When it Comes to AI, Whose Responsibility is it?

 The answer to that headline question isn’t as clear as most organizations would like it to be. Some might suggest it’s the IT department, others the engineering team, while others may point fingers elsewhere. To be clear, it starts with leadership. Leaders define the priorities, goals and objectives. They set the tone and tenor of how the company will compete to win. And when leaders say “this is important” others must rally to ensure that important thing gets done.

However, when it comes to pursuing something that hasn’t been done before within the company, ambiguity around responsibilities can creep in. Questions such as: How important is this? Is AI a business priority or a one-off initiative? Who’s going to do the work? Where’s the ROI?

Leadership isn’t referring just to the CEO or a divisional president. For something like AI and digital transformation to become a priority, to have it stick within the company, it needs a broad base of leadership acceptance. For example, it needs:

  • Finance leadership to understand how AI can impact the bottom line
  • Marketing and Sales to present and articulate new ways of commercializing products/services that users want
  • Human Resources to see how innovative work attracts and retains talent
  • Department leads to offer more interesting and meaningful work – whether it’s working on the AI initiatives directly, or AI relieving people of repetitive work in order to spend more time on thought-provoking, creative work.

And here’s how it starts: Leaders must provide a clear-cut answer. Not a “let’s-see-where-this-might-lead-us” vote of vagueness. It’s either a way forward or it’s not.  Yes or no.

This hard line is necessary because things are likely to get harder. Barriers to progress lie ahead. And if the commitment is lacking, then AI efforts will get derailed.

When a company gets decisive, then specific roles and responsibilities need to be articulated and acted upon – by the leaders who must champion AI, and through the team that must deliver on its promise. 

FOR LEADERS: Eight actions to champion AI

Too often innovation is measured solely by outcomes while overlooking inputs. Success hinges on the early and consistent “input” from leadership to the implementation team and the broader company. These eight inputs are critical to empower the team to stay focused on its transformational work.

  • Articulating the business case: The “why” behind the company strategy to pursue AI must be made clear – from the benefits for employees and the company, to dispelling myths and fears of AI.
  • Repeating the strategic priority: In order for AI to stick as a strategy, people must hear again and again that it is a strategy – from the top.
  • Dedicating a budget: Without it, AI won’t happen.
  • Building a “shed”: This is the term we use to talk about the dedicated space necessary to bring new ideas to life. Some of the greatest businesses started in a shed or garage–that maker space devoted to innovative thinking and free of everyday distractions. Every company needs one, and AI project will benefit from incubating in the shed instead of scattered across cubicles. 
  • Appointing a leader: Designate someone who is the face and lead of the initiative, someone who will be the voice of progress and transformational impact. That someone doesn’t have to be in the c-suite. 
  • Defining the target: Articulate what the end goal looks like today, knowing that there will be key learnings along the way that may alter the path. The process itself will ultimately guide and determine the final outcome. The journey (process) is as important as the destination (AI implementation).
  • Voicing expectations: These expectations might be different than what the company is accustomed to, including: the importance of key learnings along the way; achievements before project completion; focus on long-term success; incorporation of user input, ideation, prototype development and testing; starting small then scaling fast.
  • Taking an ongoing and active role: In 2019 we sought out leadership opinions by surveying organizations interested in and already invested in AI. Of those already invested 85 percent of CEOs and c-suite leaders affirmed their personal involvement in AI projects. These are leaders seeking to understand the future of business transformation and who are leading by example.
Ready to

Turn your data into immediate business value?

Our AI assessment will help you better understand your data, identify feasible AI opportunities, and provide strategic recommendations on how you can add a whole new level of intelligence to your business model.

Learn more
Connection network in servers data center room storage systems 3

FOR TEAMS: Three principles for successfully integrating AI

While each project is unique and roles and responsibilities will differ, there are some overarching considerations that will help shape the team, chart its path, and set the company up for success.

  • Assemble a cross-functional “how” and “why” team: The leadership champion needs the latitude to assemble a team of talent that spans across the enterprise. Our survey found that IT leaders manage AI projects 48% of the time. But don’t fall victim to believing AI is just an IT project. Both technical and non-technical expertise are critical to AI success. Engineers and tech-minded individuals need to know how the AI solution works, whereas design, marketing, sales and other functions help to inform the why and ensure that user experience is part of the solution.
  • Start together, finish together: From start to finish, an AI project can span a year or two, or even longer. Having continuity on the team becomes crucial to crossing the finish line. Regardless of whether teammates have heavier loads at the beginning, middle or end of the initiative, tracking the development and learning from the process is essential to growing the knowledge needed to drive digital transformation.
  • Build capacity: Companies committed to digital transformation can and should use their cross-functional teams to build in-house expertise. Building capacity for AI innovation can be done by using teams to:1. Guide and shape training for existing employees
    2. Identify desired AI and digital skillsets when hiring new talent
    3. Evaluate outside experts/consultants for partnership opportunities

If that sounds like work, it is. But the effort now will pay dividends not only with the AI project at hand, but also in how the company collaborates and innovates going forward.

In this time of digital transformation that involves AI, IoT and AIoT, and deciding if the pain is worth the gain, it’s helpful to keep these words from a former general and U.S. Army chief of staff, Eric Shinseki in mind – If you don’t like change, you are going to like irrelevance even less.

We know that efforts to innovate and transform digitally can be overwhelming. So we wrote A Practical Guide to Understanding AI. It’s easy to read and easier to understand than you might think. You can download it for free here.

Paul is our go-to guy for all things product development, and he's learned a lot about how systems work together when it comes to marketing and packaging our services to customers. His area of expertise is product development, and he is passionate about building solutions that benefit both businesses and the people in them.

A monthly newsletter focusing on the intersection of global macro-trends, strategy, and technology.


Digital transformation,
with a twist.

Create unprecedented impact with Twisthink.
Join us at Twisthink on Thursday, June 20 from 3-5pm for an exciting cross-industry event! As we reflect on a year of tech innovation, 7 GR company leaders will share key insights, offering valuable perspectives on the progress of technology.