If You’re Doing Digital Without AI, You’re Doing It Wrong

Transformation

By

May 24, 2019

There are probably no two topics more convoluted to business leaders right now than “digital transformation” and “AI.” Both are broad, require ventures into the unknown, significant investments of time and money, and a lot of visibility and pressure from the executives. The stakes feel high, and the pace at which business leaders feel pressured to execute these transformations outstrips available resources.

Interestingly, in our conversations and work with clients, we’ve observed a concerning trend where clients perceive “digital” as separate from artificial intelligence (AI), and put the digital transformation ahead of thinking through an AI strategy. The majority of digital transformations are focused on improving the mobile experience, moving toward more self-serve, and reducing call center support and costs. There’s also the catch-all need to become more “agile” or “responsive.” All of these goals are worthy of pursuit, but the focus is too narrow, and that stunts the opportunity for growth.

This separatist thinking will cost you in the long run because growth and agility come from learning—outcomes that digital alone will not solve.

Here’s why business leaders need to integrate AI needs with their digital transformations:

SPEED TO MARKET:

If you wait until your digital transformation is over to begin the work on AI, it’s too late. These need to be parallel, not serial activities. On average, it takes a well-oiled machine to complete a transformation effort within 2-3 years. If you wait until 2-3 years from now to begin your AI journey (which takes another two years minimum to develop some maturity), you have already lost and cannot catch up.

AI IS DIGITAL:

AI solutions are digital ones, so any digital transformation should, at the very least, include activities that ready the organization for AI. You can save your organization much re-work if you start simple activities to prepare your data, processes, people, and technology as part of your transformation efforts.

Thinking of how your culture needs to shift and defining the experiences you want to enable with AI will set your team on the right course and moving into the future ways of working—not just addressing the issues of the present anchored in how you work today. There are simple activities (outlined below) that you can incorporate into your efforts that begin the work of AI readiness, without slowing down your digital efforts.

Data: Data needs to be described as it is captured. This process allows data to be connected to other data sets to provide comprehensive and longitudinal insights, which support business agility and increasingly automated decision-making.

People: You need to understand the current mindsets and attitudes of your people and stakeholders towards AI and automation: what do they know about AI, and what do they think and feel about it?

Process: Processes need to be understood and blind spots uncovered where tribal (or tacit) knowledge will get in the way of consistent decision-making and execution. Business leaders need to learn the habitual things employees do and incorporate that in the documented process.

Tech: Develop a future state reference architecture rooted in interoperability vs. stovepipes and clunky integrations. This architecture is central to taking advantage of the cloud and microservices.

Culture: The team must have a shared vision for how they want to show up in the space of AI and how the future aligns with the organization’s purpose. It is equally important to define how the organization will not use AI solutions.

Experience: Consider the experiential impacts on employees, partners, and customers. Map the future state journey for each of them and make decisions on where AI solutions should play a part. Note: Just because it can, does not mean it should.

CAPTURING THE LEADER SPACE:

If you’ve decided you want to be a leader in the AI space, we have developed frameworks and critical thinking models to help you embed full-scale AI readiness into your digital transformation.

Capability vs. Maturity: Rather than taking a tech-first or even business capabilities approach to mapping your future, leaders draw a maturity curve that allows them to solve increasingly complex and impactful problems as a continuous journey rather than stringing disparate POCs together after the fact.

Vertical Scale Framework: Rather than starting small and then figuring out how to scale, leaders use systems thinking to imagine the best possible future and parse the components into actionable and feasible building blocks—new capabilities for solving real business problems. It’s thinking big but solving small, in a way that builds into the more significant problem. There has to be a path to impact, with a clear business case, rooted in a larger, meaningful vision.

Interconnected Thinking: People, process, data, and tech do not exist in the two-dimensional, hierarchical, and linear depictions that we have given them for years. Developing readiness and maturity across each is foundational, but the value is captured in understanding and visualizing the interconnections between them. This understanding is the key to removing obstacles as well as uncovering growth on your journey to becoming an adaptive, cognitive organization.

Accepting that AI is part of digital transformation is the first step. How much you choose to set yourself up for the future (which inevitably involves AI) is ultimately determined by your organization’s willingness to develop a growth mindset. With this thinking in place, you have what’s needed to not only achieve digital, but begin the cognitive transformation.


This blog was co-authored by Michael Hollar

Michael Hollar is the Global Lead for our AI & Cognitive Services offering. The last four years he has been building and leading teams at the intersection of experience design, management consulting, enterprise data and emerging tech. He brings over 20 years of data-driven solutions experience to North Highland, with work spanning enterprise data strategy, capability and org development, purpose-built custom solutions, and enterprise analytic applications.

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