Discover how DHL Group increased connectivity across the enterprise and automated multiple processes end-to-end
Enterprise AI is the use of AI technologies to drive efficiency, innovation and better user experiences across business operations. Key components include machine learning, natural language processing and data analysis. Gartner research predicts that gen AI will account for over one-third of AI software spending by 2027.
Boost productivity
AI agents can be incorporated into multiple functions and processes across the organization to complete routine tasks more efficiently than the human workforce, getting more done in less time.
Enable faster decision making
By analyzing large datasets in real time, AI can quickly uncover actionable insights. This allows decision-makers to respond to changes fast and make informed, data-driven choices, improving accuracy and responsiveness.
Enhance customer experience
Generating text with AI unlocks higher productivity levels and faster, more accurate communications which can improve customer response times. AI can also be used to personalize customer communications and deliver more relevant messages, leading to higher satisfaction and conversion rates.
Reduce costs
By automating routine processes, AI helps optimize resources and reduce labor costs, allowing businesses to either minimize headcount or reassign employees to more strategic, high-value tasks that require human input.
AI in customer service
AI can analyze calls, emails and other customer communications to determine sentiment and evaluate and prioritize cases based on this. It can then create a suggested response for the service agent to save time and enhance the customer experience.
AI in supply chain
AI can analyze data from potential vendors against set criteria and summarize the information to help procurement teams make informed decisions . It can also be used to analyze historical data and help predict future demand to aid inventory management.
AI in banking
AI can be used to analyze transaction data and detect fraudulent behavior, evaluate data to inform lending decisions and automate compliance checks.
Many organizations struggle to know where to start when it comes to deploying AI. According to recent research, 85% of business leaders consider AI an opportunity, but only 20% are currently prepared for transformation. Even those who do deploy an AI solution may struggle to see real value, and deploy small projects with limited reach, just to avoid being left behind.
"What we are finding is while there's a lot of hype around Gen AI, customers are struggling to figure out where to start and how to deliver business value," says Bizagi CMO, Samir Gulati. "There's also concern about data privacy and security. So what we've done at Bizagi is take Gen AI, and offer it within our platform in a secure way using the private Azure service and make it easy for customers to adopt AI. It's central to our strategy to focus on business productivity, business results, and business value."
"We believe that by driving enterprise-wide AI adoption as opposed to a siloed project, and embedding it in processes and building it into your application, some of the strengths of AI can be seen and felt across the enterprise, as opposed to just a few users, like developers or end users," he said in a recent webinar with Bizagi customer, FinTrU.
Why is having an AI strategy important?
Trying to incorporate AI into the enterprise without a clear strategy won’t yield the results that you want. Organizations with a stronger AI strategy will leap ahead of laggards when it comes to productivity and services in the years to come. To get the most out of the technology you need to consider:
- How implementing AI will contribute to wider business objectives
- Which areas of the enterprise the technology will have the greatest impact
- How the use of the AI will be governed
- How success will be measured
Discover 5 essential strategies for successful enterprise AI initiatives.
Steps on how to implement enterprise AI:
Identify use cases
Pinpoint and prioritize departments and processes where AI can add the most value and align with your wider business goals. Is cost reduction a priority? Or delivering faster service to your customers? Which areas of the business are not performing at the moment and how could AI support that function to achieve success?
Assess current capabilities
Compare the skills needed for AI implementation to those you already have within your organization – is upskilling required or new team members? Does your current tech stack support your AI ambitions or will you need to invest in new technologies? Evaluate data readiness – does the organization have access to high-quality, well-organized, and relevant data to train the AI model on?
Choose AI platform
Opt for a platform that offers the scalability, security and governance that meet the needs of your organization. Review how easily the new technology will integrate with existing systems and automation programs to deliver real business value. Using AI alongside or within an end-to-end automation platform is a powerful strategy because it often solves some of the barriers to AI adoption including access to data and reducing the data skills required to gain value from AI.
Train AI models
Gather, clean and label data and feed this data into your selected algorithms to help the system learn patterns to inform decisions.
Deploy solutions
Integrate the trained AI model into the production environment to make it accessible to end-users, applications, or systems. Deploying solutions now will enable you to learn from the current capabilities of AI, and then quickly leverage new features as they become available within the AI platforms.
Implement governance policies
Create and communicate guidance on how to use the technology. This should address issues like data privacy, security, ethical AI usage, and compliance to ensure responsible use of AI across the organization.
Measure ROI
Track and assess the impact of AI implementations on your business by monitoring key metrics identified at the beginning of the project. Are you seeing the expected ROI or do adjustments need to be made?
Why is it important?
- Mitigates bias in AI models
- Compliance with legal regulations and industry standards
- Promotes ethical use of the technology
What capabilities should they have?
- Aim to drive real business value – focus on improving productivity among business users as well as IT use cases.
- Ability to orchestrate AI and the other systems in your tech stack as well as the human workforce to manage end-to-end processes.
- Robust security features – data encryption, access control and anonymization ensure sensitive information remains secure.
Bizagi’s AI solution enables you to unlock the power of AI with automation. Our AI assistant Ada enables business users to answer data queries using natural language questions and AI Agents simplify routine work and boost productivity. Find out more here.
