Everyone’s an Expert

Matt Heys
Senior VP, Artificial Intelligence & Neural Genesis
Now, I say this with a certain amount of self-aware chagrin. I acknowledge that I market myself as an AI expert and I’ve even got it in my job title. However, I’m more pre-disposed to posting series of AI generated images of cats that get cuter and cuter and CUTER AND CUTER AND INFINITELY CUTER AND ULTIMATE COSMICAL LEVELS OF CUTENESS … (sorry, got carried away there) … than I am to write a whole post about “How you’re not using AI properly”. So, obviously, that makes me better, right? RIGHT?!
Ultimately, I find people fall into 3 camps:
- Haters – They believe AI is a fad, it’s hit its peak, it gets everything wrong, and you’re an idiot if you think AI is the future.
- Fanatics – They believe AI is the future, if you don’t get onboard the AI train, you’re going to be left behind, AI can do everything, and it will soon be cooking your meals and caring for your kids. Just make sure you’ve been kind to your Alexa so that it doesn’t smite you when the computers rise up and become your overlords.
- The don’t cares – They don’t care either way. If AI solves some problems for them, cool, otherwise, it’s just something abstract nerds talk about online.
Oh, and then I guess there’s the 4th category of assorted weirdos and people who should probably be on some sort of register. Like, the ones creating subservient, AI, virtual girlfriend apps. Eurgh. Or the ones creating bots to spew antagonistic, political nonsense across social media. However, I’ll give them a pass as they’re quite easy and fun to break…
So, where do I fall? Well, we’ve already established that I find myself to be superior to other people – so naturally, I don’t fall into any of those categories. I fall into my own category of ‘Sure, AI will probably end up destroying the world, but, well, we had a good innings, and, really, I don’t blame it.’
Realistically though, in the same way that the industrial revolution transformed the world; the fact we’ve eradicated certain diseases through modern medicine; and the advent of computers advanced humanity to new levels; AI is changing the world whether you like it or not. In the middle of the 19th century, many clinicians dismissed research into maternal deaths that found patients were less likely to die of infection if doctors washed their hands thoroughly between dissecting cadavers and delivering babies. The people who dismiss AI now are as misguided as those doctors. Dismissing AI as a fad that’s already hit its ceiling of progress is like delivering a baby with corpse hands. I’ve always been praised on my way with words…
AI is already changing a lot of things – for example, the way we communicate. It was very tempting to ask an LLM to write this article for me… but I have a little bit more credibility than that. Just a little. However, a lot of content on the internet is now being, at least, initially drafted by AI. I’m seeing the trademark paragraph, bullet points, paragraph structure often as I scroll online, which is a general give away for LLM content. We’re moving towards a world were emails, academic papers, government strategies, are all created from a small set of bullet points – and then, on the opposite end, are compiled back into a small set of bullet points. Perhaps we need to reflect upon our common approaches to ‘proper communication’ and remove the bluster and fluff. Let’s just speak in bullet points. Although, I’m somewhat destroying the need for this blog to exist. Let me sleep on that one and get back to you…
To be a little more sincere and serious, I think there’s a divide between good uses of AI, bad uses of AI, and silly uses of AI (which shouldn’t be discounted as genuine uses). At Cyferd, we’ve focused on how we can enable our customers to get the best out of AI. Need a new expenses app? Let’s quickly build that out for you and allow you to tweak it to your requirements. Have a risk assessment to complete? Just put your observations into the platform and we’ll generate a set of risks along with likelihood/severity scores, and suggested mitigating actions. Need a report on what’s changed in your projects over the last month? Don’t worry, we’ve seen you’ve been asking for that each month so we’ve already sent it to you.
Conversely, getting AI to generate you specialist, regionally accurate, airtight legal documents, or make complex, life-changing healthcare decisions about patient care is a bad idea (hmmm, the computer’s decided to pull the plug on this one…☠️). The challenge will be to push back on bad use cases and understand the limitations of AI, especially LLMs. Although a lot of work goes towards researching and implementing moderation features in LLMs, they’re nowhere near infallible. A lot of LLM training revolves around Reinforcement Learning from Human Feedback (RLHF), with a human saying whether results of a prompt are good. This can result in models producing things which sound very plausible and confident despite the fact they’re hallucinations. And let’s not forget some of the big embarrassments for big tech firms recently, such as Google Gemini telling users it’s safe to eat glue…thanks Reddit…
This has been a Matt Heys blog. I’m not sure if I kept to my original title very well, I tend to go on crazy tangents a lot. But hey, I’ve written ‘A BLOG’, you’ve hopefully enjoyed reading my ramblings, and I’ve done what I was asked. Happy marketing? Stop bothering me 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?
