Pipeline Discovery Services

70% of Transformation Initiatives Fail!

The success and failure of a Digital Transformation initiative starts with clearly defining the WHY. The importance of having a clear and well defined purpose or rationale behind embarking on a digital transformation journey.

We believe the Transformation of work is inevitable, but the traditional ways of progress and development are insufficient to realize true organizational change. 

Our Team of interdisciplinary Process Experts provide accurate, measurable, and growth oriented solutions in collaboration with your teams.

Curious? Let's Brainstorm and Discover your Transformation Potential

STATISTICS & IMPACTS

Digital Transformations Statistics & Facts

0 %

Of initiatives fail

9 out of 0

Projects have cost overrun.

1 in 0

Project becomes a statistical outlier, with an average overrun of 200%

Nirvana's Approach

Inverse Pipelining Methodology

We believe in the power of transformation – not just through technology, but through a profound understanding and mastery of people and processes they facilitate.

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Frequently asked questions

One of the biggest mistakes businesses make is plowing ahead without identifying why they’re taking the initiative:

  1. Figuring out which direction you want the business to take: This means defining long-term and short-term goals, creating realistic timelines, and defining where they see themselves within the industry.

  2. Be realistic about what a business can take on at a specific time: How much will it cost in time and money to purchase (or build) a solution, as well as the cost of implementation and ongoing maintenance?

  3. Define success: This requires businesses to identify metrics to track performance. Success is measured differently for every business, so setting an unattainable goal is self defeating. It’s also important to “understand customer and employee needs to build user focused value.”

  1. Precision in Defining Success: Leaders often find themselves fixated on outdated reports, metrics, and goals. The tendency to hold team members to legacy standards while expecting new outcomes hinders progress. To break this cycle, it’s essential to articulate specific measures of success aligned with the evolving objectives. Cease asking your team to adhere to outdated metrics and instead focus on the new criteria for success.

  2. Seek Input from Your Team: Tap into the insights of your team members who are familiar with your leadership style. They can identify blind spots and suggest areas where a change in leadership approach is needed. Encouraging input not only enriches decision-making but also demonstrates your commitment to evolving as a leader, inspiring team members to do the same.

  3. Embrace Incremental Changes for Significant Success: Acknowledge that consistent small changes over time yield substantial success. Rather than a complete reinvention, consider fine-tuning your leadership style for the future. For instance, if your role initially emphasized technical expertise, and your team needs to transition to a new technology, evaluate the leadership skills required to navigate this change without relying solely on your accustomed level of technical expertise.

Whether at the helm of an S&P 500 company or leading a team of five developers, leading change poses challenges. By challenging yourself to evolve alongside your team, you can expedite growth and enhance the likelihood of success. At Nirvana Consulting, we refer to this approach as “starting with self,” a potent and courageous method of leading transformation.

At Nirvana, we believe in the power of transformation not just through technology, but through a profound understanding and mastery of processes.

Our philosophy is rooted in the idea that technology is only as effective as the processes it supports. This belief guides our approach to digital transformation, where we prioritize process optimization to ensure that technological solutions are both meaningful and impactful.

 

We believe the Transformation of work is inevitable, but the traditional ways of progress and development are insufficient to realize true organizational change.

Nirvana’s Inverse Pipelining methodology centers your Business Objectives as the locus of Transformation.

Our Team of interdisciplinary Process Improvement Experts with background in Lean Six Sigma provide accurate, measurable, and growth oriented solutions in collaboration with your teams. 

Reach out to us to today to take the first step towards business Nirvana!

AI, or Artificial Intelligence, is a field of computer science focused on creating systems that can perform tasks requiring human intelligence, such as learning, problem-solving, and decision-making. It involves the development of algorithms and models that enable computers to mimic human cognitive functions.

AI works by using algorithms and data to simulate human intelligence. It involves several key components:

  1. Data Collection: AI systems require large amounts of data to learn and make predictions or decisions.

  2. Training: Machine learning models are trained on this data to identify patterns and relationships.

  3. Algorithms: These are sets of instructions that enable AI systems to process and analyze data.

  4. Inference: Once trained, AI systems use these algorithms to make predictions or decisions based on new data.

  5. Feedback Loop: Continuous learning and improvement occur through feedback and new data, enhancing AI’s performance over time.

AI techniques vary, including machine learning, deep learning, natural language processing, and computer vision, each designed for specific applications.

The time to achieve ROI with AI typically varies. For instance, a simple chatbot might take a few months to start saving customer service costs, while more complex AI projects, like predictive maintenance in manufacturing, can take a year or longer to show significant savings. It depends on the project’s complexity and data quality. Planning and patience are key.

Yes, specialized skills are often required to automate tasks or decision-making using AI. These skills include expertise in machine learning, data science, programming languages like Python, and knowledge of AI tools and frameworks. Additionally, domain-specific knowledge can be crucial for tailoring AI solutions to specific industries or tasks. Collaboration among data scientists, domain experts, and software developers is often essential for successful AI automation.