Data & AI
Where are enterprises strategically investing in their data stack to gain a competitive advantage with GenAI?
State of
enterprise
tech
20
24
Innovation Under Pressure
The Report
Innovating under pressure—insights, predictions & perspectives from 400+ enterprise leaders
Despite external challenges, enterprises continue to innovate — experimenting with new infrastructure, allocating significant spend to customizing AI models, and upgrading both the employee and customer experience.
We surveyed 421 senior tech leaders from companies representing trillions in revenue and spoke with hundreds of executives across our IGNITE enterprise network, whose perspectives on budget shifts, current investments, and key priorities we’ve distilled to create this report.
Where technology leaders place their bets within the enterprise impacts the future we build.
Whether you’re looking for a benchmarking guide, or simply trying to better connect with senior tech buyers, this is an essential tool for evaluating the current and future state of enterprise technology.
Data & AI
Where are enterprises strategically investing in their data stack to gain a competitive advantage with GenAI?
Infrastructure & Dev Ecosystem
How are emerging technologies reshaping enterprise infrastructure and the developer experience?
Digital Experience
How are enterprise leaders looking to capitalize on tech investments to improve customer and employee experience?
Cybersecurity
Where are enterprises allocating cybersecurity budgets, and how do they intend to both leverage and protect the use of GenAI?
Enterprises are allocating notable spend towards customizing AI models.
48% of leaders are fine-tuning and customizing open source or off the shelf foundational models with an additional 10% training models entirely in-house.
Out-of-the-box, hosted foundational model
Moderate fine-tuning of foundational model
Extensive customization & fine-tuning of an open source or foundational model
Training & deploying a model in-house
We are not developing GenAI applications
There is so much I can do in the next few years with GenAI without needing it to interface with the customer—so much value to squeeze out; as a knowledge management company, there is tons of unstructured data that we need to manage internally to drive value.
Data leaders are focused on the foundational data layer.
48% of leaders identified Data Warehousing as their top priority.
1. Data Warehousing & Data Lakes
2. AI Model Development
3. BI Analytics & Visualization
4. AI Governance Risk & Compliance
5. Data Preparation (Labelling, Wrangling, Cleansing)
One key challenge is having high-quality internal data sets. Without them, proof of concepts may not scale or show real value and competitive advantage. Curated data sets are crucial for making your GenAI solutions stand out and deliver sustainable benefits.
OpenAI’s models lead AI adoption, followed by customizable open source LLMs.
GPT-4 is the most widely adopted AI model, with 43% of enterprises selecting it as their model of choice.
14%
Meta Llama
36%
OpenAI GPT-3.5
8%
Anthropic Claude
9%
Google Gemini
5%
Mistral
43%
OpenAI GPT-4
We want our models in our data center, or in our own VPC to maintain complete control. This is crucial not only for regulatory and privacy concerns, but also for cost containment. Open source innovation will be key.
Enterprise-wide buy-in is critical for GenAI adoption.
56% of data leaders have established or are planning to establish a hybrid AI Center of Excellence*.
*A hybrid COE is defined as having centralized oversight and decentralized execution across different departments.
56%
Infrastructure
& Dev Ecosystem
How are emerging technologies reshaping enterprise infrastructure and the developer experience?
Enterprises are increasingly using AI copilots and agents in software development.
Over 60% of tech leaders aim to reduce coding time and improve code quality with AI copilots.
1. Reducing time spent on routine coding tasks
2. Enhancing code quality & consistency
3. Streamlining code development process
4. Augmenting code reviewed & debugging
5. Accelerating onboarding & training of new developers
Copilots boost productivity by making it easy for end users to directly access and analyze data from databases without needing technical skills or engineers to create reports.
Enterprises are reimagining architecture to improve scalability.
62% of tech leaders have deployed serverless technology in some capacity.
Deployed Serverless
We're utilizing serverless computing and pipelines throughout our processes to elevate the role of engineering. This not only enhances our nimbleness, but allows us to expose reusable capabilities in a headless manner.
Rapid adoption of cloud technologies is underway for scalability and cost efficiencies.
91% of tech leaders indicate increasing or maintaining their budget for hybrid cloud, compute or container technologies.
Increase
Maintain
Decrease
There are three key reasons to move your applications to the cloud. First, it allows you to innovate faster on behalf of your customers. Second, it can provide greater security, resiliency and flexibility. Finally, cloud done right can be highly cost effective.
Leaders are increasing investments in API orchestration, security and AI integrations.
Data Integration & Orchestration and API Security & Governance take the top two spots.
Data Integration & Orchestration
API Security & Governance
AI & ML Integration in APIs
API Gateway Management
API Lifecycle Optimization
We are leveraging APIs to deliver real-time data sets, enhance data accessibility and improve the experience of professional and citizen developers. Our proactive approach is aimed at establishing an API-centric integration landscape that is robust, flexible, and future-ready.
Digital Experience
How are enterprise leaders looking to capitalize on tech investments to improve customer and employee experience?
There is a focus to improve the CX by advancing data and analytics capabilities.
In 2024, we've seen a 64% increase in the proportion of leaders reporting Customer Data Management & Analytics as a top three priority.
Customer Data Management & Analytics
95%
2024
58%
2023
We're continuing to shift our focus from individual business line use cases to the entire customer journey across all product lines. This comprehensive, enterprise-wide transformation is key to serving our customers and requires substantial investment in core technologies and a strong foundation for enterprise data.
There's a stronger push to define success for EX technology this year.
Employee Performance & Productivity and L&D take the top two spots for EX priorities.
Learning & Development
51%
2024
29%
2023
Employee Performance & Productivity
65%
2024
36%
2023
We want to fully integrate DEX, so that employee and customer interactions are all built around the same design principles. We put as much effort into improving our EX as we do in creating an exceptional CX.
Digital leaders struggle with short-term ROI on automation investments.
Only 56% of respondents feel they are getting value out of their automation investments, listing top inhibitors as scalability, implementation challenges and cost.
1. Scalability of the solution within your organization
2. Implementation of the solution
3. Cost
4. Complexity and brittleness of the solution
Automation and standardization, especially in onboarding processes, are crucial for scaling data foundations without disrupting existing systems. Meanwhile, AI will require a human level of interaction, as trust is the flip side of the coin for value creation.
Copilots are seeing broader adoption across the enterprise.
95% of enterprise leaders indicate having a clear copilot use case in production ranging from employee services to sales.
General purpose
Employee service
Developer-focused
Marketing
External customer service
Sales
GenAI is more than just identifying use cases and distributing licenses—it's an opportunity to upskill our organization, foster new habits, and promote innovation and productivity. We started with Microsoft Copilot to familiarize the organization with AI; now we're experimenting with GenAI use cases in cybersecurity, market analysis, finance, HR, safety and sentiment analysis to name a few. We're convinced that a learning workforce that's benefited from using AI is one that’s more ready to embrace its transformative impacts.
Cybersecurity
Where are enterprises allocating cybersecurity budgets, and how do they intend to both leverage and protect the use of GenAI?
Cloud Platform Security continues to dominate as the number one priority.
49% of cybersecurity leaders select this area as a top three budget priority.
49%
Most organizations like ours have multiple cloud providers, which makes Cloud Security much more complex. Our external enterprise presence has grown exponentially, and all of that needs to be secure.
Innovation in cybersecurity thrives despite YoY budget growth slowing.
64% of cybersecurity leaders are increasing their budget allocation for Change & Innovation.
Cybersecurity remains the number one priority. Without good governance, compliance and operational resilience, an attacker doesn't need to be particularly successful to have a lot of destructive power.
Cybersecurity leaders see SecOps and AppSec as having the greatest opportunity to leverage GenAI.
64% of CISOs rank these as the biggest areas for opportunity.
1. SecOps & Threat Mgmt.
2. AppSec / DevSecOps & API Security
3. Endpoint Security
4. Cloud Security
5. Email Security
6. IAM
7. Data Security
8. Network Security
The cybersecurity landscape is undergoing significant changes. Applying AI at scale to enhance threat hunting and other security measures is crucial. Ideally, these processes should be fully automated, minimizing the need for human intervention and reducing the risk of human error.
Data Security is the number one AI adoption concern.
48% of cybersecurity leaders are focusing on capabilities to secure data used by AI systems.
48%
Securing data used in AI systems
21%
AI system access controls
11%
Protection against adversarial attacks
10%
Data poisoning protection
10%
Other
One of the biggest risks with AI adoption that I talked to the board about was the unknown regulatory risks. If you're purely investing in AI to drive efficiency then that's not so much of a concern, but if you're using AI for example, on the clinical side—that's where the risk is.
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