Only Dead Fish

Google Firestarters 24 - Deciphering Digital Transformation - The Event

On Wednesday evening we had the 24th Google Firestarters which took digital transformation as it's subject. The event had been well oversubscribed within 48 hours of registration opening, demonstrating the high level of interest that there is around this topic in agencies right now (this was supported by the IPA Future of Agencies research that I did which revealed the disparate range of agencies and consultancies that are coalescing around this burgeoning area). It's a huge subject but as always with Firestarters we wanted to take some different but challenging perspectives on the theme and we had four excellent talks that I think achieved that really well.

First up we had the brilliant Emily Webber, who has worked with people and organisations in both the private and public sectors to develop their agile capability for sustainable change, and gave a fascinating talk on scaling learning through communities within organisations. Developing a organisational learning culture in the context of transformation is something that I talked about a lot in my book, and Emily brought that to life through the lens of enabling better cross-functional progress and learning at scale within a business. She talked about how the number of connections between people in a team (and therefore communication traffic) increases exponentially as the team grows bigger, and how the organisational response to these kinds of scaling issues relies on hierarchy and the creation of functional silos.

Yet learning across the organisation is an essential element in achieving real transformation: 'learning is done better when it's done together'. Social learning theorist Albert Bandura said: 'From observing others one forms an idea of how new behaviours are performed, and on later occasions this coded information serves as a guide for action'. Collaboration is about building on top of each other's ideas, and yet so many organisations treat learning as an individualistic thing. She referenced Simon Wardley's concept of Pioneers, Settlers, and Town Planners, making the point about the different types of people needed in an organisation to bring innovation and transformation to life, and Dan North's thoughts around Delivery Mapping, talking about 'skills liquidity' and the cross-over of business need, current skills and desired skills. Emily's concept of 'Communities of Practice' enables learning horizontally across the business, and a collective intelligence that is greater than the sum of individual intelligence. When combined with a 'safe to fail' environment, fast feedback loops, and an empowered supporting network, people can really fly, it allows for decentralised learning and scales good practice.

Scaling learning through communities in digital transformations from Emily Webber

Tim Malbon followed with a witty, insightful talk on how the process, practice and culture of building digital products and services can support business transformation. In that sense, the product is the means to an end, with the process actually serving to impact a wide range of organisational practices including workflow, resourcing, processes, hiring, training, learning and so on. If transformation is the aim, the product is the how, with multi-disciplinary product teams learning (he echoed Emily's point about how people learn better together, so agency and client staffers are combined) how to prioritise, innovate and iterate - how a lean/agile product innovation team works. This becomes particularly important when you consider that real transformation comes from not only customer experience, but employee experience as well. 

I'd thought it was important for this event that we had a client perspective, from a company that is undergoing digital transformation, and so was happy to have Johan Enstrom, Digital Transformation lead at Pentland brands (Speedo, Canterbury, Mitre, etc). Johan adeptly captured just how hard the change process is in reality within companies, particularly in managing priorities across disparate levels of maturity, knowledge and capability in the business. He talked about the importance of qualities such as honesty and humility in enabling the culture that supports change and inspires creative solutions, and of understanding the nuanced levels of skills needed at each level of the organisation, and how decisions are often hard in this context because they are not always binary choices. But a shared sense of purpose that allows more fluid application of strategy is key.

Our final talk was from transformation consultant Lucia Adams who drew a lot on her experiences transforming a big global media owner. I've written before about Lucia's wisdom in saying that: ‘Digital is 10% tech and 90% human. Organisations talk about digital as if it is 90% tech and 10% human’. Her talk built on this premis, describing seven myths of transformation (drawing a lot from Steven Johnson's excellent book on Where Good Ideas Come From) . Change management, she said, is often an expensive, waterfall beast that treats people as objects to be shifted this way or that and the effort that goes into ‘transformation programmes’ is usually met with an equal and opposite cultural force. Change is not single thing, it is a 'swarm', and yet a leader needs the courage to set a direction. Eureka moments and epiphanies are a fallacy - true insights require you to think something that no one has thought before in quite the same way, serendipity needs unlikely collisions and discoveries, and error sets off the truth. Platform building she said, is by definition an exercise in emergent behaviour.

The event was broad-ranging and extremely insightful so my thanks to our brilliant speakers, to Google for hosting, and all those who came on the night and joined in the debate. As always, Scriberia did a great job of visualising the talks. If you have any themes that you'd like us to cover in future Firestarters, do let me know.

Agencies as Sensemakers

It seems fitting in the week when the ad community has decamped to Cannes, an event which seems increasingly dominated by the ad technology players, to write about agencies and technology. This is also the week of-course in which Publicis Groupe have announced that rather than spend money on flashy awards and events, they are investing in creating an internally-focused, AI-based 'professional assistant platform' to connect their 80,000 employees. It's laudable to want to empower different ways of working and to bring dispersed disciplines and resources together in new ways, but the cynic in me worries that this just another example of an overly enthusiastic focus on shiny/proprietary technology at the expense of focusing on the people stuff that really drives change - behaviours, culture, the very fabric of the way in which you work (this has been a key theme in the digital transformation work I've done with clients over the past years). I'm sure we'll see.

Anyhow, there was some really interesting feedback around this theme that came out of the interviews I did for the IPA Future of Agencies report which came out recently. The overarching metaphor/model I used for the report was Charles Leadbetter's Systems and Empathy matrix, which captured neatly the dichotomous nature of the dynamics in the market right now and the spectrum of capability along which agencies are positioned, from the systems end (characterised by data/technology/operational and business models and efficiences) all the way through to the high empathy end (founded in human insight, creativity). So it is with agencies. Large, traditional consultancies schooled in business and strategic consulting, organisation and operating model design, moving to expand design and creative capability to create more of a 'full stack' approach. More creatively and digitally led agencies broadening out from marcomms and front end into consulting, technology and digital transformation. A disparate set of consultancies and agencies converging on that top right quadrant. 

One of the key challenges in this shifting dynamic (particularly for the creatively-led end of this spectrum) is to understand the new context that technology creates for marketing and advertising. Whilst some agencies seem determined to ignore it, the reality is that this context is changing dramatically. And I'm not just talking about the growth in ad tech. Each year (friend of ODF) Scott Brinker does a heroic job of tracking and mapping the burgeoning marketing technology landscape. In fact 'burgeoning' is something of an understatement. Back in 2011 when he first started mapping the market he was already tracking 150 vendors. That number has roughly doubled each year since then so that in the latest survey which has just been released we now have over 5,000 different marketing technology solutions across a huge variety of competencies. Solutions that are increasingly platformised, enabling marketing teams to plug together a variety of different specialised and vertical solutions to create their own 'marketing stack'. It's a huge degree of complexity that is fundamentally changing the discipline, function and operations of marketing. 

More than one interviewee for the IPA research noted that agencies simply do not have enough understanding about this technological shift (and that clients themselves are struggling with this level of complexity). This is a challenge for agencies, but it's also an opportunity. It may not be necessary for many agencies to understand the operational intricacies of specific technologies (unless of-course that is a key part of their proposition), but there is an imperative on every agency to appreciate the technological context within which they are operating. Work and ideas that make sense in the context of the client organisational focus on all aspects of customer experience, a broader set of customer touchpoints, more diverse brand content delivery, and growing marketing automation and technology-driven execution are critical. 

With so many martech vendors convergence is an inevitability but there is an opportunity for agencies to make sense of new waves of technologies for clients and to distil and enable the realisation of the value that they present. In the report I focused this into three key areas:

1. Potential:-  the ability of agencies to enable clients to better understand the potential of specific technologies and trends to enable better prioritisation of client attention and investment and properly assess strategies and focus
2. Integration:- to understand how different combinations of technologies might be brought together for beneficial effect (and be larger than the sum of the parts)
3. Application:- to appreciate and advise how technologies might be applied for business benefit

As successive waves of new technology (AI, VR/AR, IoT) take hold, this role as ‘sense-maker’ for clients creates further opportunity in the operationalising and optimisation of new technologies and capability. There were a number of agencies I spoke to, for example, that were doing some really interesting work around creating the equity/practices/processes designed to help a client operationalise and scale a new capability around the globe. 

There is enormous value to be had in being the trusted adviser and enabler. But without an understanding of the broader context in which advertising and marketing now operates, agencies will really struggle to play that role.

You can download a copy of the full IPA Future of Agencies report here.

When Robots Bluff

Accepting the (current) limitations of machine learning (and some recent hiccups), it's still fascinating how AI capability is developing so fast despite the field being far from new. The 'AI beating humans' narrative (as Tom Chatfield talked about at last year's Google Firestarters on the theme of AI - describing it as the ‘usurpation narrative of human-machine interactions…a creation is pitted against its creators, aspiring ultimately to supplant them’) is not always that helpful. But still, the landmarks along this storyline (AI beating humans at Jeopardy and more recently the famous victory of Google Deepmind’s AlphaGo against GO world champion Lee Sedol) are quite compelling.

So now we have the AI system (developed by Carnegie Mellon University) that has just accumulated $1.7m worth of chips over twenty days playing against four of the world’s top professional poker players. What was particularly remarkable about AlphaGo's victory over Lee Sedol (which I remarked on at the time) was the way in which AlphaGo won, playing in highly unexpected and creative ways, with moves that left even its creators 'pretty shocked'. Wired described how:

‘…with its 19th move, AlphaGo made an even more surprising and forceful play, dropping a black piece into some empty space on the right-hand side of the board. Lee Sedol seemed just as surprised as anyone else. He promptly left the match table, taking an (allowed) break as his game clock continued to run.’

The commentator at the time noted how Sedol appeared to have trouble dealing with a highly unusual move that he had never seen before. It was a real moment in the development of AI/human relations.

And perhaps we've just passed another one since what’s again interesting about the poker example is that on its way to victory, the AI succeeded in out-bluffing its human opponents. Poker, the FT piece notes, tests different mental muscles since it involves 'strategising using imperfect information in a way that is more akin to the real world'. The AI started not so well, but then focused on fixing its own weaknesses and filling the holes in its strategy as it went. It was able to use an additional feedback loop to respond in real time to the human activity. The implications are that AI can take other imperfect information situations and derive a good strategy for that situation.

Another new milestone.

Image courtesy

The Limitations of Machine Learning

I don't claim to be an expert on machine learning and AI but there is certainly no shortage of hyperbole about it right now. But in this case for good reason I think. It's on every trends/prediction list you read but it is surely the comprehensiveness in which it will be integrated into organisational capability, customer experience (and so competitive advantage) that makes this a true mega-trend. As Fred Wilson describes it 'AI will be the new mobile' (and he should know).

So it's both refreshing and insightful to actually find something that talks about the challenges and limitations that machine learning still faces. Just after Christmas Azeem Azhar (whose Exponential View is an excellent regular read on all things AI) tweeted a photo of a slide that neatly captured some of the current limitations of deep learning. Sadly he wasn't able to attribute the source, but the list focused on aspects such as how data-hungry it can be (requiring millions of examples), and how computationally intensive it can be to train and deploy, the fact that it can be limited in its representation of uncertainty and easily fooled by adversarial examples, the complexity in optimisation (needing high degrees of skill and experimentation) and notably the lack of trust that can come from poor transparency and 'uninterpretable black boxes'. 

One of the respondents to Azeem's tweet pointed at this piece by Peter Voss which expands further on current limitations. Whilst current mainstream techniques can be very powerful in narrow domains, they will typically have some or all of a list of constraints that he sets out and which I'll quote in full here:

• Each narrow application needs to be specially trained
• Require large amounts of hand-crafted, structured training data
• Learning must generally be supervised: Training data must be tagged
• Require lengthy offline/ batch training
• Do not learn incrementally or interactively, in real time
• Poor transfer learning ability, re-usability of modules, and integration
• Systems are opaque, making them very hard to debug
• Performance cannot be audited or guaranteed at the ‘long tail’
• They encode correlation, not causation or ontological relationships
• Do not encode entities, or spatial relationships between entities
• Only handle very narrow aspects of natural language
• Not well suited for high-level, symbolic reasoning or planning

Peter usefully goes on to detail a machine learning intelligence checklist of key features.

Reading about the challenges as well as the opportunities with machine learning gives a far more rounded perspective of where we are at, and brings to life the reality of the hurdles we still need to overcome. I've no doubt that many of these challenges will be surmounted in time (perhaps some in short order given the degree of focus on the area) but my overwhelming feeling about this is that it will result in a huge demand for people who can design, build and make sense of this kind of capability. Even if, as an organisation, you can plug into API-accessible machine learning capability or access open-source libraries of machine intelligence (like Tensorflow), you still need to be able to understand where the value is, and design elegant solutions and applications. The past few years has seen a huge growth in demand for data/analytics talent at a time when supply cannot keep up. I think we'll see the same for those skilled in not only the technical side of machine learning, but particularly those who can combine technical expertise with the kind of strategic and empathetic intelligence that can effectively mine the real value that is inherent in this new capability.

Labour Clouds

'By 2040, I’m pretty confident that every skilled worker will have their own signpost. You will be your own enterprise, in a much more meaningful way than the lip service of today.'

Stephen DeWitt

This was a pretty fascinating look at what the future of work could look like, featuring Stephen DeWitt of Work Market, the rapidly growing platform that enables large organisations to create 'labour clouds' that connect skilled workers with tasks that the business needs fulfilling:

'The platform integrates all types of workers — contractors, full-time employees, and vendors — and parameterizes tasks and workflows against any number of data points and business rules. In short, Work Market hopes to instrument a wholesale rethinking of how work gets done in our society — from a world of traditional corporate employment to a world where every skilled worker can act as an enterprise of one.'

We hear a lot about the so-called on-demand or gig economy (Uber, Taskrabbit, Amazon Turk etc). There are potential downsides to this way of working that we absolutely need to be aware of, but it does strike me that digital has proved particularly adept at facilitating new types of relationships and efficiencies in all fields, and work is one area that still retains potentially big inefficiences. The scale of Work Market's ambition is impressive. Done right I do think that we could see a wholesale rethink in how work should work, and with it a reshaping of the operations and structure of businesses the like of which we haven't seen in decades.

Image courtesy