Tag: AI

One minute guide to real-world AI implementation 

McKinsey just published an excellent and comprehensive paper covering how Artificial Intelligence (AI) can deliver real value for business.

tl;dr

The only issue – at 80 pages it’s a lot to read.

A lot of the use cases focus on retail, energy and education, one angle I find particularly are the read-across of these examples into service based and business-to-business environments. There are definitely some relevant points that could map to a services/B2B worlds: for example the automation of admin tasks for teaches, more targeted sales and marketing and more personalised customer service.

Here’s my take on the key points from the document:

1. No shortcuts: first data & digital, then AI

AI becomes impactful when it has access to large amounts of high-quality data and is integrated into automated work processes. AI is not a shortcut to these digital foundations. Rather, it is a powerful extension of them.

The firs thing firm’s need to do is come up with a real business case for AI that relates to the firm’s strategy, this requires separating the hype and buzz around AI from its actual capabilities in a specific, real-world context. It includes a realistic view of AI’s capabilities and an honest accounting of its limitations, which requires at least a high-level grasp of how AI works and how it differs from conventional technological approaches.
Each new generation of tech builds on the previous one – this suggests AI can deliver significant competitive advantages, but only for firms that are fully committed to it. Take any ingredient away—a strong digital starting point, serious adoption of AI, or a proactive strategic posture—and profit margins are much less impressive. This is consistent with McKinsey findings in the broader digital space.
Technology is a tool and in itself does not deliver competitiveness improvements.

2. Areas to focus on to create real value: project, produce, promote or provide 

To fulfil the expectations being heaped upon it, AI will need to deliver economic applications that significantly reduce costs, increase revenue, and enhance asset utilization.

Mckinsey categorized the ways in which AI can create value in four areas:(1) enabling companies to better project and forecast to anticipate demand, optimize R&D, and improve sourcing; (2) increasing companies’ ability to produce goods and services at lower cost and higher quality; (3) helping promote offerings at the right price, with the right message, and to the right target customers; and (4) allowing them to provide rich, personal, and convenient
user experiences

3. Data ecosystem & staff culture to the fore 

Firms must conduct sensible analysis of what the most valuable AI use cases are. They should also build out the supporting digital assets and capabilities. Indeed, the core elements of a successful AI transformation are the same as those for data and analytics generally. This includes building the data ecosystem, adopting the right techniques and tools, integrating technology into workplace processes, and adopting an open, collaborative culture while reskilling the workforce

4. Take a portfolio approach focused on use cases in short, medium and long term, be lean, fail fast & learn

A portfolio-based approach to AI adoption cases, looking at use cases over a one- to five-year horizon, can be helpful.

In the immediate future, McKinsey suggest a focus on use cases where there are proven technology solutions today that can be adopted at scale, such as robotic process automation and some applications of machine learning. Further out, identify use cases where a technology is emerging but not yet proven at scale. Over the longer term, McKinsey’s view is to pick one or two high-impact but unproven use cases and partner with academia or other third parties to innovate, gaining a potential first-mover advantage in the future. Across all horizons, a “test and learn” approach can help validate the business case, conducting time-limited experiments to see what really works and then scaling up successes. Fast, agile approaches are important.

5. Don’t be a hammer in search of a nail … 

To ensure a focus on the most valuable use cases, AI initiatives should be assessed and co-led by both business and technical leaders. Given the significant advancements in AI technologies in recent years, there is a tendency to compartmentalize accountability for AI with functional leaders in IT, digital, or innovation. This can result in a “hammer in search of a nail” outcome, or technologies being rolled out without compelling use cases. The orientation should be the opposite: business led and value focused. This business-led approach follows successful adoption approaches in other digital waves such as mobile, social, and analytics.

McKinsey graphics on AI: