Your business’s operating model might be the biggest hurdle to AI adoption

While you can argue that nailing this is crucial for all technology implementations (and I’d agree), this is even more important when delivering AI due to its reliance on people and its potential to completely change how many of us do our jobs.

That change isn’t always what you think. Forbes recently found that: “77% of employees using AI say it has added to their workload and created challenges”, with one of the core reasons being “introducing new technologies into outdated work models and systems”. With this in mind, it’s important to think about how to unlock the transformational power of AI seamlessly.

Business shape and plans for change

It goes without saying that if you’ve found challenges in moving to business-as-usual (BAU) with other tech, AI will be equally (if not more) of a beast to overcome. When it comes to business shape, the important first step is breaking down the technology BAU environment into operational capabilities and then having a clear view of which function is owned by which area of the business.

Many have said that AI could introduce the biggest skill gap in the post-industrial era. Once you have confirmed the capabilities and functions, don’t assume that your existing teams can deliver against them – thinking about how to train, hire, and outsource to fill these gaps is also part and parcel of considering your business shape.

For years, a debate has been waged over ‘waterfall’ versus ‘agile’ management methodologies – and with AI delivery, it is essentially no different. While phases of the project will be delivered in agile sprints, the larger project will likely take a more traditional ‘waterfall’ approach (eg, design, then build, then deploy). Use the time that the waterfall approach will give you to your advantage.

You’ll need to not just think about the tech delivery roadmap but also ensure that business operations are a clear constituent part that are treated with the same regard as tech releases.

Then, embed this plan in your day-to-day workflow using both tools and routines – constantly assess the roadmap and work to mitigate risks and spot opportunities.

Lastly, don’t wait until the last minute – moving to BAU should be considered at least six months before going live. As mentioned above, you may not have the right roles and need to hire, with key personnel likely not readily available.

Empower stakeholders

When delivering organizational change, a lack of employee empowerment and buy-in is often one of the core reasons that the target operational state fails to take hold – when implementing technology or AI, this is not an option.

To avoid this failure, be sure to take people on the journey. Open early communication channels and create a continuous feedback loop for questions or queries.

Also, conduct a detailed change impact analysis. If there will be a change to your business due to new operational structures and setups, ensure that you have clearly identified the change on a role and skill level. Conducting the analysis will ensure you have a view of what you do and don’t have in the organization and where you need to invest, either by training, hiring, or outsourcing.

Make learning continuous: Unlike other martech, AI is changing on almost a daily basis. As a result, continuous business enablement needs to be considered to ensure that key stakeholders have access to artifacts and training to continue to get the most out of the technology.

Toolkit

I’m not trying to discourage the implementation of exciting new technologies like AI, quite the contrary. In some ways, it’s simpler than ever to deliver new technologies. In others, it’s never been more complex. Of course, when it comes to quick wins, it may require some simple training rather than a step-change in what your organization does.

For larger, bigger bets, it could well be the opposite – where you do need to think more holistically about what’s going to enable your business to drive long-term success? No matter the size or level of complexity of your transformation, you now have the tools you need to identify where to start.