AI
How AI Is Transforming Every Industry — What It Means for the Future of Business
It’s official: artificial intelligence is no longer the future. It’s the now—and it’s transforming every industry, from healthcare to logistics, retail to professional services. A new PwC AI Jobs Barometer report (2025) confirms what many of us have sensed: AI adoption is accelerating rapidly across sectors, reshaping job roles, increasing productivity, and changing how businesses operate from the inside out.
At Cyferd, we help businesses not just adopt AI but truly integrate it into the fabric of their operations. As AI continues to redefine how industries work, it’s essential for organisations to move beyond fragmented tools and toward unified, intelligent platforms that adapt in real time.
Real-World Examples: How AI Is Being Used in Different Industries
- Healthcare: Accelerating Diagnosis and Personalising Care
AI is helping medical professionals make faster, more accurate decisions by analysing large datasets such as patient histories, imaging, and genomics. From predicting disease outbreaks to automating administrative workflows, AI-driven healthcare solutions are improving both patient outcomes and system efficiency.
- Financial Services: Fighting Fraud and Forecasting Risk
In banking and insurance, AI is transforming how organisations detect fraud, manage compliance, and assess credit risk. AI models can spot unusual behaviour in milliseconds and help analysts prioritise threats more effectively.
- Retail: Smarter Customer Experiences
Retailers are using AI to create hyper-personalised shopping experiences, from product recommendations to dynamic pricing. Inventory management and demand forecasting are also getting an AI upgrade, improving both customer satisfaction and operational efficiency.
- Logistics and Manufacturing: Automation and Efficiency
AI is driving smarter supply chains, optimising routes, managing inventory, and predicting equipment failures before they happen. The result? Lower costs, fewer delays, and more resilient operations.
- Professional Services: Enhanced Productivity
From legal research to audit support, AI is taking over time-consuming manual tasks. This allows experts to focus on higher-value work, improving both quality and job satisfaction.
The Hidden Risks: Siloed AI and Data Quality Gaps
While AI adoption is on the rise, many organisations are facing two critical barriers to truly unlocking its potential:
- Siloed AI Systems
As businesses rush to implement AI across departments, many end up with disconnected tools that don’t talk to each other. The result? Siloed insights, redundant systems, and missed opportunities. True AI transformation only happens when data and intelligence flow across the entire business ecosystem.
- Poor Data Quality
Even the most advanced AI model is only as good as the data it’s fed. Inconsistent, outdated, or incomplete data can lead to inaccurate predictions, biased decisions, and costly mistakes. High-quality, real-time data is essential to train, refine, and trust AI-driven outcomes.
Why Businesses Must Act Now
The message is clear: every industry is using AI—and the gap between early adopters and laggards is widening. According to PwC, jobs requiring AI skills are growing 3.5x faster than average, and productivity in AI-enabled roles is significantly higher. But AI isn’t just a technology shift; it’s a business transformation.
To stay competitive, businesses must ask themselves:
- Are we using AI to enhance decision-making across departments?
- Is our tech stack flexible enough to adapt as AI evolves?
- Are we managing data quality to support trusted, accurate AI outcomes?
- Do we risk fragmentation with siloed AI tools?
The Role of Platforms Like Cyferd
Unlike static legacy systems or disconnected AI point solutions, Cyferd provides a single intelligent platform that lets organisations build, deploy, and evolve AI-enhanced applications without writing code or waiting on IT. This means:
- Seamless AI integration across the entire organisation
- One source of truth for consistent, clean data
- Real-time intelligence powering agile business decisions
- Reduced risk of silos and system sprawl
Whether you’re in finance, healthcare, logistics, or retail, Cyferd empowers you to turn AI potential into real-world performance.
Final Thought: From Buzzword to Business Driver
AI is no longer experimental. It’s embedded in the daily operations of leading organisations—and those who fail to act risk falling behind. The good news? With the right platform, the barriers to entry are lower than ever.
At Cyferd, we’re helping companies of all sizes bring AI out of the lab and into the boardroom. If you’re ready to break down silos, clean up your data, and unlock true AI transformation, the time to act is now.
Find out more About Cyferd
New York
Americas Tower
1177 6th Avenue
5th Floor
New York
NY 10036
London
2nd Floor,
Berkeley Square House,
Berkeley Square,
London W1J 6BD
Request a Demo
Comparisons
BOAT Platform Comparison 2026
Timelines and pricing vary significantly based on scope, governance, and integration complexity.
What Is a BOAT Platform?
Business Orchestration and Automation Technology (BOAT) platforms coordinate end-to-end workflows across teams, systems, and decisions.
Unlike RPA, BPM, or point automation tools, BOAT platforms:
- Orchestrate cross-functional processes
- Integrate operational systems and data
- Embed AI-driven decision-making directly into workflows
BOAT platforms focus on how work flows across the enterprise, not just how individual tasks are automated.
Why Many Automation Initiatives Fail
Most automation programs fail due to architectural fragmentation, not poor tools.
Common challenges include:
- Siloed workflows optimised locally, not end-to-end
- Data spread across disconnected platforms
- AI added after processes are already fixed
- High coordination overhead between tools
BOAT platforms address this by aligning orchestration, automation, data, and AI within a single operational model, improving ROI and adaptability.
Enterprise BOAT Platform Comparison
Appian
Strengths
Well established in regulated industries, strong compliance, governance, and BPMN/DMN modeling. Mature partner ecosystem and support for low-code and professional development.
Considerations
9–18 month implementations, often supported by professional services. Adapting processes post-deployment can be slower in dynamic environments.
Best for
BPM-led organizations with formal governance and regulatory requirements.
Questions to ask Appian:
- How can we accelerate time to production while maintaining governance and compliance?
- What is the balance between professional services and internal capability building?
- How flexible is the platform when processes evolve unexpectedly?
Cyferd
Strengths
Built on a single, unified architecture combining workflow, automation, data, and AI. Reduces coordination overhead and enables true end-to-end orchestration. Embedded AI and automation support incremental modernization without locking decisions early. Transparent pricing and faster deployment cycles.
Considerations
Smaller ecosystem than legacy platforms; integration catalog continues to grow. Benefits from clear business ownership and process clarity.
Best for
Organizations reducing tool sprawl, modernizing incrementally, and maintaining flexibility as systems and processes evolve.
Questions to ask Cyferd:
- How does your integration catalog align with our existing systems and workflows?
- What is the typical timeline from engagement to production for an organization of our size and complexity?
- How do you support scaling adoption across multiple business units or geographies?
IBM Automation Suite
Strengths
Extensive automation and AI capabilities, strong hybrid and mainframe support, enterprise-grade security, deep architectural expertise.
Considerations
Multiple product components increase coordination effort. Planning phases can extend time to value; total cost includes licenses and services.
Best for
Global enterprises with complex hybrid infrastructure and deep IBM investments.
Questions to ask IBM:
- How do the Cloud Pak components work together for end-to-end orchestration?
- What is the recommended approach for phasing implementation to accelerate time to value?
- What internal skills or external support are needed to scale the platform?
Microsoft Power Platform
Strengths
Integrates deeply with Microsoft 365, Teams, Dynamics, and Azure. Supports citizen and professional developers, large connector ecosystem.
Considerations
Capabilities spread across tools, requiring strong governance. Consumption-based pricing can be hard to forecast; visibility consolidation may require additional tools.
Best for
Microsoft-centric organizations seeking self-service automation aligned with Azure.
Questions to ask Microsoft:
- How should Power Platform deployments be governed across multiple business units?
- What is the typical cost trajectory as usage scales enterprise-wide?
- How do you handle integration with legacy or third-party systems?
Pega
Strengths
Advanced decisioning, case management, multi-channel orchestration. Strong adoption in financial services and healthcare; AI frameworks for next-best-action.
Considerations
Requires certified practitioners, long-term investment, premium pricing, and ongoing specialist involvement.
Best for
Organizations where decisioning and complex case orchestration are strategic differentiators.
Questions to ask Pega:
- How do you balance decisioning depth with deployment speed?
- What internal capabilities are needed to maintain and scale the platform?
- How does licensing scale as adoption grows across business units?
ServiceNow
Strengths
Mature ITSM and ITOM foundation, strong audit and compliance capabilities. Expanding into HR, operations, and customer workflows.
Considerations
Configuration-first approach can limit rapid experimentation; licensing scales with usage; upgrades require structured testing. Often seen as IT-centric.
Best for
Enterprises prioritizing standardization, governance, and IT service management integration.
Questions to ask ServiceNow:
- How do you support rapid prototyping for business-led initiatives?
- What is the typical timeline from concept to production for cross-functional workflows?
- How do licensing costs evolve as platform adoption scales globally?
