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Enterprise AI solutions development

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What is enterprise AI?

Enterprise AI brings artificial intelligence technologies to large organizations to streamline operations, improve decision-making, and enhance performance. It taps into machine learning, natural language processing (NLP), computer vision, and predictive analytics.

In the current market, businesses bypassing AI are setting themselves up for failure. That's why adopting AI enterprise software is essential for automating time-consuming tasks (such as in-depth data analysis and customer service), tackling operational inefficiencies, and keeping pace with innovation.

With the right AI tools, you can secure a competitive advantage, augment your team's skills, and modernize systems across various business operations — supply chain management, customer service, marketing, finance, cybersecurity, you name it.

The state of the enterprise artificial intelligence market

The AI landscape of today is marked by cautious optimism in embracing artificial intelligence enterprise software. O'Reilly's report reveals a mixed picture: Thirty-one percent of businesses have yet to adopt AI, while 43 percent are considering its adoption, and 26 percent have already integrated AI solutions into their operations.

Among those with AI in production, only 49 percent have established a governance plan to oversee AI deployment.

AI adoption and maturity

43%
Evaluating
31%
Not using
26%
In production

Source: O'Reilly

Valued at $10.08 billion in 2023, the enterprise AI market is expected to explode to over $270 billion by 2032, with a CAGR of 44.1 percent from 2023 to 2032.

Enterprise AI market size 2024 to 2033 (USD billion)

2024
$14.49B
$14.49B
2025
$20.83B
$20.83B
2026
$29.98B
$29.98B
2027
$43.16B
$43.16B
2028
$62.2B
$62.2B
2029
$89.69B
$89.69B
2030
$129.43B
$129.43B
2031
$186.89B
$186.89B
2032
$270.06B
$270.06B

Source: Precedence Research

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Contrastingly, the adoption curve of generative AI for enterprise is climbing unprecedentedly fast.

Enterprise AI Solutions_00_text

Two-thirds (67 percent) of the companies report using generative AI in their operations, while 41 percent of companies have been using AI for a year or more, and 26 percent of respondents have jumped on a gen AI train less than a year ago. Still, a third (33 percent) of businesses have yet to explore generative AI, signaling a burgeoning growth area within the enterprise AI domain.

When it comes to budget allocation for AI, the computers and healthcare industries stand out, with a significant share of respondents stating that they dedicate over 21 percent of their budget to AI initiatives. Conversely, the investment is more conservative in government, telecommunications, manufacturing, and retail sectors — with reported AI expenditures ranging between nothing and five percent of their budgets.

Share of IT budget allocated to AI, by industry

Computer, electronics & technology (hardware)
18%
18%
21% or above
14%
14%
11%-20%
21%
21%
6%-10%
32%
32%
0%-5%

Source: O’Reilly

The comparison of enterprise AI and general AI solutions

Your company has its own way of doing business, and this is where general AI solutions fall short. It’s also when enterprise AI enters the scene.

Enterprise AI

General AI

Purpose & application

Enterprise AI

Designed to solve unique, complex business challenges, optimize processes, and scale operations.

General AI

Designed for wide use across different industries and business functions.

Customization & integration

Enterprise AI

Highly customized to address specific organizational needs; requires integration with existing enterprise systems.

General AI

Offers generic functionalities with a limited level of customization; lacks integration capabilities.

Complexity & scalability

Enterprise AI

Inherently scalable, advanced solutions that handle growing volumes of data and adapt to evolving business needs.

General AI

Varies in complexity, but implies a less sophisticated use of AI and limited scalability.

Target audience

Enterprise AI

Aimed at businesses and organizations seeking strategic advantages through AI.

General AI

Targeted at individual AI users and developers searching for AI capabilities for various tasks.

Outcome & ROI focus

Enterprise AI

Developed with a focus on delivering measurable business outcomes and ROI.

General AI

Built for general use; outcomes are not tied to specific business metrics.

Essential components of enterprise AI solutions

Enterprise AI is a breed of its own that organizations can’t tame without building a baseline of competence across three core capabilities, including tech, data, and people.

Data access and quality

  • Data infrastructure: Consolidated data storage and management systems that ensure data is accessible and efficiently organized.

  • Data quality: Mechanisms to ensure enterprise data's accuracy, usability, and integrity in line with internal standards.

Machine learning models and algorithms

  • Model development: Building custom machine learning models to analyze data and predict outcomes specific to the enterprise.

  • Model training, testing, and evaluation: Training models on past data, optimizing parameters, and gauging a model’s performance based on business objectives and specific metrics.

Integration with existing IT infrastructure

  • APIs and middleware: Making application functionalities and data easily accessible for AI by implementing service interfaces and middleware.

  • Data integration tools: Technologies designed to merge, summarize, and transport data from various sources to a central system for comprehensive analysis and action.

Computational resources

  • Processing power: High-performance hardware, including CPUs and GPUs, to handle the computational intensity of enterprise AI solutions. 

  • Scalability: Easily scalable infrastructure (cloud-based or on-premises) to address the organization's growing data and processing needs.

Data security, privacy, and compliance

  • Data security: Measures to prevent unauthorized access and breaches, including encryption, tokenization, data-level access controls, and secure data transfer protocols.

  • Compliance: Commitment to following regulations related to data privacy, security, and the ethical application of AI technologies, such as GDPR.

User interface and experience

  • Dashboards and data visualization tools: Interactive platforms that enable direct engagement with AI applications and simplify data insights presentation.

  • Self-service tools: NLP-based tools that democratize AI system access, improving usability and facilitating adoption.

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Real-world use cases and applications for enterprise AI

AI for enterprise has progressed from speculative theory to practical application across sectors, redefining most business operations. It's now a critical asset for leading organizations looking to advance in the rapidly changing marketplace.

Predictive maintenance

AI algorithms analyze machinery data to spot operational anomalies and predict equipment flaws before they happen, optimizing maintenance strategies. AI-enabled health check-ups reduce downtime and maintenance costs — a crucial advantage for industries reliant on heavy machinery, such as manufacturing, energy, and transportation.

Customer experience personalization

AI models analyze consumer data to customize the customer journey based on individual tastes, needs, transaction patterns, and actions. Tailored suggestions, detailed messaging, and bespoke content delivery meet customers' expectations, making them open their hearts and wallets.

Fraud detection & prevention in finance

AI and machine learning models leverage historical data to flag anomalies in real-time transactions and proactively alert companies on potential fraudulent activities. Algorithms evolve over time, allowing organizations to detect novel fraud schemes and maintain security at an always-high level.

Supply chain optimization

By analyzing data from different touch points, AI models can forecast capacity, identify optimal inventory levels, and predict trends in a supply chain. This leads to more resilient supply chains, optimized costs, and improved delivery times.

Automated customer service & support

AI-powered chatbots and virtual assistants offer round-the-clock, omnichannel support, handling basic to moderate inquiries and escalating complex issues to human agents. This enhances response speed and elevates customer satisfaction levels.

Healthcare diagnostics & planning

In healthcare, AI is widely used in medical diagnostics, health tracking, appointment scheduling, and automation of admin tasks. Smart applications can improve patient outcomes, personalize treatment plans, and take over manual tasks so healthcare providers can focus on care.

Human resources & talent management

AI streamlines recruitment, onboarding, and employee lifecycle management, offering insights into engagement, turnover, and workforce trends, plus aiding HR in retention and workforce planning.

Process automation

Irrespective of the industry, enterprise AI software can handle routine tasks across document management, accounting, quality control, and other critical business functions. By delegating tasks to AI, companies reduce costs, enhance the accuracy of processes, and free staff for higher-value work.

Benefits of enterprise AI solutions that flip the script in the business environment

Operational efficiency and productivity

Using artificial intelligence for enterprise systems allows companies to automate tasks that require much time and human resources. This shift saves time and resources and boosts productivity across different departments.

Enhanced decision-making and innovation

AI acts as a digital co-pilot, enhancing decision accuracy by processing extensive datasets. Utilizing AI, leaders can ignite innovation, spotting opportunities for new offerings and business models through detailed trend analysis, customer feedback, and market insights.

Improved customer experience and personalization

Personalization is a basic expectation for over 70 percent of consumers. Artificial intelligence helps companies meet this requirement by giving deep insights into the customers’ needs and sentiments. Businesses can then leverage AI insights to optimize messaging, personalization, and delivery of the user experience.

Cost reduction and risk management

Leveraging AI for enterprise applications results in significant automation, yielding cost savings and optimizing resource allocation. AI also strengthens a company’s defense mechanisms, enhancing fraud detection and cybersecurity measures.

Supply chain and quality optimization

AI enhances procurement, production, and distribution of products by enabling the shift towards proactive inventory, better resource allocation, and route optimization. In manufacturing, AI-driven quality control offers unmatched speed and precision, improving product quality, reducing waste, and eliminating rework.

Competitive advantage and sustainability

AI tools analyze data to discover unseen patterns, offering businesses a competitive edge impossible with manual processes. AI's ability to interpret sensor data also provides detailed insights into environmental impacts, which helps companies meet their sustainability and corporate responsibility goals.

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Implementation challenges of enterprise artificial intelligence

While leaders acknowledge the necessity of integrating AI across the enterprise, the journey of AI adoption is peppered with challenges and obstacles.

Data quality and availability

Challenge: Accurate AI outputs require feeding on extensive, high-quality, relevant data. Yet, in many organizations, data remains trapped in outdated legacy systems, which takes a toll on its quality.

Impact: Poor data quality can compromise the accuracy of AI-driven analysis, leading to half-baked decision-making.

Integration with existing systems

Challenge: Companies struggle to integrate innovative AI solutions into the existing IT infrastructure and legacy systems without causing significant disruption.

Impact: Integration challenges can increase implementation time and costs, while uneven data access may undermine the effectiveness of the AI solution.

Talent and expertise

Challenge: The scarcity of tech talent remains one of the core barriers to enterprise AI adoption. The demand for data scientists, AI engineers, and domain experts grows, but the market fails to keep up.

Impact: This talent gap slows AI projects and increases dependence on third-party providers.

Scalability

Challenge: Scaling AI initiatives is challenging due to an inadequate data foundation, isolated work cultures, absence of AI governance, and limited resources.

Impact: Failure to move beyond the experimentation stage can result in performance bottlenecks and prevent companies from achieving ROI.

Cost

Challenge: Implementing AI calls for substantial upfront investment, including the cost of technology, infrastructure setup, talent, and change management.

Impact: High costs slow the pace and breadth of AI adoption, making executive support difficult to secure, especially with uncertain ROI.

Regulatory compliance

Challenge: AI adopters struggle to navigate the delicate balance between fostering innovation and meeting regulatory requirements, including data privacy laws and sector-specific regulations.

Impact: Compliance challenges can limit the scope of AI applications and add to the cost and complexity of implementation.

Insights from our AI expert

Insights from our AI expert

Popov Andrei
Andrei Popov
Machine Learning Engineering Manager at Vention

"Enterprises encounter a significant AI hurdle when using foundational models, or large language models (LLMs), hosted externally, as it involves transmitting sensitive data over the network, raising security concerns.

The alternative? Internal hosting of LLMs to ensure data remains protected. Yet, this approach has downsides, including the inability of open-source models to match the quality of GPT-4 and the hefty investment required for the necessary hardware like GPUs.

To address these issues, our team is actively testing the newest open-source models and refining their integration into business operations, while monitoring advancements in model efficiency. Armed with this expertise, we stand prepared to deliver superior AI solutions designed for enterprise needs.”

Enterprise AI development strategy: Nail it, then scale it

Those eager to innovate often jump into AI development head-on. However, meaningful business advancements require a foundational approach, beginning with a solid AI development strategy. At Vention, we help organizations map out their pathway to AI success, providing step-by-step guidance.

01

Discovery

Vention works closely with clients to understand their business goals and pinpoint early-value use cases that kickstart AI scaling momentum. We assess a client's existing data infrastructure, tech capabilities, and the talent required to support AI efforts.

02

Governance and ethical frameworks

We guide our clients in establishing responsible AI initiatives through essential governance frameworks and tools for managing data, AI model development, and usage. Our experts ensure the implementation of ethical AI practices that adhere to regulations like GDPR and tackle privacy issues.

03

Enabling a strong data foundation

We assist clients in handling the complexity of data management, including data access, government, and security. When needed, we develop a scalable data and AI enterprise architecture to enable advanced applications, real-time processing, and secure storage.

04

Tech stack selection

Following your unique business and infrastructure needs, our AI experts select an optimal combination of AI tools and platforms. To power up and scale your AI model delivery, we also introduce tools and methodologies that support agile development and DevOps practices.

05

Pilot project guidance

We help clients stage successful AI pilot projects and move them to production, demonstrating AI's value and building a case for broader adoption. Once launched, Vention measures the performance and impact of AI projects against predefined metrics and gradually refines applications until they fully meet the organization's needs.

06

AI solution scaling and integration

Leveraging initial pilot projects, we strategize the integration and scaling of your  AI-based enterprise solutions. Our efforts focus on enhancing model efficiency, automating data flows, and simplifying deployment to ensure successful scale-ups — backed by continuous AI model support and optimization.

07

Talent and skills development

Our company also offers training programs and workshops to help clients build an AI center of excellence within the organization and boost the impact of AI solutions. We provide access to a pool of experts, including data scientists and AI engineers, to bridge skill gaps and bolster your in-house team.

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Get a tailored enterprise AI implementation strategy to accelerate your AI adoption.

Success stories of enterprise AI implementation

Fintech

DealCloud

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DealCloud, a fintech providing CRM and deal management solutions, partnered with Vention to create several business-critical modules powered by AI and cloud technologies. Our team quickly expanded from six to 120+ experts to support DealCloud's rapid growth.

Deal_cloud_Hero

All cases

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Here’s how Vention turns enterprise AI ambitions into achievements

100+AI software engineers on board
20+years of experience in engineering
Partnerships with AWS, Google Cloud, and Salesforce
GDPR, HIPAA, PCI DSS-compliant solutions
ISOAn ISO 27001-certified company
30+business domains covered

Our enterprise AI development services

A key catalyst for digital transformation, enterprise artificial intelligence bolsters the agility of your IT systems, automates business workflows, and elevates team productivity. Yet, just adopting AI isn't the endgame — unlocking its ongoing value is crucial.

Vention isn’t just one of the enterprise AI software companies. We’re true champions of your business, building winning AI solutions to empower your large business to scale with the agility of a startup, amplify the returns on AI investments, and thrive in a hyper-competitive landscape.

Machine learning consulting

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  • Strategy road mapping

  • Technology advisory and selection

  • Process analysis and improvement

  • Development and implementation

  • Evaluation and review

  • End-to-end AI software development and integration

  • Generative AI

  • Chatbots

  • Computer vision

  • Data analytics

  • Recommendation systems

  • Anomaly detection and fraud control

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