38%
#1
23%
31%
In the race to harness generative AI, tech leaders face a dual challenge: empowering citizen developers while safeguarding against critical security risks. These are the non-technical users – in HR, marketing, finance, sales and more – who are seeing the enormous value in genAI.
Keep scrolling to learn how top companies are staying ahead in this rapidly evolving landscape.
Hallucinations
82% of organizations say their board and top leadership is receptive to move forward on AI initiatives, whilst...
Bottom-up challenges
The percentage of companies worldwide that have put in place comprehensive, consistent checks on their AI algorithms is...
#2
LLM
security
Hallucination
Access
control
#3
Data
leakage
#4
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RAG
Prompt Intelligence
Prompt Injection
PII
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Bias governance lagging
of middle management are resistant to AI
of employees are resistant to AI
According to Cisco’s AI Readiness Index, 97% of enterprises recognize the urgency to deploy AI, yet only a fraction can do so.
Mind the Gap: You’re Not Alone in the AI Adoption Journey
It’s helpful to know your starting line and understand that you're not alone in this journey. AI adoption varies significantly across industries.
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The AI adoption gap is real, and it's wide. You are not alone in this AI adoption journey.
First, consider that most organizations have not committed to a specific AI platform, enabling them to action GenAI models.
IT
Line of Business
23%
18%
25%
28%
21%
16%
"We have not done any serious evaluation of AI platforms and don’t plan to do so"
"We are in the middle of evaluating AI platforms and will select in next 6 months"
"We’ve already selected a primary AI platform and are using it"
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The only way to bridge this gap is to enable citizen developers to build their own GenAI applications – especially considering that the need forecast for developers will be quite challenging to meet.
Comparing how ready organizations are according to IT and Lines of Business
Is there an effortless way to experiment and get started? Start with compelling GenAI use cases.
150m+ developers worldwide by 2030
45m developers in the US by 2030
27m developers in the US by 2024
Data Generation
The potential for GenAI citizen developers is enormous – but organizations need to ensure security challenges are thoughtfully addressed.
AI-generated content can be unreliable or false. Hallucinations pose a significant risk, yet less than half of companies worldwide have comprehensive and consistent checks on their AI algorithms.
Tackling Top GenAI Security Challenges
14%
Ensure robust checks and balances are implemented. Start with using a trusted LLM, and then evaluate the model’s responses against user prompts and the organization’s data to determine degrees of hallucination.
Deploy robust Personal Identifiable Information (PII) protection – your data’s bodyguard.
Protecting sensitive information and preventing data leaks.
Prompt Intelligence - monitoring data from a team's prompts leads to huge insights across usage patterns and training opportunities, optimizes AI interactions, and helps avoid potential issues.
Solution
Lack of insights into AI interactions and usage patterns.
Problem
Still, less than half of companies worldwide have put in place comprehensive, consistent checks on their AI algorithms.
And to bring teams inside, there’s work to be done culturally within organizations.
Malicious actors manipulate AI responses through carefully crafted inputs.
Implement strict input validation – your AI's BS detector. By setting up robust filters and context boundaries, you're giving your AI a sixth sense for malicious inputs, keeping your system secure and responses on track.
All told, when it comes to GenAI and security, these are the areas companies find most challenging.
Shadow
LLM
usage
#5
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46%
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45%
Process Automaton
41%
Customer Interaction
39%
Employee Training
36%
Content Creation
31%
Coding Assistance
31%
Personalized Marketing
26%
Sales Outreach
Flexible AI Personalization
Easy Start
TOP
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of enterprises can deploy AI
Solution
Problem
Solution
Problem
Solution
Problem
Source: 451 Research's Voice of the Enterprise: AI & Machine Learning, Use Cases 2024
Source: IDC, GenAI Awareness, Readiness, and Commitment: 2024 Outlook — GenAI Plans and Implications for External Services Providers, AI-Ready Infrastructure, AI Platforms, and GenAI Applications, doc # US52023824, April 2024
The number of developers needed in the near future
The key use cases organizations identified to add value
Implement Retrieval Augmented Generation (RAG) - your new best friend. RAG helps improve context-specific responses, grounding LLMs in real, trustworthy information accessing company databases, documents and other internal resources.
On its own, an LLM may not have enough fine-tuned or specific organizational data to provide relevant and up-to-date information that ladders up to business insights. LLMs are generalists by design.
Solution
Problem
of employees are resistant to AI
31%
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Deploy robust Personal Identifiable Information (PII) protection – your data’s bodyguard.
Prompt Intelligence - monitoring data from a team's prompts leads to huge insights across usage patterns and training opportunities, optimizes AI interactions, and helps avoid potential issues.
Implement strict input validation – your AI's BS detector. By setting up robust filters and context boundaries, you're giving your AI a sixth sense for malicious inputs, keeping your system secure and responses on track.
A GenAI Adoption Blueprint