What problems are we solving?

“AI is here, and executives expect it will have a significant impact on their businesses, but most say they are unprepared for immediate adoption.”

This is according to a recent KPMG USA survey, and there are many reasons why this is true.

  • The technology is evolving and changing so quickly it is difficult to invest confidently. 

  • Much of the responsibility for implementing AI has fallen to the IT department when it should be business led.

  • Despite the hype, many enterprise use cases are poorly understood or simply don’t require the Gen AI ‘hammer’.

  • There is a real risk of cost blow out from even simple LLM experiments.

  • Regulations around responsible and ethical use of AI are still being developed, leaving uncertainty around compliance.

  • Building and deploying AI use cases requires a multi-disciplinary team, with a wide variety of skills, that many enterprises simply don’t have in-house.

Transfit’s combination of management consulting, Delivery Excellence framework, digital delivery practitioners, and AI / Gen AI technologists can help clear many of these roadblocks by working with your organisation holistically. Our AI / Gen AI Specialists skills include

Data

  • Data Science

  • Machine Learning / MLSecOps

  • Data Engineering

  • Data / Information Governance

Design

  • UX/UI Design and Engineering

  • LLM / Prompt Engineering

Development

  • AI Application Development

  • DevSecOps

Non-Deterministic Testing

Multi-Domain Excellence

“AI Excellence engagements and training, applying both deep domain and broad cross-functional expertise.”

Domains

Most business problems (issues, pains, inefficiencies, profitability, etc), and their corresponding opportunity for uplift, reside within or across the four key domains of People, Processes, Systems, and Data.  As such, solving business problems at the intersection of these domains takes a very experienced, diverse, and excellence minded consultant.

Adopting AI / Gen AI solutions is no exception. In fact, more than for any other technologies, implementing these solutions requires a critical impact assessment across all these domains before acting. Furthermore, Transfit can help deliver on all the following core considerations, clearing the way for implementing many of the use cases below.

Core Considerations

“BI tells you what happened, AI tells you what will happen.” As compelling as this context switch is for business, the shift from BI to AI requires a significant uplift in core Data Infrastructure and Practices.

“Knowledge is gold.” Yet, unlike real gold, corporate knowledge and IP is generally undervalued and poorly secured.

“Data Governance is critical.” Guardrails covering security, access, linage, ethics, and compliance are critical to AI adoption, hence why Data Governance must be a first class citizen in your data ecosystems.

“There is no AI without data and cybersecurity.” This is why we recommend an AI Maturity Assessment as the first step towards your AI adoption.

Leading AI / Gen AI Training

Prompt Engineering Essentials for Digital Workers

The business benefits of Generative AI are here now, but is your organisation struggling to understand how Gen AI can most immediately improve your business productivity? The best place to start is uplifting the Prompt Engineering skills across your digital workforce. Read more…

Gen AI Masterclass for Pro-Code Developers

There is a rapidly growing demand for skilled software developers to help deliver exciting, ground breaking Gen AI solutions. To help the wider Pro-Code (.Net, Java, Python, etc) developer communities address this opportunity, Transfit has developed our Gen AI Masterclass for Pro-Code Developers course to fast track this upskilling to just a few days. Read more…

Example AI / Gen AI Use Cases per Domain

People

  • Virtual Assistants: Implementing AI chatbots to assist employees with HR-related queries, improving response times and efficiency.

  • Employee Engagement: Developing AI-driven tools to measure and enhance employee engagement through personalised surveys and feedback mechanisms.

  • Recruitment and Talent Acquisition: Automating the initial stages of recruitment, such as resume screening and candidate shortlisting, using AI-generated assessments.

  • Personalised Training and Development: Creating customised training programs and learning materials based on individual learning styles and career goals.

  • Diversity and Inclusion: Analysing language and communication patterns to identify and mitigate biases, promoting a more inclusive workplace.

Processes

  • Customer Service Automation: Deploying AI chatbots and virtual assistants to handle customer enquiries, improving response times and customer satisfaction.

  • Automated Content Creation: Generating reports, presentations, and documentation automatically, saving time and reducing human error.

  • Process Optimisation: Using AI to analyse and suggest improvements for business processes, enhancing efficiency, and reducing costs.

  • Predictive Maintenance: Implementing AI-driven predictive maintenance for machinery and equipment to reduce downtime and extend asset life.

  • Supply Chain Optimisation: Utilising AI to forecast demand, manage inventory, and optimise logistics and supply chain operations.

Systems

  • System Integration: Automating the integration of different IT systems, ensuring seamless data flow and interoperability.

  • Cybersecurity: Employing AI for threat detection, risk assessment, and automated response to potential cybersecurity incidents.

  • IT Support: Using AI-driven tools for automated IT support and troubleshooting, reducing the burden on human IT staff.

  • Infrastructure Management: Leveraging AI for automated monitoring and management of IT infrastructure, ensuring optimal performance and uptime.

  • Compliance Management: Utilising AI to ensure systems and processes comply with regulatory requirements, automating compliance reporting and monitoring.

Data

  • Generative BI: Automating data cleaning, normalisation, and preparation tasks to improve data quality and readiness for analysis.

  • Predictive Analytics: Using AI to analyse historical data and predict future trends, aiding in strategic decision-making.

  • Natural Language Processing (NLP): Employing NLP to analyse and derive insights from unstructured text data, such as customer feedback and social media posts.

  • Data Augmentation: Generating synthetic data to augment real-world datasets, improving the robustness and accuracy of AI models.

  • Personalisation: Leveraging AI to analyse user data and provide personalised recommendations and experiences in real-time.

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