Defeating Dashboard Drudgery

David Thorpe
Director of Sales Engineering
The Current State of Data Insights:
Through the rise of business intelligence maturity, self-service platforms and tools coupled with big data centralization, organizations can understand more about the ways in which they operate, and how their customers and stakeholders interact.
Some have taken this further, using powerful cloud computing, real-time analytics insights and integrated machine learning methods, these companies are able to delve deeper into their data and leverage the power of forecasting to make faster, more informed decisions.
Insights Challenges:
Despite this, many organizations we speak to are still playing catch-up – whilst they may have deployed and have widespread adoption of visualization technologies, they are struggling to turn this into actionable outcomes. Reliant on business users with the right tools, experience and use cases to explore in their data, uncovering the insights becomes a search like finding a needle in a haystack.
This makes insight a ‘pull’ rather than a ‘push’ activity, finding data through which corrective outcome can be taken is difficult enough, the conditioning of this into manageable, trackable and deployable actions is almost impossible for many.
Turning Insights into Action
Recently, I watched a TEDx talk from 2018 by Data Scientist Frank Evans. In his talk, he highlights that visualizations allow for exploration of data, easily prompting questions and testing correlations and theories.
This investigative process typically follows a similar path every time, opening these dashboards, slicing and dicing data through the application of relevant categorical and variable filters, viewing only relevant segments or areas of the business, as well as applying specific date and time filters to view data over a specific period.
In doing so, they are presented with a series of charts, showing lines, bars, pies which highlight a variance, or a change in state, from which may invoke a series of questions as per a typical insights flow:
The challenge here, is twofold:
- There needs to be data-savvy organizational culture, with skills to ‘pull’ the insights from the data.
- It implies a process through which something can be done to rectify business problems highlighted through the insights.
The challenge that many businesses still face, is in capture of these insights, triaging them through a human-led review process to turn promote the provided information into a live project, or to reject.
What if, instead, we could deliver the outcomes from the insight triggers directly into agentic actions?
What Comes Next?
‘You don’t drive a car by the dashboard, but with your eyes out of the windshield’ – Frank Evans
Prompted by the unavoidable surge in AI functionality and popularity, AI-driven analytics and decision automation are becoming some of the biggest focus areas of investment for companies. We are seeing AI-powered visualizations, automated insights, and NLP-based queries for embedded analytics, as well as auto machine learning, anomaly detection and LLM-driven scenario modelling capabilities.
The true value in business insights is what you do with the information uncovered. By incorporating alerting rules based on trigger thresholds and generative AI models, trained on business context such as KPIs and core organizational priorities, next best actions with tasks and project management facilitate a move from reactive insights to proactive action.
Whilst by no means a ‘magic box’ solution – with Agentic AI, these insights are transformed into actionable recommendations, which the AI can directly implement or suggest to human decision-makers. Additionally, by employing feedback loops, agentic AI models can be optimized to ensure more accurate predictions and more efficient operations. Combined with a user interface and project management capability to review insight actions and triage tasks for next steps, we can ensure that our insights turn to action and ultimately closes the open loop on using our valuable data to improve business outcomes.
No more trawling through our dashboards to uncover insights that get left unactioned!
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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?
