Discover how DHL Group increased connectivity across the enterprise and automated multiple processes end-to-end
AI is a complex topic that is evolving fast. This leads to confusion and a lack of progress. Here you’ll find five essential strategies for successful enterprise AI initiatives.
CONTENTS
1. Pick low hanging fruit for fast ROI
What should you look for to prioritize your AI projects? The goal is to find areas where you can have maximum impact with the fastest delivery time and the lowest level of risk. We recommend looking at areas where you already have structured processes and a level of maturity when it comes to optimizing the operation. We also recommend selecting areas that have a broad impact across the business. If you’re using a technology platform that has Gen AI features built-in, then it makes sense to explore those capabilities and see if they match up to your requirements.
EXAMPLE
A financial services company uses an app to track the progress of customer complaints. After leveraging new generative AI capabilities, the app can perform sentiment analysis, pull in customer information and auto-generate text for customer emails. This saves employees time while increasing the speed and quality of communications, leading to faster complaint resolution.
2. Focus on enabling the business user
Technology leaders may be tempted to start with how AI can help with IT operations. But most enterprise organizations will have at least 10X the number of non-technical employees compared to IT staff. While AI-driven IT efficiencies might be a good thing, it’s better to focus on the larger group. In addition, due to the types of work involved, greater AI productivity gains are available for business users, giving a higher ROI. In practice this means providing enterprise apps that have AI capabilities built in. One way of doing this is by creating applications using a Gen AI-enabled low-code platform.
A manufacturing and retail company uses an app to manage RFX requests. An AI agent analyzes email request, categorizes it as an RFI, RFP or RFQ and creates a case. The case manager can review all the information that has been pulled in from the email and proceed with managing the process, saving her time and enabling her to respond faster.
3. Deliver real AI results with automation
Top barriers to generative AI adoption include skills gaps, governance, security and challenges demonstrating the value of AI. Combining AI with process automation can help solve many of these challenges, and automation platforms have created the necessary gen AI features to bring this value to life.
AI + AUTOMATION = IMPACT
1. Business Impact: By embedding AI into an automated business process, it becomes a natural part of the way the business works, and you’ll have a clear view of the impact AI brings, enabling you to avoid wasting resources on low-value AI experiments.
2. Governance: Process automation platforms are built with built-in governance at multiple levels, enabling you to control the flow of data across your organization. Without automation technology this is a significant barrier to AI innovation.
3. UI / Integration: AI services like Open AI’s ChatGPT for enterprise don’t provide a UI for the user and can be difficult to integrate with existing systems. Automation platforms with built-in AI provide a suitable UI, along with powerful integration capabilities.
4. Security: An enterprise automation platform already provides the security features you need to keep data safe and control the way AI interacts with your business data.
4. Drive enterprise-wide AI adoption
To gain the full rewards of AI you need to create a cultural change where gen AI becomes a core part of the way the business works. Leaders will benefit from identifying realistic use cases that are also broad in their impact and repeatable in many areas of the organization. That starts with understanding common use cases for gen AI that apply in an enterprise.
KEY USES OF AI IN THE ENTERPRISE
Content creation Create new emails, documents, text, copy, knowledge base articles, & replies.
Categorize Automatically detect and classify incoming requests, tickets, & cases.
Summarize Summarize or outline long meeting notes, transcripts, articles, & research.
Prioritize Automatically find and apply a priority to requests, tickets, & cases.
Obfuscate Identify and anonymize personal or sensitive data for GDPR & compliance.
Translate Remove the skill obstacle of foreign languages by translating into any language.
Organize Organize disparate data into logical groups to simplify analysis.
List Maker Get things done by seeing clear lists of information.
Analyze Detect sentiment, tone, logical groupings and SWOT.
5. Think bigger than boosting productivity
It’s clear that AI has the power to bring huge productivity benefits. But AI can deliver more than just speed and cost savings. A key capability of generative AI is answering natural language questions based on large volumes of unstructured data. By leveraging this capability, organizations can easily get access to new insights and use them to make better decisions. Through this, successful AI implementations will drive important improvements to customer experience, customer retention and revenue growth.
A bank uses an app to manage consumer loans. By leveraging an AI-powered natural language interface, employees can now look across customer loans, identifying customers that carry high risk and might need action. The app enables them to initiate processes or investigations based on the information they find.
