Did you know that you can already buy face recognition services from Amazon for just pennies per minute of video or a fraction of a penny per image? We can debate the ethics and efficacy of the service another day, what is undeniable is that artificial intelligence and machine learning are rapidly changing how we create, innovate and work. Organizations are already racing to adopt AI practices, but adoption curves still apply, and there are innovators and laggards. The difference with this curve is that because machine learning is dependent on quality data, a chasm is beginning to open between those in the early majority and those farther down the curve.

Early adopters of AI are already beginning to transform their organizations toward a future where not just any repetitive activity, but also continuous learning will be driven by machines. They are investing in their workforce and dedicating significant percentages of their budgets to AI activities. By contrast, laggards in digitalization are already missing the enterprise data models and practices that enable the quality data needed to serve as the foundation for utilizing AI, and will only fall further behind. Compounding their data deficit, learning to integrate with AI requires new strategic capacities of the workforce and of the organization. The result is that many organizations are already struggling to adopt AI, even in those areas with a clear pay-off. Only 18% of respondents have a continual process for identifying valuable AI opportunities¹. This is not because of a lack of talent, but due to a lack of strategy around identifying and implementing new opportunities.

For lagging companies, there is no way to catch up with traditional top-down organizational transformation. Organizations who are already behind must create the environments and cultures that enable distributed, innovation aka intrapreneurship.

Enter Intrapreneurship – Innovation is everybody’s job

While laggards are more likely to hire external firms to augment their workforce and get them up to speed, the most digitized companies are building internal capacity and retraining their workforce to identify and act on opportunities to utilize AI, ultimately creating AI supported processes, business models, and organizations.

Intrapreneurship traditionally refers to individuals establishing new entrepreneurial endeavors within an existing company. We call this structural intrapreneurship. Structural intrapreneurship is limited to the few employees cut out to actually create a new business, and has limited impact on the organizational culture. Infusing an organization with an intrapreneurial mindset and behaviors provides opportunities for innovation across the organization. We call this cultural intrapreneurship.

While not every employee is cut out to take a venture all the way from idea to actual implementation, every human is creative and has the capacity to be curious about their environment and activities and to create ever better processes and products given the right environment and culture.

Activating your internal talent

You can’t just expect AI skills and tools to be picked up by your employees. On the contrary, many employees are weary of AI adoption, not surprising given decades of technological replacement and optimization in organizations. As long as headcount is the primary ROI of organizational initiatives, you cannot expect to unleash creativity among your employees.

Instead, you get to find ways to reward obsoletion of processes and find ways to celebrate publicly as you provide daring employees with further opportunities so that others may follow their example.

From our work with AI leaders and their teams, and from AI Intrapreneurship bootcamps with our clients, we learned that to ensure success for those employees, a few things are essential:

  • Employees learn how to identify and evaluate opportunity
  • Establish horizons for opportunities and connected them emotionally
  • Identify and address underlying gaps in digital capacity and data
  • Create a pipeline for new internal and external projects
  • Share learnings across the organization
  • Activate an ecosystem beyond the organization

Employees learn how to identify and evaluate opportunity

Identifying signals and possibilities for new opportunities does not come naturally. Most of the workforce has been trained since school to execute on prescribed processes rather than invent new ones. Apart from the psychological safety required to explore, employees need to understand the general capabilities of AI, how it can be used, and they need to have a realistic picture of organizational capabilities (as they are developing).
On a basic level, AI can be used to:

  • Predict – Anticipate events, create scenarios for possible outcomes, and be prepared and mitigate risk
  • Automate – Most organizational processes are come down to flowcharts. Decision points and handoffs can be automated to minimize human intervention and liberate humans to focus on what is really important – human interactions
  • Insights – Turning data into information and knowledge, AI can provide insights into patterns and trends – from variances in machine functions to large social events
  • Personalization – AI can customize and humanize employee and customer experiences
  • Prescription – Especially when problems have clear definitions, AI can provide suggestions for action

When your employees understand how these general capabilities can apply in your specific organizational context, they can begin to identify opportunities for value creation.

Once identified, an opportunity needs to be evaluated. For that, methods and tools need to be made easily accessible for the employee – providing not just materials (such as some of the wonderful Kickbox models), but also coaching in the process and individual capabilities required for intrapreneurship. Coaching is ideally provided from both internal resources, who know about organizational integration, as well as external resources across the ecosystem, to keep open minds around potential new models or innovations and provide the personal capacities required to successfully innovate.

Establish horizons for opportunities and connect them emotionally

If you are too far in the future, you might be wrong. To provide context for your intrapreneurial projects and innovations, you need to be clear what horizons you are operating in. How you define them, is up to your organizational and innovation maturity and an early important step.

Creativity loves constraint. Without framing your intrapreneurial activities in your strategic context, initiatives will not ultimately serve you.

Providing a clear direction through simply communicated visions for desired future scenarios is essential to prime your workforce and emotionally anchor projects.

Identify and address underlying gaps in digital capacity and data

AI eats data for breakfast, lunch and dinner. If you are still not tracking key interactions, your data lives in disparate systems, and you don’t have defined data models, you better get to it fast. Without data, your AI efforts will be moot. Before you can become a dynamic learning organization, you will need to quantify your business. Machine Learning in particular thrives on vast amounts of data. What else could your AI learn from?

Data as such is just dumb bits and bytes. Through structuring it, it becomes information, through good questions it turns into knowledge, and only through application does it become wisdom.

Create a pipeline for new internal and external projects

Many intrapreneurial projects die young. There is no lack of ideas across organizations. Companies that have implemented idea gathering systems or attempted to crowdsource innovation have mostly been suffocating in suggestions. While many were not strategically aligned (hence the point above), even the good ones lacked pathways for implementation.

A comprehensive innovation strategy, one that goes beyond a couple of sticky note events, provides pathways for products and process optimizations from exploration to incubation, from acceleration to integration into the core business.

Within that, adaptable organizations manage portfolios and an active pipeline of new business models (externally focused) and organizational transformations (internally focused).

Now organizations get to be in perpetual beta, too.

Share learnings across the organization

While companies love their silos, ultimately processes are not that different across the organization and learnings in one area can inform and inspire others.

Key to accelerated AI adoption is sharing stories. After all, stories have programmed culture since the days when we sat around the fire. From fire we moved to electric lights and television sets, and if AI is indeed the new electricity, as Andrew Ng from Google Brain called it, we get to share our stories around it inside and outside the organization.

Activate an ecosystem beyond the organization

Governments and AI giants already in existence are innovating with AI every day. Their armies of AI talent dwarf most other large companies. To be able to compete when there is already such monopolization, organizations get to connect beyond their traditional walls. Joining forces with competitors and other ecosystem players like startups, NGOs, education and even governments will not just provide critical mass, but will also drive adoption and evolve standards that will facilitate simpler integrations.

If not now, when?

“In some cases, there is too much hype, but paradoxically, the potential opportunities and benefits of AI are still, if anything, under-hyped.”

Nigel Duffy, Global AI Innovation Leader

Like most technology adoptions and transformations, they start slow, only to be sudden. In that, there will be those who miss the boat. The race has already started. When are you getting in?

¹ “AI adoption advances, but foundational barriers remain”