Predicts 2025: AI-Powered Analytics Will Revolutionize Decision Making
26 February 2025 - ID G00822179 - 28 min read
By Peter Krensky, Georgia O'Callaghan, and 4 more
The consequences and repercussions of the enlivened analytics and AI collision continue to unfold. To navigate the opportunities and risks, D&A leaders must adapt current systems, pilot new approaches augmented by AI and build sustainable initiatives that scale.
Overview
Key Findings
- End users want to automate the migration of analytics content between platforms to save money and align with their data ecosystems. Generative AI (GenAI) and robotic process automation (RPA) promise to reduce vendor lock-in and drive more competitive pricing.
- While vendor and end-user attention has largely focused on general-purpose large language models (LLMs), there is growing interest in and adoption of small, task-specific AI models.
- Agentic AI is a top strategic technology trend, with vendors selling a vision of agentic analytics processes for data analysis. End users are eager to augment or automate decisions with specialized AI agents for decision intelligence (DI).
- End users are increasingly frustrated with the time and cost associated with building dashboards, especially as the perceived value of dashboards declines. Dashboards with low user engagement will be replaced by GenAI-created assets, many of which will become embedded narratives at decision points in business systems.
- After two-plus years of excitement, education and experiments, AI fatigue is setting in with end users. As hype fades and setbacks mount, D&A leaders must calculate and communicate new AI value expectations and initiative trajectories.
Recommendations
- Retire costly, low-usage analytics and business intelligence (ABI) tools and migrate content using vendors with automation accelerators to save time and cost. Deploy subject matter expertise (SME) oversight to ensure the accuracy and relevance of migrated content.
- Pilot small, domain-specific AI models where LLMs fall short, and implement composite approaches with multiple models for complex workflows.
- Implement agentic AI technologies that augment end users’ decision making at scale. This will improve the quality of business decisions and the adoption of DI practices. While integrating these specialized AI agents into DI platforms may boost efficiency, oversight and guardrails are essential.
- Upgrade analytics platforms with text-to-SQL and GenAI technologies to enhance decision-making capabilities. Implement effective change management strategies focused on data literacy and governance to ensure that end users can fully leverage GenAI capabilities in ABI platforms.
- Pinpoint AI fatigue sources and tackle them systematically. Recharge the organization through collaboration and mentoring, and stay true to your established strategy.
Strategic Planning Assumptions
- By 2028, GenAI and RPA techniques will automate the migration of 40% of content between analytics platforms, reducing vendor lock-in and driving competitive pricing.
- By 2027, organizations will implement small, task-specific AI models, with usage volume at least three times more than those of general-purpose large language models.
- By 2027, 50% of business decisions will have been augmented or automated by AI agents for decision intelligence.
- By 2028, 60% of existing dashboards will be replaced by GenAI-powered narrative and visualization.
- By 2027, 80% of D&A leaders will calculate and communicate new AI value expectations and initiative trajectories due to setbacks and AI fatigue.
Analysis
What You Need to Know
The landscape of analytics and AI is rapidly evolving, entering a phase where organizations are tasked with moving from experimentation to operationalization at scale. Technologies that promise transformation are being challenged to deliver measurable outcomes, driving a shift in organizational priorities. This report explores the forces shaping analytics and AI in the crucial near term, offering guidance on the opportunities and challenges that lie ahead (see Figure 1).
Central to this period is a focus on specialization and adaptability. Organizations are increasingly moving away from broad, general-purpose solutions, favoring approaches that address specific challenges with precision. This evolution reflects the growing realization that success in AI depends not only on the sophistication of the technology, but also on its alignment with business objectives and its ability to deliver focused value.
This pivotal period will also be marked by significant changes in how organizations manage their analytics ecosystems. Many enterprises find themselves constrained by a fragmented collection of tools and platforms, acquired through decentralized decision making, mergers and shifting business priorities. Such fragmented systems create inefficiencies, increase costs and complicate modernization efforts. However, advancements in automation and generative technologies are beginning to address these challenges, reducing the barriers to platform migration and system consolidation. These developments are laying the groundwork for a future where analytics ecosystems are more integrated, flexible and efficient.
Decision-making practices are also undergoing a fundamental transformation. The integration of AI into decision flows is reshaping how organizations approach strategy and operations. Insights are becoming more immediate, dynamic and actionable, as AI-driven systems begin to augment and automate key decision points. This evolution marks a shift from reliance on status dashboards to more interactive, narrative-driven insights tailored to decision makers’ needs. The ability to translate complex data into intuitive, real-time guidance will become a defining capability for analytics in the years ahead.
Despite the promise of advancements, the path forward is not without challenges. The accelerating pace of AI adoption has led to growing concerns about sustainability and the potential for diminished returns. Many organizations are grappling with the need to recalibrate their expectations, focusing on achievable outcomes rather than overly ambitious goals. This recalibration is critical to maintaining momentum and ensuring that AI initiatives continue to drive tangible benefits.
As these trends unfold, the underlying data infrastructure remains a critical enabler. Organizations must continue to invest in systems that ensure data is accessible, reliable and ready to support analytics and AI applications. Self-service analytics must balance usability with governance, empowering users while maintaining control over data integrity and proper use.
This report contextualizes the imminent and developing changes in analytics and AI within a broader narrative of transformation. The ability to adapt to these changes and align strategies with emerging trends will be critical for organizations as a new era of data-driven insights unfolds.
Strategic Planning Assumptions
Strategic Planning Assumption: By 2028, GenAI and RPA techniques will automate the migration of 40% of content between analytics platforms, reducing vendor lock-in and driving competitive pricing.
Analysis by: Georgia O’Callaghan
Key Findings:
- Many organizations accumulate multiple ABI tools due to diverse use cases, mergers, acquisitions and decentralized purchasing decisions. This results in a mix of legacy, new and domain-specialized tools that ABI teams must govern, license, maintain, integrate and manage the costs of.
- Migrating ABI tooling can be part of an analytics modernization strategy to retire outdated, end-of-life or unsupported tools. Organizations also often seek to consolidate ABI tools to reduce licensing and management costs.
- Migrating between platforms usually requires a complete redesign, especially if underlying data sources and pipelines are also changing. Organizations often overlook or underestimate the substantial cost and effort involved in a migration.
- The more analytical data modeling and data preparation has been conducted within the ABI tool itself, the harder it is to migrate away from this tool. This is because all the data transformation logic must be recreated in the new ABI platform. Organizations that use an agnostic semantic layer find migration easier.
- There is substantial change management involved in ABI tool migration, as business users typically have an affinity toward the ABI tools they use regularly or perceive as valued skills in the labor market. In addition, self-service analysts may have invested considerable time in developing skills in the ABI tool to be retired.
- Some service providers have developed ABI tool accelerators, which include a combination of scripting, RPA and GenAI techniques for report rationalization, profiling analytic content assets, recreating reports in the target ABI platform and running validation checks.
- RPA and GenAI techniques allow service providers to migrate more rapidly and efficiently between ABI platforms, reducing overall migration costs for organizations. However, current service providers tend to focus on migration between specific platforms, as opposed to being open and agnostic to any ABI tool.
- Currently, ABI migration service providers can automate the migration of between 30% and 66% of reports between certain ABI platforms. This percentage reflects the fact that most vendors still have to migrate more complex reports manually. Data assets must also be manually migrated in the majority of cases.
- The efficiency and precision of ABI migration service providers will continue to improve with further advancements in RPA and GenAI over the next few years. These service providers will also expand their portfolios to include new source and target ABI platforms.
Market Implications:
- Many organizations are currently dissuaded from migrating between ABI platforms because of the substantial time, effort and cost involved. Consequently, they may feel dependent on and locked into their ABI platform. This makes them vulnerable when it comes to licensing and pricing negotiations, and can lead to staff and skills shortages.
- In recent years, most ABI vendors have increased their pricing, leading many organizations to reconsider their decision to stay with their current platform or switch. This is because the total cost of ownership of the ABI platform over a five-year period after the price rises significantly outweighs the cost of a migration.
- With the general availability of LLMs and other GenAI applications, solution providers that offer expedited migration between ABI platforms have made significant functional and quality advancements in the last 12 months. Some service providers have built solutions that assist with automating and accelerating stages of the migration process: from report rationalization to migration to validation.
- Solution providers offering a service to migrate between ABI platforms have seen greater end-user adoption in the past year, leading providers to invest more in accelerators to increase their bandwidth for taking on new accounts. Over the next few years, continued improvements in these services will lead to greater portability and easier migrations between platforms, lowering the cost of a migration. This in turn will make ABI tool migration a more tempting proposition for organizations.
- Easier migration between ABI tools may lead ABI vendors to be more competitive when it comes to price over time in an effort to retain customers. In addition, ABI vendors that are more open and agnostic to data sources will be more attractive than others.
Recommendations:
- Identify candidate ABI tools for retirement by conducting a rationalization exercise, examining key factors such as cost and licensing model of each platform, number and complexity of reports and datasets, usage and adoption, data dependencies, and vendor support and roadmap. This should be done within the context of an existing data and analytics (D&A) strategy and objectives of the organization.
- Within each ABI tool slated for retirement, prioritize content for migration by determining use case, audience and impact. Identify areas where content can be consolidated, reinvented or retired, as appropriate. Calculate the duration and cost of in-house migration by conducting proof of concept (POC) exercises and recording the time to migrate reports of varying complexity and quality.
- Evaluate service providers that can expedite ABI tool migration. Conduct POCs with these third parties to understand their overall approach, cost structure and estimated duration of the migration. Compare the cost and duration of an internally-led migration to that estimated by the service provider to decide which approach is most suitable for your organization.
- Identify any accelerators employed by potential vendors and the mechanism involved, such as whether GenAI or RPA techniques are used or not. Be wary of delivery services that undertake ABI migrations in full manually, without the help of accelerators such as scripting, RPA or GenAI. Such manual approaches are typically very expensive and lengthy.
- If proceeding with a service provider, determine the extent to which their postmigration report validation process is manual versus automated. Most vendors still require internal subject matter experts to provide an additional layer of validation before the old content is retired.
- Regardless of the approach used, invest in change management and end-user training throughout the ABI tool migration to ease users through the transition and promote adoption of the new platform.
Related Research:
Strategic Planning Assumption: By 2027, organizations will implement small, task-specific AI models, with usage volume at least three times more than those of general-purpose large language models.
Analysis by: Sumit Agarwal
Key Findings:
- General-purpose LLMs provide very powerful language capabilities. However, many Gartner clients have reported reduced response accuracy for enterprise-specific tasks, such as answering questions or summarizing content, which require a business domain context and specific terminology.
- The diversity of tasks in a business workflow and the need for improved accuracy require more specialized models that are fine-tuned on the specific function or domain data.
- Task-specific models can be much smaller in size than general-purpose LLMs. This should result in lower compute requirements for runtime execution and faster response times. However, these models may also have smaller context windows, meaning the amount of text processed at one time is less than some of the larger models. This effectively reduces the permissible prompt length, which in turn limits the amount of supporting context in the prompt to answer the user query.
- A combination of distillation and quantization techniques has enabled faster inference and smaller memory footprint, allowing small language models to deliver comparable performances to the large model versions. This creates an opportunity to host powerful language models on a variety of deployment environments, including noncloud environments and edge devices.
Market Implications:
- Creating or customizing LLMs for specific tasks or domains requires fine-tuning general-purpose models. These models may be offered by external providers in collaboration with enterprises seeking to monetize their business knowledge through a vendor marketplace. Enterprises may also decide to fine-tune a general-purpose LLM with data that is internal to the enterprise.
- Enterprise data will serve as a key differentiator, necessitating data preparation, quality checks, versioning and overall management to ensure relevant data is structured to meet the fine-tuning requirements of the specific LLM. Effective data management and preparation capabilities for unstructured data are critical for the success of these initiatives.
- AI platform vendors, including those offering data science and machine learning (DSML) solutions with retrieval-augmented generation (RAG) capabilities, will need to expand their offerings to include fine-tuning solutions. Currently, their offerings focus on integration of various RAG components and providing a framework to automate the testing execution. However, organizations need support in managing test or evaluation data, using AI augmentation to generate such data and identifying gaps in the testing. Hosting an LLM also requires integration of model-serving capabilities across a compute cluster.
- As more LLMs with varying capabilities and usage requirements emerge, along with different model owners (internal and external), AI governance platforms must provide monitoring and oversight. Current AI observability solutions that have traditionally focused on machine learning (ML) models need to expand their functionality to include monitoring the text contained within the prompt for bias, privacy attributes, scope, safety and overall appropriate usage. Risk management requirements call for alignment with capabilities that support auditing of the solution and its compliance with internal policies and regulatory requirements. Platform vendors should offer features that enable technical teams to identify gaps or errors before they reach users, thus facilitating proactive solution improvement.
- Managing usage and costs is critical to ensure models are used for their intended purposes in a cost-effective manner. Enterprises might implement internal chargeback models to allocate consumption and maintenance costs, thereby encouraging financial discipline (see Cloud Transition Requires CDAOs to Collaborate With CFOs).
- Orchestrating inference requests across multiple LLMs requires a centralized dynamic routing mechanism, supported by guardrails, security and privacy checks. AI gateways can offer a centralized solution for managing these inference requests (see Innovation Insight: AI Gateways).
Recommendations:
- Pilot small, contextualized models in areas where business context is crucial or where LLMs have not delivered adequate response quality or speed.
- Identify use cases where a single model orchestration is insufficient, and instead, a composite approach involving multiple models and workflow steps is necessary.
- Prioritize efforts in data preparation to collect, curate and organize the data needed to fine-tune language models, thereby enhancing task-specific or business function knowledge.
- Upskill people and evaluate platforms to drive these initiatives. Upskilling should include technical and functional groups, such as AI and data architects, data scientists, AI and data engineers, API developers, risk and compliance teams, procurement teams, and business SMEs.
- Be patient and collaborate with vendors, as a contextualized AI model for every need may become a reality sooner than expected.
Related Research:
Strategic Planning Assumption: By 2027, 50% of business decisions will have been augmented or automated by AI agents for decision intelligence.
Analysis by: Pieter den Hamer, David Pidsley
Key Findings:
- Decision intelligence (DI) is emerging as a practical discipline that advances business decision making by explicitly understanding and engineering how decisions are made and how outcomes are evaluated, managed and improved via feedback. Analytics and AI work together in decision flows that define and orchestrate the specific parts needed for the support, augmentation or automation of each decision.
- AI agents are autonomous or semiautonomous software entities that use AI techniques to perceive, make decisions, take actions and achieve goals in their digital or physical environments (see Innovation Insight: AI Agents). They are a key addition to DI, as they enable more dynamic decision flows, adapting the composition of AI and analytical techniques to specific business contexts and operating conditions. Following a decision-centric approach, AI agents can orchestrate tasks from data to insight to action.
- AI agents complement human knowledge and experience in decision making by automating and managing the complexity of the analysis and retrieval of data from multimodal, internal or external data sources, tailored to the decision at hand (see Emerging Tech: Data Fabrics With Multimodal Data Focus for Generative AI-Enabled Applications). This decision-centric use of AI agents results not only in quantified insights but also in more qualitative and contextual insights and — going beyond insights — in recommended actions. In essence, AI agents for DI empower human decision makers, aiming for the synergy between human and artificial intelligence for optimal decision making.
- Beyond individual decisions and local optimization, AI agents for DI may collaborate in swarms or collectives that represent networks of decisions for more global optimization, across organizational silos and boundaries. This will evolve into an ecosystem of decision actor collaboration across the enterprise or value chain: a multiagent DI platform that is constantly learning, adapting and optimizing.
Market Implications:
- Given the ongoing demand to progress digital transformation and innovation toward autonomous business, organizations continue their adoption of analytical and AI techniques, including AI agents, furthering the possibilities of decision augmentation and automation.
- Together, AI agents within DI platforms and agentic analytics processes are pivotal to increase the impact and value creation from analytics and AI. This is achieved by applying these techniques in a more customized manner, integrating them more deeply into actual business decision making, in contrast to more general-purpose analytics and AI solutions or AI agents.
- AI agents for DI democratize the access to analytics and AI for business decision makers, making their use even easier when compared with current forms of decision support via self-service analytics. As AI agents offer natural language interaction, their adoption can be expected to reduce human use of ABI platforms via a presentation layer. Why still look at your dashboard or report if your AI agent offers you a course of action tailored to the context and decision at hand?
- AI agents for DI will work together with other AI agents. Some will handle the execution of a decision through to its intended outcome by communicating with human actors or invoking operational applications. Other AI agents may serve as decision monitors to mitigate risks, provide explanations, or evaluate decisions and foster learning.
- AI agents should not be overestimated in terms of reliability, especially when they rely on probabilistic models or potentially hallucinating LLMs at their core. Where better transparency and reliability are required for business decision making, AI agents also need to rely on symbolic AI techniques such as rules and logic-based systems, knowledge graphs or causal models.
- AI agents for decision intelligence are not a cure-all, nor are they infallible. Their use must go hand in hand with effective governance and risk management. Human decision actors still require proper knowledge and data and AI literacy. In addition, a solid foundation of reliable data — not limited to internal transactional data sources but increasingly including more external and multimodal data sources — remains essential. See Emerging Tech: Data Fabrics With Multimodal Data Focus for Generative AI-Enabled Applications.
Recommendations:
- Work with business stakeholders to identify and prioritize decisions critical to the success of the organization, and those that can benefit from the more effective application of analytics and AI. For each of those decisions, apply DI practices, including the use of AI agents, to augment human decision makers or — where desirable and feasible — for decision automation.
- Prioritize investment in a DI platform or more ready-made solutions that not only combine relevant analytics and AI techniques, but also (start to) support the use of AI agents.
- Apply decision governance and manage the risks of both the augmentation and automation of decisions. Carefully consider what level of augmentation and automation is appropriate, weighing aspects such as the impact, risks, ethics, complexity and timing of each decision.
- Strategically, reimagine the way decisions are made in the organization by evolving from a data-driven approach to a more decision-centric vision for D&A teams. AI agents for DI — autonomously or in collectives with other AI agents and empowered human decision makers — hold the potential of greatly optimizing organizational adaptability, resilience and performance.
Related Research:
Strategic Planning Assumption: By 2028, 60% of existing dashboards will be replaced by GenAI-powered narrative and visualization.
Analysis by: Tong Zhang
Key Findings:
- Text-to-SQL capabilities are quickly becoming enterprise-grade, making the transformation of traditional ABI design patterns possible. All ABI vendors are adding and improving these capabilities to ensure competitiveness on the market.
- The reasoning capabilities of LLM-enabled data storytelling go a step further by explaining why the data matters, providing a more comprehensive understanding of the insights.
- Directly querying an augmented dataset represents a significant advancement in business intelligence capabilities, reshaping self-service analytics.
- Advanced and agile data visualization can be realized by multimodality models, providing more options beyond basic charts and graphs without sophisticated designs.
Market Implications:
- Enterprises may increasingly favor ABI platforms with advanced text-to-SQL capabilities, leading to a shift in market share toward vendors offering these features. Businesses expect to leverage these capabilities to make faster and more informed decisions using near-real-time data, without intensive development effort and cost.
- The integration of strong reasoning capabilities into BI tools for data storytelling will revolutionize how businesses operate. By providing not only results but deeper insights, these advanced BI capabilities can lead to transformative impacts across all facets of operations and increased engagement of users with D&A.
- As democratization progresses, augmented analytics will become the primary approach for BI use cases. Natural language query will quickly transition from an innovative feature to a minimum requirement. When enterprises choose BI tools for self-service capabilities, their focus will shift from low-code/no-code and AutoML to prioritizing prompt engineering for agentic analytics processes as a key feature.
- No complex designs are needed once enterprises adopt BI tools with advanced data visualization capabilities. Business users can gain insights more intuitively. Multimodal models can reveal patterns and insights from multiple types of data that traditional charts might miss.
- The demand for SQL skills and BI developers will change. Enterprises will have developers focus more on complex data modeling and advanced analytics that go beyond automated solutions.
Recommendations:
- Explore new analytics platforms with text-to-SQL and advanced visualization features, or upgrading existing ones. Pay particular attention to vendors with robust foundational models supporting their BI tools.
- Prioritize self-service analyst personas in BI and enhance self-service analytics through natural language interactions with tools powered by GenAI.
- Establish and/or expand a data literacy program to ensure users grasp not only how to interact with the technology, but how to read, write and communicate with data in context.
- Establish robust D&A governance frameworks, including policies, processes and metrics, to accommodate the surge in the number of users creating and consuming analytics content. Work closely with D&A governance leaders to ensure policy alignment and feasibility in execution.
- Streamline the skill set required for BI developers by automating routine data queries and data visualization tasks, empowering BI teams to focus on more complex analytical challenges.
Related Research:
Strategic Planning Assumption: By 2027, 80% of D&A leaders will calculate and communicate new AI value expectations and initiative trajectories due to setbacks and AI fatigue.
Analysis by: Peter Krensky
Key Findings:
- Leaders and practitioners have been under prolonged pressure to deliver on the promise of generative AI since ChatGPT hit one million users. According to the 2023 Gartner AI in the Enterprise Survey, 49% of respondents cite demonstrating the value of AI as one of the top barriers to implementation.
- Gartner CDAO Agenda Survey for 2025 found that the inability to measure business impact is the second-most prominent roadblock to D&A success. The measurable, unmeasurable and perceived impact of D&A and AI in the near term will be influenced by a combination of market, societal and technological factors.
- AI fatigue takes many different forms (for example, skepticism, disillusionment, stonewalling). Leaders must recognize fatigue first in themselves and then in others. It must be validated and understood to then be managed so that it can be avoided.
- Leaders and adopters feel that popular AI applications, such as Microsoft Copilot, show positive early signs of fulfilling their promise (see The State of Microsoft Copilot 365: Survey Results), but this optimism is fragile. Setbacks and financial pressure often stem from data issues, infrastructure and maintenance costs, and talent challenges.
- AI fatigue and the resultant correction is a normal and healthy part of technology adoption, as articulated by Gartner’s Hype Cycle methodology. GenAI is moving rapidly along the Hype Cycle and looks likely to emerge stronger on the Plateau of Productivity after surviving a period of disillusionment.
Market Implications:
- The value of AI in analytics will be thoroughly tested and scrutinized. The AI good times cannot last forever. As fatigue sets in, stakeholders and executive leaders will (and should) challenge the value of AI relative to its cost. Many AI projects will be managed as assets and products with established financial values (see 2024 Data and Analytics Benchmarks to Optimize Budgeting and Staffing).
- Organizations will prioritize AI initiatives with clear and quantifiable value over projects that are perceived as speculative or experimental. The bar will be raised on AI impact and timelines. Leaders need the patience and culture to prevent setbacks from derailing promising initiatives.
- The AI Hype Cycle is highly unpredictable and volatile. Right now, the Trough of Disillusionment is being navigated. Even the current overhyped conditions and inflated expectations can go higher still. At this time, costs are more likely to trigger AI disillusionment than poor performance or impact.
- Marketing and promises around AI will become even more outlandish until fatigue becomes widespread. The marketing blitz and vendor sales push of the near future will be intense. Vendors will then have to navigate a crowded, noisy AI marketplace where customers and prospects are increasingly unreceptive to current messaging.
Recommendations:
- Identify the elements of analytics and AI that are contributing to your and your organization’s fatigue. Is it data challenges? Technology decisions? The pace of AI innovation? Internal politics? Expectations and ROI pressure? Catalog the sources of fatigue so you can address them systematically (see Tool: Data and Analytics Strategy and Operating Model Health Check).
- Find ways for individuals and the organization to recharge. Acknowledge the challenges and strains on the organization’s resources (and even more importantly, on human resources and motivation). Encourage collaboration events and mentoring so early practitioners can share successes and failures.
- AI fatigue can only be managed, not cured. Leaders, knowledge workers and society face a long road discovering and managing the impact of AI. Anticipate and prepare for setbacks to AI’s public perception and shifting organizational attitudes.
- Carefully design and construct a strategy and adhere to it. Trust the process and people that created your strategy. Allow room for adaptation, but firmly stick to the tenets that you and your organization have committed to. Be prepared to push back on stakeholders who get nervous at the first sign of trouble or jump at passing fads.
- Find a second wind and inspire your organization. AI is not a passing fad, and the organizations that thrive will be those that find a way to navigate the overwhelming innovation and enthusiasm. Focus on the elements of AI’s promise that excite you personally and evangelize them. Seek help and support in areas that are dragging you down. This may involve internal help or bringing in service providers and third parties to invigorate initiatives.
Related Research:
A Look Back
In response to your requests, we are taking a look back at some key predictions from previous years. We have intentionally selected predictions from opposite ends of the scale — one where we were wholly or largely on target, as well as one we missed.
Missed: 2024 Prediction — By 2025, 90% of current analytics content consumers will become content creators enabled by AI.
We are, as yet, nowhere near 90% of consumers acting regularly as content creators. GenAI capabilities and agents are breaking down capability barriers and empowering nontechnical users, but adoption by the consumer persona has been much slower than originally anticipated.
This research is based on Gartner inquiry data from client interactions on the subject of analytics and AI throughout 2024.
2024 Gartner Impact of GenAI in the Digital Workplace Survey. This survey sought to understand the value of generative AI (GenAI) assistants embedded in popular digital workplace productivity applications in the digital workplace, assessing their ability to enhance employee productivity and efficiency. The survey was conducted online from 16 May through 12 June 2024. A total of 152 IT leaders participated, with 61 who were members of Gartner’s Research Circle, a Gartner-managed panel, and 91 who were contacted through survey links via LinkedIn posts and outreach to clients. Respondents were from EMEA (n = 94), North America (n = 46), Asia/Pacific (n = 10) and Latin America (n = 2). Of the 152 respondents, 132 were primarily responsible for Copilot for Microsoft 365. They were highly involved in the decision-making process or management of Copilot and were required to be currently piloting or finished with the pilot of Copilot in their organizations. The remaining 20 respondents were primarily responsible for GenAI assistants apart from Copilot, such as Gemini for Google Workspace, Salesforce Slack AI and Zoom AI Companion. Disclaimer: The results of this survey do not represent global findings or the market as a whole, but reflect the sentiments of the respondents and companies surveyed.
2023 Gartner AI in the Enterprise Survey. This study was conducted to understand the keys to successful AI implementations and their impact on the broader AI that has been brought by generative AI. The research was conducted online from 19 October through 21 December 2023 among 703 respondents from organizations in the U.S., Germany and the U.K. The main sample consisted of 645 out of the 703. Organizations were required to have developed or intended to deploy at least two AI initiatives within the next three years. Respondents were required to be part of the organization’s corporate leadership or report to corporate leadership roles. Fifty-eight out of 703 are the business intelligence (BI) sample. Organizations were required to have developed or intended to deploy at least one AI initiative within the next three years. Respondents were required to be part of the organization’s corporate leadership or report to corporate leadership roles or below (senior manager and above) and to be primarily responsible for BI in their organizations. Both the main sample and the BI sample respondents were required to have a high level of involvement with at least one AI initiative, and they were required to have one of the following roles when related to AI in their organizations: determine AI business objectives, measure the value derived from AI initiatives, or manage AI initiatives development and implementation. Quotas among the main sample were established for company size and for industries to ensure a good representation across the sample. No quotas were established for the BI sample. Disclaimer: The results of this survey do not represent global findings or the market as a whole, but reflect the sentiments of the respondents and companies surveyed.
Gartner Chief Data and Analytics Officer Agenda Survey for 2024. This survey was conducted to determine the agenda and strategic challenges of the chief data and analytics officer (CDAO) role or the office of the CDAO for 2024. It also sought to inform agenda planning or potential research topics for the data and analytics practice, and track the progress of the CDAO role in organizations. The research was conducted online from September through November 2023 among 479 respondents from across the world. Respondents were required to have a CDAO, chief data officer (CDO) or chief analytics officer (CAO) title; be the highest-level data and analytics leader; have the highest-level data and analytics leader reporting to them; or be the leader with data and analytics responsibilities in IT or in a business unit outside of IT. The survey sample was gleaned from a variety of sources (including LinkedIn), with the greatest number coming from a Gartner-curated list of over 4,770 CDOs and other high-level data and analytics leaders. Disclaimer: The results of this survey do not represent global findings or the market as a whole, but reflect the sentiments of the respondents and companies surveyed.