How to incorporate AI into B2B digital experience workflows

Highlights

You’ll learn how to enhance your B2B digital experience workflows with AI

• Audit and prioritize: Identify high-impact manual workflows that should be automated
• Select tools: Choose API-first, secure AI platforms integrated via modular connectors
• Pilot and refine: Run small-scale tests, measure performance and make adjustments
• Govern and scale: Implement data governance and scale successful pilots

Ready to elevate your B2B digital experience? Leverage intelligent AI workflows to make it happen.

Manual workflows are time-consuming, error-prone and deliver inconsistent results. Given the dynamic nature of modern business environments, it would be counterintuitive to persist with it. Aside from that, customers also expect timely and tailored interactions that manual workflows may struggle to deliver. 

So, what is the alternative? B2B firms can and should look to AI-driven automation to enhance digital experience workflows. Statistics show that 92% of execs plan to implement AI for workflow automation this year. So, you cannot afford to be left behind. But, where do you start?

The business case for AI in B2B digital experience workflows

An AI workflow refers to the outlined steps that use AI to automate and enhance business tasks. Here are some key reasons to integrate AI technology for enhanced workflows.

• Reduces cost: According to this report, AI integration in workflows has been shown to reduce costs by up to 60%.
• Saves time: AI workflows automate recurring manual tasks, streamlining processes. This reduces errors and increases efficiency. Research indicates that AI-driven workflow automation can save up to 77% of time.
• Improves decision-making with predictive analytics: AI systems mine data in real-time, enabling marketers to spot trends that may not be immediately apparent. It also enhances product recommendations and overall decision-making.
• Enhances personalization: AI-driven personalization in digital experience workflows boosts conversion and retention by delivering tailored content at scale. It enhances segmentation and targeting, and uncovers actionable insights for revenue growth.

There are numerous reasons for businesses to incorporate AI into their workflows. It saves time, enhances productivity and facilitates informed decision-making.

Building AI into your business processes

Incorporating AI in business workflows begins with assessing workflows and pairing the right tools with proper use cases.

It could be as simple as integrating generative AI into your CMS or something more complex, such as lead scoring models, AI orchestration and workflow automation. Here are practical steps to integrate AI into your digital experience workflows. 

1. Audit and prepare your current workflows

Start by mapping the processes in your workflow alongside data sources and hand-off points. List the platforms your team uses to collect data and manage content. Also, identify where teams and tools transfer data to pinpoint effective AI entry points. 

Merge, cleanse and standardize data from all sources. Confirm that all available datasets comply with data privacy laws before labeling. Then, apply a unified data schema to maintain accuracy.

2. Prioritize high-impact AI use cases 

If the audit has been done properly, you'll find bottlenecks, gaps and processes that AI can improve or replace. Such areas typically require manual effort and involve large-scale data processing. 

A typical use case for many businesses is lead qualification. Typically, it involves data collection, transfer to a spreadsheet, lead analysis, scoring and other related tasks. This process can be automated with AI, with set instructions that enable the AI system to qualify leads. So, settle on a use case that makes the most impact on the business. This may include:

• Customer journey orchestration
• Omnichannel personalization
• Content management and dynamic content delivery
• Automating marketing tasks and processes 

3. Select tools and integrate them into your stack 

Once you have your use cases, research AI tools that can address them. Conduct a proof-of-concept test before purchasing the tool. Ensure that the chosen platform is API-first and offers pre-built connectors for easy integration with your existing tech stack. Given that AI systems are data-intensive, ensure that the selected solution provides enterprise-level security and complies with data privacy laws. 

4. Pilot, train and drive adoption 

Conduct pilot tests to validate your chosen AI solution. Outline metrics that you will use to track the AI system’s performance. Employ a phased approach by integrating AI solutions gradually. This ensures there is minimal disruption to current processes and enhances adoption.

Also, explain the value of AI integration to your team, and if required, train them on how to use AI solutions. Set up monitoring, as you want to be sure that integrated AI systems deliver results that align with business values and requirements. For instance, if using AI to automate content workflows, you can create guidelines and knowledge bases for AI to ensure it reflects your brand voice and tone. 

5. Measure, optimize and scale

Set and review key performance indicators that reflect technical and interactive improvements. To optimize AI digital experience workflows, monitor overrides and track anomalies. Run A/B tests to compare AI and human workflows and log feedback to improve model performance. Use holistic feedback to refine processes. If AI delivers the required improvements, you can expect to scale its usage and scope.

From test to scale: Building AI into your business processes

Start with pilot projects aligned to clear goals and measure impact using agreed KPIs. Employ an iterative approach to refine models and integrate results into the core system. AI requires high-quality data, and for that, you must establish effective data governance and consistently monitor data quality and security.

Finally, track the performance of pilot projects and scale successful ones by automating workflows, training teams, engaging stakeholders and continuously monitoring performance to accelerate company-wide AI adoption.

Avoiding common pitfalls during AI adoption

Some challenges often hinder B2B firms from expanding their use of AI. 

• Data quality issues: This Qlik report states that 81% of businesses will struggle with AI data quality issues, which will negatively impact ROI. To prevent this, deploy specific tools like a CDP to consolidate data and establish governance rules to ensure data quality.
• Skills gap and resistance to change: A McKinsey report states that 46% of leaders identify skill gaps as a barrier to AI adoption. To address this, map impacted roles, tailor hands-on training, provide quick-start guides and appoint an influential staff member as a change agent to coach and gather feedback from peers.

It is also important to load-test your infrastructure to ascertain its scalability. Finally, establish clear rules that outline the duties of AI and human experts, areas of intersection and roles and responsibilities in both cases.

Technology stack: Choosing tools for integrating AI workflows

You need a flexible, API-first solution that supports AI integration and automation to improve specific workflows. The right tools enable seamless data transfer, facilitate real-time decision-making and deliver dynamic content. Here are some tools to consider.

• Enterprise CMS systems with built-in AI features: You need a modern CMS with embedded AI features. Among other features, a CMS that supports AI workflows should have a headless architecture and personalization engines. It should also be customizable and offer repeatable content workflows.
• Automation tools that align with your existing tech: Again, API-driven tools are preferred here as they support seamless integration. AI tools with low-code or no-code builders are also great, as they enable non-technical users to create automated workflows.

Why Contentstack outpaces the competition in AI-readiness

Contentstack natively integrates AI and automation within its API-first, MACH-compliant headless CMS. To start with, that makes it a better alternative to Adobe Experience Manager, Contentful and Sitecore.

Its Automation Hub and AI platform embed generative AI, AI Connectors, Brand Kit and AI Assistant directly into content workflows. That way, you do not need third-party setups. It offers real-time data support and personalization at scale. 

You can also leverage modular blocks and visual builders for rapid model-driven content creation. Finally, its cloud-native, no-code orchestration simplifies pilot-to-production transitions, enabling you to deliver experiences faster.

Case study

Brad’s Deals

Brad’s Deals faced a complex tech stack of five CMSes that made content management slow, manual and error-prone. Contentstack Automate gave them bespoke automation to unify content and automate tasks. They cut the cost per subscriber by 70%, sped production by 95% and cut publish time by 99%.

“Automate has been an incredibly powerful tool to use. We’ve been able to quickly learn how to use the system, build automations as POCs, validate them and then deliver them to our environments in an incredibly fast-paced manner.” Eric Agnew said.

Read more to see how Contentstack's Automation solution transformed Brad’s Deals' workflow and business process.

FAQ section

What is the first step to integrating AI into B2B workflows?

The first step is to map and audit your current workflows to spot bottlenecks and gaps and note the manual procedures that can be improved with AI automation.

Can AI work with existing CMS platforms?

Yes. AI can integrate with existing CMS platforms, especially those built on API-first principles. In that case, you can incorporate AI solutions to enhance the capabilities of the content management system.

Is AI integration costly for B2B companies?

It depends. The upfront costs of integrating AI into existing workflows may be high. However, over time, AI automation reduces costs by decreasing the need for manual labor.

What is the difference between automation and AI?

Automation refers to systems that operate based on static logic and predefined rules. AI utilizes data-driven models to conduct analysis, learn, adapt, make decisions, forecast outcomes and tailor actions. 

Learn more

Artificial intelligence (AI) has numerous applications beyond content creation and optimization. AI analyzes data, automates workflows and enhances personalization. It can be integrated into B2B digital experience workflows to streamline existing processes. To start, audit your existing tech stack to identify bottlenecks, prioritize high-impact use cases and select the right AI tools to integrate.

Contentstack’s AI platform offers no-code orchestration, enabling you to automate manual work and scale processes. Whether you want to scale content production, automate tasks or deploy an LLM to power your AI strategy, Contentstack simplifies the process. Talk to us today to transform your workflows with our AI solution.