{"id":1011,"date":"2020-01-10T16:46:08","date_gmt":"2020-01-10T16:46:08","guid":{"rendered":"http:\/\/janbosch.com\/blog\/?p=1011"},"modified":"2020-01-10T16:46:14","modified_gmt":"2020-01-10T16:46:14","slug":"the-game-plan-for-2020","status":"publish","type":"post","link":"https:\/\/janbosch.com\/blog\/index.php\/2020\/01\/10\/the-game-plan-for-2020\/","title":{"rendered":"The game plan for 2020"},"content":{"rendered":"\n
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Image by Keith Johnston from Pixabay<\/figcaption><\/figure>\n\n\n\n

In reinforcement learning (a field within AI), algorithms need to learn about an unexplored space. These algorithms need to balance exploration (learning about new options and possibilities) with exploitation (using the acquired knowledge to generate a good outcome). The general rule of thumb is that the less is known about the problem domain, the more the algorithm should focus on exploration. Similarly, the better the problem domain is understood, the more the algorithm should focus on exploitation.<\/p>\n\n\n\n

The exploration\/exploitation balance applies to companies too. Most \ncompanies have, for a long time, been operating in a business ecosystem \nthat was stable and well understood. There were competitors, of course, \nbut everyone basically behaved the same way, got access to new \ntechnologies at about the same time, responded to customers the same \nway, and so on. In such a context, a company naturally focuses more and \nmore on exploitation as the reward for exploration is low. This is \nexactly what I see in many of the organizations I work with: for all the\n talk about innovation and business development, the result is almost \nalways sustaining innovations that make the existing product or solution\n portfolio a bit better.<\/p>\n\n\n\n

With digitalization and its constituent technologies \u2013 software, data\n and AI \u2013 taking a stronger and stronger hold of industry after \nindustry, the stable business ecosystem is being disrupted in novel and \nunpredictable ways. Many companies find out the hard way that their \ncustomers never cared about their product. Instead, the customer has a \nneed and your product happened to be the best way to meet that need. \nWhen a new entrant provides a new solution that meets the need better, \nyour product is replaced with this new solution.<\/p>\n\n\n\n

The only way to address this challenge is to significantly increase \nthe amount of exploration your company conducts \u2013 we\u2019re talking real \nexploration, where the outcome of efforts is unknown and where everyone \nunderstands that the majority of initiatives will fail. To achieve this,\n though, you need a game plan. This game plan needs to contain, at \nleast, four elements: strategic resource allocation, reduced effort in \ncommodity functionality, exploration of the novel business ecosystems \nand\/or new positions in the existing business ecosystem and exploration \nof disruptive innovation efforts that are enabled through data and AI.<\/p>\n\n\n\n

Many companies allocate the vast majority of their resources to their\n largest businesses. This makes intuitive sense, but fails to put a \nlongitudinal perspective on the challenge of resource allocation. A \nmodel that can be very helpful in this context is the three horizons \nmodel. This model structures the businesses the company is in into three\n buckets. Horizon one are the large, established businesses that, today,\n pay the bills. Horizon two are the new, rapidly growing businesses \nthat, however, are much smaller than the horizon one businesses. These \nare intended to be our future horizon one businesses. Horizon three are \nall the new, unproven innovation initiatives and businesses where it\u2019s \nuncertain that things will work out but that are the breeding ground for\n future horizon two businesses. Resource allocation should restrict \nhorizon one resources to maximally 70 percent of the total. Horizon two \nshould get up to 20 percent and at least 10 percent of the total company\n resources should be allocated to horizon three.<\/p>\n\n\n\n

Within horizon one, each business should grow its resource usage \nslower than revenue growth. That might mean that a horizon one business \ngrowing at 5 percent per year should cut its resource usage with 5 \npercent per year as this business is supposed to act as a cash cow for \nfunding the development of future horizon one businesses.<\/p>\n\n\n\n

In most companies, revenue and resource allocation are closely \naligned with each other, but this is a mistake from a longitudinal \nperspective. A new business will require years of investment before it \ncan achieve horizon one status and this new business can\u2019t fund itself. \nOf course, you can have it bootstrap itself, but the result will \ntypically be that competitors with a more strategic resource allocation \nwill become the market leaders in these new businesses.<\/p>\n\n\n\n

Second, reduce investment in commodity functionality. Our research \nshows that companies spend 80-90 percent of their resources on \nfunctionality and capabilities that customers consider to be commodity. \nI\u2019ve discussed this in earlier blog posts and columns, but I keep \ngetting surprised at the lack of willingness of companies to look into \nnovel ways of reducing investment in places where it doesn\u2019t pay off. \nDon\u2019t be stupid and, instead, do a strategic review of your entire \nproduct portfolio and the functionality in your products and, together \nwith customers and others, define what\u2019s commodity and what\u2019s \ndifferentiating. Once you\u2019ve defined the commodity, stop<\/em> \nvirtually all investment in it. You need those resources for sustaining \ninnovations that drive differentiation for your products.<\/p>\n\n\n\n

Third, many companies consider their existing business ecosystem as \nthe one and only way to serve customers. In practice, however, \necosystems get disrupted and it\u2019s far better to be the disruptor than \nthe disruptee. This requires a constant exploration of opportunities to \nreposition yourself in your existing ecosystem, as well as an \nexploration of novel ecosystems where your capabilities might also be \nrelevant.<\/p>\n\n\n\n

Finally, digital technologies \u2013 especially data and AI \u2013 offer new \nways of meeting customer needs that you must explore in order to avoid \nbeing disrupted by, especially, new entrants. Accept that the value in \nalmost every industry is shifting from atoms to bits, that data can be \nused to subsidize product sales in multi-sided markets, that AI allows \nfor automation of tasks that were impossible to automate even some years\n ago and, in general, proactively explore the value that digital \ntechnologies can provide for you and your customers. This is where the \nmajority of the resources that you freed up through horizon planning and\n reducing investment in commodity functionality should go.<\/p>\n\n\n\n

Concluding, at the beginning of 2020, you need a game plan to significantly increase exploration at the expense of exploitation in order to identify new opportunities and detect disruption risks and to invest sufficiently in areas that provide an opportunity for growth. This requires strategic resource allocation, identifying and removing commodity, a careful review of your position in existing and new business ecosystems and major exploration initiatives in the data and AI space. It\u2019s risky, it\u2019s scary, most initiatives won\u2019t pan out and customers, your shareholders and your own people will scream bloody murder. And yet, the biggest risk is to do nothing at all as that will surely lead to your company\u2019s demise. Will you allow that to happen on your watch?<\/p>\n\n\n\n

To get more insights earlier, sign up for my newsletter at <\/em>jan@janbosch.com<\/em><\/a> or follow me on<\/em> janbosch.com\/blog<\/em><\/a>, LinkedIn (<\/em>linkedin.com\/in\/janbosch<\/em><\/a>) or Twitter (<\/em>@JanBosch<\/em><\/a>).<\/em> <\/p>\n","protected":false},"excerpt":{"rendered":"

In reinforcement learning (a field within AI), algorithms need to learn about an unexplored space. These algorithms need to balance exploration (learning about new options and possibilities) with exploitation (using the acquired knowledge to generate a good outcome). The general rule of thumb is that the less is known about the problem domain, the more … Read more<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"generate_page_header":"","footnotes":""},"categories":[8,10],"tags":[],"_links":{"self":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1011"}],"collection":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/comments?post=1011"}],"version-history":[{"count":2,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1011\/revisions"}],"predecessor-version":[{"id":1014,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1011\/revisions\/1014"}],"wp:attachment":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/media?parent=1011"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/categories?post=1011"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/tags?post=1011"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}