Revolutionising operations and customer interactions

In today’s fast-paced and competitive business landscape, organisations are constantly looking for innovative ways to enhance efficiency, improve customer experiences, and make data-driven decisions. One of the most transformative technologies that is making waves across industries is the AI assistant.

AI assistants—powered by natural language processing (NLP), machine learning, and automation—are transforming how businesses operate. From streamlining internal workflows to enhancing customer engagement, AI assistants are offering tangible benefits for organisations of all sizes.

In this blog, we’ll explore how AI assistants can help businesses improve operations, drive growth, and stay ahead of the competition.

1. Enhancing customer support and service

One of the most powerful ways AI assistants are impacting business is by transforming customer support. AI-driven chatbots and virtual assistants can handle a wide range of customer inquiries in real time, without the need for human intervention.

Benefits:

• 24/7 availability: AI assistants can work around the clock, ensuring that customers can get the help they need at any time, even outside of regular business hours.
• Instant responses: AI assistants respond quickly to common queries, providing immediate assistance and improving overall response times.
• Consistent customer experience: AI assistants can ensure consistent responses across all customer interactions, eliminating discrepancies that might occur with human support.
• Scalability: During peak periods or holidays, AI assistants can handle a high volume of inquiries without the need to hire additional staff, ensuring that businesses can scale their support operations efficiently.

2. Automating repetitive tasks and workflows

AI assistants are highly effective at automating routine, repetitive tasks that take up valuable time for employees. By taking over menial tasks, AI can help businesses streamline operations and free up human workers to focus on more strategic activities.

Examples of tasks AI assistants can automate:

• Scheduling and calendar management: AI assistants can schedule meetings, set reminders, and send invites, reducing the administrative burden on employees.
• Data entry and documentation: AI can automate data collection, entry, and report generation, ensuring accuracy while saving employees' time.
• Customer feedback collection: AI assistants can automatically send surveys or request feedback from customers, gather responses, and analyse the results for businesses.

Benefits:

• Improved efficiency: By automating time-consuming tasks, employees can focus on high-value work that drives innovation and business growth.
• Reduced human error: AI assistants follow programmed instructions without making mistakes, ensuring that repetitive tasks are completed accurately.
• Faster response times: Automation allows tasks to be completed faster, increasing the overall speed of business processes.

3. Enhancing Data Analysis and Decision-Making

AI assistants, when integrated with business intelligence tools, can help businesses make better decisions based on data-driven insights. AI assistants can analyse large volumes of data in real time and provide recommendations that guide business decisions.

Examples of AI-driven insights:

• Sales trends: AI assistants can analyse sales data and identify trends or patterns in customer behaviour, helping businesses forecast demand and optimize inventory management.
• Market Insights: AI can analyse competitor data, customer preferences, and market conditions, providing businesses with insights that help shape marketing strategies.O
• Operational efficiency: AI assistants can identify bottlenecks or inefficiencies in workflows, providing recommendations for process improvements.
  

Benefits:

• Better decision-making: AI assistants provide real-time insights, helping managers and executives make informed decisions faster.
• Predictive analytics: AI-powered tools can forecast future trends, helping businesses anticipate market changes and adjust strategies accordingly.
• Cost savings: By identifying inefficiencies or underperforming areas, AI assistants can help businesses reduce waste and optimize resource allocation.

4. Improving marketing and personalisation

AI assistants can significantly improve marketing efforts by offering a higher level of personalization and delivering targeted content to the right audience. By analysing customer preferences, behaviour, and interactions, AI assistants can help businesses create customised marketing campaigns that drive engagement and conversions.

Examples of AI-driven marketing activities:

• Personalised recommendations: AI assistants can analyse customer browsing and purchase history to recommend products or services tailored to individual preferences.
• Automated content creation: AI assistants can help businesses generate content, such as emails, product descriptions, and blog posts, based on customer interests and previous interactions.
• Social media monitoring: AI assistants can track social media mentions, sentiment, and trends, helping businesses adjust their marketing strategy in real time.

Benefits:

• Increased customer engagement: Personalised experiences resonate more with customers, leading to hi
• Better targeting: AI can segment customers based on their preferences and behaviors, allowing businesses to target the right audience with the right message at the right time.
• Enhanced ROI: With improved targeting and personalisation, businesses can see a higher return on marketing investments.

5. Enhancing employee productivity and collaboration

AI assistants are not only beneficial for customer-facing tasks but also play a vital role in improving internal productivity and collaboration within organisations. By providing employees with access to real-time information, helping them manage tasks, and facilitating communication, AI assistants can improve overall team performance.

Examples of how AI assistants can help employees:

• Project management: AI can assist with project tracking, reminders, and deadline management, ensuring that projects stay on track.
• Internal communication: AI assistants can help employees quickly find the information they need by searching through internal knowledge bases, documents, and databases.
• Collaboration tools: AI assistants can integrate with tools like Slack, Microsoft Teams, and Google Workspace, providing employees with intelligent support and facilitating team collaboration.

Benefits:

• Time savings: Employees can quickly access information, receive reminders, and automate tasks that would otherwise take time, allowing them to be more productive.
• Improved communication: AI can enhance communication and reduce delays in getting answers or collaborating on projects.
• Task prioritisation: AI assistants can help employees prioritize tasks based on urgency and importance, ensuring that critical work gets done first.

6. Boosting sales and lead generation

AI assistants can help businesses improve their sales processes by identifying new leads, nurturing existing prospects, and automating customer interactions during the sales journey.

Examples:

• Lead qualification: AI can help qualify leads by engaging with prospects through chat, analyzing their responses, and scoring their likelihood to convert into paying customers.
• Automated follow-ups: AI assistants can automate email follow-ups, schedule calls, and send reminders, ensuring that no leads slip through the cracks.
• Sales forecasting: AI assistants can analyse past sales data and predict future sales trends, helping sales teams plan their efforts more effectively.

Benefits:

• Increased sales: By automating lead generation and nurturing processes, AI helps sales teams focus on high-priority leads, improving conversion rates.
• Efficiency gains: Automated tasks like follow-ups and lead qualification free up time for sales teams to focus on closing deals and building relationships with customers.
• Data-driven insights: AI assistants provide real-time insights into sales performance, helping teams make data-driven decisions that optimize their efforts.

7. Boosting employee training and development

AI assistants can also be leveraged for training and employee development, especially in large organizations with a continuous need for skill enhancement.

Examples:

• Training modules: AI-powered platforms can recommend training materials and courses based on an employee’s current role, interests, and skill gaps.
• Onboarding assistance: New employees can interact with AI assistants to learn about company policies, procedures, and workflows, ensuring a smooth onboarding experience.
• Performance feedback: AI assistants can track employee performance and provide actionable feedback to help employees improve and grow.

Benefits:

• Personalised learning: Employees receive tailored training recommendations based on their specific needs, leading to more effective learning.
• Faster onboarding: AI assistants help new employees get up to speed quickly by providing them with essential resources and answering common questions.
• Continuous development: AI enables businesses to offer continuous learning opportunities, fostering a culture of growth and improvement.

AI assistants are reshaping the way businesses operate by automating tasks, improving customer engagement, providing real-time insights, and enhancing productivity. Whether it’s through streamlining workflows, enhancing personalisation, improving decision-making, or

offering round-the-clock customer support, AI assistants bring immense value to organisations across industries.

As businesses continue to adopt AI-driven solutions, those who leverage AI assistants will likely see significant improvements in efficiency, customer satisfaction, and overall business performance. By embracing this transformative technology, organisations can stay competitive, boost innovation, and achieve long-term success.

If you want to discover more about the AI assistant, contact us

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In a world where speed, scale, and trust define success, Octane has emerged as a leader in enterprise AI-driven transformation. Honoured with the 2025 IBM Partner Plus Award in APAC for automation, Octane’s groundbreaking use of IBM Watsonx Orchestrate is setting a new benchmark for how intelligent automation can empower business users and scale human productivity.

The Award: Increasing performance through automation

The IBM Partner Plus Award for Automation celebrates business partners who are delivering new levels of performance with speed, scale and security, enabling systems, business processes, and people to be more efficient.

For Octane, this means:

• Speed: Accelerating workflows by over 70% through AI orchestration.

• Adaptability: Designing solutions that flex with business demands.

• Security: Ensuring compliance through enterprise-grade automation guardrails.

“This award reinforces our belief that automation should be intuitive, intelligent, and human-first,” said Amendra Pratap, Managing Director. “Our collaboration with IBM brings this vision to life.”

IBM Watsonx Orchestrate: A new paradigm in intelligent automation

Watsonx Orchestrate is IBM’s enterprise-ready solution that helps create, deploy, and manage AI assistants and agents. It blends AI and workflow automation, enabling users to interact with systems using natural language prompts, representing a powerful step toward fully autonomous agentic AI systems. It enables multi-step, goal-driven task orchestration and integration with existing business systems, connecting to multiple proprietary and third-party AI models and automation tools.

How Octane leverages Watsonx Orchestrate:

• Skill-based AI execution: Prebuilt “skills” automate repetitive actions—like sending emails, updating records, or scheduling interviews.
• No-code integrations: Plug-and-play connections to enterprise tools like SAP, Salesforce, and Workday.
• Conversational interface: Users trigger complex workflows through Slack, Teams, or email with simple prompts.

“Watsonx Orchestrate isn’t just for building chatbots—it’s an AI-powered teammate,” says Amendra Pratap, Managing Director, “Watsonx Orchestrate is your next hire. By orchestrating tasks across various assistants, agents and systems, it helps boost workforce efficiency and reduces manual load by surfacing the right tools when you need them."

Real-world impact:

What we achieved:

What sets Octane apart: Augmented intelligence, not just automation

Traditional automation follows rules. Octane uses AI to understand goals, context, and next best actions.

With Watsonx Orchestrate, Octane delivers:

• Adaptive workflows: Adjusts based on real-time data (e.g., re-routing approvals during outages).
• Multi-agent collaboration: Skills collaborate (e.g., sentiment analysis + ticket escalation).
• Continuous learning: Models improve from feedback to streamline operations over time.

“We don’t just automate tasks—we augment thinking,” says Steny Sebastian, Principal - Data and AI Platforms. “That’s how we deliver smarter outcomes, not just faster ones.”

Ready to orchestrate intelligence into every workflow?

 Explore how Octane’s award-winning AI solutions can help you scale with confidence.

Learn how advanced your organisation is with AI adoption and how Orchestrate can help. - Take the next step. Try IBM Watsonx Orchestrate at no cost, or book a consultation with an expert

Learn more about the IBM Partner Plus Awards: https://www.ibm.com/partnerplus/awards

#AgenticAI #EnterpriseAutomation #OctaneSolutions #WatsonxOrchestrate #IBMPartnerPlus #DigitalTransformation

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There’s nothing quite like starting a new role at the forefront of innovation. My first week at Octane Software Solutions was nothing short of electrifying—the highlight was a full-house customer event buzzing with energy, visionary ideas, and the promise of AI-driven transformation. 

The focus?

 IBM AI Platform,  Watsonx Orchestrate, is poised to redefine how businesses harness automation, AI agents, and predictive intelligence to unlock unprecedented efficiency. Let me take you through this exhilarating journey and why Watsonx Orchestrate, paired with Octane’s expertise, is the future of work.  

As we close out 2024, a year that has been revolutionary for AI adoption, let’s pause to reflect on the data driving this transformation. Companies have spent 2023-2024 experimenting with generative AI, deploying AI assistants, and running pilots, many of which have evolved into concrete plans for 2025. Now, the focus shifts to finding the right partner to turn these blueprints into reality. 

These statistics aren’t just metrics; they prove that AI is fulfilling its promise to amplify productivity while elevating outcomes. Here’s how Watsonx Orchestrate’s AI agents are reshaping enterprises: 

1. Enhanced User Experience 
   
   AI agents deliver intelligent, multi-turn conversational experiences that solve complex tasks seamlessly. For instance, integrating Watsonx Orchestrate with tools like IBM Planning Analytics (TM1) allows finance teams to automate data reconciliation while maintaining compliance.

2. Reduced Total Cost of Ownership (TCO) 
   
   By leaning on AI to automate tasks at scale, enterprises cut costs while boosting efficiency. Watsonx Orchestrate’s pre-built Skills and low-code studio let businesses extend existing Gen AI investments—like chatbots or co-pilots—without overhauling systems.

3. Agility & Future-Proof Flexibility 
   
   AI agents enable organisations to pivot rapidly as markets shift. With Watsonx Orchestrate’s autonomous orchestration, businesses adapt workflows in real-time, whether rerouting customer inquiries during peak demand or updating financial forecasts using TM1.

If below is what you are thinking: 

• How do you integrate AI agents into your existing digitised workflows?
• How do you maximise ROI from current AI tools?
• How do you retain control as AI evolves?

Octane: Is Your Partner for Scaling Customer-Centric AI

While many providers offer AI tools, Octane stands apart as a force for customer-centric innovation. Here’s why: 

We don’t believe in one-size-fits-all solutions. Together with IBM, Octane’s team works side-by-side with clients to:

• Map AI use cases to your unique customer journey.
• Integrate Watsonx capabilities with niche tools  
• Prioritise ethical AI practices, ensuring transparency and trust at every interaction. 

Octane: Delivering Real-World Impact with Watsonx Orchestrate—An Airline’s Journey to AI-Driven Intelligence 

Let’s cut through the hype and dive into a tangible example of how IBM Watsonx Orchestrate, implemented by Octane, transformed operations for a global airline—a case study that exemplifies the platform’s power to turn data chaos into strategic clarity. 

The Challenge: Manual Mayhem in Business Intelligence 

The airline’s Business Intelligence (BI) and Finance teams were drowning in manual processes: 

• 2-3 days wasted monthly on report generation, with analysts manually cleaning, reconciling, and validating data in IBM Planning Analytics (TM1).
• Knowledge bottlenecks: Executives relied on BI teams for real-time insights during critical meetings, creating delays and frustration.
• Human errors: Manual calculations led to costly rework, while commercial decisions were stalled by a 3-week data validation cycle. 

The stakes? Missed deadlines, strained resources, and executives flying blind in a competitive market. 

The Solution: Watsonx Orchestrate in Action

Octane partnered with the airline to integrate Watsonx Orchestrate with their existing IBM Planning Analytics deployment. In just two weeks, we automated workflows and unleashed AI-driven efficiency:

Automated Data Cleaning & Reconciliation 

• AI-Powered Automation: 

• Manual data cleaning reduced from 2-4 days/month to minutes. 

• Data reconciliation slashed from 3-4 hours/month to 1 minute. 

• Self-Correcting Workflows: 
  Watsonx Orchestrate’s AI agents flagged inconsistencies, auto-corrected errors, and validated datasets, ensuring 99% accuracy in financial reports. 

Empowering Executives with NLP-Driven Insights

• Natural Language Queries: 
  Executives could now ask, “Show me Q3 revenue trends vs. forecasts” in plain language. Watsonx Orchestrate generated real-time insights, reducing reliance on BI teams by 90%. 

• Faster Decisions: 
  Monthly reports that once took 2-3 days were generated in 10 minutes, accelerating commercial decisions from weeks to hours. 

Eliminating Knowledge Silos

• Democratized Data Access: 
  By codifying tribal knowledge into AI workflows, the airline mitigated key-person risk and ensured continuity during staff turnover. 

• Scalable Governance: 
  Octane embedded compliance checks into automated processes, aligning with IBM’s enterprise-grade LLMs for audit-ready outputs. 
  

Business Outcomes: From Friction to Flight 

"With Octane and Watsonx Orchestrate, we’re not just surviving data chaos—we’re soaring above it." - Fortune 500 Airline Client

Three Pathways to AI Transformation 

1. Test-drive Watsonx Orchestrate on our dedicated platform  

2. Client Briefing: Dive deep into a 2-4 hour session to align AI strategy with your goals.

3. Pilot Program: Co-develop a 1-4 week proof-of-concept with Octane’s AI engineers. 

The Future is Autonomous—Let’s Build It Together

Reflecting back on the event, the energy in the room was infectious. Attendees left inspired by Watsonx Orchestrate’s ability to blend autonomous AI with human ingenuity. By automating the mundane, enhancing precision, and scaling seamlessly, this platform isn’t just a tool—it’s a productivity revolution. 

As I begin my journey with Octane, I’m energised by the possibilities. Whether you’re optimising finance with TM1, Anaplan, transforming employee productivity with SAP, or reimagining customer service with Salesforce / ServiceNow, IBM Watsonx Orchestrate—powered by Octane—is your catalyst for growth. The future of enterprise productivity isn’t just automated—it’s augmented. With AI Agents handling the grind, your team can focus on what humans do best: innovating, strategising, and delivering exceptional value.

Ready to turn your 2025 AI vision into reality?

Contact Octane to discover how Watsonx Orchestrate can accelerate your journey—with the stats to back it up. 

Steny Sebastian
Principal - Data and AI Platforms
Octane Solutions 🗓️ Book me
https://www.octanesolutions.com.au/

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In today’s fast-paced business environment, companies are constantly looking for ways to streamline their operations, improve decision-making, and stay ahead of the competition. One of the tools that has gained significant attention in the world of business intelligence and analytics is IBM Planning Analytics, which harnesses the power of AI to enhance financial planning, forecasting, and reporting. One of the standout features of IBM Planning Analytics is its AI Assistant, an innovative tool that leverages artificial intelligence to provide smarter, more efficient planning and analytics capabilities.

In this blog, we’ll dive into the key features of the IBM Planning Analytics AI Assistant and explore how it is transforming business planning for organisations around the world.

What is IBM Planning Analytics AI Assistant?

IBM Planning Analytics is a cloud-based solution designed to help businesses automate their planning, budgeting, forecasting, and analysis processes. The AI Assistant embedded within the platform brings cognitive capabilities to the table, making it more intuitive and user-friendly.

The AI Assistant uses natural language processing (NLP) and machine learning algorithms to understand and respond to user queries in plain language, enabling business users—whether financial analysts, planners, or executives—to interact with the system more naturally. Instead of relying on complex formulas or spending hours running reports, users can simply ask questions like, "What was our sales growth in Q3?" or "How much did our expenses increase year-over-year?" The AI Assistant then processes these requests and provides quick, data-driven insights.

Key Features of IBM Planning Analytics AI Assistant

1. Conversational analytics

   One of the most impressive features of the AI Assistant is its ability to enable conversational analytics. Traditionally, getting insights from business intelligence tools involved navigating through multiple layers of data, setting up reports, or writing complex queries. The AI Assistant eliminates this complexity by allowing users to ask questions in natural language, just like they would talk to a colleague or consultant.

   For example, a user can ask, "What were our sales for last quarter?" and the AI Assistant can instantly pull up relevant data, graphs, or reports. This conversational interface makes it easier for non-technical users to engage with analytics and access valuable insights without having to be data experts.

2. Data-driven decision-making

   The AI Assistant doesn’t just provide static answers—it actively helps users analyse trends, identify anomalies, and make data-driven decisions. For instance, the Assistant can compare historical data, identify seasonal patterns, and even suggest potential adjustments to forecasts based on changing market conditions. This empowers decision-makers to quickly assess different scenarios and make informed choices.

   Additionally, the Assistant can provide context behind the data, such as explanations of why certain numbers are trending upward or downward. This deeper level of understanding enables organisations to plan with greater confidence.

3. Predictive analytics and forecasting

   In addition to assisting with retrospective analysis, the AI Assistant is also equipped to help users with predictive analytics. Using historical data, market trends, and other variables, the Assistant can generate forecasts for various business aspects like sales, revenue, and operational costs.

   For instance, planners can ask the AI Assistant, "What is the projected revenue for the next quarter based on current trends?" The Assistant then leverages machine learning models to provide accurate, forward-looking forecasts. By incorporating AI-driven insights, businesses can improve their planning accuracy and reduce the risks associated with manual forecasting.

4. Automated insights and recommendations

   One of the standout benefits of AI in business planning is its ability to go beyond simple reporting. The IBM Planning Analytics AI Assistant is capable of delivering automated insights and recommendations that are tailored to the needs of the organisation. By analysing past performance, the Assistant can highlight areas of opportunity or potential risk that may require attention.

   For example, if expenses are increasing faster than revenue, the Assistant might recommend strategies for cost-cutting or optimising operations. These automated recommendations allow planners and analysts to quickly address potential issues and capitalise on emerging opportunities.

5. Seamless integration with IBM Planning Analytics Workspace

   The AI Assistant is fully integrated with the IBM Planning Analytics Workspace, which is the central hub for business users to manage and analyse data. This integration ensures that users have a smooth experience when interacting with their data, whether they are leveraging the AI Assistant for ad-hoc analysis or using the broader tools available in Planning Analytics for long-term strategic planning.

   The seamless integration between the Assistant and the workspace also means that businesses can continue to rely on traditional data management and reporting workflows while taking advantage of AI-powered insights without disruption.

Benefits of IBM Planning Analytics AI assistant

• Faster Decision-Making

  The AI Assistant accelerates decision-making by delivering insights in real-time. Users can ask questions and get answers instantly, without having to manually sift through large datasets or run complex queries. This speeds up planning cycles and ensures that decisions are based on the latest data.

• Empowerment of Business Users

  With the AI Assistant, business users who may not have deep technical expertise can now access analytics and make informed decisions. This democratisation of data ensures that all teams—finance, marketing, operations—are equipped to contribute to planning processes and drive organisational success.

• Reduced Errors

  Since the AI Assistant uses machine learning models to predict and analyse data,  the likelihood of human error in forecasting and planning is significantly reduced. Automated insights and recommendations are based on sophisticated data analysis, helping to eliminate mistakes caused by manual data handling.

• Scalable Insights Across Teams

  The AI Assistant enables businesses to scale their analytics capabilities across teams and departments. Whether a team is working on financial forecasts, sales targets, or operational efficiencies, the Assistant can be used to generate insights that are relevant to each department’s specific goals and objectives. This scalability ensures that AI-powered decision-making benefits the entire organisation.

Real-World Use Cases

1. Finance Teams

   For finance teams, the AI Assistant is a game-changer in managing budgets, forecasting, and scenario planning. It can quickly identify deviations from expected results, recommend corrective actions, and forecast the financial outlook based on real-time data.

2. Sales and Marketing Teams

   Sales and marketing teams can use the Assistant to gain quick insights into customer behaviour, sales trends, and marketing ROI. By understanding which campaigns are driving results and which aren’t, they can adjust strategies on the fly and optimise their efforts.

3. Operations and Supply Chain

   Operations managers can use the AI Assistant to forecast demand, optimise inventory, and predict potential supply chain disruptions. By understanding these dynamics earlier, businesses can mitigate risks and improve operational efficiency.

Conclusion

The IBM Planning Analytics AI Assistant represents a significant leap forward in the world of business analytics. By combining artificial intelligence, natural language processing, and predictive analytics, it transforms the way businesses plan, forecast, and make decisions. With its ability to provide faster insights, automate recommendations, and empower users across the organisation, the AI Assistant is not just a tool—it’s a strategic asset that helps businesses become more agile and data-driven in their operations.

As businesses continue to face increasingly complex challenges, tools like IBM Planning Analytics AI Assistant will become indispensable for navigating the future of planning and decision-making.

Ready to take AI to the next level? Talk to us! 

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The world of finance is evolving rapidly, and AI is no longer a futuristic concept—it’s a practical tool that can transform how finance teams operate. But for many organisations, the idea of integrating AI into their workflows can feel overwhelming. Where do you start? How do you ensure success? The answer lies in taking a gradual, strategic approach. With Watson Orchestrate and IBM Planning Analytics, you can start small, prove the value, and confidently scale your AI initiatives. At Octane, we guide you through this journey, from exploring use cases to delivering impactful projects.

Why Start Small with AI in Finance? 

AI has the potential to revolutionise finance by automating repetitive tasks, enhancing decision-making, and improving accuracy. However, diving headfirst into a full-scale AI implementation can be risky. Starting small allows you to test the waters, build confidence, and demonstrate tangible results before committing to larger investments. This is where Watson Orchestrate shines—it’s designed to integrate seamlessly with your existing tools, like IBM Planning Analytics, and automate specific tasks without disrupting your workflows. 

Step 1: Explore Use Cases with Octane’s Workshops 

The first step in your AI journey is identifying where it can add the most value. We work closely with IBM client engineering team and run interactive workshops to help you explore potential use cases for Watson Orchestrate within your finance team. These workshops are designed to: 

• Understand Your Pain Points: We work with your team to identify repetitive, time-consuming tasks that are ripe for automation, such as data consolidation, report generation, or budget reconciliation. 

• Brainstorm Solutions: Together, we brainstorm how Watson Orchestrate can address these challenges, leveraging its AI capabilities to automate processes and enhance efficiency. 

• Prioritise Opportunities: Not all use cases are created equal. We help you prioritise the ones that offer the quickest wins and the highest impact. 

Step 2: Prove the Value with a Proof of Concept (POC) 

Once we’ve identified promising use cases, the next step is to validate them through a Proof of Concept (POC). A POC allows you to see Watson Orchestrate in action, delivering real results in a controlled environment. Here’s how it works: 

• Define Success Metrics: We work with you to define clear objectives and success metrics for the POC, ensuring that the results are measurable and aligned with your goals. 

• Build and Test: Our team builds the POC, integrating Watson Orchestrate with IBM Planning Analytics to automate the selected use case. We test the solution rigorously to ensure it meets your requirements. 

• Evaluate Results: After the POC, we evaluate the results together. Did it save time? Improve accuracy? Enhance productivity? These insights help you decide whether to move forward with a full-scale implementation. 

Step 3: Deliver the Project and Scale 

If the POC demonstrates value, we move into the  project delivery phase. Our team works closely with yours to implement the solution, ensuring it’s tailored to your specific needs and integrated seamlessly into your workflows. Once the initial project is delivered, you can scale the solution to address additional use cases, gradually expanding the role of AI in your finance operations. 

Real-World Impact: A Gradual Approach to AI 

Many organisations have successfully adopted AI in finance by starting small and scaling strategically. For example, an airline participated in one of Octane’s workshops and identified report generation as a key pain point. Through a POC, they automated the process using Watson Orchestrate, reducing the time required from 2 days to just 30 minutes. Encouraged by the results, they expanded the solution to automate budget reconciliation, achieving even greater efficiencies. 

Why Choose Octane? 

At Octane, we specialise in helping organisations like yours navigate the complexities of AI adoption in Finance teams. Our phased approach—starting with workshops, moving to POCs, and then delivering projects—ensures that you can dip your toes into AI without taking on unnecessary risk. We bring deep expertise in Watson Orchestrate and IBM Planning Analytics, along with a commitment to delivering measurable results.  

Take the First Step Today 

AI is no longer a distant dream—it’s a practical tool that can transform your finance team. By starting small with Watson Orchestrate and IBM Planning Analytics, you can explore the potential of AI, prove its value, and scale your initiatives with confidence. Ready to get started? Contact Octane today to schedule a workshop and begin your AI journey. Email us at  media@octanesolutions.com.au  to learn more. 

The future of finance is AI-powered, and the journey starts with a single step. Let Octane guide you every step of the way. 

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Today, we’re excited to explore a game-changing function that enhances the versatility of your Planning Analytics platform. Imagine a tool that not only streamlines your data processes but also connects your Planning Analytics seamlessly with external systems. This innovation allows you to execute HTTP requests directly within your TurboIntegrator (TI) processes, transforming your Planning Analytics into an integral part of your interconnected ecosystem. Join us as we delve into the possibilities this function brings and how it can elevate your data management strategies to new heights.

Why Execute HTTP Request is a Game-Changer

What makes this function truly versatile is its ability to connect with any external system that supports APIs. The only limit is your imagination and the capabilities of the APIs you wish to connect to. Integrating Planning Analytics with external systems allows developers to break free from traditional limitations and extend the functionality of their applications.

This session will include practical demonstrations of several use cases that highlight the power of the Execute HTTP Request. By the end, I hope to inspire you to explore how you can leverage this function to enhance your TM1 applications and workflows.

Demo 1: Hot Promotion of Objects Between Instances

Let's dive into our first demonstration on how to perform hot promotion of objects from one instance to another. Traditionally, migrating objects between instances involved shutting down the target server. However, using the Execute HTTP Request, we can do this in real-time.

1. Setting Up the TI Process:
    
   • Open the workbench in your workspace and create a new TI process.
   • Declare necessary constants and set your source and target instances (e.g., SmartCo to Demo Server).
2. Use of HTTP Execute Request:
    
   • Fetch dimensions from the source instance and check for their existence in the target instance.
   • For non-existing dimensions, save them as JSON files and use the HTTP Execute Request to migrate them to the target instance.

Let’s execute this process! Once completed, you’ll see that the dimensions have been successfully migrated.

Demo 2: Executing a Process Across Instances

Next, we'll demonstrate the ability to execute a process from one instance in another:

1. Migrate TI Processes:
    
   • Similar to dimension migration, retrieve the TI process (like Sample TI) from the source instance and save it as a JSON file.
2. Execute the TI Process:
    
   • Use the Execute HTTP Request to trigger execution from the target instance while utilising its response to capture status codes and log outputs.

After running this process, you should see that both the processes have been migrated and executed successfully.

Demo 3: Loading Currency Conversion Rates

In this demo, we will load real-time currency conversion rates from a website using its API:

1. Call the API:
    
   • Set up an HTTP GET request to retrieve USD conversion rates.
2. Extract and Utilise Data:
    
   • Capture the JSON response and extract required currency rates using JSON functions.

Run the process, and you will observe the real-time conversion rates being fetched and displayed.

Demo 4: Sending Teams Notifications

Next, I’ll show you how to send automated notifications to Microsoft Teams:

1. Integrate with Microsoft Power Automate:
    
   • Set up a Power Automate flow to send notifications.
2. Trigger Notification from System:
    
   • Use Execute HTTP Request to trigger alerts in Teams based on process execution results.

After execution, you should see notifications appear in your Teams channel.

Demo 5: Sending Emails via HTTP Requests

Finally, we'll explore how to send emails:

1. Power Automate for Email Notifications:
    
   • Again, set up Power Automate to manage email sending through appropriate HTTP requests.
2. Dynamic Email Content:
    
   • Utilise dynamic fields for subject and body based on execution results.

After executing this process, you will receive the email in your mailbox.

Conclusion

Today, we have unlocked the extensive capabilities of the Execute HTTP Request function in IBM Planning Analytics. We showcased hot promotion between instances, cross-instance process execution, real-time data fetching, as well as integration with Microsoft Teams and email notifications. 

Thank you all for attending this session. I hope you found it beneficial and feel inspired to explore the functionality of Planning Analytics further. Let’s move toward a more integrated and dynamic future in our analytics processes!

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The past few weeks have been a whirlwind, a high stakes balancing act that tested the limits of our team's resilience and expertise. We simultaneously managed two major client migrations. Both were for high profile large clients and part of their IBM Planning Analytics Modernisation initiative:

The clients

News Corp - News Corp Australia tells the stories that matter to 18.2 million Australians every month as an important part of News Corp, a diversified global media and information services company. From breaking news in the morning to deciding dinner that night, Australia trusts our brands to inform, inspire and delight across the day – including The Australian, The Daily Telegraph, Herald Sun, The Courier-Mail, The Advertiser, Mercury, NT News, Townsville Bulletin, The Cairns Post, Gold Coast Bulletin, Geelong Advertiser, news.com.au, Vogue, GQ, Kidspot, taste.com.au and plenty more. More here https://www.newscorpaustralia.com/

A long-time TM1 user was looking at refreshing its application and modernise it by going to the cloud and utilising new dashboarding capabilities of the workspace and starting some testing on AI capabilities for the Finance team. TM1 is one of the core applications within the Finance team and they could not afford to run the risk of a prolonged upgrade to the cloud.

BlueScope They are a global leader in metal coating and painting products for the building and construction industries, providing vital components for houses, buildings, structures, vehicles, and more.

They have built a solid foundation for growth with a diverse portfolio of businesses in some of the largest and fastest-growing economies of the world. They are headquartered in Australia, with our people and operations spread across North America, Australia, New Zealand, the Pacific Islands, and throughout Asia. More here https://www.bluescope.com/

BlueScope is also a long-term TM1 user with the application used for a number of areas including demand planning, forecasting and Reporting in the Finance teams. They were on an older version of on–prem TM1 and upgrading to the latest version of on-prem IBM Planning Analytics TM1

A Perfect Storm

Both projects presented unique challenges. NEWS's migration required significant user training and change management, while BlueScope's upgrade involved complex technical configurations and intricate coordination with multiple stakeholders. To make matters even more challenging, both go-lives were scheduled for the same day!

Overcoming the Odds

How did we navigate this perfect storm?

• Strong Leadership: Clear and decisive leadership was crucial in keeping the projects on track. By setting clear expectations, prioritising tasks, and making timely decisions, we were able to mitigate risks and ensure smooth execution.
• Effective Teamwork: Our team demonstrated exceptional teamwork and collaboration. By working closely together, we could share knowledge, support each other, and address challenges proactively.
• Agile Methodology: We adopted an agile approach, breaking down the projects into smaller, manageable phases. This allowed us to adapt to changing circumstances and deliver value incrementally. We have built up a comprehensive checklist for our upgrade TM1 upgrades and this makes upgrades easier and risk free.
• Robust Communication: Open and transparent communication was key to keeping all stakeholders informed and aligned. Regular status updates, clear documentation, and effective problem-solving ensured a smooth transition.

Lessons Learned

These experiences have taught us valuable lessons:

• Prioritise and Plan: Careful planning and prioritization are essential, especially when managing multiple projects simultaneously.
• Embrace Flexibility: Be prepared to adapt to unexpected challenges and changes in scope.
• Build Strong Relationships: Strong relationships with clients and team members are crucial for successful project delivery.
• Learn from Mistakes: Analyse past projects to identify areas for improvement and avoid repeating errors.

Successful outcomes for both clients

The upgrade was successful for both clients and went live on the same day. This was a testament to my team’s technical ability and tenacity to ensure we followed our upgrade checklist. All testing and end-user training went well. One of our key tenets of upgrades is to ensure that client communication around changed functionality and look and feel is explained, trained and tested. The stakeholders from our client side were great and the whole team went above and beyond during the upgrade and deployment.

Both the upgrade took under 6 weeks to complete with minimal disruption to the business.

A huge shoutout goes to Alpheus and Rajan for their stellar work on the NEWS migration, ensuring a smooth transition to the Cloud, and to Baburao for expertly managing the BlueScope upgrade, and overcoming every hurdle that came our way.

Your Turn

Have you faced similar challenges in managing multiple simultaneous projects? How did you overcome them? Share your experiences and insights in the comments below.

If you are looking to modernise or upgrade your IBM Planning Analytics, then contact us and we would be happy to guide you. 

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In this blog post, we will showcase the process of exposing the transaction logging on Planning Analytics (PA) V12 on AWS to the users. Currently, in Planning Analytics there is no user interface (UI) option to access transaction logs directly from Planning Analytics Workspace. However, there is a workaround to expose transactions to a host server and access the logs. By following these steps, you can successfully access transaction logged in Planning Analytics V12 on AWS using REST API.

Step 1: Creating an API Key in Planning Analytics Workspace

The first step in this process is to create an API key in Planning Analytics Workspace. An API key is a unique identifier that provides access to the API and allows you to authenticate your requests.

1. Navigate to the API Key Management Section: In Planning Analytics Workspace, go to the administration section where API keys are managed.
2. Generate a New API Key: Click on the option to create a new API key. Provide a name and set the necessary permissions for the key.
3. Save the API Key: Once the key is generated, save it securely. You will need this key for authenticating your requests in the following steps.

Step 2: Authenticating to Planning Analytics As a Service Using the API Key

Once you have the API key, the next step is to authenticate to Planning Analytics as a Service using this key. Authentication verifies your identity and allows you to interact with the Planning Analytics API.

1. Prepare Your Authentication Request: Use a tool like Postman or any HTTP client to create an authentication request.
2. Set the Authorization Header: Include the API key in the Authorization header of your request. The header format should be Authorization: Bearer <API Key>.
3. Send the Authentication Request: Send a request to the Planning Analytics authentication endpoint to obtain an access token.

Detailed instructions for Step 1 and Step 2 can be found in the following IBM technote:

How to Connect to Planning Analytics as a Service Database using REST API with PA API Key

Step 3: Setting Up an HTTP or TCP Server to Collect Transaction Logs

In this step, you will set up a web service that can receive and inspect HTTP or TCP requests to capture transaction logs. This is crucial if you cannot directly access the AWS server or the IBM Planning Analytics logs.

1. Choose a Web Service Framework: Select a framework like Flask or Django for Python, or any other suitable framework, to create your web service.
2. Configure the Server: Set up the server to listen for incoming HTTP or TCP requests. Ensure it can parse and store the transaction logs.
3. Test the Server Locally: Before deploying, test the server locally to ensure it is correctly configured and can handle incoming requests.

For demonstration purposes, we will use a free web service provided by Webhook.site. This service allows you to create a unique URL for receiving and inspecting HTTP requests. It is particularly useful for testing webhooks, APIs, and other HTTP request-based services.

Step 4: Subscribing to the Transaction Logs

The final step involves subscribing to the transaction logs by sending a POST request to Planning Analytics Workspace. This will direct the transaction logs to the web service you set up.

Practical Use Case for Testing IBM Planning Analytics Subscription

Below are the detailed instructions related to Step 4:

1. Copy the URL Generated from Webhook.site:
   • Visit siteand copy the generated URL (e.g., https://webhook.site/<your-unique-id>). The <your-unique-id> refers to the unique ID found in the "Get" section of the Request Details on the main page.

2. Subscribe Using Webhook.site URL:
   • Open Postman or any HTTP client.
   • Create a new POST request to the subscription endpoint of Planning Analytics.
   • In Postman, update your subscription to use the Webhook.site URL using the below post request:

• In the body of the request, paste the URL generated from Webhook.site:

{
 "URL": "https://webhook.site/your-unique-id"
}
<tm1db> is a variable that contains the name of your TM1 database.

Note: Only the transaction log entries created at or after the point of subscription will be sent to the subscriber. To stop the transaction logs, update the POST query by replacing /Subscribe with /Unsubscribe.

By following these steps, you can successfully enable and access transaction logs in Planning Analytics V12 on AWS using REST API.

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One of the standout features of Planning Analytics Workspace (PAW) is its ability to create calculations in the Exploration view. This feature empowers users to perform advanced calculations without the need for technical expertise. Whether you're using PAW or PAfE (Planning Analytics for Excel), the Exploration view offers a range of powerful capabilities. The Exploration view supports a variety of functions, such as aggregations, mathematical operations, conditional logic, and custom calculations. This means you have the flexibility to perform complex calculations tailored to your specific needs. 

This enables users to create complex financial calculations and business rules within the views, providing more accurate and tailored results for analysis and planning. All this can be done by the business users themselves without relying on IT or development teams, enabling faster and more agile reporting processes. This enables creating ad hoc reports and performing self-service analysis on the fly with a few simple clicks. This self-service capability puts the control in the hands of the users, eliminating the need for lengthy communication processes or waiting for IT teams to fulfill reporting requests.

In this blog post, we will focus on an exciting aspect of the Exploration view: creating MDX-based views that are dynamic and automatically update as your data changes. The beauty of these dynamic views is that users no longer need to manually select members of dimensions to keep their formulas up to date.

Similar to the functionality of dynamic subsets in dimensions, where each click in the set editor automatically generates MDX statements that can be modified, copied, and pasted, the exploration views in Planning Analytics Workspace also generate MDX statements. These MDX statements are created behind the scenes as you interact with the cube view. Just like MDX subsets, these statements can be easily customized, allowing you to fine-tune and adapt them to your specific requirements.

By being able to tweak, copy, and paste these MDX statements, you can easily build upon previous work or share your calculations with others.

Currently, the calculations are not inherently dynamic, however, there are techniques that can be employed to make the calculations adapt to changing time periods.

A classic example we can look at is performing variance analysis on P&L cube where we wish to add a variance formula to show the variance of current month from the previous month. There are many more calculations that we can consider from but we will focus on this analysis in this blog.

If we take our example, the current month and previous month keep changing every month as we roll forward and they are not static. When dealing with changing months or any member in your calculation, it's important to ensure that your calculations remain dynamic and adaptable to those changes. 

To ensure dynamic calculations that reflect changes in months, you have several options to consider:

Manual Approach: You can manually update the column dimensions with the changing months and recreate the calculations each time. However, this method is time-consuming, prone to errors, and not ideal for regular use.

Custom MDX Approach: Another option is to write custom MDX code or modify existing code to reference the months dynamically from a Control cube. While this approach offers flexibility, it can be too technical for end users.

Consolidations Approach: Create consolidations named "Current Month" and "Prior Month" and add the respective months to them as children. Then, use these consolidations in your view and calculations. This approach provides dynamic functionality, but you may need to expand the consolidations to see the specific months, which can be cumbersome.

Alias Attributes Approach: Leverage alias attributes in your MDX calculations. By assigning aliases to the members representing the current and previous months, you can dynamically reference them in your calculations. This approach combines the benefits of the previous methods, providing dynamic calculations, visibility of months, and ease of use without excessive manual adjustments.

In this blog post, we will focus on the alias attributes approach as a recommended method for achieving dynamic calculations in PAW or PAfE. We will guide you step-by-step through the process of utilizing alias attributes to ensure your calculations automatically adapt to changing months. By following this approach, you can simplify your calculations, improve efficiency, and enable non-technical users to perform dynamic variance analysis effortlessly.

To create dynamic calculations for variances between the current and prior month, you can follow these steps:

• Step 1: Ensure you have an alias attribute available in your Month dimension. If not, create a new alias attribute specifically for this purpose.
• Step 2: Update the alias with the values "Curr Month" and "Prior Month" for the respective months.
• Step 3: Open the exploration view in PAW and select the two months (current and prior) on your column or row dimension. 
• Step 4: Create your variance calculation using the exploration view's calculation capabilities. This could involve subtracting the P&L figures of the prior month from the current month, for example.
• Step 5: Open the MDX code editor and replace the actual month names in the MDX code with the corresponding alias values you updated in Step 2. You can copy the code in Notepad and use the "Find and Replace" function to make this process faster and more efficient.

By replacing the month names with the alias values, you ensure that the calculation remains dynamic and adapts to the changing months without manual intervention. When you update the alias values in the Month dimension, it will reflect in the exploration view. As a result, the months displayed in the view will be dynamically updated based on the alias values. This ensures that your calculations remain synchronized with the changing months without the need for manual adjustments.

Important Note: When selecting the months in set editor, it is crucial to explicitly select and move the individual months from the Available members' pane (left pane) to the Current set pane (right pane). This step is necessary to ensure that unnecessary actions, such as expanding a quarter to select a specific month, are not recorded in the MDX code generated in the exploration view which can potentially lead to issues while replacing the member names with alias values. 

This approach of using alias attributes to make calculations dynamic can be extended to various other calculations in Planning Analytics Workspace. It provides a flexible and user-friendly method to ensure that your calculations automatically adapt to changing dimensions or members.

That being said, it's important to note that there may be certain scenarios where alternative approaches, such as writing custom MDX code or utilizing a control cube, are necessary. Each situation is unique, and the chosen approach should align with the specific requirements and constraints of the calculation, however the proposed approach should still work for a wide variety of calculations in IBM Planning Analytics.

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As the world moves towards more data-driven decision-making, businesses are increasingly looking for effective planning and budgeting solutions. IBM Planning Analytics is the go-to for businesses looking for a comprehensive set of tools to help them manage their budgeting and planning process.

With Planning Analytics, businesses can access powerful analytics to make more informed decisions, leverage advanced features to create complex models, and gain better insights into their financial data.

IBM is constantly improving the functionalities and features of the IBM Planning Analytics components. This includes Planning Analytics Workspace (PAW), Planning Analytics for Excel (PAfX), and Planning Analytics with Watson. With these updates, businesses can take advantage of new features to help them manage their budgeting and planning process more effectively.

In the last 12 months, IBM has released several updates to its Planning Analytics components.

In PAW, users can now access advanced analytics such as forecast simulations, predictive models, and scenario analysis. They can also perform in-depth analysis on their data with the new Visual Explorer feature. In addition, users can now access a library of planning and budgeting models, which can be customized to fit the needs of their organization. (download PDF file to get the full details)

download PDF file to get the full details

download PDF file to get the full details

In PAfX, users can now access advanced features such as SmartViews and SmartCharts. SmartViews allows users to visualize their data in various ways, while SmartCharts allows users to create interactive charts and graphs. Users can also take advantage of the new custom formatting options to make their reports look more professional.

download PDF file to get the full details

download PDF file to get the full details

Finally, with Planning Analytics with Watson, users can access powerful AI-driven insights. This includes AI-driven forecasting, which allows users to create more accurate forecasts. In addition, Watson can provide insights into the drivers of their business, allowing users to make more informed decisions.

download PDF file to get the full details

Overall, IBM’s updates to the Planning Analytics components provide businesses with powerful tools to help them manage their budgeting and planning process. With these updates, businesses can take advantage of the latest features to quickly access data-driven insights, create more accurate forecasts, and gain better insights into their financial data.

Download the PDF file below to get the full version of each IBM Planning Analytics components.

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IBM Watson™ Studio is a platform for businesses to prepare and analyse data as well as build and train AI and machine learning models in a flexible hybrid cloud environment.

IBM Watson™ Studio enables your data scientists, application developers and subject matter experts work together easier and collaborate with the wider business, to deliver faster insights in a governed way.

Watch the below for another brief intro

Available in on the desktop which contains the most popular portions of Watson Studio Cloud to your Microsoft Windows or Apple Mac PC with IBM SPSS® Modeler, notebooks and IBM Data Refinery all within a single instal to bring you comprehensive and scalable data analysis and modelling abilities.

However, for the enterprise, there are also the versions of Watson Studio Local, which is a version of the software to be deployed on-premises inside the firewall, as well as Watson Studio Cloud is part of the IBM Cloud™, a public cloud platform. No matter which version your business may use you can start using Watson Studio Cloud and download a trial of the desktop version today!

Over the next 5 days, we'll ensure to send you use-cases and materials of worth for you to review at your earliest convenience. Be sure to check our social media pages for these.

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As business implements more and more technology to streamline their operations the benefits of saving time, minimising costs, increasing productivity and better accuracy are starting to be realised. This means this is hardly a trend that will fade anytime soon. If you or your business are not 'there yet' it’s better to catch up now rather than being left behind with the rest of the competition.

We've moved on from the times when cloud technology was an unknown territory for especially for Accounting and Finance but the industry players are now fully aware, prepared or in cloud. This leaves us to ponder where to next? I think people are now starting to set their attention to the adoption of artificial intelligence (AI) and we can see that this is happening at a much faster pace.

Both Data and work as we know it will be strongly affected by AI. With bots now able to determine and sort the information into different accounts by themselves, meaning AI is already delivering solo performances in the field. Further to this, Bots can tell and organise data coming from the same source into different categories, so if you have a monthly subscription and or one off purchases coming from the supplier, bots will automatically understand that they are of a different nature and will set them under different chart of accounts. Machine learning is also observable since these bots can learn from various human inputs to make better judgments and to adapt to accounting professionals’ behaviour patterns.

The effect or major changes due to AI on the typical Accounting and Finance role will have one of two observable outcomes:

• The menial / administrative tasks as they stand right now are slowly becoming scarcer, even some operational tasks traditionally performed by accountants, such as dedicated accounts payable and accounts receivable ones, are already being performed by AI or are outsourced.
• The other observable is the way accounting and finance will be structured within the organisation more than likely a small footprint but they have a greater impact.

The results of the above inevitably are better performance and enhanced cost reductions and although AI is already finding its way into the finance and accounting industry, it’s not been fully adopted quite yet. Without a doubt, the accounting industry has a bright future ahead with a focus more on value add than the menial.

Part 2 of this article might be a review of the tools avaialble. What do you think? Was my assessment fair? Leave comments, or feel free to reach out on social media.

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