The AI-powered Organisation – Hope for the 21st Century

Artificial intelligence is reshaping business – though not at the pace many assume. True, AI is now guiding decisions on everything from healthcare to once pie-in-the-sky prospects such as totally automated customer service is on the horizon.

AI: A Working Definition

Anyone working in artificial intelligence knows that different stakeholders define it differently. When we talk about AI in this report, we mean the science of training systems to emulate human tasks through learning and automation.

The technologies that enable AI, like development platforms and vast processing power and data storage are advancing rapidly and becoming increasingly affordable. The time seems ripe for companies to capitalise on AI. Yet, despite the promise of AI, many organisations’ efforts with it are falling short. Mckinsey have surveyed thousands of executives about how their companies use and organise for AI and advanced analytics and their data shows that only 8% of firms engage in core practices that support widespread adoption. Most firms have run only ad hoc pilots or are applying AI in just a single business process.

Why is the progress so slow? At the highest level it’s a reflection of a failure to rewire the organisation and its people. AI initiatives face formidable cultural and organisational barriers. But alternatively, it’s also been seen that leaders who at the outset take steps to break down those barriers can effectively capture AI’s opportunities.

How to make that shift? One of the biggest mistakes’ leaders make is to view AI as a PLUG AND PLAY technology with immediate returns. Deciding to get a few projects up and running they begin investing millions in data infrastructure, AI software tools and data expertise. Some of the pilots may etch out small gains but then months and years pass without bringing the big wins executives expected. Leaders also often think too narrowly about AI requirements. While cutting edge technology and talent are certainly needed, its equally important to align a company’s culture, structure and ways of working to support broad AI Adoption. But at most businesses that are not born digital, with traditional mindsets and ways of working run counter to those needed for AI.

Three Major Shifts are required:

1)    From Siloed work to Interdisciplinary collaboration

Al has the biggest impact when its developed by cross functional teams with a mix of skills and perspectives. Having business and operational people working side by side with analytics experts will ensure that initiatives address broad organisational priorities. For example, when development teams involve end users in the design of applications the chances of adoption increase dramatically.

2)    From Experience-Based leader driven decision making to data driven decision making at the front line.

When AI is adopted broadly, employees up and down the hierarchy will augment their own judgement and intuition with algorithm’s recommendations to arrive at better answers than either humans or machines could reach on their own. But for this to work, people at all levels have to trust the suggestions from AI and feel empowered to make decisions. If employees have to consult a higher up before taking action, that will inhibit the use of AI.

3)    From Rigid and Risk aversive to Agile, Experimental and Adaptable.

Organisations must shed the mindset that an idea needs to be fully baked or a business tool must have every bell and whistle before its deployed. On the first iteration AI applications rarely have all their desired functionality. A test and learn mentality will reframe mistakes as a source of discoveries reducing the fear of failure. Getting early user feedback and incorporating it into the next version will allow firms to correct minor issues before they become costly problems. Development will speed up, enabling small AI teams to create minimal viable products in a matter of weeks rather than several months.

Such fundamental shifts don’t come easily. They require leaders to prepare, motivate and equip the workforce to make a change. But leaders must be prepared themselves. We’ve seen failure after failure caused by the lack of foundational understanding of AI among senior executives. Working together with analytics academics can help leaders acquire that understanding.

Leaders have to provide a vision that rallies everyone around the common goal. Workers must understand why AI is important to the business and how they’ll fit into a new AI-oriented culture. In particular, they need reassurance that AI will enhance rather than diminish or even eliminate their roles.

The ways AI can be used to augment decision making keep expanding. New applications will create fundamental and sometimes difficult changes in workflows, roles and culture, which leaders will need to shepherd their organisations through carefully. Companies that excel at implementing AI throughout the organisation will find themselves at a great advantage in a world where humans and machines working together outperform either humans or machines working on their own. 

The actions that promote scale in AI create a virtuous circle. The move from merely functional to interdisciplinary teams initially bring together the diverse skills and perspectives and the user input needed to build effective tools. In time, workers across the organisation absorb new collaborative practices. As they work more closely with colleagues in other functions and geographies, employees begin to think bigger. The speed of innovation picks up as the rest of the organisation begins to adopt the test-and -learn approaches that successfully launched the pilots. As AI tools spread throughout the organisation, those closest to the action become increasingly able to make decisions once made by those above them, flattening organisational hierarchies. This is in turn encourages further collaboration and even bigger thinking.

Collated and Summarised by Nashya Haider, 4th July 2019