New technology offers many opportunities for professional services to improve their operations; yet has the sector been at best reactive to previous waves of innovation?
The business case for using AI in professional services is compelling. There is increasing pressure from clients to provide professional services in a more cost effective and timely manner. By the end of 2018, it’s likely we’ll see almost half of CXOs investing in AI. It’s expected the tipping point where AI technology will be mainstream for ‘white collar jobs’ is in 2025.
Our report, Machines with purpose, aims to help professionals understand why AI is important to their businesses and how to take it from theory to practice. The report details key steps, and includes many practical to-do lists, case studies and useful guides.
The four steps include:
1) Understanding AI
AI is the theory and development of computer systems able to perform tasks that normally require human intelligence. AI-based technologies include:
- Natural language processing: the ability of software to read and understand a variety of written documents such as contracts and websites in a human way
- Rule-based systems: capture and use experts’ knowledge to provide answers to tricky problems that are governed by fixed rule-sets
- Machine learning: takes place without explicit programming. By trial and error, computers learn, mining information to discover patterns in data that can help predict future events
- Robotic process automation (RPA): RPA robots are software programmes designed to automate transactional, rules-based tasks by mimicking human interactions
- Computer vision: the ability to identify objects, scenes and activities in naturally occurring images
- Speech recognition: transcribes human speech automatically and accurately. The technology improves as the machines collect more examples of conversation
2) Building a case for AI in professional services
Why should professional services consider AI? Why should they look to improve the efficiency of their operations, especially as the business models in the sector have tended to evolve around hourly charge rates? Fewer hours spent would therefore mean less revenue, wouldn’t it? A few reasons for adopting AI in professional services include: staff expectations, client demands, faster response to clients and financial gains.
3) From theory to AI strategy
Adopting AI for the sake of ‘ticking the box’ will not generate return, professional services must assess how AI fits in with their strategy and ambitions. Our report includes a detailed guide on building an AI strategy.
4) Putting AI strategy into practice
Keeping the AI strategy and high level business objectives at the heart of the implementation process is crucial for the successful implementation of AI. The process of implementing AI follows three steps:
- Proof of concept (POC): rapidly test and evaluate the feasibility of solving the business problem with a selected AI tool and draft the business case for the next stage
- Production pilot: take the POC into production with a defined, narrow scope to test the solution in the real environment and validate the business case
- Scale up: implement technology at the desired scale, identify roadmap and expansion options
The opinions expressed in this article are those of the author and do not necessarily reflect those of Tucana.