Data and AI: Winning over the sceptics by Richard Benjamins

Even if the strategy of an organization is well defined and clearly communicated, there are always those who will be sceptical, and will actively resist change. In large organizations, this is all too common — it almost always happens and needs to be dealt with. Data is not different in this respect. While it is best practice to not start the change with sceptics, but rather with ‘champions’ or early adopters, in some cases one needs to work with sceptics. This is especially true when they are the owner of important data or responsible for a business area that’s been chosen to run a big data or AI use case (see Chapter 6).

 

WHAT MOTIVATES SCEPTICS?

The following scenario is not uncommon. You have per- formed your opportunity matrix analysis to select the best use cases, balancing business impact with feasibility. But then, when you start talking to the business owner who would be impacted by the use case, he or she tells you something like, “I’ve done this for many years and I don’t need anything else to do my job.” Or, “I already know what my restrictions are (business goals, regulation, etc.), so there’s nothing to add.” You may hear, “I don’t need AI or big data until you can show me how it has worked success- fully, and what the results were, elsewhere for a business like mine.” And there you go. You started the meeting very enthusiastically, but left frustrated, wondering why there was this resistance.

There can be many reasons for such reactions. A few of them include:

• General resistance to change. Many people are working in their comfort zone and will resist anything that moves them out of that familiar space.

• Fear of not doing the job properly. Analytical use cases are almost always about improving business or reducing costs. A business owner might feel that if a data/ analytics department demonstrates that such improvement is possible, he or she might be criticized for not having proposed the same initiative earlier.

• Concern over being seen as not innovative. Today, many organizations place a lot of importance on innovation and expect leaders to explore and apply innovations as part of BAU.

• Collaborating in a data, analytics or AI initiative requires sharing data across the business area, and this automatically implies becoming transparent. Before, the business owner controlled the data, decided how to interpret it and presented the conclusions. Now, others would be able to do the same. This leads to the impression of having less control (and power) and invariably exposes the business owner to more criticism than before.

For these reasons, and more, it’s not strange to have such a frustrating first meeting. But, what are the lessons learned to win over those who are resistant to new data and analytics initiatives?

 

STRATEGIES TO WORK SUCCESSFULLY WITH SCEPTICS

The natural reaction when a head of some part of the business refuses to cooperate on a data or AI initiative is to escalate, and then use formal authority to compel participation. Depending on the seniority of the business owner, formal authority means that two senior executives have a conversation, or it means a direct order to get on board. Whatever the approach, a formal authority might force the business owner to accept, but not in an optimal way. He or she may say yes to all, but actually act in a ‘no’ manner, or perhaps come up with all kinds of excuses and artificial hurdles (privacy, security and confidentiality concerns are popular ones) that block progress. Or, the person may sim- ply assign insufficient resources, which leads to delays and lack of traction.

While there is no recipe that always works to convince sceptical people, there are some lessons learned that have worked in the past:

• Find a champion in the business area who is interested in exploring new ways of doing things, and who loves innovation.

• Keep the initial collaboration below the radar.

• Start working together, build trust, understand the problem and find the sweet spot where data or AI can really improve the business head’s situation.

• Work on a prototype, discuss the results with the champion, and make sure he or she fully understands the positive business impact.

• Have the champion socialize the prototype results across the business area.

• Enlist the champion to show the work and results to the business owner, emphasizing that control and owner- ship of the project reside fully with the business area.

If the business owner understands the essence of the work, and views it as an activity of his or her area, it won’t be seen as a threat. Instead, it’ll present itself as an opportunity to show innovation and improvement coming from the area itself. Once you’ve gotten to that point, the business owner will likely function as a spokesperson for data and AI, evangelizing to the rest of the organization.

A key aspect of this approach is that ownership and leadership of the project, and the credit for the results, are with the business area from the start. Data, analytics and AI departments often claim ownership and credit for results, downplaying the effort the business area put into getting them. In practice, that has an adverse effect. However, if successfully positioned within the business area, credit and recognition will be given to the data and analytics/AI teams as well. If not publicly, at least benefit will flow to the analytics and AI operation in the form of budget for the next year.

A mistake I made early on was to complete a successful analytics initiative with a business unit, and then publicize the project and results in company forums and executive meetings without notifying or involving the business unit. Obviously, this wasn’t appreciated by that part of the business, and it never happened again.

 

CONCLUSION

When starting new data, analytics or AI initiatives, it always pays to begin with business owners who are eager to collaborate — those true champions of change. How- ever, if for some reason this isn’t possible, and you have to work with sceptical business owners, try to make the business area the recognized owner of the project, so it’s viewed as coming from inside rather than outside. And, give full credit to the business area. Over time, almost all sceptical business owners turn around and even become evangelists for AI and big data. Be patient; it may take many months or even a year or more. (If not, they’re likely to retire soon anyway!)

There’s one other important aspect to take into account when starting data/analytics initiatives in large organizations that apply to all business areas. Don’t forget to involve any relevant area or department. If they’re not involved (when they should be) they might work as detractors rather than as facilitators. In large companies, there are generally central HQ areas with corresponding local areas in the operating businesses. For example, in data/AI/analytics, there is usually a department at headquarters and also one in each operating business. For a particular use case — let’s say, to improve marketing campaigns — there is the global marketing area, under the CMO, and the local marketing area. And, for many data initiatives, there’s also an important IT role, which exists at both the global level and the local level. The lesson is that any initiative should involve both the relevant global and local areas to become fully successful. If not, apart from hurting feelings, somewhere along the value chain the initiative may grind to a halt or at least face damaging delays.

For instance, a marketing analytics project that does not involve the local marketing area from the beginning will run into problems when a pilot needs to be performed. That simply can’t be done without the local marketing team. The local team isn’t necessarily needed for designing the technology and the marketing approach, because the global marketing team has all the required knowledge and experience.

Of course, the downside is that at the start of any initiative, too many areas and people are involved in the meetings. The best way to avoid this is to host a kick off meeting with all potentially involved areas, and then activate individual areas when they’re needed. This way, at least they’re aware of the project and bought into it to some extent.