The current model of winning work through bids and proposals is broken.
Highly inefficient, repetitive and labour-intensive processes, coupled with information being held in different systems and formats are often at the heart of the issue.
Added to that, the lack of robust internal governance and process around which opportunities to pursue, mean companies are bidding for contracts they have no real chance of winning.
Does your business calculate the internal cost and return on investment of your existing approach to work winning?
For bid teams, the current approach often means working under pressure to achieve unrealistic deadlines, just aiming to achieve a compliant submission, without having time to understand their clients’ challenges and develop a compelling submission full of solution and evidence.
This approach leads to sub-optimal return on investment from a businesses’ work winning activity and a disenfranchised bid team who are being asked to continually work under increasing pressure of workloads and deadlines.
With intelligent use of AI, technology and data, there is a better way to win.
Intelligent AI, Tech and data – the future of work winning
AI and tech are undoubtedly revolutionising the way businesses operate, and this is no different in the world of bidding / work-winning. The fact that most of this year’s APMP conferences in the UK, Europe and USA have been dominated by such content and exhibitors is a clear demonstration of this.
The AI bid writing bandwagon
In the last 12-18 months there have been an explosion of new AI tools – predominantly in the bid writing space – coming into the marketplace, promising companies can ‘do more, faster, better’. It feels like there has been a lot of jumping on bandwagons, without really understanding the biggest challenges that AI and technology can solve across the work winning lifecycle. From the tools I have trialled so far, I wouldn’t be confident in using them to generate my bid content today. I’ve also seen some AI Bid Library tools that come with the promise of ‘model’ answers or populated with your previous bid content – without any insights on the quality or currency of the content. As the old adage goes – rubbish in, rubbish out.
Undoubtedly as the quality of content produced by AI will improve as these models mature.
There’s more to winning work than writing bids
For me, the key benefit of AI is not to help companies churn out more bids or do them faster – companies are already bidding for too many contracts.
Intelligent use of AI and technology will enable organisations to take more data driven decisions on which opportunities to pursue based on better insights on the opportunity, the client and their competition. It can also drive continuous improvement through better analysis of organisations work winning performance, by using the mass of data and information at their disposal to assess key metrics, such as win rate, internal bid cost, return-on-investment and profitability.
Sure, there is absolutely a place for AI to make the process of writing bids more efficient and improve quality, however this is not the magic bullet that these current tools promise. These tools still require human insight and expertise to understand ‘what good looks like’, and to add the 10% gold-dust that transforms a bid from mediocre to outstanding.
Without the more intelligent use of AI and technology across the full Work Winning lifecycle, organisations will not benefit from AI generated bids alone.
What does the future hold for work-winning technology?
Use of AI and technology to win work is no longer the future, it is already here. Those companies who embrace that concept will do well, those that don’t will quickly be left behind.
Having said that, having tech and AI is only useful if it delivers improved performance compared with what you currently do.
My scepticism of current AI Bid Writing tools remain for now, however I predict that we will see more intelligent tech, AI and data driven platforms entering the work-winning space over the next 12 months that will revolutionise the broken, inefficient model we see today.
