Every innovative tool goes through its growing pains as it shifts from a large-scale concept to a practical one. AI is no exception to that rule. While 42% of companies deploy AI for their business processes, 40% find themselves stuck in the experimentation phase, unable to progress further, and only 17% of enterprises manage to successfully adopt artificial intelligence and achieve results that exceed their expectations. What are these 17% doing right? What kind of approach allows them to bypass barriers and become adoption leaders?
In this article, we will explore the current state of enterprise AI and what it takes to glean the full business value of the technology.
What is enterprise AI?
Enterprise AI is a term that encompasses the use of artificial intelligence (including machine learning, natural language processing, and generative AI) to accelerate organizational processes and empower business routines. AI in the enterprise can be leveraged for a vast number of tasks, ranging from basic duties to more complex operations.
What is important to understand is that enterprise AI isn’t a name for a single solution or technology. It’s a term for any application of AI in the enterprise. Using computer vision for office space management is enterprise AI – and so is leveraging AI chatbots. Therefore, it makes sense to view enterprise AI as an approach to embedding AI and generative AI into enterprise processes in compliance with organizational culture, employee routines, and best enterprise security practices.
Enterprise AI is also defined by the way the technology synchronizes with the operations and environments within organizations.
When businesses use AI in the enterprise, they utilize a solution that is fully integrated into their routines and understands the complexity of their structures. Therefore, such a solution must meet a number of criteria to match the enterprise scale.
What is enterprise scale AI?
Scalability
- Ability to adapt to growing volumes of tasks and business growth
- Seamless expansion for including more users
- Fast processing of large and small data packets
Reliability
- Consistent productivity, high accuracy and steady performance
- Minimized friction and downtime, resistance to errors
- Stable functionality despite the conditions and complexity
Security
- Reliable security measures and protocols, protection against potential exploits
- Secure user access and authentication
- Guidelines for sensitive data protection
Interoperability
- Uninterrupted interaction between digital business environments and technologies
- Flawless flaw of data within the enterprise infrastructure
- Fast response time and easy information retrieval
Governance
- Controlled AI system management within a transparent policies framework
- Compliance with legal requirements
- Robust data management and governance
Impact
- Positive outcomes for enterprise goals and long-term value, return on investment
- Alignment of tech capabilities with business objectives
- Discovery of new revenue streams
Usability
- Encouraging adoption and use of AI products due to human-friendly design and features
- Easy accessibility for beginning users
- Explainability of AI systems and tools
Flexibility
- Prompt response and easy adaptation to shifting business goals and needs
- Supporting business operations during changes
- Ability to evolve in order to meet new industry trends
Sustainability
- AI systems designed for long-term functionality and performance
- Options to upgrade the technology with innovative features
- Synergy with long-term enterprise strategies
The importance of enterprise AI
The outdated concept of AI in the enterprise views it as merely a means to transform support functions. Meanwhile, in reality, adoption leaders have managed to glean impressive business advantages by using AI in enterprise software to elevate their core business processes.
23%
Enterprise operations
- Faster report generation
- Improved risk management
- Improved productivity
20%
Sales and marketing
- More personalized offerings
- Better trend identification
- Excellent customer service
13%
Research and development
- Hidden patterns identification
- Accurate trend research
- Greater insights
5%
Supply chain
- Predictive analytics
- Quality control
- Accelerated delivery
AI in the enterprise is going to change everything about how companies operate: from the way teams and departments interact with each other to decision-making algorithms. But contrary to some expectations, AI isn’t going to call the shots or control operations in place of employees – it's going to increase human capacity by connecting talents with more data, more opportunities, and more options to manage their time. This is what makes artificial intelligence so important for the future of enterprise.
Enterprise AI use cases
The practical use of AI in enterprise applications comes in many shapes and forms. Due to its versatility, AI and generative AI are applicable to a wide range of departments within organizations, allowing them to facilitate both their internal operations and external service offerings.
AI in the enterprise: Agentic AI
Using AI agents revealed next-level possibilities for enterprise virtual assistants, going far beyond customer service. AI agents received particularly positive feedback from business employees, 54% of whom expressed their trust in AI assistants due to their neutral and unbiased interactions.
What makes these results fascinating is that, on average, AI uncertainty remains rather strong: at least 50% of employees are uncomfortable with using artificial intelligence for their work. Due to this, the warm reception of AI agents makes it possible for potential adopters to implement the technology in the most engaging and least disruptive way, enriching both customer experiences and employee routines.
Enhancing client-facing services and experiences
- Fast responses to customer queries
- Processing simple requests or commands
- Connecting clients to niche experts when there is an issue
- Providing advice and guidance for customers
- Gathering feedback and recommendations
- Delivering tailored offerings and documenting preferences
Workplace assistance
- Organizing employee work with notifications and reminders
- Connecting different departments working on the same projects
- Forwarding messages and notifications between workers
- Answering workplace-related questions
- Keeping employees up-to-date with meetings and events
- Accelerating newly hired employees’ training and onboarding
Talent Management support
- Automating Talent Management teams’ routines
- Responding to frequently asked questions from employees
- Performing basic team assessment tasks
- Updating teams and departments on policy changes
- Gathering employee feedback on changes and decisions within organization
- Sorting and organizing applications from employees
There is no doubt that AI agents will change the way departments communicate and interact with each other. We’re not only talking about communication; we’re talking about senior expert knowledge preservation with the help of agentic AI and generative AI in the enterprise. There is a lot of practical value in such tools, so we’ll see many breakthroughs in this area.”
AI in the enterprise: Data processing platforms
Unstructured data is relevant once again. However, a more accurate conclusion is that unstructured data has always been relevant, just trapped in thousands of papers, emails, and visuals. Until now, organizations didn’t have the tools or means to extract it promptly – but the arrival of AI and generative AI has made the issue of lost insights a thing of the past. Given that around 90% of data generated by enterprises is unstructured, such a development is particularly empowering for organizations.
Many departments benefit from gaining access to unstructured data. For example, sales teams gain more insights into their customers and their preferences, which gives them more versatile ideas for sales offerings. Moreover, dissecting unstructured data can reveal hidden trends and patterns that can improve their up-selling and cross-selling strategies, providing more tailored recommendations to each customer. We’re just scratching the surface here: every data-reliant process and operation can be elevated by AI gleaning information from various sources, verifying it, and bringing it to a single format
The data-processing capacities of AI aren’t limited to unstructured data alone. The use of AI in enterprise applications, from machine learning to large language models (LLMs), helps enterprises tackle many data-related issues that impede productivity and increase costs.
- Data errors
Missing values, falsified results, and duplicates are common issues when processing large amounts of data manually. When there are volumes of information to verify and organize, discrepancies can slip through the cracks and affect outcomes. AI in the enterprise automates the process of data processing and organization, timely identifying and fixing errors, thus considerably reducing the risk of profit loss or performance hiccups. - Time loss
Manual data management is a lengthy and taxing process. In the UK alone, employees waste up to 13 hours per week performing low-value tasks, such as manual data entry. This means they lack time to complete their high-priority objectives and make the most of their working environments due to repetitive, monotonous, and obligatory steps. Consequently, enterprises miss out on potential productivity growth, while employee satisfaction and happiness deteriorate. The use of AI in enterprise software allows for more time-effective and efficient data processing, enabling employees to focus on the most important tasks. - Human error
A simple data entry error or input error often leads to many complications. In many cases, such errors cause significant reputational or financial damage, potentially reaching up to $78 million. The more data employees have to work with, the higher the risk of such accidents occurring. Although employees are responsible for being attentive and assessing their every step, leaders have a responsibility to facilitate their employees' work and ensure they have the means to swiftly and easily check all the information. Leveraging AI in the enterprise addresses human error, providing teams with robust support and preventing typos and incorrect inputs.
“While AI in the enterprise can demonstrate amazing results for data, knowledge, and document management, it can only be effective if your organization has a functional and productive data management strategy. If your enterprise data isn’t organized, labeled, and clean, introducing artificial intelligence won’t achieve much. AI doesn’t automatically solve problems – it learns from your problem-solving and builds upon it. Therefore, you must ensure that your AI system has ample material and practices to learn from."
AI in the enterprise: Computer vision
Computer vision is yet another practical use case of AI in the enterprise, but it’s still in the process of finding its niche. Applying AI-powered computer vision to recruiting has yielded rather controversial results, as the technology showed clear bias towards applicants’ age, gestures, or appearance.
Such issues stemmed from incorrect assumptions about AI: adopters viewed artificial intelligence as inherently fair and unbiased, which was far from the truth. Considering that enterprise AI models are trained with organizational data sets and specific information, they base their conclusions and judgments on the materials they have at their disposal. Therefore, when provided with limited or outdated information that doesn’t include diverse and versatile criteria, AI-powered computer vision tools don’t have much to work with, delivering lackluster or even damaging outcomes.
Computer vision can be of great assistance to enterprises and industries. However, when it comes to human interactions and critical thinking, artificial intelligence isn’t going to replace talent management teams any time soon. Even with the most inclusive data sets available, it’s the professional assessment and experience of talent managers that make the greatest impact. Therefore, while AI in the enterprise can be used for hiring and recruiting, it likely won’t significantly improve end results.”
Aside from talent management, AI-powered computer vision has found much more effective applications in other enterprise management areas, delivering practical value and improving employees’ quality of work and life.
Office space management
- Monitoring available rooms for meeting and showcasing them as accessible for bookings
- Analyzing employee workspace needs for future improvements
Employee commute
- Real-time tracking of available parking lots for employees planning their route to work every data
- Timely informing employees about potential issues that can affect their commute
Security
- Improving facial recognition when monitoring people entering the building
- Tracking and reporting suspicious or potentially malevolent activities
These enterprise AI use cases aren’t the limit to the technology’s capabilities. Many more approaches to using AI in enterprise software are expected to emerge once artificial intelligence becomes an explainable and familiar tool. Nevertheless, at this point, enterprises are more focused on gleaning improvements and valuable results from already proven and verified success stories rather than experimenting and inventing new applications.
One of the biggest benefits of artificial intelligence is its versatility – if you tell it what to do and provide it with the right tools. Therefore, the abilities of artificial intelligence in data processing, performing multiple tasks, and executing several commands are limited only by your creativity.
From pilot to production: driving ROI with GenAI
Whether it’s replicating the success of well-known use cases or trying out something new, the majority of enterprises encounter the same challenge: how to guarantee ROI and get the adoption process right?
Adopting AI in the enterprise introduces a set of new variables and criteria, so it’s natural for decision-makers to feel overwhelmed and disoriented. The lack of pre-established KPIs and vague expectations doesn’t make their work any easier. Therefore, before adopters can get started with artificial intelligence, they need to conduct thorough research and preparations.”
At the same time, AI adoption leaders reap great rewards from overcoming their technology implementation barriers:
- A 1.5 increase in revenue was reported by companies that successfully embedded AI in their key operations.
- 73% of organizations that invested more than 5% of their budget into AI stated they were able to create greater competitive advantages.
- 63% retention rates were achieved through integrating AI in enterprise applications by proactive AI adopters.
How did they manage to achieve such results?
While there is no universal success template, there are several valuable insights to take from their practices and experiences.
- Identifying business goals
It all starts with one question: why?
Why does an enterprise need AI? What will it change from the perspective of business value? How is it going to look?
Leading adopters explored this matter thoroughly, researching various use cases, identifying KPIs, and comparing outcomes to their enterprises. Their main goal was to achieve full visibility of their short-term and long-term business objectives and the ways in which artificial intelligence could help them. Such efforts allowed them to root the concept of the technology in reality, developing realistic scenarios for using AI in enterprise software. - Focusing on core business processes
What makes AI in enterprise software effective is the understanding of the core components and processes that make a specific enterprise function. Since AI is a tool, adopters need to know where and how it fits. For that reason, adoption leaders started their journey with in-depth research and analysis of their core business operations, pain points, and underlying needs. Once they obtained a comprehensive view of their key processes and discovered the hidden patterns affecting them, they gained newfound clarity on what can be done.
It’s truly a basic recommendation, yet the most successful projects are built on simple, solid rules and foundations. You should always make your decisions based on what your organization needs – not what the competitors have.
- Setting high expectations
It has been found that great ambitions play a role in great achievements. Adoption leaders started their AI journey with high expectations, being at least 60% more optimistic about revenue growth than other enterprises.
However, their evaluations weren’t based solely on the estimated increased productivity. Adopters were also looking forward to empowering their workforce with the help of AI, diving deep into their current talent pool and exploring how each of the high-value skills could be improved through enterprise AI. Such an approach allowed them to both maximize the value of AI and address potential organizational resistance, as they demonstrated their loyalty to their most precious asset – their enterprise employees. - Investing in high-priority opportunities Just like intelligent process automation, the adoption of AI and generative AI in enterprises thrives on precision and order. According to multiple observations and reviews, while the ambitions of adoption leaders were grand, their steps were calculated and measured. In general, leading adopters explored only 50% of AI adoption opportunities compared to other enterprises.
They focused on the most optimal transformation candidates that offered the best time, resources, and outcome ratio, which allowed them to implement artificial intelligence within a controlled framework with clear expectations and replicable success. - Integrating AI in cost transformationCost savings and revenue also play a part in adoption leaders’ AI strategy. At least 45% of top adopters focused on how artificial intelligence could optimize growing tech costs in the long run or drive more revenue from existing functions without increasing spending. The focus on the financial side provided adoption leaders with much-needed KPIs, giving them approximate numbers of how much they could save or generate by embedding AI in enterprise software.
The issue of ROI on AI remains rather challenging for many potential adopters and decision-makers. Since using AI in enterprise applications can considerably change workflows, even making some of them redundant, enterprise leaders need to identify new KPIs for tracking performance. Connecting AI to cost transformation can facilitate that task and improve their navigation.
Implementing enterprise AI: why a trusted partner is essential
Securing productive and functional AI in enterprise applications is a complex and challenging task. When leaders know their goals and their enterprise needs, it's half the battle. They also require expertise in data science, awareness of all relevant regulations, and extensive preparation. However, with the right professional perspective and guidance, they can smooth out potential frictions and ensure that every stage of AI implementation in the enterprise seamlessly transitions to the next. For that reason, cooperating with reliable and experienced technology partners is recommended for maximizing the value of AI adoption and ensuring the most satisfying ROI.
If you’re interested in finding such a partner for your enterprise AI goals, let’s chat! At Trinetix, we provide strategic guidance and robust technological support for organizations looking to advance their exploration phase and start gleaning business value. With our teams of vetted analysts, AI, and ML engineers, you will be able to bring your enterprise AI project from concept to a fully realized solution.
