Another Week In AI & Machine Learning

Driveless

So the hot news this week is that people keep crashing into driverless cars. Wired magazine discusses the issue that the way in which the current generation of prototypes being driven on America's roads are experiencing a high level of accidents, apparently because they don't behave is the same way that ones driven by humans do. 86% of collisions in California this year have been due to being rearended or side wiped! Autonomous cars appear to be over cautious and annoy human drivers, not only by stopping unexpectedly but also because they are over compliant with the letter of the law in interpreting situations. So why don't they have signs on the car, just like a learner does to warn drivers that they need to keep clear? It's a pretty similar situation and people do tend to steer as clear as practicable from learners who they know to be unpredictable.

AWS Capability Continues to Grow

AWS also held its AWS Innovate Online Conference. In his "state of the nation" talk, Boaz Ziniman outline current capabilities available Off the Cloud (OTC) rather than Off the Shelf (OTS). Basically, AWS still provides impressive capabilities which allow a developer, data scientist or organisation to just start using AI and scale rapidly. Though they have been adding to this capability with impressively powerful capabilities around visual recognition and voice processing as well as bundled environments which are pretty much deploy and go. this is very liberating, because it provides a readily used Pay as You Go (PAYG) capability, which avoids much of the traditional issues of continuously having to research, select, implement, integrate and tune the suite of tools and infrastructure needed, as well as continuously returning to CAPEX approval and purchase processes for scaling to deal with increasing volumes of performance issues. 

There's a caveat though. Some of the capabilities, e.g. recognising a face in a crowd, which may be useful for security solutions, might also be used as the tools for enforcing a police state or merciless pursuit by paparazzi. Enabling such massive AI at scale, will to some extent be another nail in the coffin of personal privacy.

Early Adopters Surge Ahead

Finally, MIT Sloan and Boston Consulting Group released a report "Artificial Intelligence in Business Gets Real" which surveyed the application of AI in business across the globe. Whilst there were the usual scare stories about China being ahead of the West in adoption and that the gap between pioneers and laggards is growing larger there were some interesting points. The Chinese are deploying to meet efficiency and cost needs. Everyone else is deploying, because AI helps them do more and there was a message that AI will not replace jobs, but it will change the nature of work and the skills needed.

The second key message, was to experiment with something simple and demonstrate the benefits, because business leaders who had seen AI in practice, get it and are prepared to invest more, to the point that AI is almost addictive in the way in which it influences investment appetite once a business has tried it.

The final message was really around AI at scale. If you are planning on building your  future business model around AI, then you need to plan, prioritise long term investments and to get effective data governance in place. This does not just mean establishing once source of the truth, with valid, complete and coherent data. It also means having a good handle on version control. Since, if multiple applications of AI depend on the same data, it needs to be synchronised to avoid unintended problems. So Lean Data practices are fundamental to adopting AI at scale.

Finally, it was interesting to hear how paranoid Chinese companies, adopting AI, are about cyber security. Protecting their data from competitors and the continuing availability of data and AI based solutions becomes increasingly important once a company's business model has evolved to adopt AI. So the principle "Cherish your Data" (see The Way of DAU) is key to successful exploitation.

Conclusion

Human issues, ethics and common sense remain central to AI adoption, an Agile mindset encourages adoption and Data Governance is a key enabler.

Culture Shows Up Again as the Thing Driving Agility

Forbes Insights and the Scrum Alliance have got together to publish a recent report on Organisational Agility, drawn from interviews with over 1,000 C-suite executives from around the globe.

A key theme which runs through the whole report is how important Culture is to Organisational Agility. (Something which I emphasised in "The Way of DAU"). Additionally, there are some compelling findings about the benefits of organisational agility. Over 50% of respondents identified benefits in the areas of:

• Time to Market
• Speed of Innovation
• Employee Morale
• Ability to Attract Talent
• Competitiveness
• Financial Results
• Ability to Manage Across Geographies

To re-inforce these findings, the report focuses on the performance of leading Agile Organisations and Laggard Organisations. Whilst they represent similar proportions of the overall survey population (16% and 19% respectively), twice as many Leader Organisations enjoy annual growth of over 20% as Laggards do.

In all probability, the CxO community that the authors consulted, probably included a pool of organisation most interested in Agility, as an earlier report by McKinsey suggested that only 4% of organisations have completed an organisation wide Agile transformation.

The report looked at a number of things, including whether organisational agility was End-to-End or siloed in functions. Based on the opinions of the respondents, it appears that Operations and Technical Functions tend to be the most Agile (79% and 75% respectively), followed by the usual suspects in Sales and Marketing. Interestingly enough, Finance achieved a credible 64% vote of confidence, but HR was amongst the laggards with only 56% of respondents considering it an agile function. Considering that HR is often thought of as the "Guardian of Culture" this is a worrying gulf and should be a wake up call to HR to re-vitalise its mission.

One area that I would have liked more analysis on, was whether Leading Agile organisations already had positive innovative and collaborative cultures before they embraced Agile or had to focus on it as part of Agile Adoption.

Anyway, the take away is that all CxOs, irrespective of function, need to work on fostering the right culture.

Digital Era Customer Contact and Experience

In the 1980s, The theory of Customer Care was propogated widely at the same time that call centres started to evolve into contact centres. The subtle difference between a call centre and a contact centre being that a contact centre is supposed to deal with customers through all media of communication: letters, telephone calls, and direct contact in person if somone knocked on your door. With the '90s, email was added and then websites and latterly mobile apps have started to grow traction.

In parallel with this we have seen all sorts of mechanised and automated technologies arrise to help improve productivity and efficiency. IVR or Interactive Voice Response (which really is not that interactive), Power diallers to fuel direct telemarketing campaigns, backed up with scripting to ensure consistency of message were also introduced followed by features such as distributd call centres, off shoring, workflow based load balancing etc.

The touble with all these approaches has often been that they focus on work load balancing, limited levels of scalability and cost efficiency rather than personal experience. Offshoring also intorduced problems with culture and language, as well as greater possibilities of fraud and exposure of customer data to theft and exploitation, as "solid legal contracts" have proven to be paper tigers in protecting confidentiality. In fact in some countries, such as Singapore, which have reutations for innovation and fast moving companies, customer experience is terrible.

This just does not cut it in the modern digital age, when customer experience and reputation will make or break a company. We now have people pondering the future of the contact center and whether there will be any people in them to take calls with the advent of AI and chatbots as well as the use of IoT to collect customer behaviour information and change the model by which companies interact with customers.

The real issue is that many of these advances can be uselfull exploited in situations which suit "self serve" activities such as "how can I fix my thermostat or change my account settings". They don't particularly work well when there has been a service failure or a billing mix up. Assumptions that Generation Next Letter in the Alphabet will always want to contact you via a certain technical chanel are also wrong. Peoples contact method decisions are often affected by context. If they are not allowed to use mobiles at work for instance, or they are in an airport and have lost their mobile phone or iPad and urgently need help with a travel problem, then other contact options are needed.

The new technologies offer opportunities, but greater effort needs to be put into creatively exploiting them and designing a great customer experience. So it is interesting to see that a company like Creston is establsihing a global network of Customer Experience Centres to show exactly what it can do for people. It will also be interesting to see how Creston exploits this interaction chanel to improve its customer offerings as well.

Digital Experience (DX) And the Re-invention of Marketing

The Post War World of the '50s and '60s saw a massive explosion in marketing as companies tried to force feed Baby Boomers mass produced electro mechanical goods, encased in plastic. They did this by trying to create desire and brainwash people into needing their brands. This was the last hurrah for the Henry ford approach, "they can have any colour they like, so long as it is black" and the Taylorist School of management which focused on productivity and costs, whilst paying lip service to quality.

It took the massive economic melt down of the late seventies and early eighties for the Quality led management, pioneered largely by Japanese companies to gain acceptance in Europe and America, which at the same time was accompanied by the angst of Punk, as Generation X turned on mass consumerism and the break down of "A Job for Life". Customer Care, based on the concept of listening to and delighting customers, came to the forefront. New technologies like CADCAM and philosophies like Flexible Manufacturing made it possible to design, prototype and evolve products to meet customer needs and gradually Design Thinking came into being.

At the same time, the '80s saw the birth of the PC and take up of lower cost client server technology, which usurped proprietary mainframes whilst Rapid Application Development (the forerunner to Agile) sparked the first steps to evolving the Digital Ecosystem that we see today. This has pushed marketing evolution to the extent that the modern marketing function is digitally obsessed with Customer Behaviour Data, Customer Experience and Agile Marketing. So much so, that most digital conversations inevitable involve talking about Customer Experience and the Customer Journey.

So it was great to see Nimbus Ninety publish "The CX Manifesto" which sets out over 30 recommendations under the 5 headings of Personalisation, Experimentation, Trust, Data and Loyalty in its latest  quarterly publication of the Chief Disruptor magazine. This is well worth a read as it has been compiled by practitioners from London's vibrant digital scene over the first 6 months of 2018 and is based on sound experience.