Why has enterprise AI become so popular?

Just like with the development of the internet in the 1990s and cloud computing in the 2010s, many businesses have approached AI with caution: Would it live up to the hype? Or would it just be a fad that wouldn’t add real value to business and industry?

For many early adopters of enterprise AI, the results have shown that it creates a tangible competitive advantage. These businesses have seen significant improvements and efficiency gains across a spectrum of AI use cases, from data analysis, forecasting, and decision-making, to process automation, workplace productivity, and product development. Some examples:

Along with the benefits, businesses also need to understand the challenges of adopting AI, how to apply it to their own use cases and systems, the technical and training requirements, and costs. In this article, we shall explore:

• Types of enterprise AI
• Benefits and challenges
• Generative AI in CRM and ERP
• Industry use cases
• Implementation strategies and planning
• What to look for when considering a business AI platform

Types of enterprise AI

One of the most interesting aspects of enterprise AI is that it can be used to perform both routine and transformational tasks. Examples:

• Routine: An employee saves a few minutes (and some frustration) by using an AI copilot to find a document that’s been stored in the wrong place. Routine use cases may sound small, but they can really boost productivity at scale.
• Transformational: A manufacturer saves millions of pounds by using deep learning to predict when its machines will need to have parts replaced—before they break.

This extreme diversity in AI use cases is impressive but can also make it difficult to decide where to begin with AI adoption. A good place to start is by becoming familiar with the main types of business AI and their common applications.

Main types of enterprise AI

Enterprise AI advantages

As AI tools for business like copilots, chatbots, and generative AI become more sophisticated and widely adopted, organisations using these technologies are constantly discovering innovative new ways to use them. Before we explore more specific AI use cases, let’s look at some of the high-level benefits of business AI.

Workplace experience

AI plays a crucial role in improving the workplace environment by supporting and augmenting the capabilities of employees:

• Employee productivity tools: AI copilots allow employees to focus on higher-value work by helping manage emails, schedule meetings, and prioritise tasks.
• Adaptive learning systems: AI-driven learning platforms provide personalised training and development, adapting to the learning preferences and speeds of individual employees, making professional growth more engaging and effective.
• Workplace analytics: AI tools empower teams to enhance employee satisfaction and performance by analysing work patterns and providing suggestions for optimising workflows.

Strategic decision-making

The integration of AI into decision-making processes helps businesses enhance accuracy and timeliness:

• Insights driven by data: AI copilots and other enterprise machine learning tools help teams make informed decisions swiftly by analysing extensive datasets and providing strategic recommendations.
• Enhanced forecasting: Enterprise generative AI tools equip businesses to better anticipate market demands and consumer behaviour by using past data to predict future trends.
• Scenario analysis: Advanced simulation models help businesses explore various strategic scenarios, enabling effective risk management and planning.

Customer experience

AI transforms how businesses interact with their customers by providing personalised and responsive services:

• Personalisation at scale: AI analyses customer data to tailor marketing messages and product recommendations.
• 24/7 customer support: Chatbots provide instant responses to customer enquiries at any time, improving service availability and customer satisfaction.
• Customer insights: By collecting and analysing customer feedback and behaviour, AI helps businesses understand and anticipate customer needs, leading to better service and product offerings.

Enterprise AI challenges

In addition to the benefits, there are some common challenges that businesses interested in becoming AI enterprises should plan for. These are primarily cultural resistance, ethical concerns, and technical barriers. Effective navigation of these issues is crucial for the long-term success of enterprise AI adoption.

Cultural and organisational hurdles

• Challenge: While some employees may be enthusiastic about adopting AI because they see it as a tool to enhance their work, others may see it as a threat to their jobs.
• Best practices: Having transparent discussions about AI’s benefits and its role in the organisation can help align employee perspectives with strategic goals. Be understanding and compassionate if employees express concerns. Offering training programmes can also help employees gain confidence with new AI tools.

Ethical and security considerations

• Challenge: AI introduces ethical and security considerations that require special guidelines and security protocols.
• Best practices: Developing responsible AI guidelines helps ensure everyone in the organisation uses AI safely and fairly. Additionally, implementing advanced security measures helps protect sensitive data.

Integration complexity

• Challenge: Successfully integrating AI into existing systems requires strategic planning and careful implementation.
• Best practices: Beginning with pilot projects allows for gradual integration and helps identify and address potential challenges in a controlled environment.

Generative AI: enterprise AI for ERP and CRM

One of the most exciting and broadly applicable types of enterprise AI is copilots for ERP systems, which are powered by generative AI. AI copilots infuse companies’ existing ERP and CRM systems with natural language AI capabilities, which can dramatically enhance how employees operate and innovate by making processes more adaptive, intelligent, and optimised—usually without requiring major overhauling of existing systems.

Innovative applications and strategic impact

Improved user experience and productivity

Generative AI enables natural language interactions with ERP systems, streamlining processes and increasing productivity. For instance, large language models can simplify compliance by interpreting legal texts and identifying relevant criteria. This extends to CRM, where AI enhances sales and service interactions by automating responses and personalising communication based on customer data insights.

Automation of manual tasks

In addition to chat applications, enterprise generative AI tools can be used to reduce manual labour in processes like supply chain logistics and customer data management. For example, AI can automate the digitisation of delivery notes and customer interaction records, drastically cutting down costs and processing times.

Business process optimisation

By analysing data generated by business systems, enterprise generative AI tools identify patterns to optimise processes across ERPs and CRMs. They can generate ready-to-use process models and insights, and help businesses to implement best practices.

Analytics and decision-making

AI democratises data-driven decision-making by making it easier for leaders and teams alike to work with complex analytics.

ERP and CRM cloud integration

AI capabilities for ERP and CRM systems are typically cloud-based. Organisations that rely on on-premise or private cloud infrastructures may need to adopt a public cloud to implement AI solutions. Businesses wanting to keep IT on premises can create a hybrid cloud environment by adding a public cloud to their existing infrastructure. Hybrid clouds allow businesses to maintain control over critical data whilst benefiting from advanced AI, cybersecurity, and scalability solutions offered by cloud providers.

Enterprise AI examples across industries

Beyond general-purpose business solutions, enterprise AI also includes industry-specific solutions.

Automotive

Automotive companies are using AI to enhance quality control and develop technologies for safe autonomous driving.

Energy

By forecasting demand precisely and adjusting supply, AI helps amplify the integration and value of renewable energy sources.

Entertainment

Streaming services are using AI to personalise viewer recommendations, and game developers and platforms are creating more responsive, realistic environments.

Finance

Advanced fraud detection systems and robo-advisers, powered by AI, significantly improve efficiency and security in the financial sector.

Healthcare

AI has had a dramatic impact in healthcare, improving the accuracy of diagnostics, allowing care providers to personalise medicine, and enhancing treatment outcomes through cognitive analysis of clinical data.

Life sciences

Companies in drug discovery and genomic research have seen reduced costs and time to market thanks to AI, and clinicians are now able to tailor medical treatments to individual genetic profiles.

Manufacturing

Many manufacturers are using enterprise AI to enhance production efficiency, minimise downtime with predictive maintenance, and improve sustainability.

Public sector

AI is enabling organisations to automate administrative processes, optimise urban management systems for traffic and emergency services, and increase civic engagement.

Software development

Enterprise generative AI tools streamline coding and testing processes to accelerate development cycles, identify security vulnerabilities, and help developers learn new languages and frameworks more quickly.

Telecommunications

In the telecoms industry, AI-powered predictive analytics enhance service delivery and network management, helping prevent outages and offering personalised customer experiences.

Enterprise AI strategy and planning

Becoming an AI enterprise involves more than just selecting the right platform or product—it also involves a dedicated AI strategy, smart planning, and teamwork. Businesses derive the most value from their AI investments when they set clear objectives, encourage cross-team collaboration, and commit to ongoing learning. Here are some key considerations when getting started:

In-house vs. partner-led implementation

An important early step is deciding whether to plan and implement an enterprise AI platform or solution using in-house resources or to hire an expert partner.

In-house implementation

Implementing enterprise AI solutions without the help of an external partner can be cost-effective if the organisation has the necessary technical expertise, resources, and bandwidth. This also offers advantages such as complete control over project execution and confidentiality of sensitive information. However, in-house implementations may pose challenges for some organisations, including a steep learning curve and substantial investment of employees’ time.

Partner implementation

Although initially costly, hiring an enterprise AI partner provides businesses with specialised knowledge, resources, and experience. It allows organisations to confidently implement their solution knowing they’re following industry best practices. Working with a partner can also help mitigate skills gaps, and generally accelerates the process. Implementations led by partners require careful selection of vendors who align with the organisation's objectives and culture.

Assessment and goal setting

It’s critical to assess existing capabilities and set clear objectives:

• Goal alignment: Define specific, measurable goals that the AI solution should meet. These goals should align with broader business objectives and have clear metrics for success. For example, if reducing operational costs is a priority, set a target percentage reduction as a goal for AI initiatives.
• Data audit: Conduct a thorough audit of data to assess quality, accessibility, and security. This assessment will be crucial to identify obstacles and areas that need to be improved before the technical implementation begins.

Resource allocation

Assessing budgetary and human capabilities is vital to success:

• Budgeting: Allocate adequate financial resources not just for the acquisition of enterprise AI technology but also for ongoing expenses like software updates, cloud storage, and cyber security measures.
• Talent acquisition: Hiring skilled personnel or training existing employees to handle new AI tools is imperative. The technological investment will only work as planned if the people using it have the right skills.

Collaboration

Business AI projects are most successful when everyone benefiting from them takes part:

• Cross-functional teams: Form teams that include members from IT, data analytics, leadership, and end-users. This inclusive strategy helps ensure that all potential impacts and benefits are considered from the outset.
• Regular reviews: Set up regular review sessions to evaluate the progress of AI integration against the project goals. These reviews can help realign efforts with business objectives and adapt to new challenges or opportunities.

Adaptation and learning

As enterprise AI continues to evolve, businesses need to expect that their strategies and practices around using it need to evolve, too:

• Continuous training: Develop ongoing training programmes for employees to keep up with AI advancements helps staff stay informed and confident in their abilities.
• Feedback mechanism: Establish mechanisms to collect feedback on AI implementations from all stakeholders. This feedback is invaluable for refining AI strategies and tools, helping to ensure they remain effective and aligned with user needs over time.

Conclusion: How enterprise AI is redefining business

Enterprise AI—including well-established types like machine learning and newer types like copilots and generative AI—are transforming businesses by enhancing their productivity, decision-making, and innovation. The positive results experienced by early adopters of business AI indicate that AI tools for business are not just a passing trend, but a necessity for remaining competitive in the digital world.

However, becoming a successful AI enterprise isn’t just about choosing the right enterprise AI platform. It’s also necessary to foster a workplace culture that thrives with AI. This involves training, data security investments, and using AI responsibly.

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