In today’s fast-paced world, automation has emerged as a revolutionary force reshaping the technological landscape.

From self-driving cars to intelligent virtual assistants, automation is rapidly permeating various industries. Because of its increasing popularity and the importance it plays in streamlining processes and reducing costs, it has become a critical part of many organizations’ digital transformation strategy.

So far, enterprise automation has been mostly reactive. It has been implemented as a piecemeal, noninvasive method to automate routine, repetitive tasks, and structured processes and data. Business drivers, goals, and means for business processes, IT operations, and software development of enterprise automation have expanded for the next chapter of the digital journey.

According to IDC’s Future Enterprise Resiliency and Spending Survey, Wave 1 (January 2023), 30% of organizations are more than 50% done with their automation goals for their front office businesses.

The promise of automation means we will continue to see new use cases pop up. This is particularly evident with the ever-increasing amount of data that organizations need to compile, organize, and analyze. B2B buyers’ expectations for agility, flexibility, and the ability to roll out new products and services quickly have also pushed companies to embrace automation.

Digital journeys and automation are now the dynamic duo that will run a viable digital business at scale.

Rapid innovation in AI-assisted automation adds another layer of possibilities to what can be accomplished. Below, we’ll discuss a couple of ways in which automation is being used today to deliver a better customer experience overall.

Personalization at Scale

Automation plays a crucial role in the ability of businesses to tailor and customize customer experiences, communications, and offerings. Starting with data collection and analysis, automation streamlines the process of gathering customer data from multiple sources such as website interactions, purchase history, social media activity, and customer surveys.

The use of artificial intelligence (AI) and machine learning (ML) within automation enables algorithms to extract meaningful insights from the data gathered and make informed decisions. With automation tools, businesses can create and update customer profiles. Data is incorporated from various sources in real-time. These profiles give businesses a holistic view of each customer, allowing them to effectively customize their experiences.

Automation tools can also help organizations generate and deliver content in real-time. From personalized product recommendations based on browsing and purchase history to email marketing campaigns based on customer segmentation, dynamic content delivery ensures that customers receive relevant and engaging information that resonates with their specific needs.

Enterprise automation means artificial intelligence continuously supports decision-making and automated actions that proactively optimize and enrich outcomes. This process spans across the entire organization and will maximize the business value.

Proactive Engagement

Automation enables organizations to proactively engage with customers based on triggers or predefined conditions. For instance, when a customer abandons a shopping cart, an automated workflow can send a personalized email with a reminder or a special discount to encourage them to complete the purchase.

These workflows can be designed to address various customer interactions such as onboarding, upselling, cross-selling, and re-engagement. Automation can extend proactive engagement to social media platforms where businesses can monitor customer mentions, comments, and questions.

An infrastructure that’s scaled and agile delivers a great user experience. As part of digital transformation, leaders must enhance their risk and controls environment to be more intuitive and automated.

AI and ML have had a considerable impact on automation, particularly in how they’ve enabled better customer experiences. The introduction of generative AI has been met with enthusiasm. Automation use cases are already being created that have the potential to impact customer experience. Some examples of where automation within CX is headed:

Resource reallocation. Automation continues to take over manual tasks that humans perform daily, freeing up resources to focus on more complex, skill-driven activities. Everything from recruiting to medical diagnoses will be assisted by AI-driven automation, giving back valuable time to highly skilled employees to meet the unique needs of each customer.
Communication mining. Communication mining uses intelligent automation to extract valuable information and insights with AI and NPL (natural language processing). These come from various forms of communication data such as text messages, emails, social posts, customer support interactions, phone calls, recordings, and more. By mining communication data, organizations can gain insight into customer preferences. They can identify emerging trends, improve customer services, and make data-driven decisions.
Employee experience. Investing in employees and creating a positive work environment not only leads to happier, motivated, and engaged employees but improves customer service. With automation handling routine and time-consuming activities, employees can work on high-value projects and achieve higher levels of productivity. Automation can also provide opportunities for employees to develop new skills and expand their expertise. When employees feel empowered and have the right tools to be productive, they are more likely to deliver a positive customer experience.

The future of automation in CX is intelligent, automated, and engaging. The brands that achieve the most favorable results in CX are those that will merge automation and intelligent tools with human ingenuity and compassion.

It’s no secret that datacenters – the digital heartbeats powering our interconnected world – are voracious consumers of energy resources. Their energy consumption and corresponding carbon emissions are pressing concerns to those within and outside the datacenter industry.  

The importance of reducing carbon emissions as a crucial strategy to tackle climate change cannot be overstated. From the potential consequences of rising sea levels to the unpredictability of extreme weather events and disruptive impacts on ecosystems, the stakes are high. Pressure is mounting on industries worldwide to curtail their carbon footprints and contribute to global environmental efforts. 

Understanding this, the datacenter industry is far from indifferent. Almost every datacenter provider now advocates for net-zero operations. Leaders are targeting a specific date to achieve this lofty ambition. Simultaneously, IT vendors tout their innovative solutions, purporting to aid their customers in shrinking their carbon footprint. But amidst these eco-conscious promises, a troubling void becomes evident – the lack of quantitative data. 

Ironically, for an industry that thrives on data, the datacenter sector lacks comprehensive, credible data. There is a data deficit regarding power capacity, energy consumption, and carbon emissions resulting from a datacenter’s operations. We don’t have an accurate measure of the industry’s environmental impact. As a result, we don’t have a clear pathway to meeting stated net-zero goals. 

Recognizing this, IDC created new research aiming to estimate the energy consumption and carbon emissions of the datacenter market. This endeavor promises to shed new light on the datacenter industry’s environmental footprint. IDC’s research offers a quantitative lens through which to assess and compare the impact of different datacenter types and geographies. 

This dataset quantifies key parameters such as energy consumed, carbon emitted, and carbon avoided through the use of carbon-neutral energy sources. It also details power capacity, square footage, and expenditure by datacenter type, including Internet Giants, Colocation, Internal, Edge, and Telco datacenters. 

Datacenter Carbon Emissions – A Key Business Issue 

As digital transformation continues to redefine the operational landscape of organizations, another influential paradigm has become increasingly prominent – sustainability.   

IDC estimates that worldwide digital transformation investments will reach $3.4 trillion in 2026, all of which drives demand for datacenter capacity. 

“The demand for datacenter capacity is outpacing sustainability advancements” 

As part of digital transformation efforts, organizations will invest in Generative AI technologies.  These revolutionary technologies are particularly energy-hungry. Using them places an unprecedented demand on datacenter resources compared to traditional loads. For example, the energy consumption to train GPT-3 (a precursor to Chat GPT 3.5) is estimated to be 1.287 gigawatt hours. This does not include end user consumption while interacting with the model. 

It is also clear that sustainability is no longer an optional add-on or a public relations talking point. ESG (Environmental, Social, and Governance) practices create business value by fostering long-term sustainability, mitigating risks, attracting socially conscious investors, enhancing brand reputation, and driving innovation. 

These two seemingly opposing goals are creating the pre-eminent challenge for the datacenter industry. How can we meet the rapidly growing capacity demands while ensuring our operations are sustainable? 

Progress toward Net-Zero Goals 

In recent years, the datacenter industry has seen a remarkable increase in sustainability-oriented claims. In an effort to stay competitive almost all datacenter vendors are asserting their sustainability credentials. Cloud Service Providers (CSPs) and Colocation Providers are pledging to reach net-zero carbon emissions by specific target dates.

In addition, IT vendors boast about the enhanced energy efficiency of their latest chips and equipment compared to older models. While these claims are often valid and reflect a promising shift towards more sustainable practices, they can inadvertently give the impression that the datacenter industry is rapidly becoming more sustainable.  

However, this might not be the full picture. Individual components are becoming more efficient and companies are setting emission reduction goals but the overall environmental impact of the industry may not necessarily be decreasing at the same pace. Especially considering its continuous and rapid expansion. Therefore, there’s a pressing need for reliable data.

Accurate figures on energy consumption and carbon emissions of the entire datacenter industry would provide a more objective assessment of its sustainability progress. This data will help identify gaps and develop strategies to mitigate the environmental impact effectively.

So, while we must applaud the efforts and strides taken so far, it’s equally important to validate them with robust, industry-wide data to ensure a truly sustainable future for the datacenter industry. 

IDC estimates the global energy consumption of datacenters in 2022 at 382 Terawatt Hours (TWh). With a compound annual growth rate (CAGR) of 16.0%, leading to 803TWh by the year 2027. 

For more insight and information on the trends in datacenter energy growth and sustainability, please see the IDC Datacenter Deployment Model. 

Success in tech sales is “challenging”

Since its publication over ten years ago, The Challenger Sale: Taking Control of the Customer Conversation by Matthew Dixon and Brent Adamson has remained a best-seller in the business sphere. Sales teams around the world, particularly those in the tech sector, have successfully implemented the book’s teachings, which are also highly regarded among non-sales professionals.

Despite its widespread popularity, the book has often been misinterpreted as encouraging inappropriate sales behaviors. The concept of “challenging” a customer may seem counterproductive or even negative, given the long-held belief that the customer is always right. Some may mistakenly view this approach as empowering already self-assured individuals to exert more control, while, nobody wants to be perceived as a bother, especially salespeople. In truth, the book focuses less on what you say and more on the actions you can take to ensure that your words have meaning and value.

Dixon and Adamson’s extensive study reveals that top sales performers help prospects consider their business needs. By offering unique insights or confirming known facts, they connect with clients as experts, demonstrating a deep understanding of their business and earnestly aligning solutions with their needs. This approach, known as “Commercial Teaching,” is part of the “Challenger Sale” method, which the book champions as the most effective in B2B contexts, including tech sales.

Data is your great equalizer

New B2B tech salespeople may experience dissonance while learning and applying the Challenger Sale. Imposter syndrome can be common among direct and indirect sales channels, due to less experience in tech sales or lacking technical expertise. Grasping the complexity of some software and aligning its value to an organization’s unique aspects can be challenging. However, understanding prospective companies and their markets brings you closer to the Challenger Sale method, regardless of your natural selling style.

During the sales process, especially the pitch stage, knowing your potential customer and their needs helps tailor your approach. Dixon and Anderson claim that customer loyalty is often driven more by sales experience than product. Therefore, using various data types can assist sales teams in preparing for and conducting a sale:

Market data represents a sector of the broader economy and provides macro-level trends. The data tells you where, in what categories and industries, and across which segments given technologies are sold, as well as total size, share sizes, and growth rates of markets.
Contract data contains detailed records and data pertaining to various types of contracts. This can serve as a central repository for storing and managing contract-related information for easy access and reference.
Firmographic data explains the operating context for an organization. The data tells you where, in what categories and industries, and across which segments businesses compete and how they perform.
Technographic data describes a company’s relationship to technology and technology providers. The data tells you what systems they use, who they partner with in supply and value chain ecosystems, and how much they spend now and plan to spend in the future across their tech investments.
Demographic data details the population of a given organization. The data tells you who people are functionally (e.g., name, job title, position level), where and how they are organized (e.g., address, structure, department), and other factual descriptors like contact information, age, sex, education, etc.
Psychographic data illustrates the personalities of people and groups. The data tells you who people are emotionally and spiritually (e.g., values, beliefs, attitudes), how they behave (interests, lifestyles, motivators), and generally why they do they things they do.

While various data sources provide valuable business insights, some are more relevant to the Challenger Sale approach. To genuinely connect with clients on a commercial level, market, contract, firmographic, and technographic data are most crucial. Demographics aid in understanding and communicating with an organization, while psychographics can assist in messaging and persuasion. However, these insights are less likely to convince a customer to commit to your offering.

Focus on the insights that matter most

Obtaining quality data for market, contract, firmographic, and technographic insights can be challenging, as it is often unstructured, fragmented, disparate, or latent. Rarely is the data readily available in a structured format on an ongoing basis, and inconsistencies or lack of standardization can make analysis difficult.

IDC’s Data & Analytics offer you and your team instant access to comprehensive, accurate, and current data covering the gamut of commercial tech needs. This data, organized in a consistent taxonomy, has become a standard in the enterprise tech industry. With analyst-validated, continuously updated data, these user-friendly tools are designed to enable quick insight discovery and trend analysis.

Craft sales pitches that resonate with influencers and decision makers

As a sales leader or salesperson, your success in closing deals depends on your ability to influence outcomes by guiding potential buyers from their current mindset to one where they believe your solution addresses their problem. Having extensive data about the company you are prospecting, their customers, partners, competitors, and broader market context enhances your knowledge and ability to connect with your audience.

Dixon and Anderson’s book also introduces a six-step process for creating winning sales pitches based on a solid understanding of the prospect’s business. Derived from the same sales study that underpins the book, this process has become a common practice among B2B tech sales teams over the past decade. Its longevity can be attributed to its ability to help you structure your thoughts before reaching out, enabling you to present a compelling and concise case.

The six steps outlined by Dixon and Anderson can be significantly enriched with insights from IDC’s Data & Analytics line of intelligence solutions. Here, we examine the steps involved in a Commercial Teaching sales pitch and explore how leveraging IDC data can boost performance:

The Warmer introduces your pitch and quickly builds credibility by showing you understand the prospects’ challenges. Firmographic and technographic insights available in IDC Wallet help you quickly understand your prospective customer’s current state, benchmark your competitors, and draw inferences from spending budgets and forecasts. Contract data available in IDC Services Contracts Database provides insight into deal history and renewal dates, giving you the insights you need to perfect the timing of your sales activities.
The Reframe is the point in the pitch where you connect a prospect’s challenges to a bigger trend that either changes the way they think or deeply resonates with their own understanding. Using market intelligence, including deep market, share, and growth analysis, you can find your own insights or validate trends you learn of elsewhere. Go a step further with company intelligence to see how these trends play out the organizational decision-making level.
Rational Drowning is where you make your case with facts and figures. Market intelligence products, like IDC Black Book and IDC Spending Guide, provide robust spending forecast data that enable deep market, share, and growth analysis in practically any market scenario. At the same time, IDC Tracker® solutions help validate findings through tracked revenues. IDC’s other intelligence solutions detail individual and ecosystem profiles for tech buyers, partners, and vendors, giving you budget, contract, sales receipt, and other data for your business cases.

The final three steps—Emotional Impact, A New Way, and Your Solution—involve connecting the larger challenges and opportunities discussed to the prospects’ individual experiences, demonstrating how your products are the ideal solution. At this stage of the pitch, your ability to quickly adapt and communicate persuasively is crucial. Investing time and effort in preparation will yield greater success downstream.

To learn more about IDC Data & Analytics and how you can leverage intelligence products in your sales planning and processes, check out our Data & Analytics site.

For more specific information on IDC’s sales-enabling intelligence products, please visit the IDC Wallet and IDC Services Contracts Database.

Still have questions? IDC experts are standing by. To request a demo for your sales needs, please complete the form on this page. For general inquiries, you can click here.

Last month my colleagues and I on the IDC data, analytics, and enterprise intelligence research team had an inquiry with a client from a large insurance company. Part of the conversation was about the unexpected increase in costs following the migration of their data warehouse to the cloud. The situation was not necessarily atypical given the many variables involved in such a migration. What surprised me was the client’s inability to answer our question about how their workloads changed on the new data warehouse from those on the old data warehouse.  

It became clear that this data management professional wasn’t informed about how the data warehouse he worked on was being used. Was it primarily for BI workloads or AI workloads or both? Was it to support the client service function or the risk management function?

This situation happens more often than it should. We often talk about data silos, but rarely about internal knowledge silos about the use of data. Perhaps we should call them “silos of apathy”. There is certainly something amiss with the data culture in organizations where data management teams don’t know how data analysts or data scientists intend to use data or when the latter are unaware how their work contributes to business decision making processes.

To highlight the need for greater understanding and collaboration among data engineers, data analysts, data scientists, and all decision makers who rely on results of data analysis, we recently published an IDC study on the four planes of the enterprise intelligence architecture.

This conceptual model starts with the hypothesis that every organization wants to increase its enterprise intelligence. In the parlance of IDC, this means that every organization wants to be better than they currently are (and/or better than their competitors) in:

Synthesizing information
Collectively learning
Delivering insights at scale
Fostering a data culture

Our hundreds of interviews with decision makers across industries and analysis of responses from thousands of survey participants across the world have identified these four capabilities as core pillars that define enterprise intelligence. These capabilities are also measurable and help differentiate organizations that are better able to leverage data, analytics, and AI to achieve their goals.

Yet, many organizations continue to address their internal demand for data-driven decision making with discrete projects optimized for KPIs that are disconnected from the goal of lifting enterprise intelligence. These organizations build large data lakehouses, invest in the best data scientists and machine learning tools, experiment with the latest generative AI, conduct data literacy training, deploying intuitive dashboards, and implement data governance policies. What they don’t do enough is connect the dots – among different technologies, different decision-making processes, different plans, data or model ops initiatives.

Our research shows that few organizations have a comprehensive view that enables execution of the enterprise intelligence strategy with the corresponding architecture that can truly improve metrics that matter. This matters because of the growing complexity across data, analytics, AI, and decision-making vectors has resulted in organizations having issues that were highlighted in IDC’s recent Data Valuation study, where respondents cited:

Data decay: 75% of decision makers say that data loses its value within days.
Data waste: 33% of executives say they often don’t get around to using data they receive.
Data disconnect: 61% of executives say data complexity has increased compared to last year.

An enterprise intelligence strategy defines a corresponding architecture that becomes a guide to greater utilization of data for productive purposes, including greater decision velocity that drives differentiation in the digital era. IDC’s Future of Enterprise Intelligence research has found that organizations with greater intelligence have 3x-4x better business outcomes than their counterparts with nascent enterprise intelligence.

The IDC enterprise intelligence architecture is a conceptual representation of attributes, technologies, and functionality that enable the organization to execute its enterprise intelligence strategy. Our work on this view of the enterprise intelligence architecture began by defining the data control plane and evolved into four planes.

IDC Enterprise Intelligence Conceptual Architecture

Data Plane: Organizes the realities of modern data environments into three primary categories:  distributed, diverse, and dynamic data. DBAs and data architects are the personas usually associated with the data plane.
Data Control Plane: Leverages intelligence about data to take control of modern data environments through governance and engineering. Data engineers, data stewards, and data ops managers are typically involved in this plane.
Data Analysis Plane: Helps organizations explore, explain, and envision data and insights. Data scientists and data analysts, BI developers, and business analysts work in the data analysis plane.
Decisioning Plane: Has capabilities that enable decision design, engineering, and orchestration. This plane is the broadest in its use by business decision makers, executives, and even automated decisioning-systems.

Attributes, technologies, and functionality of each plane are described in greater detail in Four Planes of Enterprise Intelligence Architecture: A Conceptual View into the Data Plane, Data Control Plane, Data Analysis Plane, and Decisioning Plane, where they are depicted along with commons services across the planes (e.g. security, monitoring, knowledge management, etc.)

Attributes of the Four Planes of the Enterprise Intelligence Architecture

The four planes are also aligned with personas who must collaborate to achieve common goals rather than only optimize for peak performance within their plane.

Very few vendors address all the planes fully with packaged software, thus one of the considerations in evaluating technology providers and their products is to understand how the vendor moves or evolves within and across planes. Some vendors expand their portfolios and functionality through internal R&D, others do so through acquisitions. Extra caution is warranted when a vendor ‘skips’ a plane. For example, when they have been providing technology for the data plane and expand into the data analysis or decisioning planes. These types of moves are difficult and rarely successful.

When vendors don’t fully address a plane, customers must substitute a product from another vendor, or develop their own technology – often based on an open-source project, and often focused on immediate needs rather that strategic direction of the enterprise intelligence architecture. Whether integration of multiple software components is intentional or involuntary, it creates overhead and risks due to integration and ongoing maintenance needs. To counter such risks, it’s important to understand the extent to which a vendor’s product provides support of open data and analysis standards.

Focus your organization’s enterprise intelligence strategy to be top-down from the decisioning plane to the data plane. Too many organizations do the opposite and end up with projects resulting in great data management technology solutions that are disconnected from the goal of improving overall enterprise intelligence.

The tech industry is at a seminal moment. The combination of executive and board level interest, clearly defined outcomes, and the sheer speed of adoption makes Generative AI unlike anything we have seen before.

In this blog, we will shed light on the rapid rise of Generative AI (GenAI), its impact on tech companies, and fundamental questions related to AI technology.

The rapid adoption of Generative AI moves AI from an emerging software segment in the stack to a lynch-pin technology at the center of a platform transition.

Meredith Whalen – Chief Research Officer

GenAI – A Seminal Moment in Technology

In seven short months, GenAI has simultaneously captured the attention, imagination, and trepidation of tech and business leaders across the world.

Attention. Executives easily see how this technology will impact productivity levels and margins. The Brookings Institution forecasts GenAI will raise productivity and output by 18% over the next 10 years.
Imagination. GenAI has a wide range of applications – from horizontal use cases such as software development and marketing content creation to industry-specific use cases such as drug discovery and manufacturing design. The business benefits of the use cases are obvious, and enterprises aren’t waiting around for a business case to be developed to start experimenting. IDC’s research shows that knowledge management, marketing, and code generation are the top use cases being considered.
Trepidation. Executives see how this technology can rapidly disrupt their business model. The 20-year journey for the cloud to represent 50% of core IT spending and the 10-year journey to become a digital business will look colossally slow in comparison to the accelerated timeframes it will take for enterprises to implement Generative AI use cases at scale. The well-founded concerns around ethics, regulatory compliance, and governance will also need to be embedded in this new business model.  

Hiding in Plain Sight

How did technology with this much impact creep up on most business leaders? It didn’t. The foundational elements were being developed throughout the past decade.

Era of Multiplied Innovation. What IDC refers to as the Era of Multiplied Innovation was primarily fueled by the cloud, mobility, and the Internet. Low-cost semiconductors and virtualization enabled the cloud, which made computing elastic and plentiful. Mobility made computing ubiquitous. And the internet dropped the costs of distributing those computing bits to almost zero.
Platforms and Communities. With abundant, ubiquitous, and elastic infrastructure in place, platforms, communities, and digital ecosystems emerged. These platforms triggered a massive data consolidation process and the birth of the transformer model architecture which enabled the creation of foundational artificial intelligence models, including large language models (LLMs).
Era of AI Everywhere. Generative AI, which utilizes unsupervised and semi-supervised algorithms to generate content from previously created content such as text, audio, video, images, and code, is a trigger technology that will usher in a new era of computing – the Era of AI Everywhere. This new era will include the journey from narrow AI to widening AI and will completely change our relationship with data and how we extract value from both structured and unstructured data.

Generative AI triggers the dawn of this new era because it will drastically reduce the time and costs associated with developing solutions for a wide range of use cases associated with automation and intelligence. The rapid adoption of Generative AI moves AI from an emerging software segment in the stack to a lynch-pin technology at the center of a platform transition.  The market generally assumes that this type of platform transition requires a shift in hardware, similar to the move to client-server from mainframes, or to the cloud from client-server.  However, IDC believes that this time it will be different. This platform transition will focus more on data. This time it will be about how we use data as an input (to train, fine tune and infer foundational models) and as a business outcome (as part of the development of new use cases).

GenAI and Tech Industry Market Disruption

As Generative AI will impact most tech markets from semiconductors to professional services, tech suppliers are rapidly revising their product roadmaps and rethinking their business, pricing, and customer service models.

Infrastructure. Today, much of the value is being captured by semiconductor vendors, most notably NVIDIA, as running the training and inference workloads for the foundation models demands significant GPUs. Semiconductor providers need to have chips specifically designed for AI workloads, which is creating an opportunity for new challengers. Training AI models will also drive storage and networking investments, putting public and hybrid cloud providers in a solid position to capture share since dedicated on-prem training of foundation models is expensive.

Software. In the medium-term, well-entrenched platform and application vendors stand to benefit if they can pivot their offerings and business models fast enough. They must decide which Generative AI use cases can support direct monetization, and which will be important to implement from a defensive point of view.  For example, generative AI could transform the way we interact with enterprise software. It is potentially the biggest shift in UX design since point and click and poised for disruption by GenAI native applications startups.

As it looks like many of the costs associated with managing Generative AI models for scale, security, and privacy will fall on the shoulders of the software provider, the following key decisions are being evaluated to protect their margins:

Should they train their own foundation models or partner with model providers?
What is the new pricing model to support Generative AI capabilities?
Will SLAs need to include grounding for some use cases? And if so, should levels of support be added to deal with context and data drift?
Will getting access to customer data to train models be a part of a new set of licensing terms and conditions?
Do they need to provide indemnification on AI-generated assets?

Services. While service firms are busy helping their clients identify GenAI use cases, they are simultaneously investigating how GenAI will impact the demand for their services over the long term and how their delivery models around software development, accounting, and legal services will be automated.  Increasingly, services firms are bringing their own AI software platforms to engagements which is blurring the lines between software and services.

Security and Trust. Due to its ability to generate fake code, data, and images closely resembling the real thing, Generative AI is likely to increase identity theft, fraud, and counterfeiting cases. The LLMs are also vulnerable and could be a source of attack and manipulation. Security vendors have a ripe opportunity to develop new solutions to address these emerging challenges.

New Markets. Of course, with any disruptive technology, new technology markets will spawn. Start-ups are already emerging to provide tools to personalize models, provide contextualization for the model, increase the speed of training LLMs, and orchestrate the process. There are huge opportunities for software companies to meet the market where it stands. It may mean offering a full-stack translation service rather than translation software.

Despite all the unknowns facing the tech industry, what is clear is the need to quickly get your arms around the fundamental questions related to Generative AI and how it will drive your business model in the future.

If your organization is interested in partnering with IDC to better understand how Generative AI will impact the markets most critical to your success contact us.

We also recommend you take advantage of these recent resources from our thought leaders and tech market experts:

IDC Webinar: Unlocking Business Success With Generative AI

IDC Blog: Generative AI: Mitigating Data Security and Privacy Risks

IDC eBook: Unlocking the Power of Generative AI

IDC Blog: Can ChatGPT Transform the Customer Experience?