protection and digital sovereignty to drive social, economic, and environmental outcomes for agencies and the public.